1. introduction - moew.government.bgŸИК/… · web viewworld bank reports include among others...

Republic of Bulgaria

Advisory Services on a National Climate Change Adaptation Strategy and Action

Plan

Macro-economic implications of climate

change – Analysis

DRAFT – February 4, 2018

Climate Change Adaptation – Macro-economic Implications of Climate Change - Analysis

(Project number P160511)

Country Manager:

Practice Manager:

(Co-)Task Team Leaders:

Project Coordinator:

Antony Thompson

Ruxandra Maria Floroiu

Philippe Ambrosi, Eolina Petrova Milova

Robert Bakx

This report was produced by Sebnem Sahin, Badri Narayanang, and Zekarias Hussein, under the overall guidance of Philippe Ambrosi (Senior Environmental Economist, Task Team Leader), Eolina Petrova Milova (Senior Operations Officer, Co-Task Team Leader), and Robert Bakx (Climate Change Adaptation Expert and Resident Project Coordinator).

DISCLAIMERSThis report was produced by the World Bank team to provide advisory support to the Ministry of Environment and Water (MoEW) in Bulgaria. The findings, interpretations and conclusions expressed in this report do not necessarily reflect the views of the Executive Directors of the World Bank or of the Government of Bulgaria or its MoEW.

ACKNOWLEDGEMENTSThe team would also like to thank the Government of Bulgaria, in particular Ms. Atanaska Nikolova (Deputy Minister of Environment), Ms. Boriana Kamenova (Director of the MoEW’s Climate Change Policy Directorate) and Ms. Veronika Dacheva (Expert in the MoEW’s Climate Change Policy Directorate), Mr. Anton Gladnishki (Director at the Ministry of Finance) and his team, and other experts in and outside government institutions in Bulgaria for their excellent cooperation and support in spoken and written form. The contribution of Antony Thompson (Country Manager) in the preparation and negotiation of the Advisory Program is also acknowledged here.

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Abbreviations and Acronyms

AD-DICE Adaptation in Dynamic Integrate Climate-Economy

AEZ Agro-Ecological Zone

BaU Business as Usual

CGE Computable General Equilibrium

ENV Environment (Directorate’s Linkages; OECD)

FAO Food and Agriculture Organization

GDP Gross Domestic Product

GTAP Global Trade Analysis Project

ICOR Incremental Capital Output Ratio

IIASA International Institute for Applied Systems Analysis

IMF International Monetary Fund

IPCC Intergovernmental Panel on Climate Change

LGP Length of Growing Period

MoEW Ministry of Environment and Water

ND Net Damages

OECD Organisation for Economic Cooperation and Development

RD Residual Damages

SAM Social Accounting Matrix

TD Total Damages

ToT Terms of Trade

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Table of Contents1. Introduction...................................................................................................................................1

2. Analytical Overview........................................................................................................................3

2.1. Bulgaria CGE: Description of the model structure..................................................................6

2.2. Production functions and derived demands for inputs...........................................................8

2.3. Household demand for final goods and services....................................................................9

2.4. Modeling of the agriculture sector.......................................................................................10

3. Model Baseline.............................................................................................................................18

3.1. Business as Usual (BaU): Growth toward 2050 ignoring climate change impacts.................18

3.2. Business as Usual (BaU): Growth toward 2050 with climate change impacts.......................21

3.3. Analysis of welfare impacts..................................................................................................25

3.4. Comparing results with the OECD study...............................................................................27

4. Adaptation to climate change......................................................................................................29

References............................................................................................................................................36

Annex 1 Classification and Mapping Used for Analysis...................................................................39

List of FiguresFigure 1. Structure of the CGE analysis...................................................................................................5

Figure 2. Schematic representation of the flows in the Bulgaria CGE model..........................................6

Figure 3. Detailed Representation of the Bulgarian CGE model.............................................................7

Figure 4. Production structure in the CGE..............................................................................................8

Figure 5. Household demand for goods and services in the Bulgaria CGE model...................................8

Figure 6. Land Demand in the Bulgarian CGE model..............................................................................9

Figure 7. Types of agriculture and livestock production.........................................................................9

Figure 8. AEZs in Bulgaria......................................................................................................................10

Figure 9. Land supply in Bulgaria CGE...................................................................................................11

Figure 10. Global AEZs..........................................................................................................................12

Figure 11. Water use (million m3 ) by sector and river basin, 2011......................................................15

Figure 12. Average water tariff by sector and river basin, 2011 US$....................................................15

Figure 13. CGE BaU simulations – GDP and investment projections to 2050.......................................17

Figure 14. Population projections for Bulgaria (total population, constant fertility and migration rate)..............................................................................................................................................................17

Figure 15. CGE BaU simulations – GDP composition in 2011 (inner circle) – 2050 (outer circle)..........18

Figure 16. Household revenues from agricultural activities (2020–2050)............................................19

Figure 17. Bulgaria AEZs based on global AEZ classification.................................................................19

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Figure 18. Impact of climate change on real GDP (percentage change)...............................................20

Figure 19. Impact of climate change on domestic output....................................................................21

Figure 20. Changes in trade balance (millions of US$)..........................................................................22

Figure 21. Components of GDP: deviation from 2050 baseline due to climate change........................23

Figure 22. Welfare changes due to climate change (millions of US$)...................................................24

Figure 23. Welfare loss as a share of GDP (percentage).......................................................................25

Figure 24. Decomposing Welfare Change (as percentage of GDP).......................................................25

Figure 25. Damages of climate change and cost of adaptation............................................................27

Figure 26. Adaptation and climate change impacts on real GDP (percentage change)........................29

Figure 27. Welfare changes with and without adaptation (million US$)..............................................29

Figure 28. Impact of climate change and adaptation scenario on output............................................30

Figure 29. Changes in trade balance (millions of US$)..........................................................................30

Figure 30. Impact of adaptation and climate change on agricultural output........................................32

List of TablesTable 1. Climate related vulnerabilities by sector...................................................................................3

Table 2. AEZ disaggregation used in this CGE analysis..........................................................................10

Table 3. Bulgaria crop sector rents (millions of US$)............................................................................14

Table 4. Statistics used in the development of the Business as Usual Scenario....................................17

Table 5. GTAP 57 sector classification and mapping used for analysis (8 aggregate sectors)...............38

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1. Introduction1. The objective of this component is to assess the cascading effects from climate change on the Bulgarian economy and inform decision makers on investment needs for climate change adaptation based on an integrated analysis using socio- economic and physical climate modelling frameworks.

2. First, the socio-economic analysis evaluates the social and economic implications of climate change impacts and adaptation actions in Bulgaria and highlight the costs of inaction and the benefits of climate action within an economy-wide framework. The analysis estimates overall economic activity and per capita income, sectoral output and employment level, all with and without climate action, to provide elements in answer to the following questions: what most vulnerable sectors are, how effective is adaptation to the most significant climate change impacts, what are its costs, what are broader socio-economic benefits.

3. The economy-wide framework, so-called Computable General Equilibrium (CGE) model, developed for Bulgaria. CGEs are a class of economic models that use actual economic data to estimate how an economy might react to changes in policy, technology, or other external factors (for example, external shocks such as fluctuations in commodity prices or exchange rates or climate change impacts). The Bulgaria CGE model is developed within the Global Trade Analysis Project (GTAP) modelling framework and database, which covers 140 countries for 2011. This model and database have been widely used by the World Bank,1 the Organisation for Economic Cooperation and Development (OECD), European Union, universities and research institutes globally, for policy analysis across a wide range of topics, including green growth, climate change, and environmental sustainability.

4. Based on the most recent scientific knowledge, impacts in each of these areas were modeled and their aggregate effect on national measures of economic performance and welfare such as including gross domestic product (GDP), consumption, and investment, were estimated.

5. Second, the report integrates the socio-economic analysis with state of art climate and land use models. However, the analysis is still limited by the frontiers of climate science. The projections about direct or indirect impacts from climate change on a broad range of economic activities are still incomplete. For example, our analysis does not consider the non-market impacts of climate change such as changes in species distributions, reductions in biodiversity, or losses of ecosystem goods and services. There are also certain sectors and activities, such as tourism, which are very difficult to properly account for. Regarding agriculture and energy, climate impact assessments are relatively advanced. These are the economic sectors that were the focus on developing the adaptation scenarios for Bulgaria.

6. While integrating socio-economic and physical models, it is primordial to address the underlying uncertainties in the climate impact assessments. To overcome the risk of

1 World Bank reports include among others Georgia Country Environmental Analysis (2016), An Evaluation of Climate Change on Water Resources in the Sava River Basin (2016), Greening India’s Growth (2012), Low Water High Growth in South Asian Economies (2017).

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cumulating uncertainties at different estimation levels, the study developed the macro-economic analysis based on two climate scenarios: Optimistic and pessimistic outlooks. Each scenario incorporates different assumptions about the magnitude of climate change by 2050 and about the direction and extent of likely impacts in the market sectors analyzed. In scenario development, the optimistic and pessimistic climate outlooks were tested for high and low vulnerability assumptions for each sector (in terms of its sensitivity to climate change and its ability to adapt). As one would expect, the optimistic scenarios project either smaller climate damage or greater adaptation benefits for climate sensitive sectors compared to the pessimistic scenarios.

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2. Analytical Overview7. An economy-wide approach is the most suitable to develop and analyze scenarios on the potential impacts from climate change on economic growth and on alternative adaptation pathways. A CGE model allows to analyze the relation between the climate change impacts on productivity (for example, change in crop yield), infrastructure (for example, damage to buildings and roads), natural capital (for example, change in water availability), or populations (for example, increased health risks and lower productivity) and the dynamic demand and supply conditions and external conditions a given economy faces today (Table 1).

Table 1. Climate related vulnerabilities by sector

Sector Scope Key vulnerabilities

Agriculture

- Crops- Horticulture- Livestock- Soils

- Change in crop suitability- Water shortage and link to irrigation (reduced

soil moisture, reduced rainfall, higher evapo-transpiration, competition for water resources in summer season)

- Higher prevalence of pests and diseases- Weather extremes (droughts, floods)- Soil erosion, salinization, and desertification

Biodiversity and

Ecosystems

- Natural reserves- Plant species- Animal species- Habitats- Recreational function- Water management- Public Health

- Species extinction, invasive species – fauna and flora

- Mountain/plain/coastal zones; and current ecosystems

- Provision of ecosystems services- Link to Natura 2000

Energy

- Energy supply (hydro, thermal, nuclear, wind, solar)

- Energy infrastructure- Economic consequences

- Lower demand in winter/higher in summer- Concerns over water availability for hydro

power plants and cooling of thermal/nuclear power plants in summer

- Vulnerability of infrastructure (incl. transmission and distribution) to high temperatures and extreme events

- Energy security

Forestry

- Forest resources (timber and fuel wood)

- Recreational function - (Com)position of forests

- Impact on timber production- Impact on woodland condition from drier

conditions (overall health, location/migration, species and ecosystems’ composition)

- Impact of forest fires, pest and diseases- Extreme events, pests

Human Health - Populations’ well-being- Morbidity and mortality

- Heatwaves (and combined pollution/ozone peaks)

- Vector-borne, infectious and cardio-vascular diseases, skin cancer

- Post-hazards conditions (for example floods)

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Sector Scope Key vulnerabilities

- Marine pathogens

Infrastructure and

Construction

- Utilities infrastructure (electricity, water, sewage, internet, telephone)

- Buildings – housing and public buildings

- Civil works- Transport (road, rail, vessel)

- Disaster risk management (DRM)- Temperatures and precipitation affecting road

and railway infrastructure quality (or not), waterways

- Coastal zones- Cooling needs in buildings

Tourism- Winter tourism- Mountain/nature tourism- Seaside tourism

- Impact of shorter winter season on frequency and growth potential

- Impact of longer summer season on frequency and growth potential

- Water availability

Transport

- Road corridors- Railways infrastructure- Seaports- Pipelines- Urban traffic circulation

- Vulnerability of infrastructure (thermal stress to road and railway systems (most important modes)

- Impact of flooding (mud/landslides)- Sea level rise on port - what does it mean for

increased maintenance, climate-smart planning

Urban Environment

- Traffic infrastructure- Utilities infrastructure- Health aspects- Housing, construction and

spatial planning- Emergency response

- Are spatial plans adequate and up-to-date- Are adequate spatial plans enforced- Are emergency plans by cities up-to-date- Location of infrastructure and housing- Quality buildings and infrastructure- Extreme high temperatures and heat waves

(interaction with ambient air pollution)- Urban sprawl and forest fire risk- Flooding- Sea level rise, coastal erosion and storm surge

Water management

- Floods- Emergency response- Drinking water and

sanitation- Energy generation- Agriculture- Ecosystems, forests- Critical infrastructure

- Water resources: quality and quantity of water resources and water resources management

- Water balance across different uses including irrigation

- Disaster Risk Management arrangements (as main climate extreme, notably through floods) and River Basin and Flood Management Plans

8. The model simulations comprise the economic baseline which includes slow-onset and extreme events. Adaptation scenarios aim to measure the effectiveness of adaptation policy and investment measures.

9. The simulation results highlight the impact on Bulgarian GDP growth, fiscal and external balances, sectoral value added and employment, therefore providing information on the economic and environmental sustainability of the projected growth trends. (Figure 1).

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Figure 1. Structure of the CGE analysis

2.1. Bulgaria CGE: Description of the model structure10. This Bulgaria model developed for this report is an integrated analytical platform between an economy-wide model and land use at a granular level. The combination of climate, economics and land use models enable to estimate: (1) the ripple effect of climate-change induced impacts on employment as well as the economic and financial welfare of current and future generations; (2) the impacts on the agricultural sector in Bulgaria, with a fine granularity as various Agro-Ecological Zones (AEZs) in Bulgaria, are explicitly modeled; and (3) the net benefits from adaptation to climate change via several policy and technical levers as shown in Figure 2.

11. The underlying CGE structure is the Bulgaria GTAP-AEZ model that is presented in Lee et al. (2005). These relations are quantified for the Bulgarian economy based on the most recent data (2015) published by EUROSTAT, Food and Agriculture Organization (FAO), the World Bank, the United Nations, and the IMF.

12. The CGE framework represents households, government, producers, major trade partners and their interactions. The model describes the flow of goods from production activities to households and the flow of production factors from households to activities, as well as the payments in exchange of these goods and the use of factors as shown in Figure 2.

13. The Bulgarian industry is represented by 57 sectors where agriculture, one of the most vulnerable sectors to climate change, is represented in more details (eight crops and seven AEZs per Bulgarian classification, five AEZs based on GTAP-FAO-IIASA classification). Each sector has a different production function and maximizes profits subject to a production function that combines primary factors (including labor capital, land, and natural resources such as water, minerals, fish and forest) and intermediate inputs to produce a good. Firms pay

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Inputs-Social Accounting Matrix - GTAP (2015) for Bulgaria-Official statistics-Climate Change Impact (translated into losses in productivity and capital due to extreme events)

Analysis -CGE Model (2017-2050): Economic baseline and three adaptation scenarios-Climate Impact-Sectoral Analyses: Adaptation measures - Overall financial needs

Outputs-Impact on

GDP & growth-Wages, Income,

Consumption, Secoral VAlue Added, Trade

Policy Recommendations

(for example, Insurance, Fiscal

Resilience, Competitiveness)

Investments (for example, Renovation of

water infrastructure)

for Climate

Adaptation



wages/rental rates to the households in return for the use of land, labor, capital, and natural resources. Firms sell their outputs to other firms (as intermediate inputs), and to the private household, government, and investment (as final goods). Firms also export commodities and import intermediate inputs from trade partners. These goods are assumed to be differentiated by country of origin and so the model can track bilateral trade flows.

14. Households get income by providing factors of production (land, capital, labor) to the producers. Their consumption basket represents a variety of goods and services that are either locally produced or imported. They pay taxes to the government in return for public services such as defense, health and education. The CGE model comprises a representative household, whose disposable income is split between consumption and savings.

15. Production and consumption activities generate taxes for the government budget. Public accounts are composed of government and household savings. These funds are allocated following a public utility function.

16. Climate change impacts on productivity (for example, change in crop yield), infrastructure (for example, damage to buildings and roads), natural capital (for example, change in water availability), or populations (for example, increased health risks and lower productivity) are integrated into the model by way of so-called damage functions, further discussed in Section 3.2.

Figure 2. Schematic representation of the flows in the Bulgaria CGE model

17. The Bulgaria CGE model is an advanced version of the standard GTAP model, which traces demands for, and supplies of, a wide range of commodities including biofuels, primary sources of energy and electricity at the global scale by country and sub-region (AEZ and River Basin).

18. It also considers resource constraints and models allocation of limited resources including labor, capital, natural resources such as water and land, among its alternative uses. It divides crop producers into rainfed and irrigated and traces water and land resources and their

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demands at the spatial resolution of river basins and AEZ for Bulgaria. In this model, water can move across its alternative uses within a river basin with limited movement across AEZs and the model accounts for global trade for goods and services (Figure 3).

Figure 3. Detailed Representation of the Bulgarian CGE model

19. The static version of the model is developed to represent the current economic conditions in Bulgaria in 2011 and serves as a basis to develop the dynamic model. In this version of the model, investment is part of the model database, and is fixed. By the structure of the model there is no capital accumulation since all goods/services produced in each year are consumed the same year. The dynamic version of the model (where capital accumulates) will be used to develop the most likely growth path towards 2050.

In what follows, the production and consumption modules for the CGE model are described in more detail.

2.2. Production functions and derived demands for inputs20. Factors of production (land, capital, labor) are fixed at the national level in each year; and demand for these factors is endogenous and determined by market mechanisms. Policy changes such as removal of electricity or agricultural subsidies result in changes in the allocative efficiency of these resources that leads to welfare gains or losses (which can be estimated using the model).

21. Figure 4 displays the nested production function for Bulgaria. At the top level, the firms’ production is based on value added-energy bundle and non-energy intermediate goods that are partially substitutable. The second nest is about firms’ demand in terms of factors of

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production and various types of energy. For each intermediate input, the model assumes an Armington substitution between domestically produced and imported commodities.

Figure 4. Production structure in the CGE

Firm’s output

Imported

Region r

Value added & Energy Non-energy intermediate inputs

ESUBD

Capital-Energy composite

ESUBVA

Land Labor

Region 1

Domestic ESUBM

ELKE

Non-Electricity

Capital

Electricity

Coal

ELNEL

Petroleum products Crude oil

ELNCOAL Non-coal

Composite Energy good ELEN

Natural gas

AEZ1 AEZ18

ESAEZ

CES

Biofuels

Resources

2.3. Household demand for final goods and services22. The structure of household demand in the Bulgaria CGE model is presented in Figure 5. In this demand structure the private household uses a mix of substitutable energy products. Hence, in response to changes in the relative prices of energy products it can move away from expensive energy sources to cheaper items. Among Energy commodities, electricity demand is the most important indicator to track due to the expected increase in the air conditioner use.

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Figure 5. Household demand for goods and services in the Bulgaria CGE model

2.4. Modeling of the agriculture sector23. Figure 6 displays the land demand for farming. Besides capital and intermediate inputs, farming activities require various types of land suitable crop production. As shown in Figure 7 main land-use types in Bulgaria are croplands, followed by forests. Within the CGE model, these qualities determine the classification of the AEZ land. Thus, each AEZ is rated based on its land productivity. The higher is the land productivity, the higher the crop yield, thus the profit margin from this farming activity. Based on the literature, climate change is likely to affect either the land or crop productivity positively. Positive push on crop supply, combined with declining demographic growth is expected to lead to decreasing crop prices. In the meantime, climate events such as floods may cause direct damage to the farmers. Both cases will be examined using the model in the climate simulations section of the report.

Figure 6. Land Demand in the Bulgarian CGE model

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Figure 7. Types of agriculture and livestock production

Figure 8. AEZs in Bulgaria

Source: Kolev 2016.

Table 2. AEZ disaggregation used in this CGE analysis

LGP in days Moisture regime Climate zone GTAPP class

0 – 59 AridTropical AEZ1Temperate AEZ7Boreal AEZ13

60 – 119 Dry semi-aridTropical AEZ2Temperate AEZ8Boreal AEZ14

120 – 179 Moist semi-aridTropical AEZ3Temperate AEZ9Boreal AEZ15

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180 – 239 Sub-humidTropical AEZ4Temperate AEZ10Boreal AEZ16

240 – 299 HumidTropical AEZ5Temperate AEZ11Boreal AEZ17

>300 days Humid; year-round growing season

Tropical AEZ6Temperate AEZ12Boreal AEZ18

Source: GTAP

24. An analysis of the climate impact on agriculture in the country necessitates a good understanding of the country’s seasonal and long-term climate patterns.

25. Figure 7 displays the model structure for various land use. Table 2 maps AEZs in Bulgaria based on the GTAP-FAO-IIASA classification for global AEZs2. The AEZ9 (Temperate-moist-semi-arid) covers most of the country while AEZ10 represents mostly the Western regions.

2 It is important to highlight that the definition of GTAP differs from national sources for Bulgaria. This analysis was developed for five global AEZs for Bulgaria while national maps covers seven AEZs.

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Figure 9. Land supply in Bulgaria CGE

26. This Bulgaria CGE model was developed in a modular manner: it represents Bulgaria’s major economic sectors, including AEZs-Incremental Capital Output Ratio (ICOR) for the agricultural sector. This fine granularity in the agriculture sector is the originality of the Bulgaria CGE model. In addition to an evaluation of the climate change impact at the AEZ level, the simulation results will be displayed at the River Basin level. This method relies on analyzing climate impact on water/land as a composite factor of production.

27. The Bulgarian agricultural sector is represented by five AEZs. The competition for land takes place at the AEZ level (see Figure 9) while competition for labor and capital takes place at the national level. Sectors demand labor, capital, and natural resources such as water, minerals, fish and forest. Each factor of production is determined in a specific way: labor and capital are imperfect substitutes and mobile between sectors while land can be used in crop production, forestry or livestock sectors.

28. This study aims to enhance the existing literature by developing an analytical framework to allow to examine the interactions between climate change, crop yield, and climate risks for Bulgaria. Many papers have studied the impacts of climate change on crop yields and food security (for example, Lobell et al. [2008] and Nelson et al. [2010]). These studies demonstrate how changes in climate variables affect food security across the world. However, they do not provide a clear picture on the interactions between climatic change, crop yield, and water scarcity. More recent papers (for example, Willis et al. [2014] and Marshal et al. [2014]) have considered these interactions and show that while climate change can induce incentives for irrigation, water scarcity may limit the extent that irrigation adoption can be implemented. While these papers and the earlier work in this area provide valuable economic and biophysical analyses of the impacts of climate change for crop production and food security, they ignore the interplay between climate change and international trade. Some papers have examined the interaction between trade and climate change. For example, Reilly et al. (2002) have shown that trade can improve food security in regions where crop production will be negatively affected by climate change factors. This study and its successors

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(for example, Baldos and Hertel [2015]) usually ignore water scarcity induced by climate change and or economic factors. Liu et al. (2013) have shown that trade can mitigate the consequences of future irrigation short falls in regions where water scarcity threatens food security as well. However, this study ignores the impacts of climate change on crop yield in the presence of water scarcity.

29. The standard GTAP framework was adapted/updated for Bulgaria with further enhancements to reflect the most significant interactions between socio-economic and climate trends. The type of CGE used is a dynamic recursive model that covers 57 economic sectors and their cross-linkages, across several sub-regions in Bulgaria. This model is well-suited for an in-depth analysis of the pervasive implications from climate change on economies and societies, since it has a sufficient degree of sectoral disaggregation to represent explicitly the linkages between the most significant climate change impacts on productivity, infrastructure, and populations, and the resulting threats and opportunities for sectors and geographic areas.

30. This study aims to advance understanding of the potential ripple effects of climate change on the Bulgarian economy by examining the predicted impacts in key market activities that are deemed the most ‘climate sensitive’. These activities include crop agriculture, energy services related to heating and cooling, water supply, and regions. To highlight the climate sensitive sectors, the original database was aggregated from 57 to eight sectors as shown in Annex.

Box 1. Physical Database construction for Bulgaria CGE

The GTAP 9 Land Use and Land Cover Data Base builds on global land cover and land use data bases, as well as global forestry data. In keeping with the multi-year release of version 9, the land cover and land use database is updated to represent 2011 as shown in Figure 10. This global dataset was tailored to include multiple sources of land and water use at the River Basin and AEZ levels for Bulgaria using the most detailed and complete databases.

Figure 10. Global AEZs

Procedures followed in updating the land cover data were:

a) Aggregation and normalization of agriculture and built-up land cover. Within each 0.5 x 0.5-

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degree grid cell the percentage of land cover for croplands, pasturelands and built-up lands were added. Grid-cells wherein the combined agriculture and built-up land cover exceeded 100 percent were normalized to ensure that the sum of land cover for these cells does not exceed 100 percent.

b) Derivation of the ‘residual’ land cover and its allocation to other land cover types. The ‘residual’ land cover was calculated by deducting the normalized land cover of agricultural and built-up lands from grid-cells with uniform value of 100 percent. The ‘residual’ land cover was then allocated to grasslands, forests, shrublands and other lands using the Global Maps of Potential Vegetation.

c) Conversion of the fractional land cover data into actual area of land cover. To derive the grid-cell level areas for the different land cover types, a global map of the surface area of the earth was used.

d) Aggregation of land cover areas by AEZ and by country. The grid-cell level areas for different land cover types were aggregated by overlaying the land cover maps with a map of global AEZs and countries.

e) Calculating accessible forest area by AEZ and by country. To derive the accessible forest area by AEZ and by country, the aggregated forest area data was weighted by the share of accessible forests in total forest area by AEZ and by country. These shares were taken from GTAP 6 land cover database.

The process of adding water as a factor of production starts with the underlying GTAP-AEZ database which identifies land located in various agro-ecological and the uses (sectors). It helps to remember that when splitting the GTAP sectoral land rents into AEZs, land rent is tied to the harvested area, instead of the physically cultivated area. In other words, in the GTAP economic accounts for each country, land rents are generated from the activity on a given parcel of land during the calendar year. Therefore, the value of the land in production over the course of the entire year is what is important. The GTAP sectoral land rents are first split into 18 AEZs according to the AEZ-specific production shares as derived from multiple sources.

The above method is used in conjunction with the following formula to split the GTAP sectoral land rents into 18 AEZs and then add water as factor of production.

Rcs=Rc [∑i∈cP iY ia H ia / ∑

a∈ AEZ∑i∈c

PiY ia H ia]∗SH ws∗SH rb

Where;

Rcs is the land rent accrued to GTAP sector c in AEZ a;

Rc is the total land rent of GTAP crop sector c, (no AEZ distinction here, VFM header in GTAP);

Pi is the per-ton price of FAO crop i (invariant to AEZs)

Yia is the yield (ton/1000ha) of FAO crop i in AEZ a,

Hia is the harvested area of FAO crop i in AEZ a,

SHws is the share of water type (t¿ground or surface water, b=river basins) in total basin water supply,

SHrb is the water consumption share of sectors by river-basin

Figure 11 shows the volume of water use by river basin and sector for Bulgaria in 2011. We combined this data with data from Figure 12 that shows average water tariff across basins to generate the value of water use by sector and river basin that is needed to generate the

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SHrb(c,b) share value used in the above equation. To generate the SH ws(t , b) value, we relied on the data that showed the distribution of fresh water sources across the four river basins in Bulgaria.

As an illustration, Table 3 shows Bulgaria’s total value-added, including land rents (header ‘VFM’ from the GTAP database) for the GTAP crop sectors split into water source type, river basins by AEZs. The data show that most of the Bulgaria’s crops (value-basis) use ground water, are in the Danube river basin, and are generated either in AEZ 9 and AEZ 10, which are of temperate climate with longer growing periods.

Table 3. Bulgaria crop sector rents (millions of US$)

Source: GTAP data processing using data from Ministry of Agriculture, Foods and Forestry.

Note: Water type classification: S=Surface water, G=Ground Water River Basin classification: DB=Danube, BS=Black Sea, WA=West Aegean, EA=East Aegean

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Figure 11. Water use (million m3 ) by sector and river basin, 2011

Agriculture

Electricity

OtherInds

Services

Households

0 500 1000 1500 2000 2500 3000

EastAegean WestAegean BlackSea Danube

Source: Ministry of Agriculture, Foods and Forestry.

Figure 12. Average water tariff by sector and river basin, 2011 US$

Danube

BlackSea

WestAegean

EastAegean

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Services OtherInd Electricity Agriculture

Source: Ministry of Agriculture, Foods and Forestry

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3. Model Baseline31. The CGE model simulations refer first to the development of an economic baseline ignoring climate change impacts. Second, the economic projections in the baseline are adjusted for the estimated impacts of climate change. For Bulgaria, the cumulative loss in 2050 is around 0.5 percent GDP on agriculture and 1 percent on the overall economy (under the 2oC and 4oC scenarios), or more than half of projected annual growth in 2040–2050, as per OECD (2015)3. Third, an additional scenario will be developed at the final stage of the analysis to illustrate potential net gains from climate change adaptation. The ongoing sectoral assessments will feed into the simulations of the second (description of climate change risks and vulnerabilities) and third (description of priority adaptation options) scenarios. Early draft versions of these sectoral assessments have been used for early calibration and running of the second scenario (growth under a changing climate).

32. What follows describes the CGE model simulations refer first to the development of an economic baseline ignoring climate change impacts. Second, climate change impact from slow-set climate events and extreme events are incorporated in the model.

3.1. Business as Usual (BaU): Growth toward 2050 ignoring climate change impacts33. The economic growth path that Bulgaria is likely to follow is developed based on the historical economic trends for the country since 2011, demographic projections (developed by United Nations) that assume a constant fertility rate and constant migration trends since 2011. These statistics and IMF projections to 2022 are presented in Table 4.

34. The model displays the most likely growth path for the next 23 years based on the demographic trends (Figure 14), investment (Figure 13), international trade, improvement in ICOR and sectoral productivity. Based on official statistics on these parameters and assuming investment would represent around 23 percent of GDP for the next 23 years (Figure 13), the Bulgarian economy is expected to grow 1.3 times by 2050, or an annual average growth rate of around 1.7 percent per year.

3 OECD (2015) projects 1.7 annual growth in 2040–2050.

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Table 4. Statistics used in the development of the Business as Usual Scenario

Bulgaria 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Gross domestic product, constant prices(National currency)

76.203 76.227 76.884 77.906 80.724 83.503 85.924 88.244 90.45 92.712 95.029 97.405

Gross domestic product, constant prices(Percentage change)

1.915 0.031 0.862 1.329 3.617 3.443 2.9 2.7 2.5 2.5 2.5 2.5

Total investment(Percentage of GDP) 21.47 21.942 21.34 21.436 21.19 20.307 20.83 20.98 21.443 22.227 23.1 23.986

Current account balance (Percentage of GDP)

0.33 -0.853 1.276 0.082 -0.134 4.198 2.258 2.034 1.731 0.946 0.105 -0.825

Population, total (Number of people x 1,000)

7,395.6 7,348.3 7,305.9 7,265.1 7,223.9 7,178.0

Source: IMF.

Figure 13. CGE BaU simulations – GDP and investment projections to 2050

Source: Bulgaria CGE model results.

Note: 2011 (start year) = 100.

Figure 14. Population projections for Bulgaria (total population, constant fertility and migration rate)

60

70

80

90

100

2011 2016 2021 2026 2031 2036 2041 2046

Chart Title

Source: UN Statistics.

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Note: 2011 (start year) = 100.

35. As shown in Figure 14, population is expected to decrease by 36 percent (from 7.39 million to 4.73 million) between 2011 and 2050. Decreasing population affects two parameters in the CGE model: decreasing demand for goods and services (that leads to decrease in supply) and shrinking labor supply. Decreasing domestic demand can be compensated by exports with a boost in competitiveness. Decrease in labor supply would lead to higher wages, hence higher production cost. Thus, labor intensive sectors are likely to decline while less labor-intensive ones would expand.

36. Main expense item in household budget were housing, transport, electricity and food in 2015. The structure of the household consumption basket is expected to stay the same, except with a decrease in food expenses due to decreasing food prices.

Figure 15. CGE BaU simulations – GDP composition in 2011 (inner circle) – 2050 (outer circle)


37. Based on the model projections toward 2050, investment and government expenses will grow at a moderate rate while consumption and imports are likely to grow faster than the rest of the macro-economic accounts (Figure 15). Overall, the trade deficit is likely to persist till 2050. While the share of agriculture in trade is likely to stay constant, net exports of manufactured products (mainly machine and equipment) are likely to increase.

38. Sectoral value creation is expected to vary by sub-region even without considering climate change. As shown on Figure 16, AEZ 10 is likely to grow faster than AEZ 8 and 9. The highest value creation is expected from raw milk in AEZ 9 (Western Region) and forest/forest products in AEZ 10 (Eastern Region). Economic activities that were quasi-inexistent in 2015 are likely to grow in AEZs 8, 14 and 15 (see Figure 17 for AEZs and Annex definition of crops in GTAP).

39. It is important to underscore that the economic baseline ignores existing or planned sectoral strategies or reforms; it simply translates the growth potential based on the demand

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and supply projections for various sectors and AEZs. Demand for products comes from households, government, industry and trade partners while supply of goods relies on domestic production and imports. The market mechanisms lead to increasing prices if demand exceeds supply, and vice versa. Further details on the CGE simulations can be found in Annex 1.

40. In the next two set of simulations, these fundamental assumptions will be modified to make the analysis more realistic. First, climate change impacts will be introduced in the baseline projections. Second, coping strategies such as sectoral reforms and targeted climate interventions, will be analyzed in the final section of the analysis (to be completed by November 2017). Each set of new simulations will be compared to the economic baseline discussed above to highlight the direction (+ or -) of the projected impacts on Bulgarian GDP (and other macro-economic parameters) in 2050.

Figure 16. Household revenues from agricultural activities (2020–2050)

Revenue(thousand $, constant prices 2011)


Figure 17. Bulgaria AEZs based on global AEZ classification

Sources: GTAP, FAO, and IIASA 2017.

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Note: While Bulgarian sources define seven AEZs, GTAP-FAO-IIASA classification displays the Bulgarian territory in five global AEZs.

3.2. Business as Usual (BaU): Growth toward 2050 with climate change impacts41. In this section, the economic projections in the baseline are adjusted to include the estimated impacts of climate change. The economy-wide framework is used to estimate the ripple effect on the overall economy from specific projected climate impacts such as natural resource degradation, heat waves, more frequent extreme events, etc.

42. Climate change, operating through a variety of market drivers, can directly affect the costs and availabilities of economic outputs and inputs alike. In turn, these changes both directly and indirectly influence the level and structure of overall economic activity. For real GDP, which measures current, inflation-adjusted production, income and spending, there is ultimately a three to four percent spread between the most optimistic and most pessimistic results generated in this analysis. Under optimistic assumptions, real GDP in 2050 could be almost one percent lower in the case where temperature rises by 2oC compared with the best-case scenario where temperature rises by 4oC. By contrast, if pessimistic outcomes prevail, real GDP in 2050 could be two to three percent lower under extreme conditions.

43. According to projections from the World Economic Outlook, Bulgaria’s GDP is projected to grow annually by about 1.7 percent by 2050. This growth rate is completely wiped out if Bulgaria faces the full impact of a 2oC rise in temperature by 2050. The negative impact of climate change outweighs economic growth by more than one percent if all the pessimistic 2oC and 4oC scenarios were considered. In general, it is safe to say that climate change presents an existential threat to the prospect of future economic growth in Bulgaria.

Figure 18. Impact of climate change on real GDP (percentage change)

Optimistic Anticipated Pesmistic

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

2 Degree by 2050 4 Degrees by 2050

Source: Model simulations.

44. Figure 18 presents a first round of climate change effects; sector wide impacts of the alternative scenarios of climate change in Bulgaria by 2050. Although there are 57 sectors in

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the model, the model sectors are aggregated in eight broad categories. The aggregation from 57 to eight sectors is described in Annex 1. Here are the main observations based on the model simulations: First, climate change leads to a decline in output of the agricultural sector (represented here by the crops sector) in Bulgaria. Agriculture is unique among economic sectors in the type of impacts induced by climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (solar radiation, temperature and precipitation). Climate change alters these inputs and therefore has a direct, biophysical effect on agricultural productivity. Second, there is decline in output of the energy sector in all the developed scenarios. It is important to remember that the changes in regional households’ demand for oil, gas and electricity were modelled as changes in the overall productivity of these industries. Climate change, therefore, has a direct negative productivity shock on these sectors and hence the decline in output across scenarios. Third, the other block of sectors that experiences negative outcome is the transport and communication sectors. The overall decline in economic activity (as shown above by GDP changes) accounts for the decline in demand for output for these sectors. Fourth, a positive output response is estimated in the energy intensive trade exposed sectors which includes chemicals, steel, aluminum, cement, and ceramics sectors. The positive response following climate change is driven by a positive terms of trade change (see Figure 19) that helps to drive up export demand which helped mitigate declining domestic demand.

Figure 19. Impact of climate change on domestic output

Crops

MeatLivstk

Manufacturin

g

EnergyEITE

Utility_Const

Trans_Comm

OthServs

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2_Optimist 2_Anticipated 2_Pessmistic4_Optimist 4_Anticipated 4_Pessmistic

% C

hang

e


Note: MeatLivstk = Meat and Livestock, EITE = Energy intensive trade exposed, Utility Const = Utility and construction, OthServs = Other Services

45. The overall pattern of price changes follows the output storyline shown in the Figure 20. That is, decline in output in the face of pre-existing demand leads to rationing and higher prices are required to clear the market. Hence, sectors that are in direct line of climate change see decline in output and rising prices as a result.

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Figure 20. Changes in trade balance (millions of US$)

Crops

MeatLivst

k

Manufacturin

g

EnergyEITE

Utility_Const

Trans_Comm

OthServs

-150

-100

-50

0

50

100

2_Optimist 2_Anticipated 2_Pessmistic4_Optimist 4_Anticipated 4_Pessmistic


Note: MeatLivstk = Meat and Livestock, EITE = Energy intensive trade exposed, Utility_Const = Utility and construction, OthServs = Other Services

46. This combined with aforementioned rising commodity prices has demand and supply side consequences for the overall economy. On the demand side, for example, real incomes and real household purchases decline. With lower tax receipts and higher prices, real government purchases also decrease. Higher domestic prices discourage exports of some commodities and promote imports leading to a worsening real trade balance. As before, varying assumptions about the degree to which climate change influences import prices have a small impact on trade patterns and industry structure, but virtually no impact on predicted changes in aggregate economic performance. These combined effects are sufficient to cause a reduction in real GDP. On the supply side, real incomes decline because of losses in labor income and larger losses in productivity. Figure 21 summarizes these macroeconomic consequences of climate change.

47. Rising world prices for staple commodities may result in a substantial reduction in real income – and an increase in poverty – for households spending a large share of their income on staple grains. However, the well-being of households depends not only on changes in the cost of living, but also on changes in earnings.

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Figure 21. Components of GDP: deviation from 2050 baseline due to climate change

2_Optimist 2_Anticipated 2_Pessmistic 4_Optimist 4_Anticipated 4_Pessmistic

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

Consumption Investment Government ExpExports Imports

% C

hang

e

Source: Model Simulations

3.3. Analysis of welfare impacts4

48. Figure 23 presents estimated welfare losses for the two core scenarios along with both optimistic and pessimistic outlooks. In general, these results suggest that the market effects of climate change will have similar implications for economic welfare and for overall income, spending and production (or GDP, in short). There are however differences between the two measures. The source of difference relates to what is included or excluded from measures of welfare versus GDP. For example, GDP includes investment, which yields future consumption, and government or public spending on goods and services. Welfare, as defined in GTAP, includes private consumption involving goods, services, government purchases, and a host of other variables. Nonetheless, as shown in Figure 22 the overall estimated welfare consequences of climate change range from about 1 percent of GDP in 2050 in the most optimistic scenario to about 3.5 percent in the most pessimistic scenario.

49. The welfare impacts of the climate impacts shocks, expressed as a percentage of value-added, can be further decomposed into three components. Figure 24 illustrates this decomposition exercise for Bulgaria. The first component corresponds to the direct economic valuation of the estimated climate shocks. For the scenarios under consideration, this contribution is negative in all cases, reflecting the general worsening of production conditions in Bulgaria, but with the severity varying across the alternative scenarios that were developed. Model projections suggest the highest percentage losses owing to the direct impact of climate change in Bulgaria will materialize by the end of 2050 is the 4oC change materializes.

50. Given the relatively inelastic demand for food, the declines in production result in significant price increases for, say, agricultural commodities. These price rises affect the second component of economic welfare, the Terms of Trade (ToT). In theory, net exporting

4 The GTAP model features a representative regional household whose behavior is governed by an aggregate Cobb-Douglas utility function specified over per capita private household consumption, per capita government spending and per capita savings.

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countries tend to gain from higher priced products, that is, see a positive terms of trade impact while net importers of say, agricultural products, experience a deterioration in their ToT. The value of the ToT effect, expressed as a percentage of initial value-added, can be very important for nations that trade extensively. Figure 24 shows the impact of climate change on terms of trade and its contribution to national welfare. In most cases, the tot impact is not strong enough to outweigh the direct impact but nonetheless contributes to mitigate some of the welfare losses arising from climate change.

51. The third and final component of welfare change is the change in economic efficiency. The welfare approach aims to capture the interaction between the impacts of climate change and existing economic policies. Such policies include an array of existing polices, for example, trade policies, agricultural and non-agricultural policies, subsidies and the like. It is argued here that climate change coupled with sub-optimal global trade policies will lead to an economic efficiency loss. Therefore, a stronger negative GDP impact from combined effect of climate change and economic efficiency loss. The blue bars in Figure 24 show that indeed climate change leads to loss of economic efficiency but such loss is reversed to a certain degree, by a positive impact on terms of trade.

Figure 22. Welfare changes due to climate change (millions of US$)

Optimistic

Anticipated

Pesmistic

-2500 -2000 -1500 -1000 -500 0

4 Degrees by 2050 2 Degree by 2050

Source: Model Simulations.

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Figure 23. Welfare loss as a share of GDP (percentage)

Optimistic Anticipated Pesmistic

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

2 Degree by 2050 4 Degrees by 2050


Figure 24. Decomposing Welfare Change (as percentage of GDP)

2_Optimist 2_Anticipated 2_Pessmistic 4_Optimist 4_Anticipated 4_Pessmistic

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Efficiency Impacts Direct Impacts Terms of Trade Impacts


3.4. Comparing results with the OECD study52. This section compares the results of this study with those reported in OECD (2015) and starts by first describing the model and some important results of the study. The OECD paper uses the ENV-Linkages (Environment Directorate’s Linkages) model, a recursive, multi-region, multi-sector dynamic CGE model of the global economy. In addition to ENV-Linkage, the OECD analysis also relies on the AD-DICE (Adaptation in Dynamic Integrate Climate-Economy) model to run some long-term simulations that go beyond 2060. The projected long-term costs of climate change start with building a baseline projection of the world economy till 2060 and beyond when necessary. This baseline represents the case where the world economy evolves without any climate change. The anticipated climate change impacts are then overlaid on this baseline to help assess how the new shocks change the “observed” world economy. The model setup divides the world into eight global groupings5 5 These groupings are: OECD America, OECD Europe, OECD Pacific, Middle East and North Africa, Latin America, Sub Saharan Africa, South and South-East Asia, Rest of Europe and

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and aggregate the 57 GTAP commodities to eight sectors6. While the discussion on the climate impacts generally focuses on aggregate regions listed in the footnote, the study also reports results on some major individual economies that make up the aggregate divisions. The modelling is based on existing estimates of how selected climate impacts affect the drivers of economic growth of major world regions at the macroeconomic and sectoral level. A multitude of impact of climate change are considered in the report and the list includes: changes in crop yields, loss of land and capital from sea level rise, impact of extreme events like hurricanes, morbidity from heat and cold exposures, changes in energy demand, and changes in tourism flows. In general, the study reports that the combined effect of the above selected impacts on global annual Gross Domestic Product (GDP) are projected to rise over time to likely levels of 1.0 percent to 3.3 percent by 2060, with a central projection of 2 percent GDP loss. There are, however, substantial regional variations in this estimate of impacts of climate change.

53. The first important notable difference between our study and the OECD paper is that this specific report used a the standard and static GTAP model while the OECD report relied on a recursive dynamic CGE model that is augmented AD-DICE which is a class of integrated assessment models. While static CGE models provide a useful benchmark for policy analysis, their use in climate change modeling has been criticized in a number of ways. First, in the static CGE model, there is no optimization over time, that is, agents are seen as being myopic when it came to between period decisions such as savings and investment. Second, climate change by its very nature is a dynamic phenomenon. The fact that agents are allowed to update their information beliefs and adjust their saving and investment decisions accounts for the observed non-trivial GDP impacts of climate change under the static GTAP model, our study took as a reference the OECD report.

54. A second source of difference between the two studies is the nature and extent of climate change damages modeled in the simulation exercises. Our model derived the necessary parameters from few reliable sources and focused on three impacts of climate change: agricultural productivity, energy demand, and tourism. The OECD study on the other hand relies on a larger literature and studies the impact of a range of climate change impacts.

55. The other important difference has to do with the sectoral and regional aggregation used in this report is very different from the one reported in the OECD study. A related point is that this study uses Version 9 GTAP database with 2011 as a base year while the OECD report relies on a slightly earlier version. These are important differences with the OECD study. The differences in simulation results with the OECD study will be displayed in the Annex.

Asia6 The aggregate sectors are: Agriculture, fisheries, forestry, energy and extraction, energy intensive industries, other industries, transportation and construction, and other services

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4. Adaptation to climate change 56. The preceding section has shown that the Bulgarian economy will be increasingly vulnerable to climate risks. The rising temperature, coupled with increased variation of extreme temperature, adds economic burdens ranging from health risks to higher electricity bills. In parallel, local air and water quality continue to deteriorate. Extreme events exacerbate the situation. As a result, Bulgaria faces innumerable areas of vulnerability. However, there are many possible options for climate change adaptation.

57. Adaptation refers to actions that are taken in response to or in anticipation of projected or actual changes in climate, either to reduce the adverse impacts or to take advantage of opportunities offered by such changes (IPCC 2007). The adaptations options available to policy makers vary depending on the nature and magnitude of climate change impacts (Klein et al. 2001). Adaptation measures could be simple in nature, for example, shifting planting calendars or changing crops, or more costly ones like investing in protective infrastructures such as river or sea dykes for flood control. In some extreme cases, retreat from coastal areas may be the best strategy. It come as no surprise, therefore, that the adaptive capacities of countries differ and largely depends on economic size which determines the amount of available resources that can be devoted to successfully implement adaptation goals. Generally, developed countries have higher adaptive capacities while developing and least developed countries, which are most vulnerable to climate change, need external support to build theirs (IPCC 2010). Adaptation in particular, will require increased public expenditure, both on climate related public goods (such as information acquisition and dissemination on likely extreme events) and to protect public and private assets at risk like transportation systems, water and health systems. Eventually, adaptation could reduce or increase the stress on public budgets depending on its effectiveness, the structure of the tax system, the size of adaptation investment, and the funding sources available (Osberghaus and Reif 2010).

Figure 25. Damages of climate change and cost of adaptation

Source: Cretegny 2009.

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TD

Adaptation cost

Net Benefits

ND

RD

Cost of climate change

Change in global temperature



58. Figure 25 shows that allocating resources or investing in adaptation allows to reduce the Total Damages (TD) line from climate change to Residual Damages (RD). The total damages line is assumed to represent all the costs to society that arises from unmitigated climate change. The vertical distance between the two curves (TD-RD) shows the gross benefit of investing in adaptation measures. This, however, comes at a cost and that is represented by the difference between RD and the ND.

59. This section of the report aims to assess the impact of adaptation using the same Computable General Equilibrium (CGE) model outlined in the previous sections. CGE models consider market-driven adaptation by construction (that is, the functioning of autonomous mechanisms – primarily, demand and supply reactions to endogenous changes in relative prices) which characterizes instantaneous resource allocation across two market equilibria in response to exogenous economic shocks. Assessment of adaptation policies requires the simulation of both costs and benefits of planned adaptation measures. Measuring costs of adaptation presents complications that are beyond the scope of this study and, as a result, not reported here. Benefits of adaptation are avoided damages and are introduced in the CGE model by reducing the negative impacts of climate change. Here the report follows the work of Bruin, Delink, and Tol (2009) and take their position that adaptation helps to help reduce 33 percent of gross damages from climate change. In addition, assume that adaptation expenditure is assumed to be financed by a uniform two percent tax increase on commodities.

60. Figures 26 through 29 present the overall impact of how adaptation affects the Bulgarian economy. These figures present two climate change scenarios along with the adaptation that goes along with them. Two outlooks of climate change are considered: (1) a 2oC rise in temperature by 2050 that corresponds to an optimistic climate change impact, and (2) a 4oC temperature rise referred as a pessimistic outlook regarding the impact of climate change. To recap, the results presented below compare what happens to the Bulgarian economy when we introduce adaptation to help address the impact of climate change in the two scenarios we just described.

61. We can summarize the overall result of the impact as follows. First, in terms of real GDP (Figure 26), adaptation seems to help reduce the overall negative impact in both the optimistic and pessimistic scenarios. The magnitude of the change, however, is bigger for the 4oC pessimistic case where as the marginal change in real GDP improvement from the 2 oC optimistic scenarios is less than half of tenth of a percent. The implication here is that the stronger the anticipated impact of climate change, the stronger the need for a stricter and ambitious adaptation policy. The real GDP changes, in general, show the benefits of taking adaptation measure in the face of anticipated changes global temperature rise. Second, the estimated welfare changes (Figure 27), equivalent variations, display the same pattern of benefits, but, because they are expressed in money metric welfare form, serve to highlight the absolute magnitude of the estimated benefits. Here it is important to remember that for the optimistic 2oC climate impact, the adaptation measure had led to a decline in welfare. This is linked to the two percent tax imposed to finance the cost of adaptation and can somewhat indirectly indicate the costs of undertaking such an action.

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Figure 26. Adaptation and climate change impacts on real GDP (percentage change)

2 degree optimistic

4 degree pessmistic

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

with adaptation Without adaptation


Figure 27. Welfare changes with and without adaptation (million US$)

2 degree optimistic 4 degree pessmistic

-2500

-2000

-1500

-1000

-500

0

Without adaptation with adaptation


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Figure 28. Impact of climate change and adaptation scenario on output


Figure 29. Changes in trade balance (millions of US$)


Box 2. Scenario Descriptions and Limitations

SCENARIO DESCRIPTIONS

Within CGE models, the economy is represented by a system of equations representing production, consumption and investment. A number of sectors or regions can be represented, and factors of production are free to move to sectors where returns are highest. These models are calibrated using real-world data, and a ‘baseline’ scenario is estimated. Variables are then altered — this is referred to as a ‘shock’ — and the model is re-run to examine the impact of the ‘shock’ on the economy. For example, the impact of climate change on agriculture may be represented as a reduction in agricultural productivity. The model provides estimates of the effects of the shock on other sectors.

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Because climate change may affect various sectors of the economy directly or indirectly, interactions between different sectors must be studied to assess the impacts of climate change on agriculture. CGE models are well suited to depict interactions between agriculture and other sectors in the economy. Simulations are performed by identifying the relevant economic variables, and imposing changes in some model parameters, like: Land losses to sea-level rise have been modelled as percent decreases in the stock of productive land and capital in Bulgaria. Both modifications concern variables, land and capital stocks, which are exogenous to the model and therefore they are straightforwardly implemented. We do not have information on capital losses, thus we assumed they exactly match land losses. Changes in regional households’ demand for oil, gas and electricity are modelled as changes in the overall productivity of the respective industries. Changes in tourists’ expenditure are modelled as changes in households’ demand addressing the ‘market services sector’, which includes recreational services. Effects on agriculture is simulated through changes in crop productivity.

LIMITATIONS

Despite the increase in the use of applied general equilibrium modelling for climate policy analysis over the last few years, the methodology is not without limitations. The main problem stems from the endemic difficulty of combining theory and reality. Applied general equilibrium models need an empirical basis for their calculation. A list of the main problems with modelling follows.

The mode structure: choosing the model’s functional forms, elasticity type, tax treatment, etc., is the first obstacle the researcher must face when modelling a specific economy. As an example, the GTAP model used here applies the same functional forms across household in different regions of the world. How realistic this happens to be for policy work is always debatable. Data and parameter values: CGE models rely on social accounting matrix (SAM) to approximate the workings of an economy. However, the construction of a SAM is by no means a simple exercise. Added to this complexity is combining SAMs from a wide range of countries to develop a global SAM and a global CGE model further amplifies the problem. In addition, the different functional forms defined in the model require estimation of parameters. It is not possible to econometrically estimate all these functional parameters and some sort of approximation or expert estimate must be made. This further derives a wedge between reality and the model that is mentioned above. Model verification and validation: another important problem associated with this methodology is the lack of statistical tests to confirm the validity of the model specifications. Most general equilibrium models are calibrated from a database for a specific year. For this reason, except for simple tests to analyze the sensitivity of certain parameters included in the model, econometric procedures cannot be used to test the model’s validity. Feedback mechanisms and vulnerability: The impacts of climate change on the society and economy depend largely on the interplay with the new climate as well as on the vulnerability to extreme weather events. The degree of vulnerability is determined by factors like technical and financial capability, demographic, socio-economic and behavioral constraints and organization of the society. As these factors vary over time, vulnerability should vary as well (Tol and Fankhauser 1998). The model and approach used in this report does not explicitly take changing vulnerability into account. Also, because the GTAP model used here is somewhat aggregate in nature (sector and regions), there is only limited room for feedback loops and adjustment mechanisms.

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62. Figures 30 and 31 show the role adaptation can play in reducing some of the negative impacts of climate change on two broad sects of importance in Bulgaria. Figure 30 compares adaptation outcomes for the 2°C and 4°C scenarios for the agricultural and some of the food processing sectors that rely on agriculture as an input in their production process. The overall story that emerges from the figure is that climate change has differentiated impact on grains (for example, wheat, maize, barley) and other crops like vegetables and fruits. While adaptation policy considered here helps to reduce the impact of climate change on grains, it doesn’t, however, totally offset the negative output impact. This is not the case when we look at other crops and the food processing sectors. This calls for a differentiated approach to climate change adaptation policy for the agricultural sector in Bulgaria. Figure 31 presents climate change impacts and the role of adaptation for the energy sector. The sector representing refined petroleum products and other fuels (Petroleum in the figure) is the sector most impacted by any of the climate change scenarios considered in the report.

Figure 30. Impact of adaptation and climate change on agricultural output


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Figure 31. Impact of adaptation and climate change on the energy sector


Note: The list of abbreviations used in this figure are outlined in Table 5, Annex 1.

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Annex 1.Classification and Mapping Used for AnalysisTable 5. GTAP 57 sector classification and mapping used for analysis (8 aggregate sectors)

No. GTAP 57 Long Name Aggregate Sectors

1 pdr Paddy rice Crops2 wht Wheat Crops3 gro Cereal grains nec Crops4 v_f Vegetables, fruit, nuts Crops5 osd Oil seeds Crops6 c_b Sugar cane, sugar beet Crops7 pfb Plant-based fibers Crops8 ocr Crops nec Crops9 ctl Cattle,sheep,goats,horses MeatLstk

10 oap Animal products nec MeatLstk11 rmk Raw milk MeatLstk12 wol Wool, silk-worm cocoons MeatLstk13 frs Forestry Mnfc14 fsh Fishing Mnfc15 coa Coal Energy16 oil Oil Energy17 gas Gas Energy18 omn Minerals nec Mnfc19 cmt Meat: cattle,sheep,goats,horse MeatLstk20 omt Meat products nec MeatLstk21 vol Vegetable oils and fats Mnfc22 mil Dairy products Mnfc23 pcr Processed rice Mnfc24 sgr Sugar Mnfc25 ofd Food products nec Mnfc26 b_t Beverages and tobacco products Mnfc27 tex Textiles Mnfc28 wap Wearing apparel Mnfc29 lea Leather products Mnfc30 lum Wood products Mnfc31 ppp Paper products, publishing EITE32 p_c Petroleum, coal products Energy33 crp Chemical,rubber,plastic prods EITE34 nmm Mineral products nec EITE35 i_s Ferrous metals EITE36 nfm Metals nec EITE

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No. GTAP 57 Long Name Aggregate Sectors

37 fmp Metal products EITE38 mvh Motor vehicles and parts Mnfc39 otn Transport equipment nec Mnfc40 ele Electronic equipment Mnfc41 ome Machinery and equipment nec EITE42 omf Manufactures nec Mnfc43 ely Electricity Energy44 gdt Gas manufacture, distribution Util_Cons45 wtr Water Util_Cons46 cns Construction TransComm47 trd Trade TransComm48 otp Transport nec TransComm49 wtp Sea transport TransComm50 atp Air transport TransComm51 cmn Communication TransComm52 ofi Financial services nec OthServic53 isr Insurance OthServic54 obs Business services nec OthServic55 ros Recreation and other services OthServic56 osg PubAdmin/Defence/Health/Educat OthServic57 dwe Dwellings OthServic

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