Integrated Assessment of Climate Change Impacts

Report on Methodology andWorkshop held at the ANU 3-4 July 2005

16 June 2006

M.F.Hutchinson1, S.Dovers1, R.Letcher2, J.Lindesay3, F.P.Mills1, J.Sharples1

1 Centre for Resource and Environmental Studies 2 Integrated Catchment Assessment and Management Centre3 School of Resources, Environment & Society

1

Executive Summary.................................................................................................................41 Climate change.................................................................................................................9

1.1 Climate changes in the historical record..................................................................91.2 Future changes in Australian Climate....................................................................101.3 Abrupt nonlinear climate changes..........................................................................12

2 Potential impacts of climate change...............................................................................132.1 Water resources......................................................................................................132.2 Biodiversity............................................................................................................142.3 Agriculture.............................................................................................................152.4 Human health.........................................................................................................162.5 Forestry..................................................................................................................172.6 Marine ecosystems.................................................................................................182.7 Urban centres.........................................................................................................192.8 Tourism..................................................................................................................192.9 Cumulative Impacts...............................................................................................20

3 Integrated Assessment....................................................................................................213.1 What is IA?............................................................................................................213.2 Why is IA needed?.................................................................................................233.3 What does IA require?...........................................................................................243.4 Connections to policy.............................................................................................25

4 Integrated assessment of climate change.......................................................................274.1 Frameworks for integrated assessment of climate change.....................................27

4.1.1 A policy framework.......................................................................................284.1.2 A risk assessment framework........................................................................294.1.3 The Millennium Ecosystem Assessment framework.....................................304.1.4 A research framework....................................................................................314.1.5 Comparison of conceptual frameworks.........................................................32

4.2 Design issues for an integrated assessment............................................................324.2.1 Problem focus................................................................................................324.2.2 Project teams and personalities......................................................................324.2.3 Communication..............................................................................................324.2.4 Role of Government and Links with Policy...................................................334.2.5 Scales.............................................................................................................334.2.6 Participation...................................................................................................334.2.7 Iterative approaches.......................................................................................344.2.8 Quality control...............................................................................................34

5 Methods to support Integrated Assessment...................................................................355.1 Roles of models in IA............................................................................................35

5.1.1 Design issues for integrated assessment models............................................375.1.2 Modelling Approaches...................................................................................40

5.2 Participation...........................................................................................................435.3 Risk Assessment....................................................................................................45

6 Selecting methods for use in an integrated assessment..................................................476.1 Criteria for selecting methods for IA.....................................................................47

6.1.1 Is the method credible with the scientific community, policy community and/or the general community?......................................................................................476.1.2 Can the method answer key questions underlying the case study or meet the case study objectives?....................................................................................................476.1.3 Can the method fit into an appropriate participatory process?......................476.1.4 How easily can the method communicate uncertainty?.................................48

6.1.5 Cost – how expensive is it to develop, maintain and extend?........................486.1.6 Can it be used in training, to build capacity or for social learning?..............486.1.7 Is it useful for educating a new breed of interdisciplinary scientist?.............486.1.8 Can the method or results/lessons from the method be transferred to other case studies/ problems/areas and more broadly?...........................................................486.1.9 Can it handle multiple and/or conflicting issues?..........................................496.1.10 Can it be used in a complementary manner with other methods?.................49

6.2 A process for Integrated Assessment.....................................................................497 Case Study Selection......................................................................................................51

7.1 General context......................................................................................................517.2 Key attributes of future work in Integrated Assessment........................................52

7.2.1 Representativeness: drivers and impacts........................................................527.2.2 Representativeness: sectors, values and places..............................................527.2.3 Methodological development.........................................................................527.2.4 Data availability and institutional capacity....................................................537.2.5 Utilisation of past and current work...............................................................537.2.6 Policy and public relevance...........................................................................537.2.7 International significance and connection......................................................54

8 Matching Criteria & Methods to Assessments..............................................................568.1 Scope of an Integrated Assessment........................................................................568.2 Research approaches & reasons for an IA.............................................................578.3 Methodologies and methods for integration..........................................................578.4 Products/outputs and communication....................................................................588.5 Capacity building...................................................................................................58

9 References......................................................................................................................60Appendix 1. Policy and Integrated Assessment.....................................................................68

A1.1 Connection to policy.............................................................................................68A1.2. Integrated policy assessment................................................................................71A1.3. Scale and integrated assessment...........................................................................73

Appendix 2. Workshop Program and Participants.................................................................76

3

EXECUTIVE SUMMARY

The area of integrated assessment of the impacts of climate change, with particular emphasis on assessing vulnerability and adaptivity of key natural and human systems, is one of growing international significance. It is now recognised that this “bottom-up” approach has much to offer in making meaningful assessments of projected climate change, with real policy impact. Detailed knowledge of impacted systems is just as important as knowledge of the dynamics of possible future climates in assessing effective adaptation options. It is also important for informing decisions by managers and policymakers. The approach goes beyond traditional “top-down” scenario-based approaches, although such approaches typically form one of several starting points for integrated assessment.

Integrated assessment for climate change is a method to assess potential impacts of climate change and vulnerability of impacted systems in order to identify and implement effective adaptation options. It must take account of the cross-sector nature of the impacts of climate change, since all sectors impacted have significant interactions with other sectors. Ideally it should be designed so that scientific and engineering knowledge of the impacted sectors can have real influence on decision making and policy. This means that integrated assessment is in part a communication process that needs to be continued over a lengthy period.

Bearing in mind that no one institution has comprehensive knowledge in this multi-faceted area, ways forward need to be informed by appropriate expertise. We therefore proposed and organised a two day workshop at the ANU on 3-4 July 2005. The aim of the Workshop was to bring together Australian experts in impacts of climate change and integrated assessment with key experts in the international integrated climate change assessment community, in order to inform and facilitate further progress. Since effective integrated assessment depends on long term cross-sectoral communication between a wide range of stakeholders, as described above, the Workshop can be seen as an aspect of integrated assessment in itself. The Workshop was funded by the Australian Greenhouse Office with additional funding provided by the ARC Network for Earth System Science. The synthesis of the outputs of this Workshop, combined with an assessment of the current literature, forms the main body of this Report.

The program and list of attendees for the Workshop are provided in Appendix 2. The workshop was attended by over 40 invited international and national experts and members of key state and federal government agencies, with the explicit aim to assess approaches to integrated assessment of climate change impacts and adaptation options. The intention was to combine up to date international expertise in this area with Australian expertise in key sectors including agriculture, natural ecosystems, water resources, human health and marine ecosystems. This was combined with Australian expertise in risk management. One of the aims of the Workshop was to strike a balance in the contributors between established experts and young researchers with emerging expertise. Inclusion of young researchers was an explicit goal of the additional funding provided by the ARC Network for Earth System Science.

It was generally agreed that the Workshop was successful in informing attendees and in raising general awareness in the area. On the other hand it was also agreed that much needs to be done. It is intended that this report provide a resource document to inform further progress on integrated assessments of climate change impacts and vulnerability in the

4

Australian context. The key outcomes of the Workshop and our review of the associated literature are described below according to the main sections of this report.

Climate changeThe global mean temperature of the Earth has increased by about 0.6°C since 1900 and the Intergovernmental Panel on Climate Change has concluded that “most of the observed (global) warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations”. Australia’s climate is also changing as part of the global trend, with average temperatures over Australia between 1910-2004 increasing by 0.9°C.

Trends in rainfall are less clear, as Australian rainfall has exhibited substantial variation over both time and space. Over the past century all-Australia annual mean rainfall has increased on average with strongest increases over the central, northern and western portions of the continent and decreases in the southwest region of Western Australia. The observed decrease in winter rainfall over southwest Western Australia is most likely due to the accumulated effect of several factors including enhanced greenhouse gas emissions.

CSIRO projections for Australia using up to 13 climate models driven by the IPCC emission scenarios indicate that by 2030 annual average temperatures will be 0.4 to 2.0°C higher over most of Australia, with slightly less warming in some coastal areas and Tasmania, and a potential for greater warming in the north-west. By 2070, annual average temperatures are projected to increase by 1.0 to 6.0°C over most of Australia with spatial variation similar to those for 2030.

Projected annual average rainfall changes tend towards a decrease in the southwest and in parts of the southeast and Queensland. The projected ranges for the tropical north represent little change from current conditions. Overall, drier conditions are anticipated for most of Australia over the next century. However, this overall decrease is expected to be accompanied by an increase in heavy rainfall. This is a result of the shift in the frequency distribution of daily rainfall toward fewer light rainfall events and more heavy events. Interannual variability in ENSO leads to major floods and droughts in Australia. This variability is expected to continue under enhanced greenhouse conditions, though possibly with greater hydrological extremes as a result of more intense rainfall in La Niña years and more intense drought during El Niño years.

Mean sea-level is expected to continue to rise into the future due to the thermal expansion of sea water, melting of glaciers and polar ice-sheets. Simulations suggest that mean sea-level will rise, relative to the 1990 level, 3-17 cm by 2030 and 7-52 cm by 2070.

The dramatic decline in rainfall in southwest Western Australia and the anomalous drought in the eastern states may already serve as examples of abrupt climate change in Australia. Abrupt changes in climate are less foreseeable, provide less time to adapt and have far greater economic and environmental impacts than gradual warming. Potential impacts of climate changeAn assessment of potential impacts across the sectors shows a high degree of interaction. Water resources are a critical component of most sectors, particularly agriculture, biodiversity, human health and urban centres. The existing problems of increasing demand for water resources are likely to be exacerbated by climate change. Biodiversity is also seen to be highly cross-sectoral, with a wide range of types land ownership and interactions with

5

many policy areas. These include interactions with agricultural and forestry systems, with the increasing recognition of the role of farmers in conserving biodiversity. Human health, urban centres and agriculture all have important interactions relating to climate extremes and water supply. Tourism and marine ecosystems are also strongly linked.

Common needs arise from these sectors. These include the need to address uncertainty, with respect to the current climate as well as for projected future climates. This highlights the role of risk management and adaptive management to minimise the adverse impacts of climate variability and change. There are also commonly perceived needs for increased awareness and understanding of potential climate change impacts, for key supporting data and monitoring, for building human capacity and to address land use change. These raise issues for policy across a range of scales.

Integrated assessmentEffective environmental management requires the consideration and understanding of complex interactions between various economic, social and environmental outcomes. Models or assessments that integrate considerations and understanding developed across a broad range of fields is required to understand and evaluate the trade-offs required to inform decision making. Integrated assessment can be defined as ‘the interdisciplinary process of integrating knowledge from various disciplines and stakeholder groups in order to evaluate a problem situation from different perspectives and provide support for its solution. Integrated assessment should support policy and decision processes and should help identify desirable and possible options. It depends on integrating knowledge about a problem domain and on understanding of policy and decision making process.

Farrell and Jaeger (2006) define effective integrated assessment as one having a significant influence on the associated issue domain. This can be satisfied by a wide range of outcomes such as formulation and evaluation of policy options, improvements in scientific knowledge, prevention or delay of environmentally harmful actions, establishment of environmental regulations and enhanced prestige. They identify three key requirements to achieve effectiveness - salience or relevance (the assessment should address an issue in which users are interested and be relevant to action that users can actually take); credibility – the authoritative technical community must find the assessment acceptable; and legitimacy – (the users must believe that the process respects the rules and norms of relevant institutions and that the interests of the users have been acknowledged and taken into account).

Integrated assessment for climate change allows for cross sector assessment of the impacts of climate change, and consideration of the connections between different sections of the community and environment and the way in which this affects their vulnerability to climate change impacts. This assessment can be used to identify the extent and nature of risks facing these community and environmental sectors in light of climate change as well as potential adaptation or management options that can be used to ameliorate potential negative impacts.

Methods must be developed to serve the needs of other disciplinary components incorporated in the analysis as well as being robust and defensible in a disciplinary sense. Methods should be relatively simple while retaining appropriate levels of accuracy and sensitivity to key assumptions. Simplicity has the additional advantage that assumptions underlying the assessment can be more easily communicated and discussed with a wide range of stakeholders.

6

Integrated assessment of climate changeFour conceptual frameworks for integrated assessments were identified as particularly relevant for assessing the impacts of climate change: a framework for environmental and sustainability policy, a framework for assessing the risks of climate change, the framework devised by the Millenium Ecosystem Assessment and a framework for research integration. This is not an exhaustive list but they illustrate the key components and requirements and provide a good basis for designing the framework for future integrated assessments of climate change

There are several design issues relating to integrated assessments. Problem focusing is a key step in IA and needs to occur before methods or approaches are selected. It needs to include a substantial stakeholder collaboration component to ensure that problem focus and impacts of concern have been adequately identified. The parties involved must be able to respect and acknowledge the contribution from other disciplinary components. This should enhance participants’ understanding of the interactions between system components and provide direction for research to fill critical gaps. Since IA aims to influence both science and policy government plays an important role in the success of IA. The timeframes of science and policy often conflict. Building trust in the community and ensuring successful public participation is a process that relies on longer-term relationships. IA must aim to be flexible to a changing policy and community environment, while allowing for the needs of rigorous scientific assessment. This is one of the greatest challenges of IA.

A common feature of many successful IA exercises is the use of an iterative approach to the assessment. Complexity can be added over time as the conceptual framework is enhanced and changed. In this way, useful outputs are staged over the life of the assessment, the assessment involves learning by all participants on the nature of the impacts, trust can be built and the assessment remains flexible to a changing community and policy environment. Quality control and maintenance of rigorous scientific or policy standards requires documentation of assumptions and decisions relating to participation, review of science and assessment approaches by experts and reporting and communication of results and assumptions in plain English formats.

Methods to support integrated assessmentModels are a common approach applied as part of many integrated assessments. A model is a description of a complex process or set of processes. It may be purely qualitative, as in the case of a linguistic model, but more commonly is quantitative and represents assumptions relating to system behaviour using a set of equations and parameter values. There is a wide variety of quantitative modelling approaches that are commonly applied in IA. Models are generally built to satisfy one or more of five main purposes – prediction, forecasting, management and decision-making, social learning and development of system understanding or experimentation.

Public participation can be defined as direct involvement of the public in decision-making, and thus, in developing the tools used to inform decision makers. There are several reasons for organizing public participation. These include the possibility of more informed and creative decision making, more public acceptance and ownership of the decisions, more open and integrated government, enhancing democracy and social learning, the ultimate objective, to manage issues.

7

Risk assessment has been used as an integrating framework in many sectors including health and the environment. The basic notion is that risk is defined as the probablility of an outcome times the severity of its consequence, leading to the potential quantification of risk. However, useful forms of qualitative risk assessment also exist, and may be more appropriate where believable probability distributions cannot be assigned to the range of possible outcomes. The essential notion of risk assessment can be extended to positive as well as adverse impacts and the characterisation of uncertainty. The role of risk assessment offers a way of organising knowledge about the world, particularly those aspects that are difficult to define in precise terms.

Selecting methods for use in integrated assessmentThere are several criteria by which potential methods may be judged and selected. These include credibility in the stakeholder community, ability to meet case study objectives, appropriateness for participatory processes, ability to communicate uncertainty, expense of maintenance and further development, capacity for training and social learning, ability to be transferred to other studies, ability to handle multiple and conflicting issues, and complementarity with other methods.

Case study selectionThe complexity of relevant natural and human systems (encompassing multiple natural processes, environments, human uses and values) combine to make it impossible and indeed undesirable to undertake an integrated assessment of all possible impacts and issues relating to climate change across all relevant scales. It is possible to choose well targeted IA projects to reflect key spatial or sectoral elements that are likely to be subject to significant effects from climate change. These need to be chosen carefully and to build on existing studies to ensure that the integration is the main focus of the assessment. Key criteria for case study selection and project design include representativeness of drivers and impacts, representativeness of sectors, values and places, promotion of methodological development, data availability and institutional capacity, ability to use past work, policy and public relevance and finally international significance.

Matching criteria and methods to assessmentsScoping of a case study should fall into two phases. The first is a broad assessment of the need for and general parameters of an IA in the particular region/sector. The second phase involves consultation and collaboration with stakeholders to determine the focus of the study, its resourcing, the interests of potential users of information from the study, and to assess what aspects of interest it will be viable to include in the study (taking into account constraints such as data availability, timeframes and resourcing). For a regional study a broad scope is likely to be most useful, including as many aspects as possible (e.g. land use, ecosystem services, settlement, water issues, health).

Selecting appropriate methodologies and methods is an important component of planning an IA. Considerations in making that selection include developing a conceptual framework, incorporating integrative methodologies, using software and other tools that promote participation and discourse, integrating policy and planning processes and frameworks into the methodology and using geographic information systems to integrate data and distributed model outputs.

8

AcknowledgementThe authors gratefully acknowledge the contributors and attendees at the Workshop at the Australian National University in July 2005. Their contributions have been instrumental in compiling this report.

9

1 CLIMATE CHANGE

1.1 Climate changes in the historical record

The global mean temperature of the Earth has increased by about 0.6°C since 1900. In 2001, the Intergovernmental Panel on Climate Change (IPCC 2001)) concluded that “most of the observed (global) warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations” (IPCC, 2001). There has been an increase in heatwaves, fewer frosts, warming of the lower atmosphere and upper ocean, retreat of sea-ice and glaciers, a sea-level rise of 10-20 cm and increased heavy rainfall in many regions. Many species of plants and animals have changed their location or the timing of their seasonal responses in ways that provide further evidence of global warming.

As reported by Hennessy (2005), temperature records indicate that Australia’s climate is also changing as part of the global trend, with average temperatures over Australia between 1910-2004 increasing by 0.9°C (Nicholls and Collins 2005), and with most of this increase occurring after 1950. Minimum temperatures have increased more than maximum temperatures in most regions. The frequency of extreme hot events (e.g. hot days and nights) has generally increased since the mid-1950s, and the frequency of extreme cold events (eg. cold days and nights) has generally decreased (Hennessy et al., 2004a). The warming trend over the last 50 years in Australia cannot be explained by the natural variability of climate and most of this warming is likely due to the increased concentration of greenhouse gases in the atmosphere (Karoly, 2001).

Trends in rainfall are less clear, as Australian rainfall has exhibited substantial variation over both time and space. Over the past century all-Australia annual mean rainfall has increased on average with strongest increases over the central, northern and western portions of the continent (Collins and Della-Marta 1999; Hennessy et al., 1999; Smith 2004). These increases contrast with the well-known decreases that have occurred in the southwest region of Western Australia. Since 1950, there has been an increase in rainfall over the north-western parts of the continent while the southern and eastern parts of the continent have experienced decreases in rainfall. Natural variability is likely the major cause behind changes in rainfall, though the observed decrease in winter rainfall over southwest Western Australia is most likely due to the accumulated effect of several factors including enhanced greenhouse gas emissions (Smith, 2004; Timbal, 2004).

Changes in the evaporative demand of the atmosphere over Australia have recently been analysed. Roderick and Farquhar (2004) reported that over the last 30 years pan evaporation has experienced a continental-scale decrease. This is in keeping with similar studies conducted in the northern hemisphere (Peterson et al., 1995; Chattopadhyay and Hulme, 1997; Thomas, 2000; Moonen et al., 2002). On a more regional basis, however, the northwest of the continent has experienced decreases in pan evaporation while the eastern portion of the continent has experienced mild pan evaporation increases (Sharples et al., in prep.). The overall decrease in pan evaporation is generally thought to be due to a decrease in solar radiation caused by increased cloud and/or aerosol concentration (Peterson et al., 1995); Roderick and Farquhar, 2002, 2004; Linacre, 2004; Liu et al., 2004), although rainfall-evaporation complementarity seems to better account for regional trends and finer scale temporal behaviour (Brutsaert and Parlange, 1998; Thomas, 2000; Golubev et al., 2001; Sharples et al., in prep.). Unfortunately, the pan evaporation network in Australia prior to 1970 does not support a detailed analysis. Hence the pan evaporation data record is

10

too short to conclude that the observed changes in evaporation are a consequence of anthropogenic climate change.

Observed changes in other facets of the Australian climate include:

Extreme daily rainfall observations shows a significant decrease in both the intensity of extreme rainfall events and the number of extremely wet days in the far southwest of Australia and an increase in the proportion of rainfall falling on extremely wet days in the northeast (Haylock and Nicholls, 2000). An exception to this rule is southwest Western Australia, where there has been a marked decline in mean rainfall and a 15% decrease in heavy rainfall intensity during winter (Hennessy et al., 2004a).

The frequency of tropical cyclones in the Australian region has decreased since 1967, along with an increase in cyclone intensity (Nicholls et al., 1998); (Hennessy et al., 2004a). The trend is gradual and largely follows the downward trend in the Southern Oscillation Index, since fewer cyclones occur in the Australian region during El Niño years (Kuleshov, 2003).

Nicholls (2004) indicates that Australian droughts have become warmer over the second half of the 20th century and that the severity and impacts of drought are enhanced by increased evaporation and evapotranspiration associated with higher temperatures. The recent 2002 El Niño drought was the worst on record with average Australian rainfall between March and November the lowest ever during this period (Karoly et al., 2003).

Between 1950-2000 global-average sea-level has risen by 1.8 ± 0.3 mm per year (Church et al., 2004).

1.2 Future changes in Australian Climate

This section is based on Hennessy (2005). Computer models of the climate system, based on representations of the dynamics of the atmosphere, oceans, biosphere and polar regions are the best tools available for simulating climate variability and change. A detailed description of these models and their reliability can be found in IPCC (2001). To estimate what can be expected of climate change into the future, these computer models are used in conjunction with greenhouse gas and aerosol emission scenarios. The emission scenarios are not predictions of what will actually happen but allow analysis of the likely impacts of assumed human activities, economic growth and technological change. Climate change projections for the Australian region are based on emission scenarios developed by the IPCC that are described in the Special Report on Emission Scenarios (IPCC, 2000). These scenarios assume “business as usual” without explicit policies to limit greenhouse gas emissions, although some scenarios include other environmental policies that indirectly affect greenhouse gases, for example, policies to reduce air pollution. Finding appropriate methods to incorporate more general socio-economic factors into climate change projections poses one of the main challenges in the integrated assessment of climate change impacts.

Climate projections are presented as ranges rather than as single values. The ranges incorporate quantifiable uncertainties associated with future emission scenarios, global climate sensitivity, and model-to-model differences in the regional patterns of climate change. Global climate sensitivity is defined as the simulated global warming for a

11

doubling of carbon dioxide concentration. Regional climate projections on a 25km grid for Australia, based on the IPCC (2000) emission scenarios can be obtained from the OzClim model (CSIRO 2005).

CSIRO projections for Australia using up to 13 climate models driven by the IPCC(2000) emission scenarios indicate that by 2030 annual average temperatures will be 0.4 to 2.0°C higher over most of Australia, with slightly less warming in some coastal areas and Tasmania, and the potential for greater warming in the north-west (CSIRO, 2001). By 2070, annual average temperatures are increased by 1.0 to 6.0°C over most of Australia with spatial variation similar to those for 2030. The range of warming is greatest in spring and least in winter. In the northwest, the greatest potential warming occurs in summer.

Projected annual average rainfall changes tend towards a decrease in the southwest and in parts of the southeast and Queensland. In some other areas, including much of eastern Australia, projected ranges suggest large changes in rainfall, though it is less clear whether they will tend towards a decrease or an increase. The projected ranges for the tropical north represent little change from current conditions. Previous studies generally agree that Australian rainfall will decrease on average in most regions (CSIRO, 1996; Hulme and Sheard, 1999). Exceptions to this trend are southern Victoria and Tasmania in winter and eastern Australia in summer where rainfall is not expected to change significantly from current conditions.

Overall, drier conditions are anticipated for most of Australia over the next century. However, this overall decrease is expected to be accompanied by an increase in heavy rainfall. This is a result of the shift in the frequency distribution of daily rainfall toward fewer light rainfall events and more heavy events (McCarthy et al., 2001).

Model simulations incorporating enhanced greenhouse conditions suggest that extreme rainfall will increase in mid-latitudes, where average rainfall increases, or decreases slightly (IPCC, 2001). In addition to changes in intensity, mid-latitude storms may also change their frequency and location in response to changes in the westerlies and the Southern Oscillation (McCarthy, et al., 2001). Potential increases in the intensity of 1-in-20 year daily rainfall events have been projected for parts of South Australia (McInnes et al., 2003), for some NSW regions (Hennessy et al., 1998) and for Victoria (Whetton et al., 2002). Studies focussing on Queensland suggest increases of up to 30% by 2040 in the southeast (Abbs 2004), and an increase in 1-in-20 year daily rainfall intensity of 25% in northern Queensland (Walsh et al. 2001). Decreases in extreme rainfall are likely in the Sydney region (Hennessy et al., 2004b).

The locations of tropical cyclone genesis in the Australian region are correlated with ENSO (Evans and Allan, 1992; Basher and Zheng, 1995) so any change in the mean state of the equatorial Pacific may affect the incidence of tropical cyclones in particular locations. IPCC projections suggest that by 2070 tropical cyclone frequency may change in some regions, peak wind speeds may increase by 5-10% and peak rainfall intensities may rise by 20-30% (IPCC, 2001).

Interannual variability in ENSO leads to major floods and droughts in Australia. This variability is expected to continue under enhanced greenhouse conditions, though possibly with greater hydrological extremes as a result of more intense rainfall, and hence flooding, in La Niña years and more intense drought resulting from higher rates of evaporation during

12

El Niño years (Walsh et al., 2001). A more El Niño-like mean state over the tropical Pacific would imply greater drought frequency, as does the drying trend found over the Murry-Darling basin in recent simulations (Kothavala, 1999; Arnell 1999; Walsh et al., 2001). The incidence of wildfire in Australia is also expected to increase as the continent becomes more drought-prone (Beer and Williams 1995; Pittock et al., 1999; Williams et al., 2001).

Simulations of wind-speed show a tendency for annual-average wind-speed to increase by up to 3% by the year 2030 and up to 12% by 2070. Increases in seasonal wind-speed tend to be quite widespread in summer and spring, with the largest increases in summer-average wind-speed expected along the northern and western coasts. There is, however, a tendency for decreases in summer-average wind-speed in southeast and northern Queensland and northeast NSW. Autumn-average wind-speed is expected to increase between the latitudes 25-30°S, with a tendency for decreases to the north and south of this band. In winter wind-speed is expected to decrease between 30-35°S with a tendency for increase to the north and south of this band.

Simulations suggest that in the future annual average humidity will tend to decrease over most of the continent. Seasonally, summer and autumn humidity are expected to decrease by as much as 3% by 2030 and up to 9% by 2070. However, increases of up to 1.5% and 4% in 2030 and 2070, respectively, are possible in parts of NSW, Southern Queensland, western Northern Territory and central Western Australia. More significant and widespread decreases in humidity are expected in winter and spring, reflecting the tendency for decreased rainfall in these seasons.

Projections for solar radiation are limited by the unavailability of pertinent data and should therefore be viewed with caution. Annual-average radiation is expected to decrease in the western-half of Australia with the possibility of increases or decreases in the east. Decreases in solar radiation are strongest in summer and cover most of the western and southern parts of the continent. In autumn, decreases in radiation will affect most of Australia while in winter and spring, increases in solar radiation are expected in the south and east, with decreases in the northwest.

Mean sea-level is expected to continue to rise into the future due to the thermal expansion of sea water, melting of glaciers and polar ice-sheets (IPCC, 2001). Local and regional variations in sea-level rises are likely to occur as a result of land-sea movements and changes to ocean currents and climate forcing. Simulations suggest that mean sea-level will rise, relative to the 1990 level, 3-17 cm by 2030 and 7-52 cm by 2070.

For more detailed climate projections for individual States and Territories see (CSIRO 2005).

1.3 Abrupt nonlinear climate changes

Nonlinearities inherent in the functioning of the Earth’s climatic system provide the potential for abrupt, extreme or irreversible changes in climate. Examples of such changes that have occurred in the past include the rapid shifts in temperature in the North Atlantic during the last ice age, the formation of the Antarctic ozone hole, the mid-Holocene shift of North African ecosystems from savanna to desert and destabilisation of soil carbon under global warming. The impacts of such events are neither local nor isolated. For example, the

13

shutdown of the North Atlantic thermohaline circulation would have significant consequences for Australia.

The dramatic decline in rainfall in southwest Western Australia and the anomalous drought in the eastern states may already serve as examples of such changes in Australia. Abrupt changes in climate are less foreseeable, provide less time to adapt and thus would have far greater economic and environmental impacts than gradual warming (Mastrandea and Schneider, 2001).

2 POTENTIAL IMPACTS OF CLIMATE CHANGE

A comprehensive guide to the potential impacts of climate change has been presented by Pittock (2003). An assessment of these impacts across the sectors summarised below shows a high degree of interaction. Water resources are a critical component of most sectors, particularly agriculture, biodiversity, human health and urban centres. This well illustrates the role of the water cycle as the great global integrator (White 2005). The existing problems of increasing demand for water resources are likely to be exacerbated by climate change. Biodiversity is also seen to be highly cross-sectoral, with a wide range of types land ownership and interactions with many policy areas (Williams 2005). These include interactions with agricultural and forestry systems with the increasing recognition of the role of farmers in conserving biodiversity (Williams 2005; Chesson 2005). Human health, urban centres and agriculture all have important interactions relating to climate extremes and water supply (McMichael 2005, Troy 2005, Crimp et al. 2005). Tourism and marine ecosystems are also strongly linked (Marshall 2005).

Not surprisingly, common needs arise from these sectors. These include the need to address uncertainty, with respect to the current climate as well as for projected future climates. This highlights the role of risk management and adaptive management to minimise the adverse impacts of climate variability and change. There are also commonly perceived needs for increased awareness and understanding of potential climate change impacts, for key supporting data, for building human capacity and to address land use change. These raise issues for policy across a range of scales. It is the role of integrated assessment to address these cross-sectoral issues.

2.1 Water resources

Growing demands for food and fibre due to expansion of the human population have increased pressures on freshwater and land resources and their dependent ecosystems. In Australia, water resources are already highly vulnerable with intense competition for water supply between agriculture, power generation, urban areas and environmental flows. Projected climate changes will adversely affect the availability of water in many areas with consequent impacts on environmental flows, agriculture and other industries. The extreme variability of the Australian climate, the vulnerability of its ancient landscape and the hysteretic nature of some aquatic and land systems suggest that climate change may have far reaching consequences on the quantity and quality of fresh water. The continued impact of the January 2003 bushfires on fresh water quality and availability in the Canberra region is but one recent example. This example also highlights the significant impacts joint-probability events can have, in this case the bushfire followed by the intense rainfall

14

constituted a 1-in-400 year event. Catastrophic events such as bushfires also put additional demands on water availability as burnt forests regrow.

Water quality is also at risk from climate change as many of the microbial processes that take place within water are temperature dependent. This can then lead to infection of groundwater systems with bacteria such as ecoli. Estuarine fisheries such as oyster farms in the Hawkesbury River are also at risk due to climate change, particularly through its effect on drought. Occurrence of ‘Queensland unknown’ (QX) disease in the Hawkesbury has been linked to drought incidence. Drought is only part of the problem. Land-use changes are also important.

The National Water Initiative (NWI) recognises that there are significant knowledge and capacity building needs for its implementation. These include understanding changes to water availability and the interaction between surface and groundwater as a result of climate and land use change. Understanding of the ecological outcomes from environmental flows and the catchment processes that impact on water quality is also needed. The NWI recognises that these knowledge gaps are multi-disciplinary, involving the interaction of scientific, social and economic aspects of water, and extend beyond the capacity of any one research institution. The consequences of nonlinear changes in freshwater supply systems are mostly ignored in present water research institution in Australia (White 2005).

2.2 Biodiversity

Climate change has been identified as a major threat to biodiversity and has the potential to invalidate traditional assumptions about biodiversity management. Even though there is a lack of long-term data sets and active monitoring programs in Australia, which makes it difficult to quantify the biological impacts of climate change, there is evidence of thickening of vegetation in eucalypt woodlands as a result of increased supply of carbon dioxide and the increased establishment of snow gums in sub-alpine meadows. In the past, natural climate change has caused large-scale shifts in the geographic ranges of species, the composition of biological communities and extinctions of species. Natural systems are expected to respond to anthropogenic climate change in a similar way, but the effect will be more severe because of the extremely rapid rate of the projected change. Moreover, destruction of habitat due to human activities will prevent many species from colonising new habitat when their old habitat becomes unsuitable. The combined effects of climate change and habitat destruction would threaten many more species than either factor alone (Peters 1990).

The impacts of climate change will be further confounded by interactions and responses of invasive species and fire regimes, especially in regions where vegetation and other habitats are fragmented. Some biological responses to climate change will be nonlinear and may involve time lags.

Dealing with the pervasive uncertainties associated with climate change and its impact on biodiversity would include assessing climate change impacts in relation to a variety of other pressures and forcing factors, including mitigation. For example, mitigation strategies such as the cessation of broad-scale clearing can have a range of impacts on biodiversity. A greater understanding is particularly needed about the potential impacts of climate change and management interventions at the regional level where natural resource management efforts are focused in Australia.

15

The cross-sectoral nature of biodiversity adds another layer of complexity to adapting to climate change. Natural systems are owned and managed by governments (e.g. in state forests and national parks), non-government organisations (e.g. the growing number of groups buying land for conservation), the agricultural sector (much of the biodiversity in Australia is found on private land) and by indigenous land managers (whose land often has limited agricultural value). Additionally, expenditure or economic activity across a wide range of commercial sectors and policy portfolios affects or is affected by biodiversity. Policies and programs on regional development, trade, urban planning and taxation can have an impact on biodiversity. Integration of biodiversity, and the potential impact of climate change, will be required across a range of domestic government policies, international agreements and the private sector (Williams 2005). 2.3 Agriculture

The effect of climate change on the distribution, frequency and severity of drought is a major concern for agricultural industries in Australia. Coupled with the projected changes in rainfall and the rise in temperature and evaporation there is a high probability that Australian farmers will need to operate in a drier climate with possibly declining standards of water quality. Predicting the impacts of these changes is complicated, however, because the environmental changes interact: increased carbon dioxide boosts plant productivity and changes water use efficiency, while other changes in climate could offset or even enhance these benefits, depending on the circumstances (BRS, 2004).

The diversification of on-farm production and the use of seasonal climate forecasting have served to mitigate the impact of climatic variability on production to some degree. However, longer term climatic variations on decadal and multi-decadal timescales have yet to be considered in a fully integrated way. There is a growing realisation that longer term climate variations (including both natural and anthropomorphic drivers) contribute to the overall vulnerability of an enterprise. The assessment and management of these aspects of climate risk remains limited due to the complexity and multi-dimensional nature of the drivers in question (Crimp et al. 2005).

In southern Australia changes in winter and spring rainfall are likely to increase moisture stress on wheat crops, even in the face of some carbon dioxide fertilisation (CSIRO 2001). The positive response of wheat to increased carbon dioxide levels may also be offset by lower grain protein content. The projected decreases in frost incidence and severity are likely to result in a reduction of frost damaged fruit, though temperate fruits need winter chilling to ensure normal bud-burst and fruit set. In northern Australia, where consensus model projections show little change in simulated summer rainfall (the main growing season for pastures in that area), there may be positive impacts on plant production.

Since the late 1980s studies have been conducted on potential impacts of climate change on Australia’s grazing industry and individual components of the climate scenario, such as the impact on frequency of droughts in extensive grazing industries, impacts on carrying capacity and heat stress. This poses a problem in developing adaptation responses in the grazing industry due to the limited commonality of most sensitivity studies. A systematic approach is required to develop more comprehensive adaptation strategies for the grazing industry. These should link regional production to location and regional land use so that the

16

climatic impacts on the grazing industry can be calculate and synthesised into a comprehensive impact analysis (Crimp et al. 2005).

Under enhanced greenhouse conditions there are likely to be direct impacts on agricultural industries including changes in the productivity of agricultural lands, effects on individuals and farm businesses including mental health problems, effects on the quantity and quality of water and changes in the capacity of agricultural lands to support biodiversity conservation. Indirect, or flow-on effects of climate change on agriculture include impacts on other biophysical systems through changes in energy use, the human and social capital available to the industry and contributions of the industry to local and regional communities as well as the nation as a whole (Chesson 2005).

2.4 Human health

Projected climate changes are likely to have significant effects on human health in Australia. Expert consensus suggest that the changes in climate variability that will accompany climate change, especially the frequency, intensity and location of extreme events, will have much greater health, social and economic impacts than underlying changes in mean conditions. Future modelling of population health risk will need to take account of this dimension (McMichael 2005).

Increased thermal stress due to the higher incidence of heatwaves, coupled with an ageing population, is expected to result in an average of several hundred more deaths annually in all major cities. Unlike other countries such as the United Kingdom, increases in heat-related mortality in Australia are unlikely to be offset much by decreases in cold-related mortality. Increased atmospheric warming is also likely to result in a southward extension of the transmission zones of vector-borne diseases such as dengue fever, Ross River virus and malaria (Martens and McMichael 2001). Warming is also expected to increase the viability of malarial parasites (McMichael 1997). The risk of diarrhoeal disease is also expected to increase under enhanced greenhouse conditions. This increase will predominantly occur in summer months and especially in remote and rural communities. Indigenous communities are particularly prone with research suggesting a 15% increase in diarrhoeal hospitalisations in Aboriginal children living around Alice Springs by 2030 (McMichael 2003).

Other potential health impacts of climate change include those associated with more severe inland flooding, increased incidence of depression, suicide and other mental health problems associated with drought (Butler et al. 2005), increased frequency of food-borne diseases such as salmonellosis (D'Souza et al. 2004), a higher incidence of skin cancer due to ozone depletion and general health effects due to shortages of food and water.

The process of integrated assessment of population health, as currently practised, is usually inherently conservative in that it assumes future smooth changes in average conditions and in the extent of climatic variability. Abrupt changes and the consequences of passing critical thresholds are much less easy to foresee and model. This has been well illustrated by the integrated assessment of climate change impacts on cereal grain yields. This has been based on physiological models of how temperature and soil moisture affect plant growth. These models have not been able to take account of a change in pattern of outbreaks of plant pests and diseases.

17

A more sophisticated approach to the assessment of climate change impacts on human health would incorporate information about ongoing trends in other determinants of health outcomes believed to be reasonably extrapolatable (e.g. demographic trends in age structures), likely future contextual conditions (e.g. uptake of air-conditioning by 2050; advent of relevant vaccines and likely consequent population immunity level), and deliberate adaptive changes (e.g. mosquito control programs, heatwave warning systems, flood protection measures).

Until now there has been minimal attention paid to considering how people and health systems might respond to climate change, or interact to reduce exposure and enhance adaptive capacity. The estimation of future health impacts will be improved by an understanding of the opportunities for (and the natural limits of) adaptive responses. This work will need to be informed by an investigation of how people have responded to and managed their vulnerability to past and present climate stresses. Adaptation can be separated into two categories – planned (activities conducted by health or other government bodies), and autonomous (individual responses to changing climate). Effective adaptation will need to consider the effects of the multiple interacting stresses that influence individual and social adaptive capacity (such as social and economic status, geographic location), in addition to the driver of climate change itself (McMichael 2005).

2.5 Forestry

Anthropogenic climate change has the potential to impact on forestry in both positive and negative ways. Greenhouse conditions imply a warmer climate and a more CO2-rich atmosphere that can actually enhance plant growth and generally increased yields. On the other hand, model predictions suggest below expected yields of forestry products due to poor distribution of rainfall and temperature. Climate change is also likely to affect the timing of harvesting regimes. Sawlogs and peeler logs have a minimum small-end diameter limit and are harvested soon after the minimum limits have been reached. Climate change may affect the time it takes to achieve these limits thus altering the product yield if timing is retained or requiring longer or shorter rotations and cutting cycles (Brack and Richards, 2002; Richards and Brack, 2004ab).

Climate change also has the potential to affect the viability of certain pest and disease problems leading to catastrophic defoliation or death of forest species. Shugart et al. (2003) found for the United States that species generally migrate polewards or to higher latitudes in response to increased temperatures, but that species mix may change and rates of migration will depend on seed dispersal, the spread of insects and disease and the role of wildlife and human intervention.

The frequency and severity of drought is projected to increase in southern Australia. Hanson and Weltzin (2000) argue that drought leads to a net reduction in primary productivity, increased mortality of seedlings and saplings, and increased susceptibility to insects and disease. Moreover, drought induced reductions in decomposition rates may cause a build-up of organic material on the forest floor, with ramifications for fire regimes and nutrient recycling. Increased drought conditions in Australia are likely to be associated with increased occurrence of fire (Cary 2002). Altered fire regimes are likely to have a significant impact on forestry.

18

Howden and Gorman (1999) review the impact of projected global change on Australian temperate forests. Productivity of exotic softwood and native hardwood plantations is likely to be increased by carbon dioxide fertilisation, although the amount of increase is limited by various acclimation processes and environmental feedbacks through nutrient cycling. Where trees are not water limited, warming may expand the growing season in southern Australia, but increased fire hazard and pests may negate some gains. Reduced rainfall in recent scenarios would have an adverse effect on productivity and increase fire risk. Increased rainfall intensity would exacerbate soil erosion and pollution of streams during forestry operations (Cary 2002).

2.6 Marine ecosystems

Marine ecosystems are among the most vulnerable to climate change and many are already showing the signs of impacts that can be attributed to changes in environmental factors that are consistent with projected climate change. Climate change is expected to result in the warming of sea temperatures, changes in ocean currents and altered ocean chemistry. Mass mortalities due to coral bleaching have been reported with increasing frequency from around the world over the last decades. The Great Barrier Reef has had widespread bleaching in 1998 and 2002, although it has suffered until now relatively low levels of coral death. These temperature events are expected to have a wide range of flow-on effects throughout the reef ecosystem and dependent human communities (Marshall 2005). Lowered seawater salinity as a result of flooding of major rivers in early 1998 are also believed to have been a major factor in exacerbating the effects of inshore coral bleaching (Berkelmans and Oliver 1999).

Many marine species are highly sensitive to small changes in average sea temperature. Over a prolonged period, changes in average sea temperature of 1-2°C can impact on the growth rates and patterns of reproduction of certain marine species. Coral bleaching results when increases in average sea temperature lead to a break down in the symbiotic relationship between coral and algae living within the coral tissue. Species such as sea turtles, for which temperature plays a crucial role in determining the sex of young, provide another clear example of how climate change can affect marine creatures. Changes in sea surface temperature also correlate with the demise of kelp forests off the east coast of Tasmania. Temperature changes can also affect the balance between predators and prey, the susceptibility of organisms to disease, nutrient cycling and other energy flows, with follow-on effects to fisheries and other species that might not be vulnerable to sea temperature changes themselves.

The location and timing of ocean currents are an important factor in marine ecosystems. Currents carry the young of an enormous diversity of marine species and thus play a key role in their dispersal and the maintenance of populations. Currents are also a major influence in nutrient transport, bringing nutrient-rich waters to the surface through upwelling. Climate change is expected to alter ocean circulation patterns, leading to changes that interrupt the life cycle of many species and impact on local populations. Fisheries are likely to be affected by changes in the extent and locations of nutrient upwelling. Changes in ENSO, which influences recruitment of some fish species and the incidence of toxic algal blooms, are also likely to have an affect on fisheries. Overall, climate change is expected to lead to changes in productivity of some fisheries, though these have not yet been well documented.

19

Changes in ocean chemistry can also be expected under enhanced greenhouse conditions, with a decrease in the availability of carbonate ions a particular concern. Carbonate ions are essential in the creation of skeletons for many key marine species, such as planktonic organisms, which are thought to play important roles in ocean-atmosphere interactions through cloud formation. Changes in ocean chemistry are thus likely to diminish the critical processes necessary to maintain functioning coral reef ecosystems.

Impact monitoring programs designed to detect changes in marine ecosystems tat might be attributed to climate change are few and not well coordinated. Many impacts are expected to manifest gradually (such as shifts in the range of species), and are difficult to detect due to background natural variation and the effects of other stressors. Other impacts, such as coral bleaching, are more dramatic but are still difficult to measure due to their unpredictability. The coral bleaching response program of the Great Barrier Reef Marine Park Authority (GBRMPA) is complemented by detailed data on critical environmental variables. GBRMPA is collaborating with Australian and overseas authorities to examine the likely effects of future climate change on reef ecosystems and dependent human communities.

2.7 Urban centres

Climate change has already effected planning and policy in major urban centres and projected changes in rainfall and temperature are likely to cause further problems for urban areas (Troy 2005). Over the last few years, cities in eastern Australia have seen their potable water supplies diminished significantly and as a consequence harsh water restrictions have been imposed. While cutting back on garden watering might make a short term saving in some cities, the irony is that such measures actually increase urban micro-climate temperatures that in turn result in more households using air conditioning. The associated increase in energy consumption then directly increases production of greenhouse gases. Energy consumption is also likely to increase in cities under enhanced greenhouse conditions due to increased temperatures alone.

Population pressure has caused the reshaping of cities towards an increase in the density of infrastructure and housing. This again leads to an increase in energy consumption including that embodied in the buildings and that due to enhanced heat-island effects. Positive feedbacks through the use of cooling systems ensue. The current conventions and attitudes to water use and the existing water delivery infrastructure have become impediments to attempts to reshape consumption and bring it more into line with environmental capacity to meet demand. New approaches are needed to reshape demand of both water and energy of Australian cities (Troy 2005).

Urban infrastructure is also potentially at greater risk of extreme weather events such as hail-producing thunderstorms, lightning strikes and windstorms. Cities in coastal areas are particularly vulnerable to sea level rises and increased risk of storm surges. If current trends of increasing migration to coastal settlements and building of infrastructure near beaches continue, the above-mentioned risks will become more profound. Follow-on effects are likely to impact sectors such as emergency management and the insurance industry. Building local resilience as part of the recovery from emergency situations is likely to place demands on social capital.

2.8 Tourism

20

Tourism is an important industry for many regional centres, as well as for Australia as a whole, and can be impacted by projected climate changes in a number of ways. Many northern Australian coastal resorts rely heavily on the attractiveness of the coral reefs, most notably those associated with the Great Barrier Reef, but also others in Western Australia. The appearance, and ultimately the function of coral reefs are threatened by global warming through more frequent coral bleaching events, which can lead to death of corals and their replacement by algae and weed-based ecosystems that are far less attractive. Damage to coral reefs due to increases in the intensity of tropical cyclones is likely to exacerbate this problem (Pittock, 2003). Infrastructure associated with coastal resorts is also susceptible to the effects of climate change. The impacts of sea level rise and more severe storm surges on coastal resorts are discussed in McInnes, et al. (2000).

Higher temperatures will accentuate algal blooms. Algal blooms and shoreline erosion related to sea level rise are also factors impacting upon the tourism industry in other locations such as the Gippsland Lakes in Victoria (Gippsland Coastal Board, 2002; 2003), for example.

Several studies have considered tourist preferences in relation to climate, particularly temperature and thermal comfort. These studies suggest that, subject to some regional variations, increasing thermal indices and physiological discomfort, and the possibility of increased risk of tropical cyclones might reduce tourism in some tropical destinations in Australia, while warmer conditions may make some cooler destinations more appealing (Pittock, 2003).

The ski industry is also likely to be affected by climate change, with significant reductions in natural snow depth and snow cover duration. The immediate response of the ski tourism industry might be to increase artificial snow-making although such a response is likely to become less viable in the long term (Hennessy, et al., 2003).

2.9 Cumulative Impacts

In many of the above categories of impacts, it may be that crucial vulnerabilities will be exposed where multiple impacts occur cumulatively, whether different impacts at one time or cumulation of impacts over time. As Australia is a developed country with highly evolved institutional, policy and informational systems, many impacts will, despite costs incurred, not overwhelm coping capacities (although larger-than-expected events and impacts may). However, a combination of climate-related impacts may challenge coping capacities – for example, drought and related fire, or storm surge in combination with a hail event, may impact on multiple areas and assets at a given time. This presents a challenge to integrated assessment methods, but integrated assessment should by definition have greater purchase on such possible scenarios than narrower assessment techniques.

21

3 INTEGRATED ASSESSMENT

3.1 What is IA?

Effective environmental management requires the consideration and understanding of complex interactions between various economic, social and environmental outcomes. Any management option or decision (including ‘do nothing’) will lead to both costs and benefits being incurred by different groups and potentially positive and negative impacts on the environmental system. These costs and benefits may occur over very different time scales, with many costs associated with environmental damage not being seen for years and in some cases decades, while some economic and social costs may be more immediate. There is an increasing awareness of the complexity of evaluating these types of trade-offs to inform decision-making. In general, models or assessments that integrate considerations and understanding developed across a broad range of fields are required to understand and evaluate these trade-offs.

Integrated assessment (IA) can be defined as ‘the interdisciplinary process of integrating knowledge from various disciplines and stakeholder groups in order to evaluate a problem situation from different perspectives and provide support for its solution: IA should support policy and decision processes IA should help identify desirable and possible options.

Hence IA builds on two major methodological pillars: Approaches to integrating knowledge about a problem domain Understanding of policy and decision making process.’ (Pahl-Wostl, 2004)

Integrated assessment provides a vehicle for addressing all key issues affecting the sustainability of a system by combining the knowledge and understanding from different research areas, such as economics, psychology, ecology and hydrology. A better understanding of the complex interactions occurring within the system must include the needs and concerns of communities and industries, as well as the environment.

According to Farrell and Jaeger (2006) IA is part of a long term communication process and needs to be cognisant of the associated issue domain. This includes the principal actors within the sectors - with a range of interests, resources and beliefs, who seek strategies to advance their interests in the face of climate change; institutional settings - that regulate interactions within the sectors and with the world outside; behaviours – the decisions, policies and agreements that emerge from these interactions; and finally the impacts of these behaviours on the world - such as enhanced resilience to climate change and improvements in environmental quality.

Farrell and Jaeger define effectiveness as having a significant influence on the issue domain. This can include a wide range of outcomes such as formulation and evaluation of policy options, improvements in scientific knowledge, prevention or delay of environmentally harmful actions, establishment of environmental regulations and enhanced prestige. Farrell and Jaeger identify three key requirements to achieve effectiveness:

Salience or relevance – the assessment needs active participation from the organisations impacted. The assessment must address an issue in which users are interested and be

22

relevant to action that users can actually take. The assessment process must be able to adapt to changes required by the user community.

Credibility – the authoritative technical community must find the assessment acceptable.

Legitimacy – political acceptability and fairness. The users must believe that the process respects the rules and norms of relevant institutions and that the interests of the users have been acknowledged and taken into account.

IA for climate change has been defined as ‘an interdisciplinary process of combining, interpreting and communicating knowledge from diverse scientific disciplines in such a way that the whole cause–effect chain of a problem can be evaluated from a synoptic perspective with two characteristics: (i) it should have added value compared to single disciplinary assessment; and (ii) it should provide useful information to decision makers (Rotmans and Dowlatabadi, 1997)’ (quoted from van der Sluijs, 2002). IA for climate change allows for cross sector assessment of the impacts of climate change, and consideration of the connections between different sections of the community and environment and the way in which this affects their vulnerability to climate change impacts. This assessment can be used to identify the extent and nature of risks facing these community and environmental sectors in light of climate change as well as potential adaptation or management options that can be used to ameliorate potential negative impacts.

Integrated assessments have a number of common features (adapted from Jakeman and Letcher, 2003).

1. IA is a problem-focussed activity, needs driven; and likely project based. IA is rarely theoretical. The approach emphasises the importance of learning by case studies and through applications (frequently project based research). In terms of climate change, this means that IA is likely to focus on specific geographic or sectoral issues with an emphasis on learning from the experiences of this assessment for alternative situations. An IA must also be scoped to issues and impacts of interest to policy makers and the general community, rather than those of purely scientific interest.

2. IA links policy to research. IA emphasises the importance of close links between research and policy. Research questions need to be defined in conjunction with policy makers to ensure that the focus of research activity is aimed at delivering information required to inform better policy. Researchers must also be sensitive to the timeframes of policy makers and the need to provide the best available science in the time available, rather than attempting to hold policy back until the science (or data) is considered to be ‘good enough’. IA and policy relating to such complex issues thus needs to embrace the concept of an iterative and adaptive approach to both science and policy development to allow for the potential conflict between scientific and policy timeframes.

3. IA must focus on key elements, and while aiming to be inclusive of a broad view of impacts and connectivities, must be managed carefully to ensure that the assessment is feasible. Processes and impacts that are important but for which there is limited information should be included in the assessment in some form to acknowledge their importance. Exclusion of these processes can lead to the perception that these issues are of little interest or importance in the assessment.

23

3.1 Why is IA needed?

In general there has been a trend towards taking account of many different values - ecological, cultural, social and economic - in decision making processes (Palmer, 1992; Syme et al., 1994). Ewing et al. (1997) state that this has been a consequence of the increasing dissatisfaction that decision makers feel with 'the outcomes resulting from 'narrowly-focussed, incremental, and disjointed' environmental management'. They maintain that '[i]t is now well-recognised that earlier approaches to environmental management usually failed to deal with the many interconnections and complexities within and between, the physical and human environment'. Born and Sonzogni (1995) echo this view of past approaches to environmental management when considering water resources stating that '[o]ver the years, much of water resources management has been of limited purpose, focused on only a portion of the watershed, and implemented incrementally'.

Integrated assessment is a response to the change in policy and research agendas from compartmentalised treatment of distinct sectors and issues, to the broader and more integrated agenda of sustainable development (or ecologically sustainable development (ESD) in Australian policy and law – eg. EPBC Act 1999). In particular, ESD seeks to bring environmental, social and economic considerations together, and recognises the interdependence of resource sectors, economic management, and human development. This demands integrated approaches in research, in policy support methods, and in policy making processes. Climate change is a major ESD issue, and IA has a clear role in informing policy considerations, but IA and related approaches are similarly developing in areas such as catchment management and landscape-wide policy responses to biodiversity loss. It is advisable to ensure that evolving IA methodological developments in such areas are cross-referenced so as to maximise learning.

Mirroring this integration is the need for integration in modelling and assessment of natural resource and environmental systems to provide the answers required by integrated system managers. For example, Park and Seaton (1996) stress the importance of linking scientific research to policy, and see the need for an integrated approach, particularly with the social sciences, for making this come about. Geurts and Joldersma (2001) state that 'policy analysts that use traditional formal modeling techniques have limited impact on policy makers regarding complex policy problems'. They argue that 'these kinds of problems require the combination of scientific insights with subjective knowledge resources and improved communication between various parties involved in the policy problem'. Villa and Costanza (2000) argue that different modelling approaches need to be integrated into higher-level simulation models because of the 'increasing complexity and multidisciplinarity of environmental research and management problems, the spatial and cultural delocalization of research groups, and the increasing recognition of the need for a multiplicity of scales to be considered at the same time'.

From a Government perspective IA encourages the use of a cross-portfolio or whole-of-government approach to managing climate change issues. While climate impacts are of obvious interest to the Australian Greenhouse Office, climate change is likely to have impacts on a range of interests such as tourism, water availability, agriculture, health, biodiversity (see Section 1). These will impact on urban and rural communities and regional economies. Possible adaptations or options to ameliorate these impacts will have implications for sectors such as mining, transport and energy. Clearly effectively dealing

24

with the impacts of climate change will require considerable cooperation between Government agencies, across Federal, State and Local government jurisdictions. This will require development of a shared terminology and understanding of the issues as well as the actions required. IA is one method for developing such an understanding.

3.2 What does IA require?

The features of IA place certain requirements and restrictions on methods used as part of an integrated assessment of climate change. Methods must be developed to serve the needs of other disciplinary components incorporated in the analysis as well as being robust and defensible in a disciplinary sense. Methods must remain relatively simple while retaining appropriate levels of accuracy and sensitivity to key assumptions. Simplicity has the additional advantage that assumptions underlying the assessment can be more easily communicated and discussed with a wide range of stakeholders, a key requirement of IA. Given the importance of including key issues where very limited information is available, assessments often contain components that are based on assumptions or expert or local knowledge.

Some of the following considerations should commonly arise in integrated assessments of natural resources management issues: Climate variability and episodes – these often have a profound effect on outcomes.

Variability can affect the returns of an investment in production as well as the response of an ecosystem while episodes such as floods can have an inordinate effect on outputs. Both raise issues of appropriate time periods and time steps over which to assess scenarios.

Representations of process complexity – once the basic processes and causal relations are decided upon, often there is still much scope for selecting the level of underlying detail including the spatial and temporal discretisation of process representation in an IA. Data paucity, especially of system behaviour, should limit the assessment complexity.

Beyond business-as-usual scenarios – the nature of environmental or social decline may mean substantial changes to the current situation are required. Other public and private investments, policy incentives and institutional arrangements will be needed to change resource activities.

Consideration of long leads and time lags – the timeframes for returns on investments and for ecosystems to respond to changes affect both the period and the temporal resolution over assessments are made.

Narrowing assessment objectives – in addition to simplifying types of models, scales, system boundaries etc., it is critical to keep the level of integration of issues and disciplines manageable in any integrated assessment exercise.

Uncertainty – it is desirable to reduce and, where possible, characterise uncertainty; the latter needs methodological attention by IA researchers.

System representation – there is a need to balance the extent of the capacity to characterise feedbacks and interactions with keeping assessment components and linkages effective but efficient.

Participation – refers to the inclusion of interest groups, multiple government portfolios or general community members in the assessment process and is a common approach used in integrated assessment. Participation aims at encouraging an environment of ‘learning by doing’ within the assessment. This learning is multidirectional – participation is not solely about educating community, but also acknowledges the

25

importance of the knowledge they bring to an assessment. Participation also aims at increasing the ownership of communities of the climate change issue as well as of the decisions required to mitigate impacts. It seeks to increase adoption and acceptance of final decisions by including a broad representation of government and community interests in their development.

Government coordination – by its nature IA generally involves a range of issues that cross various levels of Government interest and responsibility. This requires both cross-portfolio cooperation as well as cooperation between Federal, State and Local Governments. This means that an IA must operate in an environment or changing government priorities and will rely on a good degree of cooperation between government interests. Researchers must be sensitive to these needs and must be flexible in their approach to allow for the challenges this creates. Governments must commit to the demanding task of this cooperation, committing time and other resources to ensure its success.

Teams and communication – IA requires the use of experts from specific fields as well as generalists capable of thinking across a broad range of issues. Teams which undertake IA will represent a range of scientific, community and government interests. One of the challenges of IA is communication within these teams. This involves the development of a shared language and understanding of the problem. It also requires team members develop trust and respect for each other skills and expertise. This can be very time consuming and also means that the choice of team members is key to the success of IA.

3.3 Connections to policy

Integrated assessment is essentially a science focused on the aims and needs of policy and policy makers. Appendix 1 provides a detailed discussion of the connections between IA and policy. This section provides a brief summary of key points in this discussion.

A comprehensive approach to policy making includes four key phases: problem framing; policy framing; policy implementation; and policy monitoring and evaluation. This approach is described in more detail in Section 4 (as a potential IA framework). IA has a clear role to play in the problem framing component of policy development and to a lesser extent can play a role in policy monitoring and evaluation. Thus it is possible to embed IA directly in the policy development and implementation process.

Four different types of policy learning can also be identified: instrumental learning; government learning; social learning and political learning. It is likely to IA adds principally to the process of social learning, which explicitly seeks to redefine problems and goals, considering how useful constructions of policies and goals are. It is also possible that IA will be used for instrumental learning, where learning is focused on the critique of instruments in achieving goals. It is also possible that IA will lead to political learning in some situations, where political actors learn about the most effective ways to engage with and influence political and policy processes. Government learning is an unlikely outcome from IA, given that it is focused on understanding how well administrative arrangements and processes have allowed policy implementation.

Finally IA has the potential to aid in interagency and cross-sectoral integration of policy initiatives. While this is one of the benefits of an IA approach, it must be said that

26

achieving such integration will still be very difficult and will require substantial resources and commitment across agencies for this to be potential to be achieved.

27

4 INTEGRATED ASSESSMENT OF CLIMATE CHANGE

As discussed in Section 3, the key distinguishing elements of Integrated Assessment are cross-sectoral integration and a high degree of involvement by “stakeholders.” The degree of integration, the type of integration, and the level of involvement by “stakeholders” in the assessment process must be determined for each assessment. This section provides an overview of some of the conceptual frameworks that have been devised to describe integrated assessment processes relevant to climate change some of the approaches or paradigms used for these assessments, and some of the most common tools (e.g. models) used.

4.1 Frameworks for integrated assessment of climate change

Four conceptual frameworks for integrated assessments were identified as particularly relevant for assessing the impacts of climate change: (1) a framework for environmental and sustainability policy (Dovers, 2005), (2) a framework for assessing the risks of climate change (Jones, 2001), (3) the framework devised by the Millenium Ecosystem Assessment (MEA, 2005) and (4) a framework for research integration (Brinsmead, 2005). This is not an exhaustive list of the frameworks that are relevant to integrated assessment of climate change but they illustrate the key components and requirements and provide a good basis for designing the framework for future integrated assessments of climate change. A brief overview of each follows.

28

4.1.1 A policy framework

The process of creating public policy is integrative and ‘stakeholder’ participation is a key feature. Consequently, the policy making process provides one important conceptual framework for integrated assessment of climate change. A summary of the four steps of the policy making process is shown in Figure 1.

Figure 1. Detail of framework for analysis and prescription of environmental and sustainability policy (adapted from Dovers (2005))

_____________________________________________________________________

I. PROBLEM FRAMING1. Discussion and identification of relevant social goals 2. Identification and monitoring of topicality (public concern) 3. Monitoring of natural and human systems and their interactions 4. Identification of problematic environmental or human change or degradation 5. Isolation of proximate and underlying causes of change or degradation 6. Assessment of risk, uncertainty and ignorance 7. Assessment of existing policy and institutional settings 8. Definition (framing and scaling) of policy problems

II. POLICY FRAMING 9. Development of guiding policy principles 10. Construction of general policy statement (avowal of intent) 11. Definition of measurable policy goals

III. POLICY IMPLEMENTATION12. Selection of policy instruments/options 13. Planning of implementation 14. Planning of communication, education, information strategies15. Provision of statutory, institutional and resourcing requirements 16. Establishment of enforcement/compliance mechanisms 17. Establishment of policy monitoring mechanisms

IV. POLICY MONITORING AND EVALUATION 18. Ongoing policy monitoring & routine data capture19. Mandated evaluation and review process20. Extension, adaptation or cessation of policy and/or goals

GENERAL ELEMENTS:

In policy process:- coordination & integration- public participation- description & communication- transparency & accountability

In institutional arrangements:- persistence- purposefulness- information-richness & sensitivity- inclusiveness- flexibility

29

4.1.2 A risk assessment framework

Risk assessment and management is another common integrative process that can incorporate ‘stakeholder’ participation, so it offers a second framework for integrated assessment of climate change. A summary of the risk assessment process as it may be applied to climate change is shown in Figure 2 from Jones (2001). IPCC is the Intergovernmental Panel for Climate Change and FCCC is the United Nations Framework Convention for Climate Change. This framework was developed on the basis that the impact of climate change can be defined via thresholds which demark significant changes in biophysical or behavioural states (Jones, 2001). It was adapted from the risk assessment framework for impact assessment to incorporate “stakeholder” participation at most stages of the risk assessment and management process (Jones, 2001).

Thresholds

Key Climate Variables

Scenarios

Sensitivity Analysis

Risk Analysis

Autonomous Adaptation

Planned Adaptation

Stakeholders

FCCC

IPCC

ThresholdsThresholds

Key Climate Variables

Scenarios

Sensitivity Analysis

Risk Analysis

Autonomous Adaptation

Planned Adaptation

Stakeholders

FCCC

IPCC Key Climate

Variables

Scenarios

Sensitivity Analysis

Risk Analysis

Autonomous Adaptation

Planned Adaptation

Stakeholders

FCCC

IPCC Key Climate

VariablesKey Climate

Variables

ScenariosScenarios

Sensitivity Analysis

Sensitivity Analysis

Risk Analysis

Risk Analysis

Autonomous Adaptation

Autonomous Adaptation

Planned Adaptation

Planned Adaptation

StakeholdersStakeholders

FCCC

IPCC

FCCC

IPCC

Figure 2. Risk assessment framework for assessing climate change impacts (from Jones, 2001)

This framework shows stakeholders as central to the IA process, with stakeholders feeding into all steps of the assessment. The IPCC is a scientific body. This framework shows this scientific group as identifying key climate variables in conjunction with stakeholders. From these variables scenarios are identified, sensitivity analysis is undertaken and thresholds are identified. These then feed into a risk analysis. The potential for autonomous adaptation is assessed, and planned adaptation recommended. These planned adaptations are fed back as policy to the FPCC, the United Nations policy body on climate change.

30

4.1.3 The Millennium Ecosystem Assessment framework

The conceptual framework for the Millennium Ecosystem Assessment, shown in Figure 3, places human well-being as its central focus while recognizing that biodiversity and ecosystems have intrinsic value and all three are considered when people make decisions concerning ecosystems (MEA, 2003). The framework assumes people and ecosystems interact dynamically and recognizes that each also interacts with external factors independently of the other (MEA, 2005). The framework also recognizes that a multi-scale approach is necessary (MEA, 2005).

LIFE ON EARTH - BIODIVERSITY

Human well-being and poverty reduction• Basic material for a good life• Health• Good social relations• Security• Freedom of choice

Indirect drivers of change• Demographic• Economic (eg. Globalisation, trade, market and policy framework)• Sociopolitical (eg. Governance, institutional and legal framework)• Science and technology• Cultural and religious (eg. beliefs, consumption choices)

Drivers of change• Changes in local land use and cover• Species introduction or removal• Technology adaptation and use• External inputs (eg. fertiliser use, pest control, and irrigation)• Harvest and resource consumption• Climate change• Natural, physical and biological drivers (eg. evolution, volcanoes)

Strategies and interventions

Ecosystem services• Provisioning (eg. food, water, fiber, and fuel)• Regulating (eg. climate regulation, water and disease)• Cultural (eg. spiritual, aesthetic, recreation, and education)• Supporting (eg. primary production,

soil formation)

LOCAL

short termlong term

REGIONAL

GLOBAL

LIFE ON EARTH - BIODIVERSITY

Human well-being and poverty reduction• Basic material for a good life• Health• Good social relations• Security• Freedom of choice

Indirect drivers of change• Demographic• Economic (eg. Globalisation, trade, market and policy framework)• Sociopolitical (eg. Governance, institutional and legal framework)• Science and technology• Cultural and religious (eg. beliefs, consumption choices)

Drivers of change• Changes in local land use and cover• Species introduction or removal• Technology adaptation and use• External inputs (eg. fertiliser use, pest control, and irrigation)• Harvest and resource consumption• Climate change• Natural, physical and biological drivers (eg. evolution, volcanoes)

Strategies and interventions

Ecosystem services• Provisioning (eg. food, water, fiber, and fuel)• Regulating (eg. climate regulation, water and disease)• Cultural (eg. spiritual, aesthetic, recreation, and education)• Supporting (eg. primary production,

soil formation)

Ecosystem services• Provisioning (eg. food, water, fiber, and fuel)• Regulating (eg. climate regulation, water and disease)• Cultural (eg. spiritual, aesthetic, recreation, and education)• Supporting (eg. primary production,

soil formation)

LOCAL

short termlong term

REGIONAL

GLOBAL

Figure 3. Integration framework of the Millennium Ecosystem Assessment (from MEA, 2003)

31

4.1.4 A research framework The final framework presented is a framework for integrated assessment as a research concern. Figure 4 shows integrated assessment as the integration of three main components: an integrated description of the problem; an integrated set of adaptation of mitigation options and an integrated evaluation of options. It emphasises that this integration process must occur within its own socio-political environment. This includes social, political and institutional environments. Brinsmead (2005) also emphasises that due to finite resources an ‘iterative bootstrapping’ process must be applied, which uses existing understanding and incorporates this as part of the assessment so that a more detailed and elaborate understanding may be developed over time. He emphasises that it is not important whether a process is top-down or bottom-up but whether the resultant description represents relevant real world features, and whether it does so sufficiently accurately for its purpose.

Figure 4. Research framework for Integrated Assessment of Climate Change, adapted from Brinsmead (2005)

32

4.1.5 Comparison of conceptual frameworksWhile all the conceptual frameworks put some emphasis stakeholder engagement they are quite different in the emphasis they place on research and science versus policy development. The risk assessment framework, for example, focuses early attention on the IPCC whose role is to assess scientific, technical and socio-economic information on climate change impacts. Steps outlined are largely scientific or research based leading to consideration of planned adaptations to climate change. These adaptations then link to the FCCC, a main policy instrument of the United Nations on climate change. Policy is largely limited to this final link in the assessment and is not considered explicitly early in the assessment process. In comparison the policy framework considers integrated assessment to centre around the policy development process. In this framework research plays a role in problem framing and monitoring but is not the primary component of the assessment. Policy framing and implementation are shown to play much more substantial roles. Choice of an appropriate framework will depend on the emphasis that is placed on policy versus research and thus the audience for the work. In undertaking any particular integrated assessment, development of a specific framework to be used in that application may be necessary to ensure that the expectations of the policy, science and general communities are in line. The research framework focuses largely on the components of problem description, identification of adaptations and evaluation of mitigation options. It acknowledges that this integrated assessment process must be undertaken within a socio-political environment, including a stakeholder participation setting but puts very little emphasis on describing links between the assessment process, stakeholders and policy.

4.2 Design issues for an integrated assessment

This section summarises a number of issues relating to the design of integrated assessments. It is based largely on Letcher and Jakeman (2005), Farell (2005), Farrell and Jaeger (2006) and Rotmans (2002).

4.2.1 Problem focus

Problem focusing is a key step in IA and needs to occur before methods or approaches are selected. It needs to include a substantial stakeholder collaboration component to ensure that problem focus and impacts of concern have been adequately identified. The problem should be specific enough to get started but general enough to allow reorientation of the project outcomes as the problem evolves over the life of the assessment.

4.2.2 Project teams and personalities

The ultimate success and lessons leant through an IA will depend critically on the personalities and aims of those involved in the project. One key requirement of IA is that the parties involved are able to respect and acknowledge the contribution from other disciplinary components. Integration should not be about simply linking different components models or methods. It should not only enhance participants’ understanding of the interactions between system components, but should also provide direction for research to fill critical gaps among the disciplinary components of the project.

4.2.3 Communication

33

The communication required within the research team and between researchers and stakeholders is important but extremely time- and energy-consuming. A significant component of any IA project is communication between these groups. This means that IA or ISM is not always an appropriate technique for considering management problems. Where a problem is relatively simple or has a very short time frame, the time necessary to manage this communication properly means that a simpler, less comprehensive approach should be used. In general a good rule to live by is that if you don’t intend to pay due attention to stakeholder views then you shouldn’t ask for them in the first place. A project that claims to be participatory but that does not allow appropriate time and resources for building trust between researchers and stakeholders risks alienating, as well as disenfranchising, stakeholder groups and making future management efforts more difficult.

4.2.4 Role of Government and Links with PolicyIA is science for policy – it aims to influence both science and policy directions through close collaboration between scientists, community and government. As such it is clear that government plays an important role in the success of IA. IA must often contend with restructures of government departments and changes in emphasis or direction of policy. The timeframes of science and policy often conflict – science usually requires long time frames to undertake comprehensive assessments. In addition, building trust in the community and ensuring successful public participation is also a process that relies on longer-term relationships and trust being built. In contrast, policy timeframes are often rapid. IA must be developed to be flexible to a changing policy and community environment, while allowing for the needs of rigorous scientific assessment. This is one of the greatest challenges of IA.

4.2.5 Scales

If integrated assessment is to engage multiple disciplines and inform policy processes, IA projects should take into account, respectively, the embedded scales in disciplinary traditions, and scales of policy responsibility and governance, and not only considerations of scales informed by natural system functions and modelling techniques. On the first, disciplines across the natural and social sciences and humanities have very different scales, spatial and temporal, and cognisance of cross-scale processes, embedded in their theory and methods, and explicit recognition and exploration of these is necessary in the development of a specific IA approach (see further Appendix A). On the second, IA needs to negotiate a balance between scientific rigour and policy relevance in selected scales, at minimum to ensure that model and other information outputs are congruent with policy responsibilities, locations of responsible authority and cross

4.2.6 ParticipationPublic participation is a key component of most IA exercises. There are many choices to be made in designing a participatory approach. There are different ways of approaching participation including a variety of methods which can be used to facilitate participation. In addition for any IA there are many types of stakeholders ranging from industry or interest groups to individual members of the general public. An IA may engage with different groups or individuals in different ways for different reasons. These aspects of participation are discussed in more detail in Section 5.

34

4.2.7 Iterative approachesIA focuses on learning by doing. This learning is on the part of scientists, community and policy makers. A common feature of many successful IA exercises is the use of an iterative approach to the assessment. Frequently learning is enhanced by developing a common shared understanding, or conceptual framework, for the connections between processes, impacts and values and starting with simple assessment approaches for each of these components. Complexity can be added to these components over time, the conceptual framework can be enhanced and changed. Decisions to add these types of complexity over time should be made on a needs basis, that is, complexity is added where experience shows the framework or assessment to lack sufficient detail to be useful. In this way, useful outputs are staged over the life of the assessment, the assessment involves learning by all participants on the nature of the impacts, trust can be built by experiencing the response of the project team to concerns over lack of detail and the assessment remains flexible to a changing community and policy environment.

4.2.8 Quality controlAn important concern in the development of IA is quality control and maintenance of rigorous scientific or policy standards. This can mean documentation of assumptions and decisions relating to participation, review of science and assessment approaches by experts and reporting and communication of results and assumptions in plain English formats capable of being assessed by members of the general public. An approach to quality control needs to be developed early in the life of the project and properly resourced throughout the project.

35

5 METHODS TO SUPPORT INTEGRATED ASSESSMENT

This section outlines some common methods used in Integrated Assessments. These methods comprise commonly only a component of any assessment. Assessments normally require a combination of many different approaches, including models, risk assessment, stakeholder participation and qualitative research approaches.

5.1 Roles of models in IA

Models are a common approach applied as part of many integrated assessments. A model is a description of a complex process or set of processes. It may be purely qualitative, as in the case of a linguistic model, but more commonly is quantitative and represents assumptions relating to system behaviour using a set of equations and parameter values. There is a wide variety of quantitative modelling approaches that are commonly applied in IA. This section summarises the most common of these approaches. It is derived heavily from Letcher and Weidemann (2004) and Jakeman et al. (2005).

When choosing the type of modelling approach to be used it is important to consider two main issues: what is the purpose of the model; and, what types of data are available and what requirements are there on the scales and formats of model outputs? This section focuses on the purposes of model building. The next section discusses issues of scales and data.

Models are generally built to satisfy one or more of five main purposes:1. Prediction 2. Forecasting3. Management and decision-making4. Social learning5. Development of system understanding or experimentation.

These purposes place different requirements on the model structure, scales and accuracy.

PredictionPrediction involves estimating the value (quantitative or qualitative) of a system output in a specified time period given knowledge of the system inputs in the same time period. Models are often developed to predict the effect of a change in system drivers or inputs on the system outputs. For example, a model may predict a change in the probability of an algal bloom occurring in a dam given that there is going to be an increase in the level of nutrients delivered to the dam. Predictive models may be very simple (often empirical) or may be more complex. In many cases increased complexity of a model does not lead to improved predictive performance, so many successful predictive models have relatively simple structures that are well grounded in observations. Predictive models are generally required to have some level of accuracy in reproducing historic observations of system outputs from observed inputs. For integrative models, validating the predictive accuracy of these models is often difficult due to a lack of appropriate data for validation.

36

ForecastingForecasting refers to predicting the value of a system output in future time periods, without knowledge of the values of system inputs in those periods. For example, a model may use observed rainfall today to forecast the chance of rainfall tomorrow. Time series methods are very commonly used for forecasting problems. The accuracy of forecasting models is commonly tested considering the difference between ‘forecast’ values and historic observations.

Management and decision-makingModels are frequently developed for management and decision-making purposes. The term ‘decision support system’ is often applied to models that have been developed to aid decision-making. These models may be simulation based (ie. developed to answer ‘what if’ type questions) or optimisation based (developed to provide the ‘best’ option under a given objective subject to constraints). Tools such as multi-criteria analysis are essentially optimisation-based models developed to provide the optimal outcome under multiple objectives. Usually these tools also provide information about the sensitivity of the optimal outcome to assumptions, such as weights placed on different objectives. Management and decision-making models are usually required to be able to accurately differentiate between decisions or management options. This usually requires the model to give accurate estimates of the magnitude and direction of changes in system outputs in response to changes in system drivers.

Social learningThe use of models for social learning is an increasingly important development area in integrated assessment. This is consistent with the multiple values and interests represented in the problem of and response to climate change issues (for an overview of the theory and practice of social learning, see Keen et al 2005). Participatory models building and application may utilise a range of tools, including IA, qualitative systems approaches (eg causal loop diagrams), more detailed qualitative models, and a variety of participatory deliberative methods (eg. inclusive multicriteria methods). One area of rapid development is the use of agent-based models (Parker et al. 2002; Srbljinovic and Skunca 2003, Brown et al. 2004). In this case, models are developed to allow individuals (not the model builder) to learn and experiment so as to inform their understanding of the way in which the system may work and the way their individual actions may interact with the actions of others to create system outcomes. Models developed for the purpose of social learning generally have a large emphasis on the importance of social interactions between individuals or groups and may include representations of many less well-known or understood processes. The emphasis of accuracy in models developed for social learning tends to fall more on the plausibility of interactions and outcomes than the predictive accuracy of the model. Uses of IA models for social learning is closely tied to issues of public participation, and similarly qualifications and care should be exercised with respect to transparency and clarity of roles and expectations (see section 5.2 below).

Developing system understanding/experimentationModels are frequently developed to summarise and integrate available knowledge or understanding of system components in order to improve understanding of the entire system and the way it may react to changes in system drivers. Models that are developed to improve system understanding or for experimenting on a system may include components that are less certain (to test the potential effect of the assumed structure on the system) than

37

those used for prediction, forecasting or decision-making. These models tend to be ‘research’ models, accessible to the model builder and other researchers, as opposed to social learning models that are generally developed with a large non-technical audience in mind. As with social learning models, model accuracy tends to be considered in terms of plausibility and possible implications for the system rather than history matching.

5.1.1 Design issues for integrated assessment models

There are a number of design issues that must be considered which strongly influence the best choice of modelling approach for any application. The key issues are outlined below. This section relies heavily on Letcher and Weidemann (2004).

5.1.1.1 Types of data

There are two main types of data able to be used to construct a model: quantitative data and qualitative data. Quantitative data includes time series data, spatial, or survey data. This data refers to the measurable characteristics or fluxes in a system. Qualitative data or information includes expert opinion, stakeholder information or some types of information derived from surveys and interviews. Almost all model development relies on both quantitative and qualitative information. For example even purely quantitative models rely on theory or knowledge about systems interactions in the development of their underlying conceptual frameworks. However, some modelling approaches allow qualitative information and data to be explicitly incorporated in not just the system conceptualisation but also the calibration and parameterisation of the model. In this report the distinction between a model’s ability to use quantitative or qualitative data refers to whether or not the approach allows explicit incorporation of this data in model parameterisation, rather than during model conceptualisation. Of particular importance in IA, where multiple values and stakeholders and analytical approaches will interact, is for clear understanding of the characteristics and limits of different data streams being incorporated into models, and for clear connection along the continuum between highly qualitative conceptual models and more detailed quantitative ones.

5.1.1.2 Treatment of space

There are essentially four different approaches to treating space in a model. 1. Non-spatial models do not make reference to space. For example regional and national

economic impacts arising from a change in the management of a system (eg. modelled using a choice modelling approach) may not refer to any particular spatial scale.

2. Lumped spatial models provide a single set of outputs (and calculate internal states) for the entire area modelled. For example the impact of a change in nutrient delivery to a lake may be modelled using a simple function as a total change in biomass for the entire lake system. In this case the lake system is not disaggregated into smaller units and the interactions between parts of the lake system are not considered.

3. “Region”-based spatial models provide outputs (and calculate internal states) for homogenous sub-areas of the total area modelled. These sub-areas are defined as

38

homogenous in a key characteristic(s) relevant to the model eg. homogenous soil types or similar production systems. For example the lake system may be disaggregated into areas within 1-2m of the shore line, the creek leading into the lake and the deeper lake systems. Interactions between these three ‘regions’ are then considered by the model. The model is also able to output impacts for each of these regions.

4. Grid or element-based spatial models provide outputs (and calculate internal states) on a uniform or non-uniform grid basis. Neighbouring grid cells may have the same characteristics but will still be modelled separately, as opposed to homogenous region based spatial models where these areas would be lumped together. For example when considering the impact of land use changes on terrestrial ecosystems the landscape may be divided into a uniform grid, where the descriptors of that grid cell are based on either a single measurement or an average of measurements in that cell (eg. landcover, species distribution, soils). These cells may then be modelled either independently or as a connected series of cells (ie. each cell affects the outcomes in neighbouring cells) depending on the way in which the model has been conceptualised.

For integrated models the entire model may not operate using a single approach. For example, a grid based lake hydrodynamic model may be used to feed a single spatially averaged output to an economic or ecological model. The spatial approach of the integrated model is generally at most as disaggregated as the least spatially distributed model in the integrated system. Disaggregation of models to different spatial scales can lead to many difficulties in integrated models, as the spatial scales of interest in one component model may be quite different to those of a model from a different discipline. This is discussed in more detail when individual integration approaches are overviewed (section 4).

5.1.1.3 Treatment of time (temporal scales)

There are three main approaches to dealing with time in models.

1. Non-temporal models are those that do not make reference to time. For example, key ecological attributes of a landscape may be considered to be patch size and connectivity. These may be modelled for different scenarios from a static land use or management decision using appropriate ecological indicators. This is essentially a simple model of ecological impact of land use change that has no reference to time.

2. Lumped temporal models generally provide outputs over a single time period, such as average annual outputs. For example many nutrient and sediment models output an average annual load, rather than an annual or daily time series. By definition a model that is developed for forecasting purposes cannot be lumped temporally.

3. Dynamic models provide outputs for each time-step over a period. For example a model may calculate the change in the system condition each day, month or year. This approach is usually taken when the response of the system to a time varying input (such as rainfall) is required.

As with their treatment of spatial scales, integrated models do not necessarily have to integrate components working at the same temporal scale. The outputs of one component may be aggregated or disaggregated before being input into another component model. The

39

main consideration here is that the choice of aggregation or disaggregation method is generally subjective and may affect the model outputs. Any such effect needs to be considered when interpreting model results, and if the effect is too great the model may need to be modified to remove this problem (for example component models may need to be redesigned to work at a different scale).

5.1.1.4 Treatment of Uncertainty

Uncertainty is an important consideration in developing any model, but is particularly important, and usually difficult to deal with, in the case of integrated models. Uncertainty in models may be derived from uncertainties in system understanding (i.e. what processes should be included, how do different processes interact), from uncertainties in data and measurements used to parameterise the model or from uncertainty in the base line inputs or conditions used for model runs (eg. world prices for a crop may change – does this change the model recommendation?).

Some integration approaches are able to explicitly deal with uncertainty in data, measurements or base line conditions. Other approaches require comprehensive testing of the model to allow this understanding to be developed. The level of testing required to develop this understanding is rarely carried out however, largely due to time and other resource constraints given the complexity of such a task for even relatively simple integrated models. For example the sensitivity of a model to changes in one or even two parameters at a time may be tested but analysis rarely involves more complicated combinations of parameter changes. The results from such a complex testing regime can also be quite difficult to interpret. Very few approaches explicitly consider uncertainty introduced by the system conceptualisation or model framework.

Finally model uncertainty must be considered in the context of the purposes of the model. For example the variation of a system output from the observed value may be very important for forecasting models, but may be much less critical for decision-making or management models. In this case the user may be more concerned with being able to accurately distinguish between the magnitude of impacts from two alternative management options (or scenarios).

As well as explicit handling of uncertainty in model construction, problem definition and participatory aspects of IA require recognition of added dimensions of uncertainty. First, constructions of uncertainty other than scientific ones are important in how society and policy systems define problems and consider responses. Wynne (1992) presents a widely used typology including tractable risk, uncertainty, indeterminancy and ignorance, while Smithson (1989) offers a detailed categorisation of types of uncertainty and their sources, under the primary separation of error (to be ignorant) and irrelevance (to ignore). Significantly, Smithson emphasises important political and social dimensions of uncertainty. Second, discussions of uncertainty should have reference to the two main expressions of uncertainty and risk in Australian policy: the Precautionary Principle (stated in over 120 Australian statutes, see Peel 2005; Fisher et al 2006) and Australian Standard/New Zealand Standard 4360: Risk Management (Standards Australia 2004a).

A particular challenge in the case of climate change, given the possibility of abrupt change, is identification and handling of residual uncertainty, that remaining after either identification of tractable or easily identifiable risks in an assessment procedure, or after

40

treatment of identified risks in a practical management context. (See also section 5.3 Risk Assessment below.)

5.1.1.5 External vs. Internal Optimisation

There are two main approaches to considering management interventions or decisions in models. The first of these is scenario-based, where the model is developed to consider the impacts of implementing options (often referred to as ‘what if?’ approaches). This type of approach is developed to allow the user to explore the results of various actions and the effects and trade-offs these involve.

The second approach is optimisation, where the model explicitly determines the best intervention or decision according to a specified objective (maximise net returns, minimise environmental costs) subject to various constraints. In this case the model user is generally presented with a single ‘best’ option or intervention. The objective function may be defined as a weighted combination of multiple outcomes.

5.1.1.6 Appropriate Software

Software development must be undertaken with a clear picture of the target audience, the specific issues and the uses. While a sophisticated, object-oriented based software platform may be both useful and desirable in some circumstances, in other cases a spreadsheet-based model may be more useful for extending project ideas and science. Having different software products aimed at different audiences can also be a useful outcome of a project. On the other hand, software development should not be the primary objective of the work undertaken. The software is a tool to enhance communication and interaction between different disciplinary teams. It should be a focus of the project primarily in so far as it encourages communication of ideas and enhanced understanding of the integrated nature of the problem.

5.1.2 Modelling Approaches

Given the different definitions of what constitutes integration and the varied purposes of developing integrative models, various approaches to developing integrated models have been developed. This section provides a classification for previous integrated models before providing a brief overview of applications of each approach. It concludes with a framework for choosing the appropriate approach to integration given the requirements placed on the model (taken from Letcher and Weidemann, 2004; Letcher and Jakeman, 2005). A summary of each of the approaches, the types of model applications for which they are appropriate and the way in which they deal with the model considerations outlined here is given in Table 1.

5.1.2.1 System dynamics

System dynamics (e.g. Deaton and Winebrake, 1999) is a modelling approach that investigates and manages complex feedback systems (eg. aquatic food webs). Many authors consider it to be a philosophy of model development rather than a type of model. Nodes in

41

the conceptual framework generally represent state variables, while the links or arrows between nodes represent functions transforming one state variable to the next. The conceptual frameworks for system dynamics models often contain feedback loops. These loops may be very complex, and/or only be supported by perceived ‘plausible’ connections. Thus, system dynamics models are most commonly used to improve systems understanding and to compare simulation responses rather than decision-making and policy. However, in theory the latter could be achieved. Examples of the use of a systems dynamics approach to model integration in climate change research can be found in Simonovic and Davies (2006), Fiddaman (2002) and Fiddaman (1997).

5.1.2.2 Bayesian networks

Bayesian networks consist of a series of nodes and links that conceptualise a system. Feedback loops cannot be included in this approach. They are fundamentally a decision-making tool. The nodes in the system are variables. The links are defined by conditional probability distributions, thus providing a measure of certainty in the causal relationship between each node. In this way this integrated approach differs from others that use deterministic, rather than probabilistic, methods to determine the relationship among variables (Borsuk et al., 2004). The implicit ability to account for uncertainty means that Bayesian networks are able to make use of ‘soft’ sources of data, such as expert opinion, where observed data is not available (Sadoddin et al., 2003). Bayesian Decision Networks also include decision variables, which allow for management options to be implemented, and utility variables, which reflect the benefit or cost of a particular decision. Examples of the use of Bayesian Networks to consider the impacts of climate change can be found in Koivusalo et al. (2005) and Ticehurst et al. (in press).

5.1.2.3 Metamodels

Metamodels are essentially a simplification of the processes within more complex models. Data-mining techniques such as regression are often used to develop metamodels. Bouzaher et al. (1993) suggest that metamodels are useful to approximate and aid in the interpretation of simulation models. The mere size of the output from complex models can make them difficult to view and interpret. Metamodels can provide look-up tables, or simpler functions to represent the information found in the more detailed models. In integrated modelling metamodels can be used to completely replace a complex model, or complex components of a model. In the latter, the metamodels can be coupled into an integrated system. Examples of the use of met-models in integration for climate change can be found in Martens (1998) and van Kooten et al. (2004).

5.1.2.4 Coupled component models

Coupling component models involves combining models, typically from different disciplines, to arrive at an integrated outcome. Conceptually each node in the framework represents a model of a particular issue. The links between models pass the generated data. The links maybe be manually linked external to the original models, or may be more tightly linked where the component models share inputs and outputs (e.g. Merritt et al., 2004;

42

Letcher et al., 2004). Coupled component models are generally able to incorporate feedback loops.

Coupled component models can account for non-trivial temporal and spatial discretisation. This is particularly relevant in climate change assessment where complex processes must often be integrated over large, and varying, spatial and temporal scales. Examples of the use of coupled complex models as a methods of integration in climate change research can be found in Krol et al. (in press), Nijkamp et al. (2005) and Pasquer et al. (2005).

5.1.2.5 Agent-based models

An agent- or actor-based model is essentially a type of coupled component model. It focuses on the interactions between agents (individuals) in a system (e.g. Brown et al., 2004), where agents adapt to changes to their environment. When two or more agents exist at the same time, share resources and communicate with each other, it is called a multi-agent system.Agent-based models are efficient at identifying large-scale outcomes resulting from often simple, local interactions between individuals. For this reason, and because they tend to be very hypothetical, agent-based models are usually applied in social and ecological science. An example of the use of an agent-based model used for assessing climate change can be found in Janssen and de Vries (1998).

5.1.2.6 Expert Systems

An expert system is a type of qualitative model where prior knowledge is encoded into a knowledge base and then logic used to infer conclusions (Davis, 1995). The knowledge base determines the success of the system (Forsyth, 1984). Given a problem, the expert system simulates the problem-solving task(s) (Kidd, 1987). The conceptual diagram for an expert system refers to questions about the nature of the system directed at the user. The response to these questions then dictates the route down which the procedure looks for a solution. Examples of the use of expert systems for assessing the impacts of climate change can be found in Hood et al. (in press) and Huang et al. (2005).

The strengths and weaknesses of these approaches for different applications are summarised in Table 1.

Table 1. Appropriate use of integrated modelling techniques (from Letcher and Jakeman, 2005)

System dynamics

Bayesian Networks

Meta Modelling

Coupled Complex Models

Agent based

Models

Expert Systems

What is your reason for modeling/type of application?Predictive X X X XForecasting X X XDecision-making X X X X XSystem understanding

X X X X

Social learning X X X XWhat types of data do you have available/want to use to populate your model?Qualitative and quantitative data

X X

43

Quantitative data only

X X X X

Do you want your model to focus more on a complex description of specific processes in the system or have a greater breadth of coverage of interactions in your system?Depth of specific processes

X

Breadth of system XCompromise X XBoth X XDo you want your model to provide explicit information about uncertainty caused by model assumptions?Yes X XNo X X X XAre you interested in investigating the interactions between individuals and their impact on the system, or only the aggregated effect of human behaviour?Interactions between individuals

X

Aggregated effects X X X X X

5.2 Participation

Public participation can be defined as direct involvement of the public in decision-making, and thus, in developing the tools used to inform decision makers. Clearly it can occur at various levels. Mostert (in press) describes six levels of public participation: information supply; consultation; co-thinking; co-designing; co-decision making; and self-control.He proposes several reasons for organizing public participation. These include the possibility of: more informed and creative decision making more public acceptance and ownership of the decisions more open and integrated government enhancing democracy social learning, the ultimate objective, to manage issues

Mostert states that it is important that public participation is organized well to avoid limited and unrepresentative response from the public, disillusionment, distrust, less public acceptance, more implementation problems, less social learning, and complication of future participatory processes. He stresses the need for sensitive processes, taking into account the culture (e.g. natural and socioeconomic conditions, ideology) and subculture (e.g. environmentalists, industrialists, managers). He argues that if environmental management is to be participatory, research supporting environmental management should also be participatory. Not only should the public have access to research results, presented in an understandable way, but it should also have a say in what is researched and how, and participate in the research process itself.

Integrated assessment and ‘independent’ experts can provide an important and useful mechanism for raising the level and quality of public participation in environmental management. Involving communities in model development or other non-model based assessments can not only add to the validity of the final results but can also create an opportunity for constructive interaction between stakeholders. This allows them a less threatening focus for developing a shared system understanding than interactions focused on resolution of specific environmental conflicts. An integrated assessment can capture a shared understanding of system processes and can allow people to manage disagreements about system assumptions. Delivery of models through software or development of a

44

decision-support system can permit the model developed to be reused in order to make management decisions after the end of the research project. Conflict over management options can often be resolved as conflict over key system assumptions. In these cases conflict may be managed by identifying areas of disagreement or gaps in knowledge, and by improving system understanding through targeted data collection or system observation. Such attempts at resolution of conflict tend to be positively received by stakeholders. They feel that their concerns are being addressed by the process.

There are numerous methods available for public participation. Good summaries of these methods and their strengths and weaknesses are given in World Bank (1996) and Mayoux (2006). It is important to note that not all methods are suitable for all situations. Methods need to be tailored for the situation or issue at hand. Often a combination of methods is required throughout the life of an assessment.

World Bank (1996) define four different categories of participation approaches: workshop-based methods; community-based methods; methods for stakeholder consultation; and methods for social analysis. Table 2 summarises the methods discussed by World Bank (1996).

A further set of participatory methods under rapid development and increasing implementation in policy are those gathered under the general term ‘deliberative designs’ (for an overview see Munton 2003). These involve structured processes of participation, featuring various means of informing representative groups of stakeholders or the general community about the issue at hand, and deliberative processes of recommending responses to that issue or decision problem. Such approaches include citizen’s juries, consensus conferences, deliberative polling, inclusive (non-deterministic) multi-criteria methods, and planning cells.

Key factors to any participatory approach being incorporated effectively (and equitably) into an IA process include: clarity as to roles and responsibilities; definition of the purpose of participation; transparency of methods (ie. not ‘black box’ models); careful definition of ‘stakes’ and of the relevant community; and, if the participation is linked to policy or management processes, sufficient longevity of engagement (ie. not simply as a swiftly curtailed data input). Sensitivity to the volunteer and limited nature of stakeholder inputs to research processes needs also to be maintained.

Table 2. Participation approaches adapted from World Bank (1996)

Method DescriptionCollaborative Decision Making: Workshop-based MethodsAppreciation-Influence-Control (AIC)

A workshop-based technique where stakeholders produce a visual influence diagram of the project or issue considering social, political and cultural factors along with technical or economic aspects. Appreciation is developed through listening, influence through dialogue and control through action.

Objectives-Oriented Project Planning (ZOPP)

Workshops focus on development of a project planning matrix. Stakeholders are asked to set priorities and plan for implementation and monitoring. Builds stakeholder team commitment and capacity.

Collaborative Decision Making: Community-Based Methods

45

Participatory Rural Appraisal

A family of methods and approaches that emphasise local knowledge and enable local people to do their own appraisal, analysis and planning. Uses group animation and exercises to facilitate information sharing, analysis and action among stakeholders.

SARAR Aims to build on local knowledge and strengthen local capacity to assess, prioritise, plan, create, organise and evaluate. Allows for training of local trainers and facilitators. Encourages participants to learn from local experience rather than external expertise and provides a multi-sectoral, multilevel approach to team building.

Methods for Stakeholder ConsultationBeneficiary Analysis (BA)

A systematic investigation of the perceptions of stakeholders to ensure their concerns are heard in and incorporated into project and policy formulation. Involves lengthier, repeated and more meaningful interaction among stakeholders.

Methods for Social Analysis (SA)Social Impact Assessment (SIA)

A systematic investigation of the social processes and factors affecting impacts and results. Aims to: identify key stakeholders and establish appropriate framework for their participation; ensure project objectives and incentives for change are appropriate and acceptable to stakeholders; assess social impacts and risks; and, minimise of mitigate adverse negative impacts.

Gender Analysis (GA)

Focuses on understanding and documenting differences in gender roles, activities, needs and opportunities in a given context. Involves disaggregation of quantitative data by gender and highlights different roles and learned behaviour of men and women based on gender attributes which vary across culture, class, ethnicity, income, education and time.

5.3 Risk Assessment

Risk assessment has been used as an integrating framework in many sectors including health and the environment (Jakeman et al., 2005). The basic notion is that risk is defined as the probablility of an outcome times the severity of its consequence, leading to the potential quantification of risk in a number of ways. However, relevant and useful forms of qualitative risk assessment also exist, and may be more appropriate where believable probability distributions cannot be assigned to the range of possible outcomes. The essential notion of risk assessment can be extended to positive as well as adverse impacts and the characterisation of uncertainty. According to Jasanoff (1993) the role of risk assessment is to ‘offer a principled way of organising what we know about the world, particularly about its weak spots and creaky joints.”

Kammen and Hassenzahl (1999) present much of the central theory and methods including order of magnitude estimation, cause-effect calculations, exposure assessment, fault-tree analysis, and managing and estimating uncertainty. In Australia, an increasing number of policy sectors are approaching risk in a manner consistent with the risk management standard (Standards Australia 2004a), including areas as diverse as finance, product design, chemical engineering, emergency management and environmental management. The Standard has been developed considerable since its first edition in 1995, and is supported by application guidelines (Standards Australia 2004b). Wild River and Healy (2006) provide an

46

overview of the standard, and detail on implementing risk assessment consistent with it in environmental management. The standard emphasises a systematic and integrated approach to risk management, including the following core elements:

Establish the context; Identify risks; Analyse risks; Evaluate risks; Treat risks; Communication and consultation (throughout process); and Monitor and review (throughout process).

The Standard is flexible, allowing for a variety of specific techniques (qualitative and quantitative, strategic and more specific) to be employed within this framework. IA processes dealing explicitly with risk should refer to and ideally maintain consistency with the Standard, representing as it does an accepted, overarching framework for dealing with risk.

Risk assessment has been used as an integrating framework in many sectors including health and the environment (Jakeman et al., 2005). The basic notion is that risk is defined as the probablility of an outcome times the severity of its consequence, leading to the potential quantification of risk in a number of ways. This notion can also be extended to positive as well as adverse impacts and the characterisation of uncertainty. According to Jasanoff (1993) the role of risk assessment is to ‘offer a principled way of organising what we know about the world, particularly about its weak spots and creaky joints.” Kammen and Hassenzahl (1999) present much of the central theory and methods including order of magnitude estimation, cause-effect calculations, exposure assessment, fault-tree analysis, and managing and estimating uncertainty.

47

6 SELECTING METHODS FOR USE IN AN INTEGRATED ASSESSMENT

The previous sections outline a number of the methods available for use in integrated assessments. The choice of methods to be used in an integrated assessment depends primarily on the question or problem focus. Without defining an application area (such as a sector, group of sectors, region etc) on which the assessment is to focus it is not possible to recommend a specific method. However once an application area has been defined it is possible to propose several criteria by which potential methods may be judged and selected. This section provides an outline of these criteria and then gives an overview of an approach to integrated assessment that may be applied.

6.1 Criteria for selecting methods for IA

Once an application area has been chosen and the problems and impacts associated with this have been well-defined a method (or set of methods) may be chosen for the considering to the following criteria.

6.1.1 Is the method credible with the scientific community, policy community and/or the general community?

In order for an integrated assessment to have impact on policy makers and decisions affecting a problem, it must have a degree of credibility with three separate audiences: the scientific community; the policy or decision-making community; and the general community including stakeholders. An assessment that is credible with one of these communities may lack credibility with another audience. For example scientific credibility is arguably achieved by publication of the results and methods of the assessment in the peer reviewed literature. While this may improve the credibility of the assessment with government or community members, their experience of the assessment and interactions with the members of the IA team is much more likely to colour their judgment of the credibility (or lack thereof) of the assessment. Thus the process of IA must attempt to appeal to the judgment of these three audiences and not attempt to satisfy only one if it is to achieve its aims.

6.1.2 Can the method answer key questions underlying the case study or meet the case study objectives?

Most obviously the methods chosen must be capable of addressing the problems underlying the case study or application area. They must be capable of assessing the types of impacts focused on by the assessment while maintaining credibility and other criteria. This means that methods should not be chosen until after an application focus has been selected and well-defined. This is essentially a warning against the ‘have model will travel’ approach to scientific assessment. IA is often referred to as a problem in search of a method rather than a method in search of a problem.

6.1.3 Can the method fit into an appropriate participatory process?

Some problems require a large degree of collaboration with stakeholders. However some methods do not lend themselves to use within a collaborative process due to their complexity, their large computational requirements or their lack of credibility with the

48

general public or policy and decision-making audiences. Decisions must be made as to the degree of public participation that is desirable or required and the ways in which the public might be engaged. After this, methods can be chosen that complement this participation strategy rather than conflict with the goals of the IA and the participation.

6.1.4 How easily can the method communicate uncertainty?

Some methods (such as Bayesian networks) explicitly communicate uncertainty in a fairly straightforward and understandable way. Other methods do not easily allow for uncertainty to be estimated let alone communicated. Where uncertainty is a key consideration then a method that allows for it to be explicitly incorporated and easily communicated is desirable.

6.1.5 Cost – how expensive is it to develop, maintain and extend?

This criteria is often the most important in terms of achieving the outcomes desired by the funder and project team. Integrated Assessment is expensive in terms of money, time and resources (including the good will of both stakeholders and researchers). Before setting off to undertake an Integrated Assessment, the question of whether IA is the best approach to tackle the problem should be considered, and, if it is considered to be the best approach, then sufficient time and resources must be budgeted for the IA activity. Once this budget is set, it is then important to choose methods that fall within the scope of the time and resources committed. Generally very inclusive and collaborative participatory approaches and the development of very complex models can be considered to be the most expensive of IA activities. Underestimating the cost of IA activities can lead to expensive failures that alienate the community towards future assessments.

6.1.6 Can it be used in training, to build capacity or for social learning?

Some methods lend themselves to building capacity in researchers, decision makers or technical staff as well as within the community more broadly to understand a problem from multiple perspectives and to apply a systems approach. These methods may be a better choice where capacity building or social learning are goals of the IA exercise.

6.1.7 Is it useful for educating a new breed of interdisciplinary scientist?

Sometimes a method or an Integrated Assessment might have its greatest influence through training new scientists, decision makers and technicians in interdisciplinary, systems based approaches. A method might be very useful for this type of education role and could be selected even though this might not be the primary purpose of the assessment.

6.1.8 Can the method or results/lessons from the method be transferred to other case studies/ problems/areas and more broadly?

Most useful integrated assessment has a strong applied focus. However, clients and other funding bodies generally want to fund research that develops methods, approaches or results that are able to be applied more broadly. It is important when developing and applying methods in an assessment to consider whether those methods or the recommendations arising from the research can be applied to other areas, sectors or problems. Most of the emphasis that practitioners of IA have placed on the benefits of IA has been the learning experience of participants rather than explicit results or products (such as models) arising

49

from the assessment (see for example Jakeman and Letcher 2003; Janssen and Goldworthy 1996).

6.1.9 Can it handle multiple and/or conflicting issues?

The main purpose of IA is to address problems in which trade-offs are a major issue. Where a problem has a simple solution that is of benefit to all stakeholders and does not involve interactions with any other environmental or social systems, IA is not usually beneficial. Thus most problems in IA involve multiple and usually conflicting issues and impacts. Methods selected must be capable of addressing these issues.

6.1.10 Can it be used in a complementary manner with other methods?

Some methods lend themselves to use in conjunction with other methods. For example a Bayesian Network approach can be used to integrate information from complex numerical models, surveys and expert elicitation. Other approaches such as coupled complex models may be difficult to integrate with other methods unless they are used in conjunction with such a complementary method.

6.2 A process for Integrated Assessment

These criteria highlight the importance of using an appropriate process for Integrated Assessment. The success of an IA exercise will generally depend less on the methods selected than on the process in which they are embedded. This section briefly outlines one process that has been developed and applied in a number of very diverse IA exercises in Australia (see Jakeman and Letcher 2003; Letcher and Jakeman 2003; Letcher et al. 2004; Merritt et al. 2004; Newham et al. 2004; Merritt et al. 2005 for details). The process is described in Table 3.

Table 3. IA process where model development is one key outcome (from Letcher and Jakeman, 2005)

Step Comment1. Clearly identify the aims and objectives of the integrated assessment, including stakeholders and potential audiences for the results and any other products of the IA 2. Build an understanding of the constraints and issues in the case study as well as possible targets and measures of system performance

Reviewing existing information on the case study area including management reports, previous studies and other ‘grey literature’ information.

3. Develop an initial conceptual framework, identifying key drivers, including management and development options as well as state and utility variables and their interactions

Generally developed in-house using information sourced from reports and other information available on the issue in discussion with a few key stakeholders.

4. Workshop the initial conceptual framework and general scenarios with stakeholders to get feedback on missing scenarios, links between system components and impacts that should be

A very broad consultation with many different stakeholder groups seeking feedback across social, economic and environmental issues. This should include a discussion of the specific scenarios to be considered and the types of impacts of greatest concern.

50

considered in the assessment. 5. Revise the initial framework using stakeholder feedback obtained in step 3.

This step produces a working version of the conceptual framework for assessment to be focused on. Specific scenarios and key impacts to be considered should be identified.

6. Identify existing data and information available to populate the assessment.7. Identify and fill key knowledge or information gaps

Reviewing the working conceptual framework to identify processes, links and impacts for which no or very limited information exists. A workplan to fill gaps with information is then constructed. Feedback to the community on the limitation of data used and factors not able to be included should be provided to inform expectation of the assessment capabilities.

8. Populate the assessment with data and other information

Very time consuming and is usually the primary focus of traditional model building or assessment practice (usually the prime focus in budgets as well!). This may include development of quantitative models, or qualitative integration such as reports and analysis of information. If a model is to be developed it should generally be coded in such a way as to allow the end-users of these results access to the model, scenarios and results.

9. Review the conceptual framework and assessment model with stakeholders

The assessment and results are demonstrated to stakeholders. Feedback is sought on the accuracy and validity of the results and conclusions from the assessment.

11. Revise the assessment, results and conclusions in the face of stakeholder feedback.12. Distribute the assessment to relevant stakeholders or other user groups with appropriate training or information on its use

Results, models and/or conclusions are generally distributed through workshops run with people identified, by the client and/or through the project activities, as key users, or through reports which have had some iteration with stakeholders to ensure they are adequate and understandable.

51

7 CASE STUDY SELECTION

This section describes some of the issues which need to be considered in selecting case studies of climate change in which integrated assessment may be applied.

7.1 General context

Clarifying the purpose and context of IA is a first step in scoping further work. Integrated assessment of climate change impacts has four underlying purposes: (i) to improve characterisation of the phenomena which drive climate change impacts; (ii) to better define the range of possible impacts on different components of interdependent natural and human systems (issues, sectors, values); (iii) to identify, test and improve approaches and methods; and (iv) to identify effective options to address the impacts of climate change. The second aim is the most apparent and widely accepted, however all four are interrelated and should be considered together. The fourth purpose is the ultimate aim of Government in IA of climate change.

These four general purposes, and the complexity of relevant natural and human systems (encompassing multiple natural processes, environments, human uses and values) combine to make it impossible and indeed undesirable to undertake an integrated assessment of all possible impacts and issues relating to climate change across all relevant scales. Simply, the number of variables and parameters, and the time and effort involved in such a comprehensive assessment, stands at odds with the purposes of undertaking IA and the time frames within which research, policy agencies and the community would wish to gain insights. Moreover, such a comprehensive integrated assessment would need to be spatially-bound to approach any degree of thoroughness, and it is doubtful that more than one or two such studies could be undertaken, meaning that the outputs may be of limited transferability or operational usefulness across regions, populations, environments and sectors. What is possible and desirable is a few well targeted IA projects which are chosen to reflect either spatial or sectoral elements of key importance which are likely to be subject to significant effects from climate change. These needs to be chosen carefully to take advantage of the opportunities presented by IA for learning both by government and researchers as well as by the general population. They should build as much as possible on existing studies to ensure that the integration is the focus of the assessment, rather than building primary disciplinary data sets or understanding.

This context defines the scoping task as one of designing a coordinated suite of programs and projects rather than a small number of more complete assessments, ensuring – through definition of these and connection between them and previous or existing work – that more rather than fewer climate variables, impacts categories, impact contexts and societal values are captured, through a range of approaches and methods. The following identifies major considerations to be taken into account in framing, enabling and funding future IA work, stated as key attributes of a suite of programs and projects. In the interests of both efficiency and effectiveness, it is crucial that these attributes (or criteria) are maximised across projects rather than projects being designed in isolation from others.

52

7.2 Key attributes of future work in Integrated Assessment

Attributes are organised under seven general categories, and serve as an initial definition of criteria for case study and project design (see Table 4). Within and across these categories, there are tensions between criteria, and it is emphasised again that the task is to maximise the breadth and depth of future work across projects – that is, specific projects cannot address all criteria or resolve all such tension, but a portfolio of connected work may do so. As criteria, the following is informing rather that prescriptive in potential use – part of the point of their identification is to make choices and the implication of choices explicit and transparent.

7.2.1 Representativeness: drivers and impactsAccounting for multiple climate change impact categories and multiple potentially impacted sectors is the intent and rationale of IA. However, as noted above, it is unlikely to be possible, practical or desirable to capture all or even most in the foreseeable future within a small numbers of IA processes (unless at a broad scoping level). Nevertheless, a suite of programs and projects can and should seek representativeness in including a wide range of climate-driven phenomena (eg. rainfall, temperature, extreme events) and impact types (infrastructure, disease distribution, agricultural production, biodiversity, etc). Sufficient attention should be paid to both potential positive and negative impacts of climate change.

7.2.2 Representativeness: sectors, values and placesSimilarly, IA by definition must deal with multiple sectors and values, yet capture of all or even most in a single exercise would be impossible. Again, the widest range of sectors, values and places can only be captured within a coordinated suite of IA work. Work should cover the most apparent and well-known (eg. coastal settlements, sensitive environments such as the Reef or Alpine areas, agriculture, health, large cities) but also ones less well-recognised in discussions of impacts thus far (eg. fisheries, remote and Indigenous communities, biodiversity in urban areas, small-scale tourism, etc).

Given the unlikelihood of a sufficiently large number of regional case studies being undertaken to capture all sectors and values, achieving such representativeness would require a mix of spatially-defined (ie. region, major city) and issue or sectoral-focused projects (ie. health, agriculture, infrastructure). Reflecting considerations discussed under (f) Policy relevance below, IA work should include both ‘iconic’ environments and places (eg. the Reef, Kakadu National Park or similar) as well as less well-known places which nonetheless collectively embody significant local or wider values. IA should also consider differentiated vulnerabilities within subsets of regional communities or within industry sectors.

7.2.3 Methodological developmentIA is not a method per se, but rather a style of research and application that has an emerging broad framework within which it selects methods appropriate to the problem being investigated (see Section 4). Future work should be designed in such a way as to test and drive improvement in specific methods and in the understanding of the IA process more generally. Very few methods are not contested, especially across disciplinary divides and the research-policy interface, so testing of methods will only be effective if the IA process is open to allow extended peer review communities to operate. For example, different modelling approaches to characterising natural processes (see Section 4.1) should be employed in a comparative fashion. Likewise, different methods of assessing social and

53

economic impacts should be employed in a way that allows comparison of robustness from both a scientific and policy-relevance perspective (eg. standard Social Impact Assessment, vulnerability assessment techniques and deliberative approaches, or standard and extended cost-benefit analyses). An explicit choice must be made whether to compare like methods in unlike settings, or to utilise similar settings to contrast and compare methods – either choice is valid but different in intent and implementation. Ideally various strategies can be linked across projects in an informing manner.

An important part of methodological development is to improve the capacity of IA and component methods to identify, differentiate and explore interactions between climate-related and other factors (eg. demographic, cultural, natural, trade, policy-driven) that also determine the vulnerability or adaptability of sectors and communities.

7.2.4 Data availability and institutional capacityFuture IA work should span regions and sectors that are both data-rich and institutionally well-resourced and capable, and data-poor and lacking receptive or capable institutional settings. This is for two reasons: to maximise methodological development; and to ensure that not only currently topical or previously well-studied places and issues are explored. Especially at regional scale, but also across sectors and issues, the institutional capacity and thus ability to engage with an IA process varies significantly. Given the costs and time required for major data gathering or consolidation, data-poor regions or sectors, these should be regarded as opportunities to apply and test methods suited to such conditions (eg. Bayesian Networks or expert systems) rather than as imperfect settings for other, more data-intensive methods (eg. coupled complex or agent-based models). The capability of methods to function and produce useful outcomes in the face of uncertainty should also be an explicit part of the research scoping and design.

7.2.5 Utilisation of past and current workThere has been some work in IA or equivalents in the past, and a number of current projects and processes. Future work in IA should build on such work, rather than engage in discrete projects. Regions and sectors not subject to IA or similar work may nonetheless have existing knowledge or capacity resources that would enable more rapid advance in assessing at least some impact categories – for example, rural demographic and socio-economic assessment undertaken in relation to structural adjustment or water policy change could well underpin some IA work. This may also be the case with vulnerability assessments undertaken for emergency management or public health reasons.

This suggests that, before defining future work, close engagement with R&D providers and funders, and relevant agencies, should occur to identify both possible partners and existing preliminary or complementary work (such as LWA, MDBC,other industry based Research and Development Corporations). If the regional scale is pursued for IA, then existing data gathering, management and policy processes at that scale provide potential synergies (eg. CMAs, NAP and NHT processes, etc).

7.2.6 Policy and public relevanceTo ensure support for IA work (eg. funding, collaboration), successful application in particular settings (eg. engagement of agencies and communities), and usefulness of outcomes (ie. relevance to the mandates of policy makers), IA needs to connect with policy problems. This relevance can be considered in three ways: the current mandate and agendas of policy agencies; actual policy processes directly associated with either impacts or

54

impacted sectors; and community concerns and social values. These three may coincide, or differ, and all are strongly determined by the scale of focus – different levels of government, non-traditional spatial scales of management (eg. CMAs), community perspectives varying with scale, socio-economic or cultural character of regions, etc. A key variable is the loci of management or policy responsibility for coping with impacts, which may be located at one or more locations, including state and federal government, local government or catchment organisation, Indigenous council or local community, industry sector, land parcel, and so on. Central to maximising relevance is the necessity of joint problem framing and research design, engaging policy agencies, resource managers, etc as well as relevant stakeholder and community perspectives. The process of problem framing in itself represents an area of imperfect and fragmentary expertise and methodological development (eg. qualitative joint modelling, deliberative scoping approaches), and thus should be considered an integral part of the research and development process rather than a preliminary step undertaken in isolation. Close engagement of agencies and stakeholders throughout the research process is a feature of IA, without which relevance of either the problem or outcomes is unlikely. If IA appears unlikely to inform the coping capacity of communities, industries or management agencies, then support for IA is unlikely. Past extreme events may be useful as an initial focus to engage some interests.

Given that IA is a process that encourages learning and that generally leads to changes in the understanding of issues and possible adaptations over time, it is important that adaptability in research design and focus should be maintained, both within extended projects, and across projects over time. This also allows for risks which are currently deemed to be very low probabilities to be incorporated in the assessment over time if it becomes apparent that their risk was initially underestimated.

An important characteristic of useful and adaptive IA is the ability to produce outputs in stages, allowing feedback to users and review and improvement of the research and assessment process. Ensuring early outputs does require balancing timeliness with rigour and trustworthiness, including clear mutual understanding of the status of results between researchers and users. A staged approach beginning with inclusive problem definition and scoping phases suits the development of such outputs.

7.2.7 International significance and connectionClimate change science, impact assessment, and more specifically IA represents a rapidly advancing domain, with a number of key networks and research initiatives. It is important that IA in Australia be connected to endeavours elsewhere in the world to enable use of best available knowledge and to allow comparative methodological development. This may be achieved by: involving key international figures in Australian research projects or vice versa; establishing formal connections between Australian processes and those elsewhere; encouraging presentation of Australian research designs and outcomes to international audiences; or linking specific Australian and international projects.

Table 4. Summary of attributes of further work in IA (criteria for case study and sectoral focus selection

Attribute/criteria Case studies (spatially defined)

Issue, sector or human value focus

Representativeness of drivers and impacts:

- climate-related phenomena

55

- impact categoriesRepresentativeness of sectors, values and places:

- production sectors- values (health, biodiversity,

etc)- regions/communities

Methodological development:- comparative potential

(methods/cases)- models, other- purchase re uncertainty

Data availability:- rich/poor

Institutional capacity to support IA:- strong/weak

Existing or current work:- IA or equivalent- Non-integrated, but relevant

Policy and public relevance:- policy agencies, processes and

agendas- public/community topicality

International connections (linked processes and projects, comparable studies, institutional links)

Note: this matrix is intended as a simple illustration of the use of the attributes in (i) seeking to maximise coverage across programs and projects, and (ii) is requiring explicit recognition of the aims and potentials of IA work.

56

8 MATCHING CRITERIA & METHODS TO ASSESSMENTS

This section summarises several issues which should be considered in the context of identifying either regional case studies or case studies based around sectoral issues. These issues are considered in light of defining a specific regional case study – North Queensland (see Box 1),

8.1 Scope of an Integrated AssessmentThe scope of an IA of climate change impacts could be broadly and generically defined as being the minimum scope to meet the objectives (i.e. the scope is necessarily determined by the objectives of the IA). It is therefore essential to identify the justifications for the IA, and to define its objectives, before attempting to define the detailed scope of the study.

Box 1. Justifying an IA of Climate Change Impacts in north QueenslandThe justifications for and objectives of a climate change impacts IA will be determined largely by the characteristics of the particular sector or region, and the relevant climate change impact issues there. Possible reasons for undertaking a climate change impacts IA in the north Queensland region include the following: This World Heritage area has important cultural and economic values, connected to its

status as the single most biodiverse region in Australia; adaptation to climate change is important to maintain those values.

It is a well-defined economic region with a socio-economic fabric that is dependent on its biodiversity and cultural values, including the iconic status of the Great Barrier Reef.

The system is potentially vulnerable to climate change impacts, for example in terms of heritage values (threats to the reef and the rainforest), and in the area of health (increases in vector-borne diseases).

The region is already under significant pressure caused by impacts from other processes, including land-use change.

Objectives of such an IA case study would include: Making an argument for the necessity for mitigation action to reduce greenhouse gas

emissions. Given known high-probability stresses on a global scale (rising temperatures, ocean acidification), the GBR and montane tropics will be vulnerable. Their preservation requires mitigation action. In addition, the gap between adaptive capacity and residual risk in the region is large.

Contributing to understanding the costs of inaction on climate change mitigation (including social, environmental and economic costs), and focus on the need to reduce avoidable costs.

Providing knowledge necessary for adaptation to local and state government agencies, management authorities and individuals, in consultation with stakeholders. This could include considering issues like the effects of population growth on the resiliance of the overall system.

Establishing baseline monitoring and ongoing data collection to allow assessment of the effectiveness of actions taken.

Contributing to the development of adaptive policy processes and frameworks.

Scoping should thus fall into two phases. The first is a broad assessment of the need for and general parameters of an IA in the particular region/sector (which would also identify any prior relevant work and the ongoing validity of any previously-identified priorities). This stage should include discussion of the starting point for developing the IA; should it be the stakeholders’ interests, or the characteristics of the system itself? The second phase involves

57

consultation and collaboration with stakeholders to determine the focus of the study, its resourcing, the interests of potential users of information from the study, and to assess what aspects of interest it will be viable to include in the study (taking into account constraints such as data availability, timeframes and resourcing). For a regional study a broad scope is likely to be most useful, including as many aspects as possible (e.g. land use, ecosystem services, settlement, water issues, health).

8.2 Research approaches & reasons for an IAIntegrated assessments are likely to be effective where multiple interests require integrated actions to address climate change impacts, because it can provideconsistency within and across sectors a systems perspective across all parts of a region/system additional explanatory power to that available from studies of separate parts of the system, often derived by considering the interactions among parts of the systema means of serving the overlapping needs of a range of stakeholders with interests in different issues (e.g. the Millennium Ecosystem Assessment, a model that provides both integration and the possibility of extracting specific reports for different stakeholders)

A range of tools and research approaches is available for IA, ranging from literature surveys and data appraisals to a variety of modelling techniques. In general, it is probably most effective to identify a small number of relatively high-level studies that require integration to be successful, excluding studies that are not highly inter-related. It is important to then recognise potential linkages, interactions, synergies and amplification within the study, and to focus on the principal co-variants. An example of a strongly inter-related system (characterised by people-environment linkages and interdependencies) is the rate of recovery of the Great Barrier Reef (GBR) following high temperature events, which is dependent on water quality, which in turn is related to land use.

Suggestions for specific approaches to IA centre on the need for staging, and include beginning with a review of existing work to build upon identifying components of the system that can be modelled (e.g. fauna, forests, reef,

catchments, sediment, settlement, land use) undertaking a GIS mapping exercise to define the spatial dimensions of current

knowledge and data, informed by prior definitions of the desirable scope and focus of the IA

looking at the impacts of climate change on iconic natural resources first, e.g. the GBR (primary impacts), and then considering the resulting impacts on tourism, etc. (secondary impacts)

8.3 Methodologies and methods for integrationA wide range of methodologies and methods can be applied to IA, including models. A range of these were discussed in Section 5. However, it is important to recognise that models can be difficult to work with and are not applicable in all cases. Selecting appropriate methodologies and methods is an important component of planning an IA. Considerations in making that selection include first developing a conceptual framework, e.g. the Millennium Ecosystem Assessment

(Figure 3) or the National Land and Water Resources Audit (http://www.nlwra.gov.au/) incorporating integrative methodologies to achieve a whole that is greater than the sum

of the parts

58

using software and other tools that promote participation and discourse amongst a wide range of previously non-communicating parties

integrating policy and planning processes and frameworks into the methodology using both GIS, to integrate data and distributed model outputs, and qualitative system

approaches

A discussion of the considerations that should be made in choosing an appropriate set of methods for an IA was given in Section 6.

8.4 Products/outputs and communicationIt is important that the products of an IA study should be useful to and usable by a range of current and potential stakeholders and other groups. Some key elements of a report that succeeds in effective information delivery are: an over-arching narrative storyline (such as that for a book or movie on the subject of

the IA) a well-written Executive Summary a small number of key synthesising diagrams highlighting of simple bottom-line messages clear recommendations for action a focus on solutions as well as problems tailoring (language, format) to the target audience/s delivery in multiple forms, including web, newsletters, formal publication, media, etc.

This can only be achieved if there is a strong communication and education plan built in to the study; and if the communication teams understand the science as well as the target groups. An important decision that will shape the nature of the communication strategy is the desired end point that should be reached along the continuum from awareness to behavioural change; this should be specified in the communication plan.

A significant barrier to effective communication is stakeholder fatigue. This can be overcome using a number of strategies, including: ensuring that the material has an exciting story to tell, and has both relevance and

substance regular communication on short timelines on issues of substance, to maintain interest generating excitement by using ‘post-emergency’ (or dramatic event) opportunities (e.g.

storms, floods, etc) to raise issues using event recurrences, anniversaries and other public events to maintain visibility of

issues, at a range of levels An effective way of engaging the local community is using credible local experts as advocates; this also helps to maintain interest in and action on the issues addressed in an IA. It is important to develop local capacity so that the outcomes of the IA can be implemented and monitored, without ongoing dependence on the IA community.

8.5 Capacity buildingA key to successful capacity building in IA is to involve state and local planning agencies and policy makers as collaborators, rather than stakeholders, in the IA process. Planners (social, physical, urban design, etc) are particularly important in this respect, as they develop the systems within which action occurs to implement the existing regulatory frameworks. There is a question, however, as to the extent to which it is the role of the IA

59

community to engage in capacity building, beyond the collaborative relationships mentioned above.

60

9 REFERENCES

Abbs, D. J. (2004) A high resolution modelling study of the effect of climate change on the intensity of extreme rainfall events. Staying afloat: Floodplain Management Authorities of NSW 44th Annual Conference, Coffs Harbour, NSW, May 2004, 17-24.

Arnell, N. W. (1999). Climate change and global water resources. Global Environmental Change, 9, S31-S46.

Basher, R. E., and Zheng, X. (1995). Tropical cyclones in the southwest Pacific: spatial patterns and relationships to the Southern Oscillation and sea surface temperature. Journal of Climate, 8, 1249-1260.

Beer, T., and Williams, A. (1995). Estimating Australian forest fire danger under conditions of doubled carbon dioxide concentrations. Climatic Change, 29, 169-188.

Berkelmans, R. and Oliver, J.K. (1999). Large scale bleaching of corals on the Great Barrier Reef. Coral Reefs 18: 55-60.

Born, S. M., and Sonzogni, W. C. (1995). Integrated environmental management: strengthening the conceptualization. Environmental Management, 19(2), 167-181.

Borsuk, M.E., Stow, C.A., and Reckhow, K.H., 2004, A Bayesian network of eutrophication models for synthesis, prediction and uncertainty analysis, Ecological Modelling 173, 219-239.

Bouzaher, A., Lakshminarayan, P.G., Cabe, R., Carriquiry, A., Gassman, P.W., and Shogren, J.F., 1993, Metamodels and nonpoint pollution policy in agriculture, Water Resources Research. 29, 1579-1587.

Brack, C. L., and Richards, G. P. (2002). Carbon accounting model for forests in Australia. Environmental Pollution, 116(1), 187-194.

Brinsmead, T. (2005). Methodology for integrated assessment of climate change impacts and adaptation options, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Brown, D.G., Page, S.E., Riolo, R., and Rand, W., 2004, Agent-based and analytical modelling to evaluate the effectiveness of greenbelts, Environmental Modelling & Software 19, 1097-1109.

BRS. (2004). Science for Decision Makers: Climate Change Adaptation in Agriculture. Bureau of Rural Sciences, Canberra, Australia.

Brutsaert, W., and Parlange, M. B. (1998). Hydrological cycle explains the evaporation paradox. Nature, 396, 30.

Butler, C.D., Corvalan, C.F., and Koren, H.S. (2005). Human health, well-being, and global ecological scenarios, Ecosystems, 8, 153-162.

Cary, G.J. (2002). Importance of changing climate for fire regimes in Australia. In: Bradstock, R.A. et al. (eds), Flammable Australia: The Fire Regimes and Biodiversity of a Continent. Cambridge University Press, Cambridge, UK, pp 26-46.

Chattopadhyay, N., and Hulme, M. (1997). Evaporation and potential evapotranspiration in India under conditions of recent and future climate change, Agricultural and Forest Meteorology, 87, 55-73.

Chesson, J. (2005). Major issues for agriculture, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Church, J. A., White, N. J., Coleman, R., Lambeck, K., and Mitovica, J. X. (2004). Estimates of regional distribution of sea level rise over the 1950-2000 period. Journal of Climate, 17, 2609-2625.

61

Collins, D. A., and Della-Marta, P. M. (1999). Annual climate summary 1998: Australia's warmest year on record. Australian Meteorological Magazine, 273-383.

Crimp, S., McKeon, G., Howden, M. (2005). Methods in integrated assessment: Agriculture/Grazing, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

CSIRO (1996). Climate Change Scenarios for the Australian Region. Commonwealth Scientific and Industrial Research Organisation, Atmospheric Research, Aspendale, Victoria, Australia.

CSIRO (2001). Climate Change Projections for Australia. CSIRO Atmospheric Research, Aspendale, Victoria, Australia.

.CSIRO (2005). OzClim - Climate Change Scenarios for Australia. CSIRO Marine and Atmospheric Research. http://www.cmar.csiro.au/ozclim/index.html

Davis, J.R., 1995, Expert systems and environmental modelling, in Jakeman, A.J., Beck, M.B., and McAleer, M.J. (Eds.) Modelling Change in Environmental Systems, Wiley, Chichester, pp. 505-517.

Deaton, M.L., and Winebrake, J.J., 1999, Dynamic Modelling of Environmental Systems, Springer, New York.

Dovers, S. (2005). Environment and sustainability policy: creation, implementation, evaluation. Federation Press, Sydney.

D'Souza, R. M., Becker, N. G., Hall, G., and Moodie, K. B. A. (2004). Does ambient temperature affect foodborne disease? Epidemiology, 15, 86-92.

Evans, J. L., and Allan, R. J. (1992). El Nino-Southern Oscillation modification to the structure of the monsoon and tropical cyclone activity in the Australiasian region. International Journal of Climatology, 12(611-623).

Ewing, S. A., Grayson, R. B., and Argent, R. M. (1997). Research Integration in ICM: Review and Discussion Document.Centre for Environmental Applied Hydrology, University of Melbourne.

Farrell, A. (2005). International overview of Integrated Assessment processes, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Farrell, A., and Jaeger, J. (2006) Assessments of Regional and Global Environmental Risks: Designing Processes for the Effective Use of Science in Decision making. Resources for the Future, Washington, DC.

Fiddaman, T. (1997). Feedback Complexity in Integrated Climate-Economy Models. Ph.D. Thesis. MIT Sloan School of Management.

Fiddaman, T.S. (2002). Exploring policy options with a behavioral climate-economy model, System Dynamics Review, Vol. 18, No. 2.

Fisher, E., Jones, J. and von Schomberg, R. (eds). Implementing the precautionary principle: perspectives and prospect. Elgar, Cheltenham.

Forsyth, R., (1984). The expert systems phenomenon, in: R. Forsyth (ed.) Expert Systems: principles and case studies, Chapman and Hall, London, pp. 3-8.

Geurts, J. L. A., and Joldersma, C. (2001). Methodology for participatory policy analysis. European Journal of Operational Research, 128, 300-310.

Golubev, V. S., Lawrimore, J. H., Groisman, P. Y., Speranskaya, N. A., Zhuravin, S. A., Menne, M. J., Peterson, T. C., and Malone, R. W. (2001). Evaporation changes over the contiguous United States and the former USSR: A reassessment. Geophysical Letters, 31, L13503, doi: 10.1029/2004GL019846.

Handmer, J. (2005). Adaptation issues in emergency management, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

62

Hanson, P.J. and Weltzin, J.F. (2000). Drought disturbance from climate change: response of United States forests. The Science of the Total Environment 262: 105-220.

Hennessy, K.J. (2005). Climate change scenarios and impacts, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Hennesy, K.J., Collins, D. and Salinger, J. (2004a). Appendix 3: Observed and projected trends in extreme weather and climate events in Australia and New Zealand. In: Carter et al. (eds), Characterising the 21st Century and beyond: Guidance on Scenarios for Authors of the Working Group II Fourth Assessment Report, 77-90.

Hennessy, K., McInnes, K., Abbs, D., Jones, R., Bathols, J., Suppiah, R., Ricketts, J., Rafter, T., Collins, D., and Jones, D. (2004b). Climate Change in New South Wales, Part 2: Projected changes in climate extremes. Consultancy report for the New South Wales Greenhouse Office, CSIRO Atmospheric Research and Bureau of Meteorology, 79pp.

Hennessy, K., Suppiah, R., and Page, C. M. (1999). Australian rainfall changes, 1910-1995. Australian Meteorological Magazine, 48, 1-13.

Hennessy, K.J.,Whetton, P.H., McGregor, J.L., Katzfey, J.J., Jones, R.N., Page, C.M.. and Nguyen, K. (1998). Fine Resolution Climate Change Scenarios for New South wales – Annual Report 1997-98. CSIRO Atmospheric Research Consultancy Report for New South Wales Environmental Planning Authority.

Hennessy, K.J., Whetton, P.H., Bathols, J., Hutchinson, M.F., Sharples, J.J. (2003). The impact of climate change on snow conditions in Australia. CSIRO Atmospheric Reserarch. Consultancy report for the Victorian Department of Sustainability and Environment, NSW National Parks and Wildlife Science, Australian Greenhouse Office and Australian Ski Areas Association, 47pp. http://www.cmar.csiro.au/e-print/open/hennessy_2003a.pdf

Hood, A., Cechet, R., Hossain, H. and Sheffield, K. (in press) Options for Victorian agriculture in a new climate: Pilot study linking climate change and land suitability modeling, Environmental Modelling & Software.

Howden, S.M. and Gorman, J.T. (eds) (1999). Impacts of Global Change on Australian Temperate Forests. Working Paper Series 99/08. CSIRO Wildlife and Ecology, Canberra, Australia, 146 pp.

Huang, Y.F., Huang, G.H., Hu, Z.Y., Maqsood, I. and Chakma, A. (2005). Development of an expert system for tackling the public's perception to climate-change impacts on petroleum industry, Expert Systems with Applications, 29(4): 817-829

Hulme, M., and Sheard, N. (1999). Climate Change Scenarios for Australia. Climatic Research Unit, University of East Anglia, Norwich, UK.

IPCC (2000). Special Report on Emission Scenarios: Summary for Policymakers., Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK See also http://sres.ciesin.org/

IPCC. (2001). Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

Jakeman, A. J., and Letcher, R. A. (2003). Integrated assessment and modelling: features, principles and examples for catchment management. Environmental Modelling and Software, 18, 491-501.

Jakeman, A. J., Letcher, R. A., and Newhan, L. T. H. Integrated catchment modelling: issues and opportunities to support improved sustainability outcomes. 29th Hydrology and Water Resources Symposium, Canberra.

63

Janssen, M. and de Vries, B. (1998). The battle of perspectives: a multi-agent model with adaptive responses to climate change, Ecological Economics, 26(1): 43-65.

Janssen, W., and Goldworthy, P. (1996). Multidiscplinary research for natural resource management: conceptual and practical implications. Agricultural systems, 51, 259-279.

Jasanoff, S. (1993). Bridging the two cultureds of risk analysis. Risk Analysis 13(2): 123-129.

Jones, R. (2005). Climate change risks and integrated assessment, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Karoly, D. (2001). Interannual and longer-term variations in Australian mean climate parameters from observations and model simulations. Understanding the climate of Australia and the Indo-Pacific region: 13th Annual BMRC Modelling Workshop, BMRC Research Report No. 84, Bureau of Meteorology, Australia, 49-52.

Karoly, D., Risbey, J., and Reynolds, A. (2003). Global Warming Contributes to Australia's Worst Drought. WWF Australia.

Keen, M., Brown, V. and Dyball, R. (eds). (2005). Social learning in environmental management: towards a sustainable future. Earthscan, London.

Kidd, A.L., 1987, Knowledge acquisition an introductory framework, in: A.L. Kidd (ed.) Knowledge Acquisition for Expert Systems: a practical handbook, Plenum Press, New York, 194pp.

Koivusalo, H., Kokkonen, T., Laine, H.,A., and Varis, O. (2005). Exploiting simulation model results in parameterising a Bayesian network – A case study of dissolved organic carbon in catchment runoff, In Zerger, A. and Argent, R.M. (eds) MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2005, pp.421-427. ISBN: 0-9758400-2-9. http://mssanz.org.au/modsim05/papers/koivusalo.pdf.

Kothavala, Z. (1999). The duration and severity of drought over eastern Australia simulated by a coupled ocean-atmosphere GCM with a transient increase in CO2. Environmental Modelling and Software, 14, 243-252.

Krol, M., Jaeger, A., Bronstert, A. and Güntner, A. (in press). Integrated modelling of climate, water, soil, agricultural and socio-economic processes: A general introduction of the methodology and some exemplary results from the semi-arid north-east of Brazil, Journal of Hydrology.

Kuleshov, Y. A. Tropical cyclones in the southern hemisphere: influence of the El Nino-Southern Oscillation phenomenon. Seventh International Conference on Southern Hemisphere Meteorology and Oceanography, Wellington, 202-203.

Letcher, R. A., Jakeman, A. J., and Croke, B. F. W. (2004). Model development for integrated assessment of water allocation options. Water Resources Research, 40, W05502, doi:10.1029/2003WR002933.

Letcher, R., and Jakeman, A. J. (2003). Application of an Adaptive Method for Integrated Assessment of Water Allocation Issues in the Namoi River Catchment, Australia. Integrated Assessment, 4(2): 73-89.

Letcher, R., and Weidemann, S. (2004). Modelling Economic-Ecological Impacts in NSW Coastal Catchments- Feasibility Study. iCAM, The Australian National University, Canberra, Australia.

Letcher, R.A., Croke, B.F., Jakeman, A.J., Merritt, W.S., (in press) An integrated modelling toolbox for water resources assessment and management in upland catchments: Model description, Agricultural Systems.

64

Letcher, R.A., Croke, B.F., Merritt, W.S. and Jakeman, A.J.(in press). An integrated modelling toolbox for water resources assessment and management in highland catchments: Sensitivity analysis and testing, Agricultural Systems.

Letcher, R. and Jakeman, A.(2005). A role for models in integrated assessment, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Linacre, E. T. (2004). Evaporation trends. Theoretical and Applied Climatology, 79, 11-21.Liu, B., Xu, M., Henderson, M., and Gong, W. (2004). A spatial analysis of pan evaporation

trends in China, 1955-2000. Journal of Geophysical Research, 109, D15102, doi: 10.1029/2004JD004511.

Lynch, A. (2005). Context sensitive integrated methodologies, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Marshall, P. (2005). Climate change vulnerability/adaptation issues for marine ecosystems, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Martens, P., and McMichael, A. J. (2001). Vector-borne diseases, development and climate change: An editorial comment. Integrated Assessment, 2, 171-172.

Martens, W. J. M (1998). Climate change, thermal stress and mortality changes, Social Science & Medicine, 46(3): 331-344.

Mastrandrea, M. D., and Schneider, S. H. (2001). Integrated assessment of abrupt climatic changes. Climate Policy, 1, 433-449.

Mayoux, L. (2006). Participatory Methods, http://www.enterprise-impact.org.uk/word-files/ParticMethods.doc

McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J., and White, K. S. (2001). Climate Change 2001: Impacts, Adaptation, and Vulnerability. Cambridge University Press, Cambridge, UK and New York, USA.

McInnes, K. L., Suppiah, R., Whetton, P. H., Hennessy, K. J., and Jones, R. N. (2003). Climate change in South Australia: Report on assessment of climate change, impacts and possible adaptation strategies relevant to South Australia. CSIRO Atmospheric Research, Aspendale, Victoria.

McMichael, A. J. (1997). Integrated assessment of potential health impact of global environmental change: Prospects and limitations. Environmental Modelling and Assessment, 2, 129-137.

McMichael ,A.J. (2005). Health impacts of climate change, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Merritt, W. S., Croke, B. F., Jakeman, A. J., Letcher, R. A., and Perez, P. (2004). A biophysical toolbox for assessment and management of land and water resources in rural catchments in Northern Thailand. Ecological Modelling, 171: 279-300.

Merritt, W. S., Croke, B. F., Jakeman, A. J., Letcher, R. A., and Perez, P. (2004). A biophysical toolbox for assessment and management of land and water resources in rural catchments in Northern Thailand. Ecological Modelling, 171:, 279-300.

Merritt, W. S., Jakeman, A., and Croke, B. F. (2005). Sensitivity Testing of a Model for Exploring Water Resources Utilisation and Management Options. Environmental Modelling and Software, 20: 1013-1030.

Millennium Ecosystem Assessment (2005). Ecosystems and human well-being: current state and trends: findings of the Condition and Trends Working Group, edited by R. Hassan, R. Scholes, N. Ash, The millennium ecosystem assessment serices, Volume 1.

65

Moonen, A.C., Ercoli, L., Mariotti, M., and Masoni, A. (2002). Climate change in Italy indicated by agrometeorological indices over 122 years, Agricultural and Forest Meteorology, 111, 13-27.

Mostert, E., in press, Participation for sustainable water management, in Sustainable Management of Water Resources: an Integrated Approach, C. Giupponi, A. Jakeman, D. Karssenberg and M.Hare (Eds.), Elgar.

Munton, R. (2003). Deliberative democracy and environmental decision-making, in Negotiating environmental change: new perspectives from the social sciences, F.Berkhout, M.Leach and I.Scoones (Eds). Elgar, Cheltenham.

Newham, L. T. H., Ticehurst, J. L., Rissik, D., Letcher, R., Jakeman, A. J., and Nelson, P. (2004). Assessing the sustainability of coastal lakes using a Bayesian decision network approach. NSW Coastal conference 2004: Seachange - the Balancing Act, Lake Macquarie NSW, pp. 63-69.

Nicholls, N. (2004). The changing nature of Australian droughts. Climatic Change, 63, 323-336.

Nicholls, N., and Collins, D. (2005). Observed climate change in Australia over the past century. Energy and Environment, in press.

Nicholls, N., Landsea, C., and Gill, J. (1998). Recent trends in Australian region Tropical Cyclone activity. Meteorology and Atmospheric Physics, 65, 197-205.

Nijkamp, P., Wang, S. and Kremers, H. (2005). Modeling the impacts of international climate change policies in a CGE context: The use of the GTAP-E model, Economic Modelling, 22(6): 955-974.

Pahl-Wostl, C. (2004) Why do we need a Society for Integrated Assessment, The Integrated Assessment Society, November 2004 .

Palmer, D. (1992). Methods for analysing development and conservation issues: the Resource Assessment Commission's experience. Resource Assessment Commission, Australian Government, Canberra.

Park, J., and Seaton, R. A. F. (1996). Integrative research and sustainable agriculture. Agricultural Systems, 50, 81-100.

Parker, D.C., Berger, T., Manson, S.M (2002). Agent-Based Models of Land-Use and Land-Cover Change. Review of an International Workshop, October 4-7, 2001. LUCC Report Series No.6.

Pasquer, B., Laruelle, G., Becquevort, S., Schoemann, V., Goosse, H. and Lancelot, C. (2005). Linking ocean biogeochemical cycles and ecosystem structure and function: results of the complex SWAMCO-4 model, Journal of Sea Research, 53(1-2): 93-108.

Peel, J. 2005. The precautionary principle in practice: environmental decision-making and scientific uncertainty. Federation Press, Sydney.

Peters, R. L. (1990). Effects of global warming on forests. Forest Ecology and Management, 35, 13-33.

Peterson, T. C., Golubev, V. S., and Groisman, P. Y. (1995). Evaporation losing its strength. Nature, 377, 687-688.

Pittock, A.B. (2003). Climate change: an Australian guide to the science and potential impacts. Australian Greenhouse Office, Canberra.

Pittock, A. B., Allan, R. J., Hennessy, K. J., McInnes, K. L., Suppiah, R., Walsh, K., and Whetton, P. H. (1999). Climate change, climatic hazards and policy responses in Australia. Climate, Change and Risk, T. E. Downing, A. A. Oltshoorn, and R. S. L. Tol, eds., Routledge, London, UK, 19-59.

Richards, G. P., and Brack, C. L. (2004). A continental biomass stock and stock change estimation approach for Australia. Australian Forestry, 67(4), 284-288.

66

Richards, G. P., and Brack, C. L. (2004). A modelled carbon account for Australia's post-1990 plantation estate. Australian Forestry, 67(4), 289-300.

Risbey, J., Milind, K., and Patwardhan, A. (1996). Assessing integrated assessments. Climatic Change, 34, 369-395.

Roderick, M. L., and Farquhar, G. D. (2002). The cause of decreased pan evaporation over the past 50 years. Science, 298(1410-1411).

Roderick, M. L., and Farquhar, G. D. (2004). Changes in Australia pan evaporation from 1970 to 2002. International Journal of Climatology, 24, 1077-1090.

Rotmans, J. (2002). Scaling in integrated assessment: problem or challenge? Integrated Assessment, 3, 266-279.

Rotmans, J. and Dowladabati, H. (1997). Integrated Assessment Modeling, in Human Choice and Climate Change, Vol. 3 eds. S. Rayner and E.L. Malone, Battle Press, Columbus, OH, 291-377.

Sharples, J. J., Hutchinson, M. F., and Kesteven, J. L. (2006). Spatio-temporal trends in pan evaporation over Australia 1970-2003. In prep.

Shugart, H., Sedjo, R. and Sohngen, B. (2003). Forests and Global Climate Change. Pew Center on Global Climate Change, Wahington DC, USA.

Srbljinovic, A. and Skunca, O. (2003). Am introduction to agent based modelling and simulation of social processes. Interdisciplinary Description of Complex Systems 1(1-2): 1-8.

Simonovic, S. P., and Davies, E. G. R. (2006). Are we modelling impacts of climatic change properly? Hydrological Processes 20: 431-433, doi: 10.1002/hyp.6106.

Smith, I. (2004). An assessment of recent trends in Australian rainfall. Australian Meteorological Magazine, 53, 163-173.

Smithson, M. (1989). Ignorance and uncertainty: emerging paradigms. Springer-Verlag, New York.

Standards Australia (2004a). AS/NZS 4360: The Australia/New Zealand risk management standard. Standards Australia International Ltd, Sydney.

Standards Australia (2004b). HB 4360:2004 Risk management guidelines: companion to AS/NZS 4360. Australian Standards International Ltd, Sydney.

Syme, G. J., Butterworth, J. E., and Nancarrow, B. E. (1994). National Whole Catchment Management: A Review and Analysis of Processes. 1/94, LWRRDC, Canberra.

Thomas, A. (2000). Spatial and temporal characteristics of potential evapotranspiration trends over China. International Journal of Climatology, 20, 381-396.

Ticehurst, J.L., Newham, L.T.H., Rissik, D., Letcher, R.A. and Jakeman A.J., (in press) A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia, Environmental Modelling & Software.

Timbal, B. (2004). Southwest Australia past and future rainfall trends. Climate Research, 26, 233-249.

Troy, P. (2005). Climate change vulnerability/adaptation issues for Australian ciies, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

van der Sluijs, J.P., (2002). Integrated Assessment, Definition of, Volume 4, Responding to global environmental change, pp 249–250, Edited by K. Mostafa Tolbain, Encyclopedia of Global Environmental Change, Editor-in-Chief T. Munn, John Wiley & Sons, Ltd, Chichester, 2002.

van Kooten, G.C., Eagle, A.J., Manley, J. and Smolak, T. (2004). How costly are carbon offsets? A meta-analysis of carbon forest sinks, Environmental Science & Policy, 7(4), August 2004, Pages 239-251.

67

Villa, F., and Costanza, R. (2000). Design of multi-paradigm integrating modelling tools for ecological research. Environmental Modelling and Software, 15, 169-177.

Walsh, K., Hennessy, K., Jones, R., McInnes, K. L., Page, C. M., Pittock, A. B., Suppiah, R., and Whetton, P. (2001). Climate change in Queensland under enhanced greenhouse conditions - third annual report, 1999-2000. CSIRO Atmospheric Research, Aspendale.

Whetton, P. H., Suppiah, R., McInnes, K. L., Hennessy, K. J., and Jones, R. N. (2002). Climate change in Victoria: high resolution regional assessment of climate change impacts. CSIRO consultancy report for Department of Natural Resources and Environment Report, Victoria, 44pp.

White, I. (2005). Water resources and water security, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Wild River, S. and Healy, S. (2006). Guide to environmental risk management. CCH Australia, Sydney.

Williams, J. (2005). The Biodiversity sector – major climate change vulnerability/adaptation issues, Workshop on Integrated Assessment of Climate Change Impacts, Centre for Resource and Environmental Studies, 4-5 July 2005.

Williams, A. J., Karoly, D. J., and Tapper, N. (2001). The sensitivity of Australian fire danger to climate change. Climatic Change, 49, 171-191.

World Bank. (1996). The World Bank Participation Sourcebook, The International Bank for Reconstruction and Development (World Bank).

Wynne, B. (1992). Uncertainty and environmental learning: reconceiving science in the preventative paradigm. Global Environmental Change, 2:, 111-127.

68

APPENDIX 1. POLICY AND INTEGRATED ASSESSMENTIt can be assumed that IA is intended, either directly or indirectly, to inform policy decisions by governments and other members of the policy community. If so, this can be viewed as a subset – and potentially a particularly complex one – of the broader issue of science-policy linkages. Three broad issues arise from considering the connection of IA to policy: (i) how IA as a research activity connects to policy processes; (ii) the prospects for integrated policy assessment within policy systems; and (iii) issues of scale. This section notes key considerations in relation to these three issues, and concludes by discussion some implications for methodological development and selection and for case study selection.

A1.1 Connection to policyThe general issue of science-policy linkages is a fluid and problematic area, with significant tensions over: the ‘independence’ of pure as opposed to applied science; the roles and responsibilities of scientists (and researchers more generally) in relation to informing versus formulating policy; the time scales over which thorough theoretical and methodological development, let alone empirical investigation, occurs compared to those over which policy decisions may need to be made; the comprehensibility of scientific methods and findings to a policy audience; and the changing nature of research provider-funder relationships in recent years.1

It seems apparent that these tensions – and others – are particular problematic in the case of IA and climate change. The phenomenon of climate change, and especially its impacts at meaningful scales, is pervaded by uncertainty yet highly topical politically. Moreover, the very nature of integrated assessment involves multiple disciplines and policy sectors, in turn bringing into both research and policy a larger number of different interests and knowledge systems (the latter including formal disciplines) into collaborative interactions. And, any policy interventions will only produce measurable, substantive outcomes in the longer term, affected by multiple factors in interacting natural and human systems, making the identification of cause-effect linkages a challenging prospect.

The precise nature and potential of research-policy connections will of course vary profoundly across specific contexts. At the broadest level, some guidance can be provided for such context-specific research design by exploring: (i) different parts of the policy process, and thus which particular parts an IA exercise might seek to connect with; and (ii) different froms of policy learning, assuming that the design and outcomes of an IA has, at least in part, the intention of contributing to learning how to formulate and deliver more effective policy interventions aimed at ameliorating or avoiding adverse climate change impacts (or conversely, taking advantage of potentially beneficial changes).

On the first, Table 1 offers a detailed representation of the policy process, constructed specifically for the environment and sustainability domain.2 (Note that such ‘models’ should not be taken to convey the way in which policy is made, or even how it might best be made, but rather identifies the elements of a comprehensive policy process.) IA would be most relevant in process of problem framing (I, 3-6, 8) and to a lesser extent policy monitoring 1 For a review, see Nowotny, H. Scott, P. and Gibbons, M. (2001). Re-thinking science: knowledge and the public in an age of uncertainty. London: Polity Press2 This construction is from Dovers, S. 2005. Environment and sustainability policy: creation, implementation, evaluation. Sydney: Federation Press. For an alternative, generalised model, see Bridgman, P. and Davis, G. 2004. The Australian policy handbook. Sydney: Allen and Unwin.

69

(IV, 18). Cognisance of general principles and imperatives (V) and of the roles of and responsibilities for other elements would be necessary contextual knowledge for scientists engaged in policy-oriented IA.

Table 1. Framework for analysis and prescription of environmental and sustainability policy

_______________________________________________________________

I. Problem framing: 1 Discussion and identification of relevant social goals 2 Identification and monitoring of topicality (public concern) 3 Monitoring of natural and human systems and their interactions 4 Identification of problematic environmental or human change or degradation 5 Isolation of proximate and underlying causes of change or degradation 6 Assessment of risk, uncertainty and ignorance 7 Assessment of existing policy and institutional settings 8 Definition (framing and scaling) of policy problems

II. Policy framing: 9 Development of guiding policy principles 10 Construction of general policy statement (avowal of intent) 11 Definition of measurable policy goals

III. Policy implementation: 12 Selection of policy instruments/options 13 Planning of implementation 14 Planning of communication, education, information strategies15 Provision of statutory, institutional and resourcing requirements 16 Establishment of enforcement/compliance mechanisms 17 Establishment of policy monitoring mechanisms

IV. Policy monitoring and evaluation: 18 Ongoing policy monitoring & routine data capture19 Mandated evaluation and review process20 Extension, adaptation or cessation of policy and/or goals

_____________________________________________________________________

V. General elements, throughout policy and institutional systems: In policy processes: - policy coordination and integration (across and within policy fields)- public participation and stakeholder involvement- transparency, accountability and openness - adequate communication mechanisms (multi-directional, democratically structured).

Institutional arrangements:- persistence over time- purposefulness via mandate and goals- information-richness & -sensitivity, including gathering, use and ownership- inclusiveness in policy formulation and implementation- flexibility, through evaluation, experimentation and learning.

_____________________________________________________________________

70

On the second issue, policy learning Table 2 indicates four broad forms of policy learning, and the actors involved, and the outcomes of learning.3 Instrumental and government learning operates with respect to existing constructions of policy problems and goals, whereas social and political learning explicitly seek to redefine problems and goals. It is likely that IA will contribute principally to social learning, in terms of informing the definition of policy problems, given that climate change impacts are uncertain and social and political consensus on policy problems and goals is yet to materialise. That raises the sensitive issue of informing versus formulating policy problems, a tension which can be minimised through clear recognition of the roles of research and policy actors in any collaborative IA project.

Table 2. Policy learning: forms and purposesForm What is learned? Who learns? To what effect?Instrumental learning

How well instruments have allowed the achievement of goals

Members of the policy network, especially government officials engaged in policy formulation and implementation

Better design and implementation of policy instruments to achieve predetermined policy goals

Government learning

How well administrative arrangements and processes have allowed policy implementation

Members of the policy network, especially senior officials responsible for design and maintenance of policy process

Better design of administrative structures and processes within the bureaucratic systems (and engaging outside that system)

Social learning

How useful are our constructions of policies and goals

The broader policy community, including both more and less closely engaged actors within and outside government

Reframed problems and related goals, through changed cause-effect understanding or altered social preferences

Political learning

How to most effectively engage with and influence political and policy processes

Policy actors wishing to (i) change policy agendas and outcomes or (ii) defend current agendas and outcomes

Change in: problem definition; policy goals; and/or membership of the policy network.

Final broad guidance on negotiating the connections between IA and policy can be found in the area of knowledge utilisation in public policy, again offering a simple yet well-informed framework for considering how information (especially scientific research outcomes) is or could be used in policy systems, beyond the simplistic and now dated assumption of linear connection and uncritical uptake of scientific findings (instrumental use). Table 3 proposes a simple taxonomy of the use of composite sustainability indicators in policy systems, and the similarity of that field to the likely composite and complex nature of outcomes of IA suggests its relevance to IA.4 Reinforcing the observation regarding policy learning, the outcomes of an IA might occasionally be used instrumentally (directly driving policy change), but most commonly would be used conceptually, contributing to the formulation of 3 From Dovers (2005) op cit, drawing on the broader policy and institutional learning literature.4 From Hezri, A.A. 2004. Sustainability indicator system and policy processes in Malaysia: a framework for utlilisation and learning. Journal of Environmental Management. 73: 357-371.

71

policy problems. While both researchers and policy officials engaged in IA would not intend for the outcomes to be used politically, tactically or symbolically, the political realities of the climate change debate instruct such individuals and agencies to understand and expect such use of science.

Table 3. A taxonomy of indicator useNature of response Degree of rationality

High LowPositive Instrumental use Political use

- use for action - support predetermined use

Ordinary Conceptual use Symbolic use- for enlightenment - ritualistic assurance

Negative (not used) Tactical use- delaying tactic- substitute for action- deflect criticism

IA and similar research initiatives do not exist in isolation in either space or time, but are inextricably linked to previous, concurrent and future research and applications. Often, a small range of organisations will be involved, and the potential exists for mutual learning – both methodological and policy-oriented – across participating groups. Recent work on the evolution of and learning within large scale assessment processes may inform the progress of IA in the Australian context, informed as it is by both empirical analysis of assessment processes and the organisational learning literature, recognising and taking into account structural, cultural, contextual and personal variables and identifying different forms of learning that contribute to improved assessments and applications.5

A1.2. Integrated policy assessmentThe imperative of policy integration stems from the foundational principle of sustainable development – codified in over one hundred Australian statutes and in international treaties as well as much policy – of integrating environmental, social and economic consideration in policy.6 The logic of policy integration is that attention to environmental problems n isolation – methodologically or in institutional and policy terms – fails to deal with the systemic and indirect causes of environmental degradation, which requires attention to other policy settings which create the incentives or disincentives for environmentally damaging behaviour. It incorporates a number of aspects and approaches which can be briefly noted here.

The first aspect is two purposes or degrees of integration. Environmental policy integration refers to be embedding of environmental considerations into policy analysis and formulation

5 This draws specifically on Siebenhuner, B. (2002). How do scientific assessments learn? Part 1: conceptual framework and case study of the IPPC. Environmental Science and Policy: 5: 411-420.6 Generally, see Lenschow, A. (ed). Environmental policy integration. London: Earthscan; and Dovers (2005) op cit, chapter 10..

72

in non-environmental sectors and portfolios. The other is a more overarching and complete requirement or intent of full integration of environment, social and economic factors.

With respect to both these purposes or degrees, there are institutional or policy process approaches, and methodological or decision-support options, respectively summarised in Tables 4 and 5.

Table 4. Institutional and policy process options for policy integrationGeneral form of integration

Major options (selected)

Policy processes Overarching environment and/or sustainability policy, or policy development defined by problems that traverse policy sectors (eg. oceans, biodiversity, energy, etc).

Policy assessment processes: strategic environmental assessment; sustainability assessment; regulatory impact review; environmental risk assessment.

Legislative review for consistency with sustainability principles.

Insertion of environment and sustainability consideration in agency decision making through statutory expression of sustainability principles.

Agency reporting on environment and/or sustainability (incl triple bottom line accounting).

Inter-agency and cross-sectoral (within jurisdiction)

Connecting existing parts: cabinet review processes; ministerial councils, inter-departmental committees or taskforces; joint policy programs; information sharing; parliamentary committees.

Merging wholes or parts: portfolio and agency re-organisation (super ministries, mergers, etc).

Whole-of-government mechanisms: offices or commissioners of environment or sustainability; councils for sustainability; sustainability legislation.

Table 5. Methods for policy integration (selected)Category Main examples

1. Economic/neo-economic

Extended cost benefit analyses (incorporating values not measured economically in traditional CBA); non-market valuation (eg. contingent valuation, hedonic pricing, travel cost method); choice modeling; multi-criteria analysis; natural resource accounting; agent-based modelling.

2. Integrated Various forms of bio-economic and related modeling, sometimes

73

assessment and modeling

including agent-based models and scenario modelling, including integrated assessment of climate change impacts

3. Policy assessment methods and procedures

Strategic environmental assessment, sustainability assessment and integrated policy assessment, extending assessment of proposals beyond the project scale of environmental impact assessment to the assessment of policies, plans and programs in non-environmental policy sectors.

4. Discursive approaches

Planning cells, collaborative planning, citizens juries, consensus conferences, and a range of inclusive approaches (see Chapter 9).

There is overlap in the integrative intent of IA and The relevance of institutional options for policy integration. The relevance of the options in Table 4 will largely be more indirect and contextual, and variable depending on the IA process, the policy environment, and the relationship between these. IA in the context of this volume is one option listed under (2) in Table 5, however various other methodological options may be incorporated into a broader IA exercise. Awareness of the breadth of integrative methodologies would be a necessary input to design of an IA, even if the broadening of IA to include, for example, social values via discursive approaches, is unlikely. No methodological approach has attracted broad support or is likely to be suitable under all or even many conditions, so a preparedness to choose and utilise methods from a number of options is necessary.

It is clear that policy integration will never – and should not be expected to – produce single metrics that allow composite measures of the social, environmental and economic implications of a policy decision. The same impossibility would clearly be the case with IA methods.

A1.3. Scale and integrated assessmentElsewhere in this document (3.2, 4.3) the issue of scale is dealt with, and is a recurrent theme in climate change research and IA. However, most discussion is about scale issues in joint modelling between biophysical sciences and sometime with a few social sciences (especially economics). If we accept IA as being, either actually or potentially, an undertaking inclusive of a larger range of disciplines from the social and natural sciences and the humanities, and as being closely related to policy processes, then the issue of scale becomes more problematic, for two reasons. First, the spatial and temporal scales that underpin theory and method in different disciplines vary widely and are often not explicit or well-explained when viewed from outside that discipline – the issue of embedded scale.7 Second, the spatial and temporal scales over which legal, policy, institutional and political actors and processes operate are rarely congruent with those over which climate change operates or with which the disciplines dominant in climate impacts research are familiar with.

Here, this issue can only be identified and briefly illustrated. As with many issues in interdisciplinary and policy-oriented research, the first challenge is to make disciplinary (theoretical and methodological) differences explicit and understood early in the research design phase, at least allowing for the possibility of subsequent theoretical and

7 This term, and much of this discussion, is taken from Dovers, S. 2004. Embedded scales: interdisciplinary and institutional issues. Millennium Assessment Conference: Bridging scales and epistemologies, Alexandria, 17-20 March 2004.

74

methodological development to integrate perspectives and approaches. The following uses some simple (and to a degree simplified) examples to illustrate differences in embedded scale:

Discipline/sub-discipline: Typical scales (spatial, temporal)

Neoclassical economics: - spatial: individual, household, firm, economy (jurisdictions), trade systems- temporal: short term (months-years).

Economic history: - spatial: national state, sector- temporal: longer term (decades-centuries).

Ecology:a) ecosystem theoryb) community ecologist

a) ecosystem; multiple, but longer termb) community; multiple, but shorter term.

Law:a) commonb) statute

a) legal traditionb) jurisdiction, enactment/repeal.

Psychology: - individual, days-years.

Meteorology: - spatial: local-regional-global (but not jurisdictional)- temporal: days-years-centuries.

Sociology, anthropology: - group, years-decadal.

Chemistry: - non-spatial, instantaneous.

Although simplified and selective, the above illustrates the different embedded scales across and even with disciplines. We may note, however, that some disciplines and research traditions have – or at least had – a tradition of natural-social science interactions at multiple scales, the most obvious one being geography (Barnett et al 2003). To expand on this, it is necessary to understand the underlying logic of a particular scale – that is, the reasons why a particular discipline, theory, method or application utilises a particular scale. The following is again simplified, but illustrates the point.

Apparent scales (examples) Underlying logics (examples)

Spatial:- individual, household, policy or industrial sector, locale, bioregion, catchment, sub-national, nation state, inter-governmental, regional, global.

- consumption, distribution of taxa, nutrient fluxes, jurisdictions, administration, legal competence, information availability, trade flows, transport systems and other infrastructure, international treaties and agreements.

Temporal:

75

- instantaneous, hours, days, weeks, months, seasonal, annual, decadal, generational, geological.

- chemical reactions, half-lives, life cycles, flowering, agricultural production, human longevity and fertility, political mandate, profit reporting, tax cycles, memory, data relevance, evolution.

All the reasons in the second column above may be highly relevant to either and integrated assessment, or to the utilisation of the outcomes of an IA in the policy process.

76

APPENDIX 2. WORKSHOP PROGRAM AND PARTICIPANTS

Workshop on Integrated Assessment of Climate Change Impacts

4-5 July at the Australian National University

Organised by ANU Centre for Resource and Environmental Studies

Sponsored by Australian Greenhouse Office, Dept of Environment and Heritage

and the ARC Network for Earth System Science

AimsThe area of integrated assessment of the impacts of climate change, with particular emphasis on assessing vulnerability and potential adaptivity of key natural and human systems in specified regions, is one of growing international significance. Integrated assessments require a wide understanding of natural and human systems and their interdependency, as well as consultation with stakeholders.

The purpose of the workshop is to bring together international and Australian experts in integrated assessment methods and in climate change impacts, in the presence of key State and Federal stakeholders, to address three main aims:

1. To identify the main methods for integrated assessment of the impacts of climate change. Methods will include “top down” scenario driven approaches and “bottom up” vulnerability based approaches.

2. To assess the strengths and weaknesses of these approaches for the assessment of climate change impacts and adaptation options with reference to key Australian sectors.

3. To identify 3-4 regional case studies to test these approaches in the Australian context.

The workshop is for two days. The first day will address aims 1 and 2 with presentations of integrated assessment methodology across sectors by key invited international and Australian experts. The second day will address synthesis of the methodologies presented on the first day and progress to identification of 3-4 regional case studies as required by aim 3. Detailed synthesis of workshop outputs in the light of the current literature will be circulated to the Australian Greenhouse Office and to workshop participants.

77

ProgramSunday evening – 3 July6.00 Welcome Drinks at University House

Day 1 - 4 July

Introduction9.00 Workshop logistics – Mike Hutchinson, ANU CRES9.10 Key questions to be addressed by workshop – Jo Mummery, AGO

Major climate change vulnerability/adaptation issues by sector9.30 Agriculture – Jean Chesson, BRS9.40 Water Resources – Ian White, ANU CRES9.50 Biodiversity – Jann Williams, Latrobe University10.00 Marine Ecosystems – Paul Marshall, GBRMPA10.10 Urban systems/infrastructure – Patrick Troy, ANU CRES10.20 Human Health – Tony McMichael, ANU NCEPH10.30 Emergency management – John Handmer, RMIT10.40 Morning Tea

International Overview of Integrated Assessment Methodology11.10 Alex Farrell, Energy & Resources Group, University of California, Berkeley12.10 Discussion12.30 Lunch

Methods in Integrated Assessment1.30 Climate change scenarios and impacts – Kevin Hennessy, CSIRO1.50 Climate change risks and integrated assessment – Roger Jones, CSIRO2.10 Integrating quantitative models – Alex Farrell, University of California, Berkeley2.30 Incorporating population health impacts – Tony McMichael, ANU NCEPH2.50 Agriculture/grazing – Steve Crimp, Qld Dept Natural Resources3.10 Afternoon Tea3.40 Context-sensitive integrated methodologies – Amanda Lynch, Monash University4.00 A role for models in integrated assessment – Rebecca Letcher, ANU iCAM4.30 Methodology for integrated assessment – Tom Brinsmead, University of Newcastle4.40 Criteria for assessment of case studies on Day 2 – Stephen Dovers, ANU CRES.5.15 Close

Workshop Dinner6.30 for 7.00 Vivaldi’s ANU

Day 2 – 5 July

9.30 Clarification of methods and their strengths and weaknesses – discussion led by Rebecca Letcher ANU CRES

10.30 Morning Tea11.00 Scoping of case studies – discussion led by Stephen Dovers, ANU CRES12.30 Lunch1.30 Matching methods to case studies – discussion led by Will Steffen, AGO/BRS3.00 Afternoon Tea3.30 AGO perspective on workshop – John Higgins, AGO4.00 Close

78

Workshop Participants

Jim Allen Rebecca LetcherSA Dept of Environment & Heritage ANU ICAM/CRES

Anne Bennett Amanda LynchWA Dept of Premier and Cabinet Monash University

Bryson Bates Paul MarshallCSIRO Land & Water GBRMPA

Tom Brinsmead Tony McMichaelUniversity of Newcastle ANU NCEPH

Jean Chesson Frank MillsBureau of Rural Science ANU CRES/RSPSE

Steve Crimp Jo MummeryQld Dept Natural Resources Australian Greenhouse Office

Dale Dominey-Howes Neville NichollsMacquarie University Bureau of Meteorology

JeanDouglass Tamara O’SheaAustralian Greenhouse Office Qld EPA

Steve Dovers Pascal PerezANU CRES ANU RSPAS

Alex Farrell Neil PlummerUniversity of California, Berkeley Bureau of Meteorology

Paul Graham Paul PurdonCSIRO NT Greenhouse Unit

John Handmer Hugh SaddlerRMIT Energy Strategies

Kevin Hennessy Jason SharplesCSIRO Atmospheric Research ANU CRES

John Higgins Will SteffenAustralian Greenhouse Office AGO/BRS

Jack Holden Ros TaplinVictorian Greenhouse Policy Unit Macquarie University

Mark Howden Nigel TapperCSIRO Sustainable Ecosystems Monash University

Lesley Hughes Graham TurnerMacquarie University CSIRO Sustainable Ecosystems

Mike Hutchinson Pat TroyANU CRES ANU CRES

Tony Jakeman IanWhiteANU ICAM/CRES ANU CRES

Roger Jones Jann WilliamsCSIRO Atmospheric Research Latrobe University

Jenny Kesteven Oliver WoldringANU CRES NSW Greeenhouse Office

Janette Lindesay Andrew ZuchANU SRES Qld Dept of Natural Resources

79

80