1

KLIMOS working paper 5 - March 2011

Ecological footprint of mobility of development organizations

Joana Almeida, Miet Maertens, Bruno Verbist

DRAFT 11 March 2011.

Not yet for citation.

2

Correct citation Almeida, J., Maertens, M., Verbist, B. (2011). Ecological footprint of mobility of development organisations. KLIMOS working paper 5, KLIMOS, Leuven, Belgium.

About the authors Joana Almeida (MSc.) is a PhD-student working on lifecycle assessment of bio-energy crops at KULeuven, Celestijnenlaan 200E, Box 3411, B-3001 Leuven. Prof. Miet Maertens is a lecturer at the Division of Agricultural and Food Economics at KULeuven, Celestijnenlaan 200E, Box 3411, B-3001 Leuven.

Dr. Bruno Verbist is the project leader of KLIMOS, Celestijnenlaan 200E, Box 3411, B-3001 Leuven.

For correspondence: Bruno.verbist@ees.kuleuven.be

Acknowledgements

Funding for the KLIMOS – consortium is kindly provided by DGD (the Directorate General for Development Cooperation; www.dgos.be) through VLIR-UOS (the ‘Vlaamse Interuniversitaire Raad – Universitaire Ontwikkelingssamenwerking’; ‘Flemish Interuniversity Council – University Development Cooperation’; www.vliruos.be).

About KLIMOS KLIMOS (Dutch acronym for “Climate Change and Development Cooperation”) gathers researchers from the various Flemish universities and colleges and from non-governmental organizations with a view to provide policy support to DGDC in matters related to climate change and their integration in development cooperation initiatives. Details of the KLIMOS consortium members and partner organisations can be found at: http://www.kuleuven.be/klimos

Copyright This material is considered to be an international public good that can be freely copied for use in a non-commercial context, provided that the source is acknowledged.

Layout Anne-Marie Reynaers

3

Table of contents

1. Introduction ...............................................................................................................................5

1.1. Development cooperation agents and environmental responsibility. Error! Bookmark

not defined.

1.2. Ecological and carbon footprints ....................................... Error! Bookmark not defined.

1.3. Reducing and offsetting emissions ................................... Error! Bookmark not defined.

Baseline emission cuts .................................................................. Error! Bookmark not defined.

The offset market ............................................................................ Error! Bookmark not defined.

Dynamic carbon networks ............................................................. Error! Bookmark not defined.

2. Case studies.............................................................................. Error! Bookmark not defined.

2.1. Methods................................................................................. Error! Bookmark not defined.

ATOL and CDI-Bwamanda............................................................ Error! Bookmark not defined.

Belgian Technical Cooperation ..................................................... Error! Bookmark not defined.

2.2. Results................................................................................... Error! Bookmark not defined.

3. Discussion ................................................................................. Error! Bookmark not defined.

4. Conclusions ............................................................................... Error! Bookmark not defined.

5. Key Recommendations for development aid institutions.... Error! Bookmark not defined.

6. References ................................................................................ Error! Bookmark not defined.

4

Abbreviations

BTC – Belgian Technical Cooperation

CDM – Clean Development Mechanism

CDP – Carbon Disclosure Project

CSR – Corporate Social Responsibility

FTE – Fulltime Equivalent

GHG – Greenhouse Gas

GIZ – Gesellschaft für Internationale Zusammenarbeit

GWP – Global Warming Potential

IPCC – Intergovernmental Panel on Climate Change

LCA – Life Cycle Assessment

NGO – Nongovernmental Organization

VCS – Voluntary Carbon Standard

WRI/WBCSD - World Resources Institute/World Business Council for Sustainable

Development

Objectives

This study analyses the potential of assessing the environmental sustainability of development organizations by quantifying their ecological and carbon footprints. Our objective is to analyse the criteria that should be met for better environmental accounting of North-South development cooperation projects by performing a carbon footprint analysis on available data from institutions. We focus on the impact of mobility on the carbon footprint, owing to its relevance in North-South cooperation and its burden on a system’s greenhouse gas emission profile. We aim to yield specific insights on the ecological footprint of mobility in development organizations and projects and set forward policy options for more environmentally sustainable practices.

5

1. Introduction

1.1. Development cooperation agents and environment al responsibility

Energy and material flows from any individual or organizational activity have impacts, which are taking a worrisome toll on the Earth’s resources. Initiatives and policies aiming to curb irreversible resource depletion and climate change are both being exhorted and criticised. The open debate and widened awareness promoted the onset of an environmentally conscious society. Likewise, organizations are stimulated to aim for both economical performance as well as the application of environmentally sound practices. Both objectives are formalised into corporate social responsibility (CSR) programs. By acknowledging own impacts and pointing towards increasingly responsible planning, organizations find themselves to be better fit to face the scrutiny of stakeholders as well as competing in a carbon constrained economy (Lash and Wellington, 2007). Immediate advantages of improvement strategies pertain to reducing costs and enhancing operational efficiency whilst reducing the consumption of energy and goods. Simultaneously, they conform to current or future regulatory policies by reducing their emissions or by offsetting their emissions through carbon trading (Holland, 2003; Kleiner, 2007). At last, displaying an ecological concern seems to benefit the organizations’ image at the eyes of public opinion and offer advantageous market repositioning (Heffernan, 2010; Kleiner, 2007; Lash and Wellington, 2007).

Development organizations should in principle support and stimulate the overlap of development objectives with environmental CSR. They may do so by advocating for it and promoting compliance and accountability (Vives, 2004). Donor agencies can provide financial and technical support for the implementation and CSR strategies in the private sector (Fox and Prescott, 2004). Not least important is the incorporation of transparent and rigorous environmentally responsible conduct it in its own operation (Fox and Prescott, 2004). Development cooperation agencies can foster environmental standards and policies, which assess, report and regulate the agencies’ activity own output to the environment.

It is only natural that development cooperation institutions adopt and implement environmental responsibility programs, since the ultimate goal of any form of CSR is the same as of development cooperation: enhancing the quality of life of populations (Vives, 2004). By enabling environmentally sound practices, institutions are addressing the susceptibility of the well-being of populations to the status of their environment. This equilibrium is particularly devious issue in developing countries. The overexploitation of its ecosystems falls short from granting the generality of its inhabitants with above-poverty standards. This is due to the use of these regions’ biocapacity to sustain foreign practices, such as natural resources withdrawal and carbon sinking, and is likely to be aggravated with increasing population and quality of life (Goldfinger et al., 2008).

Incorporating these concerns into the mainstream of a development cooperation agent renders it fitter to face external scrutiny as well as to promote and guide environmental impact mitigation and regulation within its partners in the South. Organizations seek straightforward indicators of environmental performance, which would easily mesh with general management approaches. The ecological and the carbon footprints have been suggested to corporations and other organizations as a resonant and efficient display of their environmental performance (Hoffmann and Busch, 2008; Holland, 2003; Lash and Wellington, 2007).

1.2. Ecological and carbon footprints The ecological footprint gauges the pressure put upon the ecosystem services by any individual, organization, activity, event or product. It does so by measuring the resource demand implied and the extent to which it exceeds or not the ability of the planet to generate that same amount of resources (Kitzes and Wackernagel, 2009; Wackernagel et al., 1999). It commonly expresses that demand by the amount of land required to fulfil it, thus an area.

6

There does not exist a formal protocol for differentiated inventorying in the base of the ecological footprint calculation. Despite the publication of standards (Kitzes et al., 2009a), discussion and improvement of methods has not ceased (Kitzes et al., 2009b; Kitzes and Wackernagel, 2009; Fiala, 2008).

The most common method for calculating an organization’s ecological footprint consists of the component-based calculation (Hoekstra, 2009). The first step is to define the boundaries of the system, i.e. identify the activities associated with the organization’s operation that will be included in accounting (Kitzes et al., 2009a). Within those boundaries, the items that are consumed by the organization are inventoried and allocated to the associated amount of bioproductive area required through conversion rates (Hoekstra, 2009). The sum of that demand will configure the ecological footprint in a component-based calculation. Usually, different types of land uses are weighted according to biological productivity (Kitzes and Wackernagel, 2009; Hoekstra, 2009).

Within the ecological footprint framework, the carbon footprint would entail the area of productive land and ocean required to sequester carbon emissions (Kitzes et al., 2009b; Wiedmann and Minx, 2008). However, the relationship between carbon footprint and ecological footprint was rendered merely nomenclatural by its current use, since it expresses a global warming potential, being commonly expressed in kg of CO2-equivalents (Pandey et al., 2010). Nonetheless, it is argued that non-CO2 gases should be left out since that turns a land requirement measure into a climate impact measure (Wiedmann and Minx, 2008).

The carbon footprint commonly expresses the total amount of greenhouse gas (GHG) emitted by a product, activity, individual or organization. With regard to the Kyoto Protocol targets for GHG emission reductions, it became a tool to measure an institution’s fitness towards the stress of carbon-constrained settings. Still, despite its widespread use and popularity, there is not a consensual definition of the carbon footprint among literature (Pandey et al., 2010; Wiedmann and Minx, 2008). Available protocols and published studies reveal unstandardized procedures, mainly regarding boundary definition (Fig. 1). Although many organizations opt for reporting only direct emissions, many authors defend the need to look upstream in the supply chain of materials and services in order to fully disclose the GHG profile of organizations (Matthews et al., 2008; Pandey et al., 2010; Peters, 2010; Weidema et al., 2008; Wiedmann and Minx, 2008).

Energy and material flows are thus implicitly inventoried in the organizations’ information system, such as financial records and statistics. The first step of calculating an organization-level ecological or carbon footprint is to set a scope of its activities hence defining a boundary within which all flows of materials and energy will be taken into account. Hence, it is an open discussion on how much upstream to go when accounting for emissions (Pandey et al., 2010; The Carbon Trust, 2007).

Pandey et al. (2010) reviewed various guidelines for GHG accounting. Among them, the GHG Protocol developed by WRI/WBCSD provides general sector-specific standards and calculation tools (GHG Protocol Website). ISO has developed the entity-based standard 14064 (ISO, 2006). Both guidelines are fundamentally similar and constitute two well-accepted frameworks for carbon footprinting (Pandey et al., 2010; Peters, 2010; The Carbon Trust, 2007).

The protocol by WRI/WBSC and the ISO 14064 define increasingly larger scopes of embedded carbon. The first level refers to direct emissions from processes and activities within an organisation (scope 1 emissions in Fig. 1).These are abundant in companies in sectors involving manufacturing or processing. In the type of agency at hand, they pertain to transportation with vehicles owned by the organization, when applicable (GHG Protocol Website; ISO, 2006).

Scope 2 emissions are indirect but mandatory as well, according to both the GHG Protocol and ISO 14064. These emissions originate from the generation of all sorts of energy imported by the institution but generated elsewhere (GHG Protocol Website).

7

Other indirect emissions may arise from waste disposal, mobility of commodities and staff and upstream processes in the supply chain of purchased energy and materials (scope 3) (GHG Protocol Website, ISO, 2006). Scope 3 emissions are not mandatory but their importance is considerable for a robust analysis (Peters, 2010). Since transport is responsible for a large share of total anthropogenic GHG releases, these indirect emissions are especially relevant for activities highly reliant on mobility. For example, the tourism sector has been criticised for its high share of aviation-based GHG emissions (e.g. Høyer, 2000; Gössling et al., 2002). Likewise, development aid organizations engaging in North-South cooperation programs rely on frequent air travels of its workers and collaborators, increasing their carbon footprint significantly.

Because direct measurements are often complex, GHG emissions are generally not measured, but rather estimated. Operation and process-related factors (e.g. a travelled distance with a certain vehicle or the electricity consumption of a building) are identified and measured. Each of these factors is then multiplied with an average GHG emission factor and then summed up to obtain final carbon footprint results (Pandey et al., 2010).

Figure 1 – Simplified methodology for carbon footpr int assessments. The three scopes of emissions considered in the GHG Protocol and in the ISO 14064 standard guidelines arise from different orders of inputs. The quantified inputs are then converted into GHG emissions and summed up to a final carbon footprint score.

1.3. Reducing and offsetting emissions The ecological and the carbon footprint provide organizations with baseline data to develop emission reduction strategies. Institutions can actively reduce their own emissions or pay for emission reduction elsewhere. This is currently driven by regulations (e.g. emission caps) or internal reasons like image improvement.

1.3.1. Baseline Emission cuts

Organizations are encouraged to in first instance cut emissions by reducing consumption that is under their own control. This leads to optimal bottom line savings for the organisation involved as well as along the supply chain (Holland, 2003; The Carbon Trust, 2007).

Reducing direct and indirect consumption and emissions has clear advantages such as cost savings and improved operability (Holland, 2003). In a scenario of financial distress,

8

decreasing expenditure is more appealing than paying for emission reductions elsewhere by offsetting (Sekerka and Stimel, 2010).

1.3.2. The offset market

Organizations confronted with emission limits or ‘caps’ can opt for GHG offsetting in the regulatory carbon markets. Carbon trading in regulatory market falls under the Kyoto Protocol and the EU trading schemes. Organizations with excess emissions can buy certified credits (Certified Emission Reductions or Emission Reduction Units, under the Clean Development Mechanism (CDM) or Joint Implementation) from carbon-offset suppliers or implement own offset projects (Lovell and Liverman, 2010).

Alternatively, the voluntary carbon market offers verified and non-verified emission reductions, depending on whether the projects are attested by external audits or self-developed standards. Besides buying credits, organizations may, once again, develop own projects (Lovell and Liverman, 2010). There is a great number of organizations that aims to be ahead of regulations, especially corporations in the industrial sector. However, unclear assessment and reporting standards has not always led to credible emissions reductions (Southworth, 2009).

To remedy this, robust certification programs have been established for offsetting, like the Voluntary Carbon Standard (VCS) and the Gold Standard. The VCS provides well-accepted guidelines for certification, while the Gold Standard registers and validates programs and carbon credits.

Organizations can opt for carbon storage in biological sinks (e.g. in forests) equivalent to what has been released or for emission reduction programs aimed at the replacement of fossil energy use by renewable ones. Forestry has been growing in popularity as a compensation mechanism, particularly in the voluntary market, because of potential co-benefits like preservation of biodiversity or being pro-poor. Offsetting through forestry might be especially appealing to development aid organizations owing to the potential benefits to local populations.

1.3.3. Dynamic carbon networks

Rather reducing carbon emissions in a direct way, belonging to a dynamic carbon network can be interpreted as a statement. Some authors (Gell, 2008a; Gell, 2008b; Gell, 2010; Sekerka and Stimel, 2010) take an optimistic point of view and claim that the global climate change and the emergence of emission-reduction policies triggered a shift in social and economic thinking which will eventually set out a new global order of entity level networking. In this context, the participation of organization in so-called dynamic carbon networks aiming for increased sustainability and reduced GHG emissions would lead to organizations to reduce their carbon intensity. Hence, carbon-constrainment would spread up the supply chain and dynamically reduce the carbon footprints of institutions (Gell, 2008b).

An example of this would be the Kauri network (www.kauri.be) that promotes sustainability through a participatory platform of organizations that are concerned about their environmental performance and its impact on society. The association is coordinated in a way that promotes knowledge exchange finally yielding into the uptake of better practices within each organization.

2. Case studies This section explores how important mobility is in the environmental sustainability of some selected development organizations, what are constraints regarding data availability and possibilities for improvement. The focus is on development cooperation institutions with North-South activities that are based in Belgium.

According to the latest Global Footprint Network report (Global Footprint Network, 2008), the per capita ecological footprint of Belgium is 8 global hectare (g ha). This is well above the

9

European average (4.7 g ha), or even above the average of the high-income countries (6.1 g ha). In fact, Belgium’s per capita footprint is only challenged by the United States of America (8 g ha), Denmark (8.3 g ha), and some oil-producing countries such as Qatar and the United Arab Emirates (10.5 and 10.7 g ha). Also in terms of per capita carbon footprint, Belgium stands among the 10 most polluting nations. An average person in Belgium emits 16,5 t CO2 eq per year, versus 29 t CO2 eq for an American and 0.7 t CO2 eq for a Malawian (data from 2001 which does not feature all the countries in the World) (Hertwich and Peters, 2009).

On a more positive side, Belgium is a country where corporate social responsibility (CSR) has received much attention, having CSR-related legislation and governmental policies relatively important in the European scene. Of those, environmental CSR has received specific attention, namely concerning emission reduction. There is a strong organizational and citizen-level awareness of environmental issues (Europe CSR, 2010).

Because of data limitations and time constraints, we opted for well-accepted and simplified approaches.

2.1. Methods The initial approach for data collection and footprint assessment was to consult experts and tap into the databases of organizations that estimated the ecological footprint on a professional basis. This did not yield sufficient data.

Another approach was to retrieve data from the annual activity reports published online by all the 116 North-South non-governmental organizations (NGO’s) registered in Belgium (BTC website), as well as the Belgian Technical Cooperation (BTC). BTC and 5 of the 116 NGO’s included environmental performance information in their reports. The information conveyed pertains mainly to electricity and paper consumption as well as remarks on measures aiming at decreasing consumption and the promotion of less-impactful mobility.

Recognizing the potential weight of indirect emissions and resource demand, we calculate the footprints of only those institutions that supplied enough data for scope 3 emissions to be accounted for. Of those six institutions, two (ATOL and CDI-Bwamanda) provided sufficient information in quantity and explicitness to calculate ecological and carbon footprints(ATOL, 2009; CDI-Bwamanda, 2009). The supplied information specified energy and paper consumption and distances covered by work-related travelling (both long-distance and home-work commuting). Although incomplete for a thorough inventory of scope 1 to 3 emissions, these two NGO’s supplied the most complete data set (Table 1).

Upon request BTC provided extra information from their footprint assessments by consultants (Bailly et al., 2008; CO2Logic, 2010).

2.1.1. ATOL and CDI-Bwamanda

SCOPE DEFINITION AND INVENTORY

System boundaries were, hence, defined according to data availability (table 1). There is a clear resemblance between carbon and ecological footprints in terms of scope definition routines. Hence, it was considered that the boundaries are the same for carbon and ecological footprints.

Table 1 – Available data quantified by CDI-Bwamanda and ATOL. CDI-Bwamanda ATOL Paper Paper Toners Electricity Electricity Heating oil Heating oil Long distance work-related

travels Plane*

Long distance work- Car Home-work commuting Car*

10

Plane Train

related travels

Bus Car Home-work commuting Train

* other modes were mentioned but not specified

The data from CDI-Bwamanda allowed the most complete inventory. Besides the consumption of paper, toners, electricity and heating oil, it extensively describes mobility. It specifies distances travelled in different modes of transport both for work-related long-distance traveling and daily commuting between home and office. ATOL only specifies the distances made by airplane and car, besides the consumption of paper, electricity and heating oil. It is mentioned that public transport is used as well for short and long distance trips and daily commute, but the distances are not specified.

Regarding data from office activities, both NGO’s at hand only specify data in the North. Moreover, it is not explicit whose mobility is accounted for (if all workers and collaborators in the North and the South, or if only of workers in the North).

Data pertains to 2009 and is displayed and analyzed per year. CDI-Bwamanda made available data from 2008 as well. Thus, the ecological and carbon footprints of this NGO were calculated for both years in order to assess changes. In order to allow some comparability between institutions, the footprints were calculated as well per Full Time Equivalent (FTE), or the number of staff members working full-time (Table 2).

Table 2 – Full Time Equivalents of CDI-Bwamanda and ATOL in 2009. Full Time

Equivalents CDI-Bwamanda 4,79 ATOL 7,1

Assuming that system boundaries were considered the same for both footprints assessments we followed the increasing scopes frameworks for data allocation. This way, we considered that there will be direct (scope 1) and indirect (scopes 2 and 3) emissions and resource demands.

Given the nature of the organizations at hand, data belonging to scope 1 were non-existent. Possible own vehicles use was thus assumed as being included in mobility emissions and resource demand accounted for in scope 3. Scope 2 data include electricity purchased by the institution and heating oil, as suggested by the ISO guidelines. Scope 3 data comprise transport, paper and toners and upstream processes from scope 2 and 3 inputs’ supply chain. These upstream processes, or background data, were retrieved from the Ecoinvent® (Swiss Centre for Life Cycle Inventories, Switzerland) life cycle assessment (LCA) database. In this way, this analysis accounts for consumption and emissions at all levels, i.e., in an LCA perspective.

Table 3 – Data for footprint calculation from CDI-B wamanda and ATOL’s annual reports concerning commodities, energy and fuel consumption and work-related transport use. CDI-Bwamanda 2008 2009 ATOL 2009 Paper 150 kg 150 kg Paper 195,3 kg

Black 5 p 5 p Electricity 8558 kWh Toners Colour 3 p 3 p Heating oil 1775 l

Electricity 491 kWh 479 kWh Work-related air travel 318657 km Heating oil 4312 l 4174 l Home-work

commuting by car 3000 km

Plane 218880 km 136400 km Train 2650 km 2480 km

Long distance work-related travels Bus 15 km 15 km

11

Car 11589 km 13561 km Car 26682 km 27075 km Home-work

commuting Train 3691 km 2305 km

Background data include even the disposal phase of every component, such as infrastructure for electricity production or the vehicles used for transport. However, waste generated during the institutions activity (e.g. paper recycling) is not accounted for owing to data unavailability.

Although water consumption is generally included in the ecological footprint (Hoekstra, 2009), it was omitted in this study due to lack of data.

ASSESSMENT

Instead of compiling and applying GHG emission and bioproductive land requirement factors to the inventoried data, the ecological and carbon footprint calculations were performed in SimaPro® software (PRé, The Netherlands). SimaPro® is a robust tool for life cycle and footprint assessment, which allows immediate and unambiguous retrieval of results as well as incorporation of extensive LCA data from databases.

Within SimaPro® the practitioner may choose the impact calculation method depending on the aim of the analysis. This study was based on the dedicated method Ecological Footprint and results were retrieved for the characterization phase in ha a (hectares of arable land). We opted not to present weighted results owing to uncertainties involved in this step (Hoekstra, 2009).

For the carbon footprint, IMPACT2002+ was used. This method assesses life cycle impacts in various categories. Global Warming Potential (GWP) is one of them and it assesses the total emissions of all GHG involved, expressing them in t CO2 eq (tones of CO2 equivalents). The carbon footprint was assessed as well with the single issue IPCC 2007 GWP 100a method, as suggested by Kumar (2010) (data not shown). The crosschecking was satisfactory since the results were relatively similar. The only remarkable difference was that the IPCC 2007 GWP 100a considered paper consumption as an offsetting process, presumably owing to the carbon sequestration during tree growth. This slightly decreased the total value of the footprint.

Table 4 – Calculation methods and respective units used to determine the ecological and carbon footprints in SimaPro®. Category Method Unit Ecological footprint Ecological Footprint ha a Carbon footprint Global Warming Potential (GWP) from

IMPACT2002+ t CO2 eq

When accounting only for CO2, and leaving out other GHG, GHGs which a higher global warming potential are excluded. The carbon footprint is thus often expressed as a CO2 equivalent unit to also include those other GHGs.

2.1.2. Belgian technical cooperation

This case study is based on two reports of carbon footprint of the Belgian Technical Cooperation (BTC): One report refers to 2007 (Bailly et al., 2008) and another to 2009 (CO2Logic, 2010). Both reports refer to the Bilan Carbone® (ADEME, France) guidelines, which comply with ISO14064 and the GHG Protocol, emphasizing the importance of indirect emissions (ADEME, 2009). Main lines and conclusions are summed up in table 5.

Table 5 – Summary of the reports from the carbon fo otprint of BTC. 2007 2009 Methodological remarks North and South

Only CO2 North and South

Total footprint 2278 t CO2 3246 t CO2 eq

12

Air travel share of emissions 971 t CO2 43% (excluding scholars)

63% (undisclosed passenger rolls in BTC)

Largest contributors (excl. air travel)

Energy for the buildings and travel by car in the South

Energy for the buildings and travel by car in the South

Emissions per employee Similar in North and South Similar in North and South

The report from 2007 attempts to include data from the Brussels head office as well as offices in the South regarding electricity and other energy forms consumption, business travel by staff and consultants and commuting by employee. It excludes use of materials, paper, water, waste and outsourced activities. Although the scholarship program accounts for 37% of BTC’s air mileage of 2007, air travel from this program was considered outside BTC’s boundary and not accounted for (Bailly et al., 2008).

The 2007 report acknowledged some data gaps and does not consider non-CO2 GHG. Although it is recommended that all GHGs are taken into account, we argue that for this type of institutions, the emissions of GHG’s different from CO2 is limited (Forster et al., 2007).

The 2010 report compared data from 2007 to 2009 and resolved many of those recommendations. Data were collected from the Brussels head office. Data regarding representations in the South still refer to 2007. Adjustments made to the carbon accounting system led to a more accurate estimation of air travel in 2009 (CO2Logic, 2010).

2.2. Results The ecological and carbon footprint analysis both NGO’s (Fig.2 and 3) point towards the prominence of mobility and transport in the environmental performance of these institutions (at least 60%). As expected, air travel involved in North-South cooperation hold the largest share in GHG emissions (41% in CDI-Bwamanda and 78% in ATOL). It is also the main contributor to the ecological footprints (39% in CDI-Bwamanda and 70% in ATOL).

For CDI-Bwamanda heating also gives a large contribution (39% and 36% in the carbon and ecological footprints, respectively). Heating is the second largest contributor for the carbon footprint of ATOL as well although it only has 13% of the share. However, in the ecological footprint, electricity is the second contributor (15%), followed by heating (12%). Comparing both institutions, it is clear that the combined contributions of heating and electricity to the ecological footprint are equivalent, although CDI-Bwamanda emits more from electricity consumption than from heating.

13

Figure 2 – Different contributions (%) to the total ecological footprints of NGO’s CDI-Bwamanda and ATOL. The smaller chart on the right represents the composition of the mobility contribution to the total ecological footprint.

14

Figure 3 - Different contributions (%) to the total carbon footprints of NGO’s CDI-Bwamanda and ATOL. The smaller chart on the right represents the composition of the mobility contribution to the total carbon footprint. Within the mobility sector, CDI-Bwamanda’s footprint shows that home-work commuting is the second largest contributor. This data is not comparable to ATOL’s performance owing to lack of data.

Because ATOL did not fully disclose overland distances, either in daily home-work travels or longer distance work-related traveling, it can be expected that its ecological and carbon footprints are an underestimation. Consequently, the proportion of air travel is overestimated.

15

Figure 4 – Ecological and carbon footprints of ATOL in 2009 in arable ha land and ton CO2 eq. We compared also the carbon and ecological footprints of CDI-Bwamanda in 2008 and 2009 (Fig. 5). The most significant difference between those two years is a 48% decrease in air-travelled distances. This difference resulted in a 20% decrease of both footprints in 2009.

Figure 5 – Ecological and carbon footprints of CDI- Bwamanda in 2008 and 2009. Results displayed per year in arable ha and ton CO2 eq.

16

ATOL’s overall annual carbon and ecological footprints are higher compared to CDI-Bwamanda owing to a higher distance traveled by plane (Fig.4 and 5). On the other hand, when comparing the footprints per FTE (Fig. 6), there is an inversion of this scenario. If ATOL’s inventory had been complete, this result would be an appropriate comparison for environmental efficiency of these two NGO’s. Since ATOL’s performance is in reality expected to be higher, we can assume that their footprints are not significantly different.

Figure 6 – The carbon (blue bar) and ecological (re d bar) footprints of CDI-Bwamanda and ATOL calculated per FTE.

According to the 2010 report the annual carbon footprint increased from 2278 t CO2 eq in 2007 to 3246 t CO2 eq in 2009 (Table 5 and Fig.7). This steep increase is to large extent due to the fact that there was a lot of uncertainty regarding the destinations and thus distance travelled by air in 2007.

Energy consumption within the buildings (electricity and heating/cooling) was the second largest contributor and showed a moderate decrease over those 2 years. In 2009 electricity consumption of the head office became carbon neutral because of the purchase of ‘green electricity’ generated from renewable sources (CO2Logic, 2010). Commuting was the third emission sources both in North and South. Business traveling has a large weight in the South as well, owing to the extensive use of the car. The carbon footprint per employee was in 2009 lower than in 2007 and is similar in North and South (2.9 and 3 t CO2 eq respectively and excluding air travel).

17

Figure 7 – The carbon footprint of BTC from 2007 an d 2009.

Air travel from BTC staff accounts for 43% of the total CO2-footprint (Table 5 and Fig. 7). This is followed by emissions due to the consumption of electricity and business travel by car, for which the representations in the South contribute the most. Commuting using public transport by employees of the head office is the fourth largest emitter.

If air travel, which might be considered a common North-South item, is excluded, 38% of emissions arise from Brussels head office while the South representations account for 62%. Perhaps surprisingly, emissions per employee in North and South are similar (circa 3.4 t CO2 eq) (CO2Logic, 2010).

We then compared these outcomes with the carbon footprint of the Gesellschaft für Internationale Zusammenarbeit (GIZ) the former German Technical Cooperation (Fig. 10). In 2009 each GIZ worker emitted 11 t CO2 (Brandes et al., 2010), which is nearly 1.4 times higher than the BTC’s worker footprint (7.9 t CO2 eq) and nearly the average per capita footprint of Germany (15 t CO2 eq) (Hertwich and Peters, 2009). Air travel corresponds to 76% of the total 16,712 t CO2 emitted in by GIZ activities in 2009 (Brandes et al., 2010).

18

Figure 8 – Comparison of the carbon footprint per w orker of the NGO’s CDI-Bwamanda and ATOL, the BTC and the German GIZ.

SENSITIVITY ANALYSIS

Given that heating (oil) and air travel had the highest impact on the footprints of CDI-Bwamanda, we probed the carbon footprint sensitivity to changes in heating system and distances travelled by plane:

- heating provided by wood pellets or natural gas or solar thermal panels assisted by natural gas (we assumed that the Belgian weather would not allow solar thermal by itself);

- reduction of air travel with 10%, 30%, 50% and 70%.

19

Figure 9 – Sensitivity analysis of the carbon footp rint of CDI-Bwamanda in 2009 to decreases in air travel and changes in heating syst em. Each axis represents a different scenario, where the dotted line is the actual carbon footprint and the full line reflects the reduction potential for each measure mentioned on the tip of each axis.

Improvements in the carbon footprint of CDI-Bwamanda can be made in heating and air travel (Fig.9). Although natural gas-fed heating does not consist of a significant improvement (4% reduction), opting for wood pellets would allow a 33% reduction in the overall footprint. Solar panels complemented with natural gas, would represent a 17% reduction.

GHG emissions from air travel could be reduced by 20% and 29% respectively, if kilometers flown were cut by 50 and 70%. More easily attained cuts in air travel by 10 or 30% would entail 4 and 12% reductions, respectively.

20

3. Discussion

In general, the results of the footprints of ATOL, CDI-Bwamanda and BTC show a similar pattern. This is because energy consumption and mobility make up the largest part of the ecological footprint. These results were well expected. Development cooperation institutions are low-demanding in terms of commodities and energy inputs and have a small supply chain compared to e.g. industrial corporations. On the other hand, they rely heavily on the mobility of their workers and collaborators between head offices in the North and project and partner sites in the South. Knowing how impactful aviation is, it could be expected that this would be the main burden in the footprints of these institutions.

Information in the annual reports includes prescriptive measures to reduce the environmental impact of these NGO’s. The same applies to the other institutions which disclose this type of data. These emphasized the importance of mobility of workers, and especially air travel. Several institutions affirm promoting “eco-friendly” commuting, either by making bicycles available to their workers, offering reduced-price public transport or introducing telework.

Other reduction targets on the spotlight of several institutions were paper and electricity consumption. According to our results, these have minor contributions to the total ecological footprint. However, we agree that the benefits of low consumption might be hidden by the simplified methodology used in this analysis. A more obvious advantage is, clearly, the economic savings.

Options, such as changing the power source of a heating system implies high investments, which might be prohibitive to small organizations. Large reductions in air travel would likely have a large impact on the organisations’ operations. There are clear trade-offs in emission cuts versus operations.

Experts had warned us that the comparability between institutions is low because of different boundary settings. We can only agree. Although we attempt to compare the carbon footprints of two NGO’s, the BTC and its German counterpart, we acknowledge that there is little comparison to attain. The issue lies in methodological options, being boundaries the focal point. Even if two footprint studies claim that they encompassed emissions from scopes 1 to 3, the effective data availability may not allow the inclusion of all emission drivers. This was the case in comparing the two NGOs. Notwithstanding the use of a common assessment methodology, we reached no solid terms for comparison owing to bottom-line differences in scope. When performing its own carbon footprints or supplying data for external analysis, organizations constrain the order of emissions that will be accounted by selecting which data will be included or disclosed.

Moreover, there might be discrepancies in background data, i.e. the processes and materials implied in the chain of the first order data. For example, LCA databases and software compile and compute extensive data sets which feed into each entry. When resorting to LCA software and databases (as we did in the NGO case study), the carbon footprint of inputs from upstream in the supply chain is also accounted for and would consequently lead to a relatively higher footprint than was considered in the BTC (and the GIZ) footprints.

When a practitioner applies pre-defined conversion factors for emission calculation, the choice of those factors is intervening in the background data as well. This is because different sources compute different factors depending on what they consider to underlie the emissions at hand. Hence, the conversion factors for emission calculation also configure boundary setting.

A broader and more profound insight on the status quo of eco-sustainability of development organizations would benefit from the availability of systematic databases. Besides extensive data sets on inputs and emissions, these would entail the standardization of carbon footprinting. Rather than guidelines which give room to maneuver different data availabilities, databases would arise from specific uniform requirements regarding boundary settings and

21

calculation procedures. The trade-off for decreasing uncertainty and improving footprint quality would be the harder effort from organizations and individuals.

Another step that must be taken for carbon accounting is defining an appropriate yardstick that would provide a carbon efficiency, i.e., the amount of carbon equivalents emitted in relation with attained goals. In the first case studies we opted to use FTE, but it could also be the number of people reached by the projects or the amount of money invested in them. Other types of institutions could use their profit or units of products sold. This once again highlights the need for a concerted effort of institutions as well as policy makers.

Regardless of the low comparability between the carbon footprints of the BTC and the two NGO’s, the outcomes of our case studies stress the importance of air travel for the overall carbon footprint of development cooperation agencies. However, aviation-related GHG emission accounting is not straightforward (as discussed by e.g. (Strasdas, 2006) and Gossling et al., 2007). IPCC puts into evidence that the aviation-based emissions trigger reactions whose relation with greenhouse effect is not thoroughly understood (Forster et al., 2007).There is very high uncertainty implied in the radiative forcing induced by aviation. Although it is accepted that emission calculation is an estimation rather than a measurement, the average effects of GHGs and thus its contribution to the compound calculation of a GWP is not standardized nor devoid of error (Forster et al., 2007).

As discussed in (Southworth, 2009), many corporations with heavy supply chains, often related to the industrial sector, have engaged in voluntary measures of various natures to offset their environmental impacts ahead of regulations. Many others are taking measures to lawfully comply. The Carbon Disclosure Project (CDP) (CDP website) also gives an example of commitment to disclose and abate from this sort of organization (CDP website). The CDP is a large database of climate change information, providing corporations with emission reporting and disclosing and sustainability indexing.

Irrespective of their motivations and mitigation success, pollutant corporations have been working to place themselves in the spotlight of eco-sustainability policies. Eco-sustainability has become a differentiator in competitive areas and a driver for market positioning. Hence, it can be argued that these corporations feel a stronger need to sooth their image, while institutions associated with lower impact activities have not yet come under such intense scrutiny from markets or the general public.

The development sector of neighboring EU countries is taking action. The Gesellschaft für Internationale Zusammenarbeit (GIZ) is implementing reduced consumption and optimized mobility of workers. Furthermore, they are offsetting mobility emissions through renewable energy generation projects with partners in the South. The overall goal for 2014 is 100% offsetting using the Gold Standard (Brandes et al., 2010). Similar actions are reported by the (DFID) in the United Kingdom that implemented monitoring and rationalization of its energy consumption of buildings and equipment down to the tea boilers level. Moreover, it accounts for and offsets all emissions from air travels involved in its activities and established reduction targets (DFID, 2010). Likewise, the Agence Française de Développement claims to account its carbon emissions with the Bilan Carbonne® and offset them by financing energy efficiency and promoting energy self-sufficiency in the South (AFD, 2010). Despite the thorough description of actions and reduction targets, information on the total carbon footprint of these organizations was not fully disclosed in their reports.

If promoting development is an institution’s mandate, then eco-sustainability ought to be innate. Development cooperation agents ought to position their strategies in environmental impact-lean paths. The current scenario suggests that the institutions whose finality is to fight poor living conditions are not taking the leading roles in fighting resource scarcity and mitigate climate change. Still, despite indices of acknowledging the issue and of undergoing actions, the overall delivery falls short from being satisfactory.

The scant information conveyed by development aid organisations in Belgium suggests that there are only few internal policies for emission cutting. Baseline cuttings focus on promoting commuting by public transport or bicycle, reducing paper and electricity consumption, opting for renewable electricity providers and waste recycling. On the other hand, the effort to

22

neutralize the impacts of air travel seems to rely mainly on voluntary offset programs instead of baseline reductions. In fact, it should be expected that by taking up more projects and collaborators, institutions will face commodity and energy consumption increases. Likewise, the growing burden of GHG emissions from air travel seems unavoidable. Offsetting emissions is a logical second best option. On offsetting, institutions ought to choose competent providers, which thoroughly account emissions and estimate offset needs. Compensation providers for aviation and reached the conclusion that, like prices, calculation factors vary a lot (Gossling et al., 2007).

We did not assess the impact of offsetting in the carbon footprints we calculated, because the reports did not disclose how this was done. Although we do not wish to discuss the validity of carbon sequestration projects, we stress the need to ponder on the implications on development of the chosen compensation projects. For example, a recent report by the United Nations Food and Agriculture Organization (FAO) urges the need for integrated food and energy production among small farmers in developing countries. This would provide them with autonomy for energy production thus enhancing food security and improved livelihoods. Simultaneously, there are environmental benefits such as carbon sequestration, GHG emission reduction or improved natural resource management (Bogdanski et al., 2010). This report gives several examples of specific actions that can be undertaken in this field.

An article in Science by Casillas and Kammen (2010) shows that it is possible to increase and decentralize access to energy combined with carbon abatement in poor rural communities in Nicaragua. Casillas and Kammen (2010) attested the success of implementing carbon abatement measures in concertation with development goals.

4. Conclusions

Despite some methodological controversy, the ecological footprint and, especially, the carbon footprint have been adopted as easily comprehensible indicators of entity-level eco-performance. However, its value is just only if organizations assume high standards of completeness, accuracy and consistency over time. Moreover, this reasoning should encompass as many development institutions as possible.

In order to position themselves in the center of the eco-sustainability paradigm, development cooperation agencies have to incorporate environmental issues transversally to all cooperation projects. This must consist of a systematic, long-term and geographically wide accounting of impacts along the life cycle of their projects. This requires the coupling of financial accounting with carbon accounting. Choosing adequate reduction and offsetting strategies is a next step, while promoting sectorwide low-footprint policies ought to be an ultimate goal.

Air travel increased in many activities by development agencies. It also became the single largest contributor to both the ecological and the carbon footprint. Drastic reductions of air travel might not always be feasible. On the other hand, one can expect development cooperation organizations to behave environmentally responsible because of their mission and intrinsic reasons of existence. Thus, these institutions must develop strategies to deal with the impacts from air travel. For this purpose, organizations from all sectors are resorting to compensation mechanisms to hinder the effects of carbon emissions. Within the offsetting market, development cooperation agencies can look for integrative solutions for both mitigation and livelihood improvement.

23

5. Key recommendations for development aid institut ions

PAIR CARBON ACCOUNTING WITH FINANCIAL ACCOUNTING

All in all, institutions must adhere to the concepts of ecological risks and carbon constrainment. Financial records can provide an excellent basis to keep track of the emissions of an organsation, but will likely require some adjustments as was the case for BTC. Every consumed item has a corresponding GHG emission that should be accounted for in footprint assessments.

REPORT, DISCLOSE AND OFFSET

Emission reduction through a set of management changes and investments, e.g. in energy efficiency or better commodity use, are important measures. Still, they will likely not suffice in attaining a zero footprint. The largest impact on the environment is quite likely to arise from air travel from its staff and collaborators. More and more organizations concerned with their environmental performance opt for offsetting projects, often voluntarily. When reduction is not possible, the second best option is to offset emissions.

INVEST IN EMISSION REDUCTION OR POVERTY ALLEVIATION? CHOOSE BOTH:

GHG offsetting projects can consist of development cooperation projects and bring relevant benefits in livelihood improvement amid developing countries. Furthermore, if you advocate for climate mitigation and its bond with development goals you will both improve your image and pressure your peers to follow. Ultimately, sector and trans-sector carbon effectiveness frameworks are established and climate change-poverty fight models are launched.

6. References

6.

ADEME, 2009. Bilan Carbone® Enterprises-Collectivités-Territoires. Guide méthodologique – version 6.0 – objectifs et principes de comptabilisation, Agence de l’Environnement et de la Maîtrice de l’Energie, France.

AFD, 2010. AFD 2009 Annual Report, Agence Française de Développement, France.

ATOL, 2009. Jaarverslag 2009, ATOL, Kessel-Lo, Belgium.

Bailly, J., Hanekamp, E., van Merksteijn, C., 2008. BTC CO2 Footprint 2007, Prospect C&S, Partners for Innovation, Belgium Development Agency, Brussels, Belgium.

Bogdanski, A., Dubois, O., Jamieson, C., Krell, R., 2010. Making integrated Food-Energy Systems work for people and climate, Food and Agriculture Organization, Rome, Italy.

Brandes, E., García-Cortés, S., Haupt, B., Jarchow, U., Koch, M., Loos, S.P., Mack, R., Mundt, E., Ollech, S., Pfautsch, R., Rauschelbach, B., von Reden, K., Weber, C., Winkelman, S., Wolf, R., 2010. Environmental Report 2009, Deutsche Gesellschaft fur Technische Zusammenarbeit, Eschborn, Germany.

Casillas, C.E. and Kamen, D.M., 2010. The energy-poverty-climate nexus. Science, 330(6008): 1181-1182.

CDI-Bwamanda, 2009. Jaarverslag 2009, Centre de Dévelopement Intégrale de Bwamanda, Heverlee, Belgium.

CO2Logic, 2010. BTC Carbon Footprint Report 2009, CO2 Logic, Belgium Technological Cooperation, Brussels, Belgium.

24

Europe CSR, 2010. Guide to CSR in Europe, Europe Corporate Social Responsibility.

DFID, 2010. Carbon Reduction Delivery Plan, Department for International Development, London, UK.

Fiala, N., 2008. Measuring sustainability: Why the ecological footprint is bad economics and bad environmental science. Ecological Economics, 67(4): 519-525.

Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe, D.C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz M., Van Dorland, R., 2007. Changes in Atmospheric Constituents and in Radiative Forcing. In: Solomon, S., Qin, D., Manning, M., Chen Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA.

Fox, T. and Prescott, D., 2004. Exploring the role of development cooperation agencies in corporate responsibility. Development cooperation and corporate social responsibility: exploring the role of development cooperation agencies Conference. 22-23 March, 2004, Stockholm

Gell, M., 2008a. Business transformation in carbon-constrained markets. The Environmentalist(66): 1-6.

Gell, M., 2008b. Dynamic Carbon Footprinting International Journal of Green Economics, 3(3): 269–283.

Gell, M., 2010. Carbon-constrained health care enterprise. Journal of Evaluation in Clinical Practice, 16(1): 220-227.

Global Footprint Network, 2008. Living Planet Report 2008.

Goldfinger, S., Wackernagel, M., Niazi, S., Peller, A., Kaercher, M., Kitzes, J., Ewing, B., Silvestri, F., Hayes, K., Wakabayashi, T., Humphrey, S., Loh, J., Chapagain, A.K., Orr, S., 2008. Africa: ecological footprint and human well-being, WWF, Global Footprint Network and Swiss Agency for Development and Cooperation, Switzerland.

Gossling, S.,Broderick, J., Upham, P., Ceronc, J.P., Dubois, G., Peeters, P., Strasdas, W., 2007. Voluntary Carbon Offsetting Schemes for Aviation: Efficiency, Credibility and Sustainable Tourism. Journal of Sustainable Tourism, 15(3): 223 — 248.

Gössling, S., Hansson, C., Hörstmeier, O., Saggel, S., 2002. Ecological footprint analysis as a tool to assess tourism sustainability. Ecological Economics, 43(2-3): 199-211.

Heffernan, O., 2010. Seen to be green. Nature Reports Climate Change, 4: 48.

Hertwich, E.G. and Peters, G.P., 2009. Carbon footprint of nations: A global, trade-linked analysis. Environmental Science & Technology, 43(16): 6414-6420.

Hoekstra, A.Y., 2009. Human appropriation of natural capital: A comparison of ecological footprint and water footprint analysis. Ecological Economics, 68(7): 1963-1974.

Hoffmann, V.H. and Busch, T., 2008. Corporate Carbon Performance Indicators. Journal of Industrial Ecology, 12(4): 505-520.

Holland, L., 2003. Can the principle of the ecological footprint be applied to measure the environmental sustainability of business? Corporate Social Responsibility and Environmental Management, 10(4): 224-232.

Høyer, K.G., 2000. Sustainable Tourism or Sustainable Mobility? The Norwegian Case. Journal of Sustainable Tourism, 8(2): 147-160.

25

Kitzes, J., Ewing, B., Wermer, P., 2009a. Ecological Footprint Standards 2009. Global Footprint Network: 1-20.

Kitzes, J., Galli, A., Bagliani, M., Barrett, J., Dige, G., Ede, S., Erb., K., Giljum. S, Haberl, H. Hails, C., Jolia-Ferrier, L., Jungwirth, S., Lenzen, M., Lewis, K., Loh, J., Marchettini, N., Messinger, H., Milne, K., Moles, R., Monfreda, C., Moran, D., Nakano, K., Pyhälä,A., Rees, W., Simmons, C., Wackernagel, M., Wada, Y., Walsh, C., Wiedmann, T., 2009b. A research agenda for improving national Ecological Footprint accounts. Ecological Economics, 68(7): 1991-2007.

Kitzes, J. and Wackernagel, M., 2009. Answers to common questions in Ecological Footprint accounting. Ecological Indicators, 9: 812–817.

Kleiner, K., 2007. The corporate race to cut carbon. Nature Reports Climate Change: 40-43.

Kumar, S., 2010. Carbon footprint calculation with SimaPro. LCA-Newsletter - PRé India. (4): 1-4.

Lash, J. and Wellington, F., 2007. Competitive advantage on a warming planet. Harvard Business Review, 85(3): 94.

Lovell, H. and Liverman, D., 2010. Understanding Carbon Offset Technologies. New Political Economy, 15(2): 255-273.

Matthews, H., Hendrickson, C., Weber, C., 2008. The importance of carbon footprint estimation boundaries. Environmental science & technology, 42(16): 5839-5842.

Pandey, D., Agrawal, M., Pandey, J.S., 2010. Carbon footprint: current methods of estimation. Environ Monit Assess: 1-26.

Peters, G.P., 2010. Carbon footprints and embodied carbon at multiple scales. Current Opinion in Environmental Sustainability, 2(4): 245-250.

Sekerka, L.E. and Stimel, D., 2010. How durable is sustainable enterprise? Ecological sustainability meets the reality of tough economic times. Business Horizons: 1-10.

Southworth, K., 2009. Corporate voluntary action: A valuable but incomplete solution to climate change and energy security challenges. Policy and Society, 27(4): 329-350.

ISO, 2006. ISO 14064 Greenhouse gases -- Part 1: Specification with guidance at the organization level for quantification and reporting of greenhouse gas emissions and removals. International Organization for Standardization, Switzerland.

Strasdas, W., 2006. Standards for voluntary carbon-offset schemes. Enhancing their ef- fectiveness to reduce GHG emissions from individual air travel. In: Tourism and Climate Change Mitigation Conference, Westelbeers, The Netherlands.

The Carbon Trust, 2007. Carbon footprint: an introduction for organisations. Carbon Trust. 12pp

Vives, A., 2004. The role of multilateral development institutions in fostering corporate social responsibility. Development, 47(3): 45-52.

Wackernagel, M. Onisto, L., Bello, P., Linares, A.C., Falfán, I.S.L., García, J.M., Guerrero, A.I.S., Guerrero, M.G.S., 1999. National natural capital accounting with the ecological footprint concept. Ecological Economics, 29(3): 375-390.

Weidema, B., Thrane, M., Christensen, P., Schmidt, J. and Løkke, S., 2008. Carbon Footprint. Journal of Industrial Ecology, 12(1): 3-6.

Wiedmann, T. and Minx, J., 2008. A definition of carbon footprint. C. C. Pertsova, Ecological Economics Research Trends: Chapter 1, Nova Science Publishers, Hauppauge, NY, USA.: 1-11.

26

WEBSITES

Belgian Technical Cooperation website: www.btcctb.org (consulted on January 2011)

The Greenhouse Gas Protocol Initiative website: www.ghgprotocol.org (consulted on January 2011)

Kauri website: www.kauri.be (consulted on February 2011)

The Carbon Disclosure Project website: www.cdproject.net (consulted on January 2011)