Final Report

Home , Economy of Ukraine, Energy in Ukraine

The consulting company of DIW Berlin

Final Report

June 2014

Project “Capacity Building for Low Carbon Growth in Ukraine”

Low Carbon Growth in Ukraine Final Report

DIW ECON GmbH Dr. Lars Handrich

Mohrenstraße 58 10117 Berlin Germany

Phone +49.30.20 60 972 - 0 Fax +49.30.20 60 972 - 99

[email protected] www.diw-econ.de

DIW ECON GmbH Dr. Ferdinand Pavel ii Mohrenstraße 58 10117 Berlin

Low Carbon Growth in Ukraine Final Report

Table of Contents

Management-oriented summary ...... 1 Executive summary ...... 3 1. Sectoral economic analysis ...... 7 1.1 Introduction and summary of main results...... 7 1.2 The Benchmarking Approach ...... 9 1.3 Benchmarking the metal industry ...... 11 1.3.1 Database ...... 11 1.3.2 The metal industry in selected countries ...... 13 1.3.3 Efficiency benchmarking ...... 15 1.3.4 Adjustment for structural characteristics ...... 17 1.3.5 Summary and implications for the metal industry in Ukraine ...... 21 1.4 Benchmarking the non-metallic minerals industry ...... 23 1.4.1 Database ...... 23 1.4.2 The non-metallic minerals industry in selected countries ...... 24 1.4.3 Efficiency benchmarking ...... 26 1.4.4 Adjustment for structural characteristics ...... 27 1.4.5 Summary and implications for the minerals industry in Ukraine ...... 31 1.5 Benchmarking the chemical industry...... 32 1.5.1 Database ...... 32 1.5.2 The chemicals industry in selected countries ...... 33 1.5.3 Efficiency benchmarking ...... 34 1.5.4 Adjustment for structural characteristics ...... 36 1.5.5 Summary and implications for the chemicals industry in Ukraine ...... 41 1.6 Conclusion ...... 41 2. Short analytical papers on economic analysis of policy options to support low carbon policies ...... 43 2.1 Towards a low carbon growth strategy for Ukraine ...... 43 2.2 Assessing the innovation potential in Ukraine ...... 44 2.3 Policy options for LCD in industry ...... 45 2.3.1 Introduction ...... 45 2.3.2 Status quo and the planned policies in industry ...... 46 2.3.3 Potential for emission reduction in industry: Assessments from the literature ...51 2.3.4 What can the government do for the low carbon development? ...... 51

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3. Impact assessment of identified policies and measures and development of BAU scenarios until 2020 and 2050 ...... 53 3.1 How to assess the potential of identified policies for low-carbon economic growth in Ukraine ...... 53 3.2 Model description ...... 54 3.2.1 Building blocks ...... 54 3.2.2 Production technology ...... 55 3.2.3 Power sector ...... 57 3.2.4 Heat supply sector ...... 58 3.2.5 Taxes and subsidies ...... 59 3.2.6 GHG emissions ...... 60 3.2.7 Rest of the world ...... 60 3.2.8 Behavioural setup and equilibrium ...... 61 3.2.9 Dynamic elements ...... 62 3.2.10 Model closure ...... 63 3.2.11 Database ...... 64 3.3 Scenario analyses ...... 70 3.3.1 The Business-as-Usual (BAU) Scenario ...... 70 3.3.2 Results of the Business-as-Usual scenario ...... 82 3.3.3 The Energy-Efficient-Investments (EEI) scenario ...... 86 3.3.4 Results of the Energy-Efficient-Investments (EEI) scenario ...... 91 4. Short-term, ad hoc expertise and consulting to decision makers and key stakeholders for current topics on the political agenda ...... 97 4.1 Introduction ...... 97 4.2 The challenge ...... 98 4.3 Analysis ...... 99 4.3.1 Expected yearly levels of GDP growth until 2020 ...... 99 4.3.2 The intensity of GHG emissions per GPD until 2020 ...... 100 4.3.3 Expected national GHG emissions: new adjusted estimations ...... 101 4.3.4 Costs of reaching the 2012 Doha Amendment ...... 101 4.4 Conclusions ...... 102 5. Revised concept of a low carbon development plan of Ukraine: From Stabilisation to Sustainable Economic Growth ...... 103 6. Findings from the economic assessment of a domestic ETS and possibilities for linking the ETS in Ukraine with other FSU countries ...... 105 iv

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References ...... 107 Appendix A ...... 114 Appendix B-1 ...... 117 Appendix B-2 ...... 129 Appendix C-1 Benchmarking for sustainable and economically viable technology options Appendix C-2 Towards a low carbon growth strategy for Ukraine Appendix C-3 Assessing the innovation potential in Ukraine Appendix C-4 The 2012 Doha Amendment to the Kyoto Protocol: Implications and Recommendations for Ukraine Appendix C-5 Benchmarking for sustainable and economically viable technology options Appendix C-6 Towards ratification of the 2012 Doha Amendment to the Kyoto Protocol by Ukraine: The revised projections of national GHG emissions Appendix C-7 MRV and linking a potential ETS in Ukraine with other systems Appendix C-8 From Stabilisation to Sustainable Economic Growth

Low Carbon Growth in Ukraine Final Report

List of Figures

Figure 1: Technical efficiency levels of metal industries in selected countries (in 2007) ...... 16 Figure 2: Structural characteristics of the metal industry in selected countries (2007) ...... 19 Figure 3: Technical efficiency levels adjusted for structural characteristics ...... 21 Figure 4: Technical efficiency levels of minerals industries in selected countries (2007) ...... 26 Figure 5: Structural characteristics of the minerals industry across countries (2007) ...... 30 Figure 6: Technical efficiency levels of chemicals industries in selected countries (2007) .... 35 Figure 7: Structural characteristics of the chemicals industry in selected countries (2007) ... 38 Figure 8: Technical efficiency levels adjusted for structural characteristics ...... 41 Figure 9: GHG Emissions by the Ukrainian industry, million tons of CO2-equivalents ...... 47 Figure 10: Direct GHG emissions and the demand for energy by industrial enterprises, as foreseen by different ministries. Yearly expected percentage changes over 2010- 2017...... 50 Figure 11: Schematic representation of a multi-level CES production function ...... 56 Figure 12: Schematic representation of the CES production function electricity sector ...... 58 Figure 13: Gross value added by key sectors in Ukraine (2011) ...... 65 Figure 14: GHG emission intensity in Ukraine and OECD Europe (1990-2010) ...... 73 Figure 15: Primary energy intensity of sectoral output in BAU (TJ per mln UAH in constant prices of 2001) ...... 74 Figure 16: Electricity intensity of sectoral output in BAU (GWh per mln UAH in constant prices of 2001) ...... 76 Figure 17: Installed generation capacity in the BAU scenario ...... 78 Figure 18: Fuel mix of produced electricity (in percent of total generation, BAU scenario) .... 79 Figure 19: Fuel mix of produced heat (in percent of total generation, BAU scenario) ...... 80 Figure 20: Assumed development of the import price (in real terms) for natural gas (USD per thousand cubic meters) ...... 81 Figure 21: Real GDP growth rates in BAU scenario ...... 82 Figure 22: Growth of sectoral gross value added in the BAU scenario ...... 83 Figure 23: Structure of gross value added in the BAU scenario ...... 83 Figure 24: GHG emissions intensity of GDP in the BAU scenario (kt CO2eq per bln UAH in prices of 2011) ...... 84 Figure 25: GHG emissions in the BAU scenario (kt CO2eq) ...... 85 Figure 26: Primary energy intensity of sectoral output in manufacturing in the EEI scenario (TJ per mln UAH in constant prices of 2001) ...... 89

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Figure 27: Primary energy intensity of sectoral output in transport in the EEI scenario (TJ per mln UAH in constant prices of 2001) ...... 89 Figure 28: Primary energy intensity of sectoral output in heat supply in the EEI scenario (TJ per mln UAH in constant prices of 2001) ...... 90 Figure 29: Composition of the required energy efficiency investments (total net present value, in bln UAH of 2011) ...... 91 Figure 30: Dynamics of key indicators (EEI relative to BAU scenario in each year) ...... 92 Figure 31: Decomposition of the total GDP impact of the EEI scenario in 2050 (in % relative to the baseline level of 2011) ...... 93 Figure 32: Decomposition of the total emissions impact of the EEI scenario in 2050 (in % relative to the baseline level of 2011) ...... 93 Figure 33: Sectoral GVA impacts of the EEI scenario (difference in GVA between EEI and BAU in 2050 relative to the level of 2011, %) ...... 94 Figure 34: Profitability of economic sectors in the EEI scenario (net present value of incremental profits, as compared to BAU) ...... 95 Figure 35: Profitability of key manufacturing sectors in the partial EEI scenario with only manufacturing measures (net present value of incremental profits, as compared to BAU) ...... 96 Figure 36: GHG Emissions in Ukraine and available AAUs for CP2 based on Doha Amendment ...... 98 Figure 37: Available AAUs for CP2 (Doha Amendment) and estimated GHG Emissions in Ukraine...... 99 Figure 38: Expected yearly levels of GDP until 2020: Outlook of 2011 vs. outlook 2013. .... 100 Figure 39: Abatements needed to keep with requirements of the Doha Amendment ...... 102

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List of Tables

Table 1: Comparison of capital and emission intensities across countries, metal industry (2007) ...... 14 Table 2: Comparison of capital and emission intensities across countries, minerals industry (2007) ...... 25 Table 3: Comparison of capital and emission intensities across countries, chemicals industry (2007) ...... 34 Table 4: Programme for Energy Efficiency and Energy Saving in Industry (2009-2017), selected numbers ...... 48 Table 5: Demand for energy in industry: Official forecast ...... 49 Table 6: NERA estimations of GHG emissions from industry until 2030. Yearly % change. ..51 Table 7: Energy use and GHG emissions by activity ...... 67 Table 8: GDP growth 2012-2019 ...... 71 Table 9: GHG abatement volume and incremental investments in Ukraine by activity ...... 88

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List of Abbreviations

AAU Assigned amount unit BAU-scenario Business-as-usual scenario CAGR Compound annual growth rate CP1 First Commitment Period (Kyoto Protocol) CP2 Second Commitment Period (Kyoto Protocol) EE Energy efficiency EEI-scenario Energy-efficiency-investments scenario ETS Emissions trading system FSU Former Soviet Union GDP Gross domestic product GHG Greenhouse gas LCD Low-carbon development LULUCF Land Use, Land-Use Change and Forestry MAC Marginal abatement cost QELRO Quantified emission limitation or reduction objective UAH Ukrainian Hryvna UNDP United Nations Development Program UNFCCC United Nations Framework Convention on Climate Change

Low Carbon Growth in Ukraine Final Report

Management-oriented summary

This Final Report has been prepared by DIW ECON as part of the project “Capacity Building for Low Carbon Growth in Ukraine.” Commissioned by the United Nations Development Program (hereinafter referred to as "UNDP"), supported by the International Climate Initiative of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety of Germany and in service to the Ukrainian government, this report provides the results of the economic analysis of a low carbon economic growth trajectory for Ukraine.

In particular, the report summarises the work of the consultants on the construction of a Computable General Equilibrium (CGE) model for the economy of Ukraine, the accompanying analytical work, as well the results of policy modelling and policy advice. The project was implemented from June 2012 until June 2014.

The Final Report presents the deliverables as specified as # 6 and # 7 of the contract concluded between UNDP and the consultants. The deliverables contain the following output:

1. Sectoral Economic Analysis - development of the specific forecasting models of certain sectors Ukrainian economy (energy, heat supply, etc.) Chapter 1 of this report contains the final version of the sectoral economic analysis of Ukrainian industries. The analysis covers the most important industrial sectors of the Ukrainian economy - the metals industry, the non-metallic minerals industry and the chemical industry in Ukraine. The developments in the energy sector are discussed in detail in Chapter 3 of this Final Report.

2. Short analytical papers on economic analysis of policy options to support low carbon policy analysis Chapter 2 presents three short analytical papers:

 Policy Paper No. 2 “Towards a low carbon growth strategy for Ukraine”

 Technical paper No. 1 “Assessing the innovation potential in Ukraine”

 “Policy options for low-carbon development in industry” (earlier version presented in Chapter 3 of the Third Project Report)

Low Carbon Growth in Ukraine Final Report

3. Impact assessment of identified policies and measures at the macroeconomic and sectoral levels; development of BAU economic scenarios of Ukraine’s development to 2020 and 2050, including but not limited to official government forecasts and development strategies. Chapter 3 contains the description of the computational tool that we apply for the analysis of the low carbon development options as well as the results of the comprehensive future baseline (BAU) scenario and of the additional energy-efficient-investments (EEI) scenario, both covering the period 2011-2050. The results cover main macroeconomic and sectoral indicators, energy use and GHG emissions.

This section also contains an analysis of the differentiated (by scenario) development paths of energy efficiency for several selected energy intensive industries of the agricultural, energy and manufacturing sectors of the Ukrainian economy (agreed as # 7 within the contract amendment 1).

4. Short-term, ad hoc expertise and consulting to decision-makers and key stakeholders for current topics on the political agenda Chapter 4 discusses the implications of the climate talks in Doha for Ukraine.

5. Concept of a low carbon development plan of Ukraine setting out a vision of the country’s new development path up to 2020 and 2050. Chapter 5 contains the executive summary of Policy Paper 6 “From Stabilisation to Sustainable Economic Growth” (s. Appendix C-1) that sets out the plan of economic development for Ukraine.

6. Findings from the economic assessment of the proposed domestic emissions trading system and possibilities for linking the Emissions Trading System (ETS) in Ukraine with other Former Soviet Union countries, particularly Kazakhstan. Chapter 6 discusses the issue of measurement, reporting and verification in case of linking a future linking a potential ETS in Ukraine with other systems.

Low Carbon Growth in Ukraine Final Report

Executive summary

This is the final report prepared by the consultants of DIW ECON in the frame of the on-going project on development of a low carbon growth strategy for Ukraine. The project was commissioned by UNDP and is funded under the international climate change initiative of the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. The project is jointly implemented by DIW ECON and Thompson Reuters. The beneficiaries of the consulting services are the State Environmental Investment Agency (SEIA) of Ukraine and the Government of Ukraine.

The project aims to strengthen the institutional capacity of Ukraine to design and implement long-term policies and measures directed at reducing emissions of greenhouse gases and enhancing absorption by sinks. In particular the project will support the Government of Ukraine in developing a low emission pathway for Ukraine‟s long-term economic development. To this effect the project aimed to:

 Develop new generation GHG models and comprehensive projections of GHG emissions;

 Prepare the concept of Ukraine's low carbon growth strategy until 2020 and 2050;

 Prepare an enabling environment for the introduction of a domestic emissions trading scheme in Ukraine;

 Improve the measurement, reporting and verification of greenhouse gas emissions;

 Strengthen the institutional capacity to implement climate change policies in Ukraine.

The final project report by DIW ECON summarizes the work done in order to reach these goals.

Chapter 1 contains the final version of the sectoral analysis of Ukrainian industries. We apply the methodology of international efficiency benchmarking. As a result, a technological yardstick allowing to quantify the potential for reducing greenhouse gas emissions is determined for each sector. For the metal industry, we estimate an emission reduction potential of up to 30 percent when taking into account the specific production structure of the metal industry in Ukraine. In absolute terms, this corresponds to a GHG emission reduction potential of up to 27 Mt of CO2 equivalent per year. For the non-metallic mineral products industry a full realisation of the reduction potential would result in abating 11 Mt of CO2 3

Low Carbon Growth in Ukraine Final Report

equivalent per year. For the chemicals and chemical products industry our analysis shows that there is an emission savings potential of at least 1 Mt of CO2 equivalent per year through technical improvement. The analysis also shows an additional emission savings potential through scale adjustments.

Chapter 2 includes three papers that were presented in the earlier reports.

Policy Paper No. 2 “Towards a low carbon growth strategy for Ukraine” argues that promising sectoral policies leading to sustainable growth include the modernization of the capital stock in the industrial sectors, the liberalization of the energy market, deregulations in the heating and electricity sectors as well as improved heat containment in residential buildings and the introduction of fuel taxes for private transport.

Technical Paper No. 1 “Assessing the innovation potential in Ukraine: Recent track record and implications for low-carbon development” evaluates the capacity of patented technology in Ukraine to induce economic growth and concludes that growth of Ukrainian GDP was driven much less by R&D than in other nations, implying that Ukraine‟s domestic capacity for a growth path driven by technological innovation is still limited at the moment. We conclude that the switch of the Ukrainian economy towards a low carbon economic growth trajectory will require initially large transfers of technology from abroad. This should be accompanied by efforts to increase domestic research capacity.

Paper “Policy options for low-carbon development in industry” reviews the existing policy proposals for future development in Ukraine‟s industry and shows that the current policies of Ukraine are not well coordinated and do not rely strongly enough on exploring the existing high potentials for energy saving in industry. It then formulates a set of energy efficiency policies consisting of fiscal, financial, market and informational policies, and measures to be implemented by the government

Chapter 3 contains the description of the computational tool that we apply for the analysis of the low carbon development options as well as the results of the comprehensive business- as-usual (BAU) scenario and an additional energy-efficient-investments (EEI) scenario covering the period 2011-2050. The BAU scenario is characterized by 4% annual average growth rate of GDP and decreasing GHG intensity of the economy. The latter is achieved by the continuing improvements of energy efficiency in the industry as well as by the massive 4

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investment in the modernization of the power sector. The BAU scenario assumes the realization of the current IMF support program in the part of the reduction of energy subsidies and increase of heat and gas tariffs.

The energy-efficient-investments (EEI) scenario investigates the economic effects of additional investments into measures improving energy efficiency in industry, heating, and transport. The scenario is constructed based on the results provided by Thomson Reuters Point Carbon in the framework of this project. The results suggest the advantage of investments into energy efficiency in industry a compared to energy efficiency of residential buildings due to more rich feedback effects on the overall economy.

Chapter 4 discusses the implications of the climate talks in Doha for Ukraine. The presented analysis (finished before the unfolding of the political crisis in Ukraine) shows that the emission target imposed by 2012 Doha Amendment for the second commitment period from 2013 to 2020 is achievable with some additional political efforts and is economically reasonable for Ukraine.

Chapter 5 illuminates how liberalizing the energy market, including abolishing subsidies, can ignite sustainable economic growth. To unlock Ukraine‟s growth potential, investments for economic diversification and capital stock modernization are needed. Such investments can be spurred in the long-term by government measures that underpin property rights, fight corruption and minimize red-tape, along cost-covering energy tariffs. As neither the economic nor the political situation in Ukraine allow to wait, a complementary policy of public support for kick-starting private investments in capital stock modernization, infrastructure and buildings is essential for energy efficiency and economic diversification. As public budgets will not be able to provide the necessary funds, the government needs to secure funding through international donors willing to support economic reconstruction and reduce carbon emissions that mitigate pressures on global warming along the way.

Currently no further steps have been undertaken in Ukraine to implement an ETS. Instead, the actual political discussion is focusing on introducing a CO2-tax. Therefore, the assessment of possibilities for linking the ETS in Ukraine with other FSU countries is of hypothetic value. The contribution of DIW ECON included in Chapter 6 covers the major issues in case an ETS would be developed and implemented in the future. It is predominantly dealing with linking of systems allowing for trading of emissions on the level of 5

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companies. The term “linking” is treated as full direct connection of existing emissions trading systems allowing non-restricted bi- or multilateral trading.

Low Carbon Growth in Ukraine Final Report

1. Sectoral economic analysis

This section presents the findings of a detailed sectoral analysis on GHG mitigation potentials in the metal, the non-metallic minerals and the chemical industry in Ukraine.

1.1 Introduction and summary of main results

Determining green growth potentials in Ukraine requires detailed analyses at the sector level. This includes assessing economic viability as well as environmental sustainability of different industrial activities. The focus of the analysis is on the metal industry, the non-metallic mineral products industry (i.e. production of clinker, lime, glass and soda ash) and the chemical industry in Ukraine.

We use an international benchmarking approach based on the economic concept of efficiency. Our yardstick for comparing the performance of industrial activities in different countries is given by:

 High levels of desired outputs (production volumes or revenues),

 Low levels of undesired outputs (greenhouse gas emissions), and

 Low levels of factor inputs (labour, capital, energy).

Our international benchmark approach includes the following steps:

 First, we compare the different industrial activities across relevant countries to identify the countries with the highest level of efficiency (i.e. with the highest volumes of gross output with lowest level of emissions from a given set of inputs).

 We then analyze whether and to what extent special characteristics of the production structure (such as the volume of aluminium production in the metal industry) need to be considered when comparing the estimated efficiency levels.

 Third, we assess whether the efficiency levels are determined by the general level of economic development in the country (i.e. whether the country is a high-, medium or low- income country) or by specific regulatory conditions in the energy sector.

Low Carbon Growth in Ukraine Final Report

Based on these three steps we determine a technological yardstick of international best practices. Against this benchmark, we then quantify the potential for reducing greenhouse gas (GHG) emissions in each of the three industrial sectors in Ukraine.

The analysis yields the following results:

 Metal Industry

 Structural characteristics (primary steel making and the production of aluminium and ferroalloys) need to be considered when comparing efficiency levels across countries.

 For the metal industry in Ukraine, we identify significant potentials for reducing GHG emissions:

 As long as Ukraine‟s current level of economic development and its specific conditions in the energy sector are considered, the emission reduction potential is estimated at up to 23 Mt of CO2 equivalents (or equivalently 25 percent of the emission level in the metal sector in 2007);

 Without explicit consideration of the country‟s level of economic development and its specific conditions in the energy sector, the estimated emission reduction potential amounts to 27 Mt of CO2 equivalents (or equivalently 30 percent of the emission level in the metal sector in 2007).

 Non-metallic minerals industry

 Structural characteristics are found to have no impact for an international comparison of efficiency levels.

 The emission reduction potential of the non-metallic minerals industry in Ukraine is estimated at up to 11 Mt of CO2 equivalents (or equivalently 47 percent of the emission level in the minerals sector in 2007).

 Chemical Industry

 Structural characteristics (the production of ammonia) need to be considered when comparing efficiency levels across countries.

 The emission reduction potential of the chemicals industry in Ukraine is estimated at up to 1 Mt of CO2 equivalents (or equivalently 5 percent of the emission level in the chemicals industry in 2007).

The approach of the analysis and its results are described in more detail below. 8

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1.2 The Benchmarking Approach

The key objective of the benchmarking approach is to identify technology options for a given industrial sector to best combine economic viability and sustainability. The yardstick for this is a balanced combination of:

 High levels of desired outputs such as production volumes (in physical units) or revenues (values),

 Low levels of undesired outputs like emissions or pollution, and

 Low levels of factor inputs like labour or energy use, or production costs.

The focus of the benchmarking approach is on technologies that are currently in use in Ukraine, while theoretical solutions and technologies that are not yet implemented are not considered. Thus, only technically as well as economically feasible and viable solutions are considered as benchmarks.

In economic terms, a combination of high (desired) outputs and low inputs (as well as undesired outputs) is considered to be efficient. In principle, efficiency levels can be related to technology scale as well as price levels (see Box 1). For practical applications, however, such a comparison is strongly limited to the availability of data and relevant information. In particular, micro-level benchmarking of different installations or companies within a country requires access to private and often confidential information. Alternatively, we propose benchmarking the same industry across different countries. That way, detailed company- or even installation-specific information is compensated by aggregate information from a range of countries, which is more easily available. Moreover, such an international benchmarking also allows for identifying international best practices.

Low Carbon Growth in Ukraine Final Report

Box 1: The concept of economic efficiency

Efficiency is an economic concept that describes the optimal use of production factors in production processes. In economic terms, efficiency is evaluated as the relationship between the quantities of primary factor inputs such as labour, capital or energy (henceforth inputs) and specific goods such as steel, chemicals or food (henceforth outputs) which are produced from these inputs. Efficiency is typically defined as either:

 The lowest-possible amount of inputs for the production of a given set of outputs (input- oriented efficiency); or

 The highest-possible level of outputs that can be produced from a given set of inputs (output-oriented efficiency).

Modern efficiency measurement starts by decomposing overall economic efficiency levels into several subcomponents that can be measured separately:

 Technical efficiency describes the ability of a firm to obtain optimal combinations of input and output quantities.

 Scale efficiency describes the ability of a firm to produce output while optimising all scale economies.

 Overall efficiency describes the ability of a firm to obtain optimal combinations of input and output quantities while optimising all scale economies.

 Price efficiency is the most restrictive criterion which also reflects the ability of a firm to combine inputs and outputs in optimal proportions, given their respective price levels.

In the present analysis, the focus will be on technical efficiency.

In order to determine the countries with the most-efficient combination of inputs and outputs (i.e. the most efficient technologies) we apply a specific empirical estimation technique, the Data Envelopment Analysis (DEA). This is a well-established methodology for estimating different efficiency measures (as described in Box 1) based on a large variety of different input and output measures. For each country, it estimates the share of input factors that are used efficiently or, in other words, in line with international best practice. Hence, a value of one refers to a fully-efficient country while all values below one indicate an inefficient use of input factors.

Low Carbon Growth in Ukraine Final Report

In practice, it is common to estimate these efficiency scores in two steps. The first step calculates efficiency levels based on the number of outputs that are produced from a given set of inputs (as described above). In a second step, these efficiency results are related to a variety of contextual factors which might have an impact on efficiency. These can include for example the level of development of a country, its resource endowment or its production structure. This allows taking into account specific country-related or activity-related characteristics to explain differences in efficiency across countries and for determining emission reduction potentials in the different countries.

1.3 Benchmarking the metal industry

This section describes the results from applying the international benchmarking approach to the basic and fabricated metals industry in Ukraine. The focus of interest is the relationship between the inputs used in the production processes in different countries (i.e. labour, capital and energy) and the respective outputs in terms of gross output and greenhouse gas (GHG) emissions. For ease of notation the basic and fabricated metals industry is henceforth referred to as metals industry.

1.3.1 Database

The data on the metal industry in different countries stems from four key sources:

 the World Input Output Database (WIOD) which has been compiled by a consortium of scientific organizations with financial support of the European Union1,

 the Steel Statistical Yearbook of the Worldsteel Association2,

 the United States Geological Survey Mineral Resources Program3, and

 the United Nations Framework Convention on Climate Change (UNFCCC)4.

The WIOD database is broken down by various industries that are based on the International Standard Industrial Classification (ISIC) of the United Nations Statistics Division. In this standard, the metal industry is classified into “Manufacturing of basic metals” and

1 http://www.wiod.org/ 2 http://www.worldsteel.org/ 3 Minerals Yearbook, http://minerals.usgs.gov/ 4 National Inventory Submissions 2012, http://unfccc.int/national_reports/annex_i_ghg_inventories/ 11

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“Manufacturing of fabricated metals”.5 This includes the production of pig iron, crude steel (primary or secondary fusion), precious and non-ferrous metals as well as metal casting and the production of fabricated metal products.6 The UNFCCC database contains detailed information on the different industrial sections as well as on industrial products. The USGS Minerals Yearbook and the Steel Statistics Yearbook of the Worldsteel Association solely provide data on industrial products.

The choice of countries to be included in the international benchmarking exercise is based on the objective to primarily cover potential technological leaders of the sector. The chosen country sample includes 16 European Union (EU) countries and 11 non-EU countries (as listed in Table 1) for which the following information is available:

 GHG emissions (in thousand tonnes of CO2 equivalent, source: WIOD 2012)7,

 Energy Use, Emission Relevant (in TJ, source: WIOD 2012)8,

 Gross Output (in millions of US dollars, source: WIOD 2012),

 Number of persons employed (in thousand persons, source: WIOD 2012),

 Real fixed capital stock (in millions of US dollars, source: WIOD 2012),

 GDP per capita in Purchasing Power Parities (PPP) (in international US Dollar, source: WEO 20139)

 Total primary energy consumption per dollar of GDP (energy intensity) (Source: EIA 201310)

 Production of pig iron (in thousand metric tonnes, source: Worldsteel Association),

 Production of crude steel (in total and by production process, all in thousand metric tonnes, source: Worldsteel Association),

 Production of non-ferrous metals (aluminium and ferroalloys, in thousand metric tonnes, source: United States Geological Survey Mineral Resources Program)

The most recent information available for all countries is of the year 2007 which therefore is chosen as base year in the benchmarking analysis.11 Ukraine is included in all sources

5 ISIC Rev 3.1 division 27 and 28. http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=17. 6 Note that the analysis does not include the mining of metal ore and the production of coke. 7 UNFCCC for Kazakhstan and Ukraine. 8 UNFCCC for Kazakhstan and Ukraine. 9 IMF World Economic Outlook Database (WEO) 2013. http://www.imf.org/external/ns/cs.aspx?id=28. 10 U.S. Energy Information Administration (EIA). International Energy Statistics. http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=90&pid=44&aid=8 12

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except from WIOD. The missing levels of gross output, persons employed and capital stock are therefore taken from national statistics.

1.3.2 The metal industry in selected countries

Table 1 gives a first impression of the performance of different countries in the metal industry. The first two columns (I and II) refer to sustainability (emissions per output) and the third and fourth column to economic viability (output per capital input) of the production processes in the different countries. For ease of comparison, the three top performers of each column are shaded in grey. With respect to sustainability (columns I and II) Brazil, France, Italy, Spain and Turkey show top performance, while Bulgaria, Canada, Finland, Poland, Romania and Russia are the top countries with respect to economic viability (columns III and IV)12.

11 In fact, 2007 is a good choice for a base year since it is the last year before the start of the global economic crisis. 12 Note that values for columns III and IV are not available for Kazakhstan since there is no reliable estimate on capital stocks. 13

Low Carbon Growth in Ukraine Final Report

Table 1: Comparison of capital and emission intensities across countries, metal industry (2007)

Emissions per Volume of Emissions per volume of Revenue per production per revenue production capital stock capital stock 2007 (tons of CO2-eq (tons of CO2-eq (tons of metal per thousand US- per ton of metal products per $) product) (US-$ per US-$) thousand US- $) (I) (II) (III) (IV)

Australia 0.84 2.03 1.28 0.71 Austria 0.47 1.00 2.15 0.79 Belgium 0.33 0.83 1.91 1.16 Brazil 0.70 0.40 0.47 1.08 Bulgaria 6.25 2.13 0.81 5.09 Canada 0.47 1.07 3.30 1.38 Czech Republic 0.96 1.08 1.57 1.63 Finland 0.35 0.87 2.84 1.24 France 0.18 0.76 2.27 0.64 Germany 0.29 0.90 2.32 0.82 Hungary 0.46 1.00 1.89 1.15 India 1.28 1.15 0.80 0.94 Italy 0.14 0.60 1.31 0.38 Japan 0.28 0.80 1.03 0.36 Kazakhstan 6.31 2.81 Korea 0.53 1.02 1.98 1.23 Mexico 0.53 0.91 1.97 1.15 Netherlands 0.28 0.64 2.17 1.05 Poland 0.63 1.39 3.31 2.15 Romania 1.79 1.05 1.85 3.91 Russia 3.68 1.56 2.02 4.35 Slovakia 1.05 1.06 2.16 2.69 Spain 0.19 0.61 1.50 0.42 Sweden 0.24 0.70 1.86 0.81 Turkey 0.20 0.57 1.73 0.94 Ukraine 3.88 1.15 1.87 1.09 United Kingdom 0.45 1.27 2.55 0.78 United States of America 0.34 2.29 2.25 0.66

Source: DIW ECON based on WIOD, Worldsteel Association, UNFCCC, State Statistics Service of Ukraine, Agency of Statistics of the Republic of Kazakhstan

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The isolated comparison of the different indicators does allow identifying the leaders in each respective category, but not for deriving conclusions about the overall performance of a country with respect to efficiency, i.e. about the best combination of sustainability and economic viability. This is the objective of the benchmarking approach to be carried out in the next section (section 1.3.3). Two further issues have to be considered:

 First, the costs of production (inputs) do not only include capital but also other key inputs such as labour and energy use. These three inputs will be considered in the international benchmark analysis.

 Second, a range of different products are produced in the metal industry including pig iron, crude steel, non-ferrous metals (aluminium) based on different production processes. A feasible efficiency measure must therefore take into account the relevant structural characteristics of the metal industry. This is to be done in section 1.3.4.

1.3.3 Efficiency benchmarking

The efficiency benchmarking of the metal industry is based on output-oriented efficiency measures of technical efficiency (see Box 1). In other words, we identify the countries that are able to produce the highest volume of gross output with the lowest level of emissions from a given set of inputs (output-oriented efficiency). All efficiency estimates are given as indices ranging from zero to one, with one indicating best performance. For example, a technical efficiency score of one for a given country indicates that in no other country within the sample the metal industry produces more outputs from the same combination of inputs. Likewise, a technical efficiency score below one suggests that in at least one other country the metal industry is capable to produce more outputs from the same inputs.

The following set of inputs and outputs is used for benchmarking the metal industry:

 Inputs

 Real fixed capital stock in the metal industry

 Number of persons employed in the metal industry

 Energy used in the metal industry

 Outputs

 Gross output generated in the metal industry

 GHG emissions generated by the metal industry 15

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Figure 1 shows the estimated technical efficiency levels of the metal industry in selected countries for the year 2007. Separate results for scale efficiency and overall efficiency (technical & scale efficiency) are shown in the Appendix A.

Figure 1: Technical efficiency levels of metal industries in selected countries (in 2007)

Source: DIW ECON

The results of the efficiency benchmark with respect to technical efficiency can be summarized as follows:

 In 15 out of the 27 countries in the sample (Belgium, Bulgaria, Canada, Finland, France, Germany, Hungary, Italy, Japan, Korea, Poland, Spain, Sweden, the United Kingdom and the United States) the metal industry operates technically efficient. These countries determine the technology frontier of the metal industry in an international comparison.

 In the remaining countries (i.e. Australia, Austria, Brazil, Czech Republic, India, Mexico, the Netherlands, Romania, Russia, Slovakia, Turkey and Ukraine) the metal industry suffers from technical inefficiency.

 The estimated technical efficiency level for Ukraine is 0.57. Only Brazil, the Czech Republic and India have a lower technical efficiency level in the metal industry.

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 Out of the 15 technically efficient countries 6 countries (Germany, Italy, Japan, Korea, The United Kingdom and the United States) are technically efficient but not overall efficient (see Appendix A). This is due to the fact that these countries operate at a too large scale, i.e. underutilization of available production capacities.

Productivity measurement over time In order to assess the change of productivity over time a special index is used. It measures the technical and productivity changes over time and can be explicitly decomposed into a measure of efficiency change and the rate of technological progress. Applying this index to our model, a yearly improvement of total productivity of on average 1.6 percent is estimated for the period 1998 to 2007. This change is driven by a reduction in efficiency of on average 0.5 percent per year and a technological progress of on average 2.2 percent per year.

1.3.4 Adjustment for structural characteristics

This section is aimed at analyzing the relationship between technical efficiency and the production structure of the metal industry across countries. That is, we want to assess whether differences in the efficiency scores may be attributable to differences in the production structure of the metal industries across countries. If that is the case, the estimated efficiency scores of section 1.3.3 have to be adjusted for country specific structural characteristics.

The choice of production variables for our analysis is based on the classification of UNFCCC for metal products as presented in the GHG emissions inventory submissions13. This classification includes the following metal products:

 Pig iron

 Crude steel

 Crude steel produced in oxygen blown converters (OBC)

 Crude steel produced in open hearth furnaces (OHF)

 Crude Steel produced in electric arc furnaces (EAF)

 Aluminium

 Ferroalloys

13 United Nations Framework Convention on Climate Change (UNFCCC) (2013): Annex I Party GHG Inventory Submissions. Sectoral report for industrial processes. 17

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The production of crude steel can be divided into primary and secondary steelmaking. Oxygen blown converters (OBCs) and open hearth furnaces (OHFs) are methods of primary steelmaking in which pig iron is transformed into crude steel. Electric arc furnaces (EAFs) in contrast are mostly used for secondary steelmaking based on metal scrap. The difference between primary and secondary steelmaking is of particular relevance since they represent different production chains and differ strongly in energy consumption and generation of greenhouse gas emissions.

Taking into account these differences in production chains and energy consumption, we group the different metal products into three categories:

 Pig iron & primary steel (OBC and OHF)

 Secondary steel (EAF)

 Aluminium & ferroalloys

Figure 2 gives an overview across countries of the production volumes of these product groups in a) absolute and b) relative terms. Japan, the United States and Russia are the biggest metal producers in absolute terms with production volumes of more than 120 million tons. Bulgaria and Hungary are the countries with the lowest volumes of metal production with less than 10 million tons. In Ukraine, total metal production amounts to about 80 million tons in 2007. In relative terms, the share of pig iron & primary steel is on average the highest across countries, ranging from 35 percent in Spain to over 95 percent in the Netherlands. In Ukraine, the share of pig iron and primary steel amounts to over 95 percent. The second biggest share of production across countries is secondary steel. The countries with the highest share of secondary steel are Mexico, Spain, Turkey and Italy. In Ukraine, secondary steel production has a share of 2 percent. While primary and secondary steel are produced in all countries14, aluminium and ferroalloys are only produced in 19 of the 28 countries and with shares of less than 5 percent in most cases. In Ukraine, the production of aluminium and ferroalloys accounts for 2.5 percent of total production15. Only in Kazakhstan, Canada and Australia the production of aluminium and ferroalloys exceeds 10 percent of total metal production.

14 Except from Kazakhstan where no secondary steel is produced. 15 In the context of our analysis total metal production refers to the sum of production of pig iron, crude steel, aluminium and ferroalloys. 18

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Figure 2: Structural characteristics of the metal industry in selected countries (2007)

a) Output composition in absolute terms

Source: DIW ECON * Volumes of production for Japan cut from above for better representation. Full data: 176 million tons of pig iron and primary steel, 31 million tons of secondary steel, 0.9 million tons of non-ferrous metals.

b) Output composition in relative terms

Source: DIW ECON 19

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Apart from the shares of production, the following variables are included in the estimation:

 GDP per capita as a measure for the stage of economic and technical development of the country;

 Energy intensity as a measure for the regulatory framework conditions in the energy sector;

The time period considered is from 1997 to 2007. Apart from a positive influence of GDP and energy intensity on technical efficiency our estimation yields the following results:

 A negative influence of the share of pig iron and primary steelmaking on technical efficiency.

 A positive influence of the share of secondary steelmaking on technical efficiency

 A negative influence of the share of aluminium and ferroalloys on technical efficiency.

These results imply that a focus in production on pig iron, primary steelmaking, aluminium and ferroalloys – all of them highly energy intensive products – goes to the disadvantage of efficiency while a focus of production on secondary steelmaking is positively related to technical efficiency. Due to this statistically measurable influence of the production structure on efficiency, structural characteristics need to be considered when comparing efficiency levels across countries.

Figure 3 shows the technical efficiency scores adjusted for country-specific structural characteristics. The green bars represent the efficiency scores as estimated by the international benchmark analysis (see also Figure 1). The grey and red marks represent the adjusted efficiency scores for each country. The grey marks represent the adjusted efficiency scores when taking account of the specific production structure of each country. The red marks represent the adjusted efficiency scores when additionally taking into account the level of economic development of each country and the regulatory conditions in the energy sector.

In case of Ukraine, the adjustment for structural characteristics (i.e. taking account of the focus on primary steelmaking and aluminium production) leads to an increase in the efficiency score from 0.57 to 0.7 (see grey mark Figure 3). This implies that given the specific production structure of its metal industry, Ukraine has an efficiency improvement potential of

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0.3.16 If we do not only take into account the specific production structure of the metals industry in Ukraine, but also its relatively low level of GDP per capita and its high level of energy intensity, we estimate an adjusted technical efficiency score of 0.75 (see red mark in Figure 3). In that case, Ukraine has an efficiency improvement potential of 0.2517 in the metal industry.

Figure 3: Technical efficiency levels adjusted for structural characteristics

Source: DIW ECON

1.3.5 Summary and implications for the metal industry in Ukraine

The international benchmark analysis of the metal industry has placed Ukraine among the countries with poor performance in terms of technical efficiency. A closer look at the production structure of the metal industries across countries has given evidence that structural characteristics are relevant for an international comparison of efficiency. Given Ukraine‟s specific production structure in the metal industry, i.e. its focus on highly energy

16 Maximum value of technical efficiency minus adjusted efficiency score (1 – 0.70 = 0.3). 17 Maximum value of technical efficiency minus adjusted efficiency score (1 – 0.75 = 0.25). 21

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intensive products, the estimation results imply the following emission reduction potentials through technological improvement:

 An emission reduction potential of up to 30 percent when taking into account the specific production structure of the metal industry in Ukraine. In absolute terms, this corresponds to a GHG emission reduction potential of up to 27 Mt of CO2 equivalents.18

 An emission reduction potential of up to 25 percent when taking into account the specific production structure of the metal industry in Ukraine as well as its level of economic development and the regulatory framework conditions in the energy sector. In absolute terms, this corresponds to a GHG emission reduction potential of up to 23 Mt of CO2 equivalents.19

18 As exemplary calculated for the year 2007. 19 As exemplary calculated for the year 2007. 22

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1.4 Benchmarking the non-metallic minerals industry

This section describes the results from applying the international benchmarking approach to the non-metallic minerals industry, i.e. the production of clinker, lime, glass and soda ash. As before, the focus of interest is on the relationship between inputs used in the production processes in different countries (labour, capital stock and energy used) and outputs in terms of gross output and GHG emissions. For ease of notation the non-metallic mineral products industry is henceforth referred to as minerals industry.

1.4.1 Database

The two main sources providing data on the minerals industry in different countries are:

 The World Input Output Database (WIOD) which has been compiled by a consortium of scientific organizations with financial support of the European Union20, and

 The United Nations Framework Convention on Climate Change (UNFCCC)21.

Additional data stems from:

 The United States Geological Survey (USGS) Mineral Resources Program22, and

 The Organisation for Economic Cooperation and Development (OECD) 23

The choice of countries to be included in the benchmarking analysis is based on the objective to primarily cover potential technological leaders of this sector. The chosen country sample for the minerals industry includes 18 European Union countries and 10 non-EU countries (as listed in Table 2) for which the following information is available:

 GHG emissions (in thousand tonnes of CO2 equivalent, source: WIOD 2012)24,

 Energy Use, Emission Relevant (in TJ, source: WIOD 2012),

 Gross Output (in millions of US dollars, source: WIOD 2012),

 Number of persons employed (in thousand persons, source: WIOD 2012),

20 http://www.wiod.org/ 21 National Inventory Submissions 2012, http://unfccc.int/national_reports/annex_i_ghg_inventories/ 22 Minerals Yearbook, http://minerals.usgs.gov/ 23the SDBS Structural Business Statistics (ISIC Rev 3), http://stats.oecd.org/Index.aspx?DataSetCode=SSIS_BSC 24 For Kazakhstan and Ukraine GHG emissions are calculated based on UNFCCC data. 23

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 Real fixed capital stock (in millions of US dollars, source: WIOD 2012),

 Total production of clinker (in kilo tonnes, source: UNFCCC, USGS),

 Total production of glass (in kilo tonnes, source: UNFCCC, OECD),

 Production of lime (in kilo tonnes, source: UNFCCC, USGS),

 Production of soda ash (aluminium kilo tonnes, source: UNFCCC, USGS)

 GDP per capita in Purchasing Power Parities (PPP) (in international US Dollar, source: WEO 201325)

 Total primary energy consumption per dollar of GDP (energy intensity) (Source: EIA 201326)

Since the most recent information for all countries is available for 2007, this year is chosen as base year for the benchmarking analysis. 27 Ukraine is included in all sources except from WIOD. The missing levels of gross output, capital stock, persons employed and energy used in the minerals industry are therefore taken from national statistics.

1.4.2 The non-metallic minerals industry in selected countries

Table 2 gives a first impression of the performance of different countries in the non-metallic minerals industry. The first two columns (I and II) refer to sustainability (emissions per output) and the third and fourth column to economic viability (output per capital input) of the production processes in the different countries.28 For ease of comparison the three top performers in each column are shaded in grey. With respect to the sustainability (columns I and II) Czech Republic, Finland, Ireland, the Netherlands and Romania show top performance while Canada, Romania, Russia, Ukraine and the United Kingdom are the top countries with respect to economic viability (columns III and IV). Since the production data is not completely available for all countries, some of the entries in Table 2 are left blank.

25 IMF World Economic Outlook Database (WEO) 2013. http://www.imf.org/external/ns/cs.aspx?id=28. 26 U.S. Energy Information Administration (EIA). International Energy Statistics. http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=90&pid=44&aid=8 27 In fact, 2007 is a good choice for a base year since it is the last year before the start of the global economic crisis. 28 For countries where data on production is not completely available, values for columns II and IV could not be provided. 24

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Table 2: Comparison of capital and emission intensities across countries, minerals industry (2007)

Emissions per Volume of Emissions per volume of Revenue per production per revenue production capital stock capital stock 2007 (tons of CO2-eq (tons of CO2-eq (tons of mineral per thousand US- per ton of mineral products per $) products) (US-$ per US-$) thousand US-$) (I) (II) (III) (IV)

Australia 1.08 0.83 Austria 0.85 1.15 1.42 1.05 Belgium 1.43 1.04 1.05 1.44 Brazil 1.56 0.94 Canada 1.09 2.05 Czech Republic 0.85 0.60 1.05 1.48 Denmark 1.17 1.25 1.26 1.18 Finland 0.61 0.82 1.77 1.32 France 0.77 0.98 1.68 1.32 Germany 0.78 0.92 1.46 1.22 Hungary 1.48 0.97 1.03 1.56 India 3.32 0.58 Ireland 0.73 0.55 1.09 1.44 Italy 1.07 1.13 1.00 0.95 Japan 0.81 0.85 0.69 0.66 Kazakhstan 5.78 1.08 Korea 1.30 1.33 Netherlands 0.32 0.89 1.64 0.60 Poland 1.41 0.91 1.52 2.35 Portugal 1.34 0.88 1.13 1.73 Romania 3.82 0.70 1.27 6.99 Russia 5.04 1.14 1.79 7.91 Slovakia 1.33 0.88 1.72 2.58 Spain 1.35 1.29 1.23 1.28 Sweden 0.94 0.97 1.59 1.54 Turkey 1.45 1.16 Ukraine 4.41 1.26 1.09 3.80 United Kingdom 0.85 0.93 1.95 1.78 United States of America 1.65 1.17 1.57 2.21

Source: DIW ECON based on wiod.org, UNFCCC, USGS, OECD, State Statistics Service of Ukraine

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1.4.3 Efficiency benchmarking

The efficiency benchmarking of the minerals industry is based on output-oriented efficiency measures of technical efficiency (see Box 1). All efficiency estimates are given as indices ranging from zero to one, with one indicating best performance.

Figure 4 shows the outcome of the international efficiency benchmark analysis with respect to technical efficiency of the minerals industries across countries. The estimation results for scale efficiency and overall efficiency are shown in the Appendix A.

Figure 4: Technical efficiency levels of minerals industries in selected countries (2007)

Source: DIW ECON

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The technical efficiency performance of the countries in the sample can be summarized as follows (for the year 2007):

 In 12 of the 28 countries in the sample (Australia, Canada, Finland, France, Germany, Ireland, Japan, the Netherlands, Slovakia, Sweden, the United Kingdom and the United States) the minerals industry operates technically efficient. These countries determine the technology frontier of the minerals industry in an international comparison.

 In the remaining 16 countries (Austria, Belgium, Brazil, Czech Republic, Denmark, Hungary, India, Italy, Korea, Poland, Portugal, Romania, Russia, Spain, Turkey and Ukraine) the minerals industry is technically inefficient.

 The estimated technical efficiency level for Ukraine is 0.53. Only India has a lower technical efficiency level in the minerals industry.

 Out of the 12 technically efficient countries, Germany, Slovakia, Sweden, the United Kingdom and the United States are technically efficient but not overall efficient (see Appendix A). This is due to an inefficient scale of production which means that these countries operate at a too large scale, i.e. underutilization of available production capacities.

Productivity measurement over time In order to assess the change of productivity over time a special productivity index is used. It measures the technical and productivity changes over time and can be explicitly decomposed into a measure of efficiency change and the rate of technological progress. Applying this index to our model, a yearly improvement of total productivity of on average 2.2 percent is estimated for the period 1998 to 2007. This change is driven by an average efficiency change of 0.3 percent per year and an average technological progress of 1.9 percent per year.

1.4.4 Adjustment for structural characteristics

This section is aimed at analyzing the relationship between technical efficiency and the production structure of the minerals industry across countries. We take a closer look at structural characteristics of the non-metallic minerals sector in order to assess whether differences in the efficiency levels across countries may be attributable to differences in the production structure of the non-metallic minerals sectors. If that is the case, the efficiency

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scores estimated in section 1.4.3 have to be adjusted for country specific structural characteristics.

According to the International Standard Industrial Classification (ISIC) of the United Nations Statistics Division the minerals industry is described as “Manufacturing of other non-metallic mineral products”.29 This includes among others the production of cement, glass and glass products, ceramic products, lime, plaster as well as articles of concrete, plaster and cement. For the purpose of our analysis we will focus on the production of clinker, lime, glass and soda ash. This choice of variables is based on the classification of UNFCCC for mineral products as presented in the GHG emissions inventory submissions.30

 Clinker is an intermediate product in the production of cement and is made of limestone and clay or shale. When these raw materials are heated in the cement kilns, they are formed into lumps or nodules which are called clinker. To produce cement, the clinker - sometimes together with a small portion of calcium sulphate - is pulverized into fine powder. This procedure is used to produce Portland and other types of hydraulic cements.

 Lime is calcium oxide or calcium hydroxide and is made out of limestone. The limestone is heated in different types of lime kilns to decompose the carbonates. Inter alia it is used as building and engineering material and as chemical feedstock.

 Glass production can be divided into four major manufactured products: containers, flat (window) glass, fibre glass, and specialty glass. The first two types are the most common ones and are almost completely soda-lime glass. This glass is produced by melting silicon dioxide, sodium carbonate, and lime with a small amount of aluminium oxide and other alkalis and alkaline earth.

 Soda ash production can be divided into the production of natural and synthetic soda ash. The natural soda ash is produced from trona or sodium-carbonate-bearing brines whereas the synthetic soda ash is produced by one of several chemical processes that use limestone, salt and coal as feedstock. It is commonly used as raw material in glass, chemicals, detergents, and other important industrial products.

29 ISIC Rev 3.1 division 26. http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=17. 30 United Nations Framework Convention on Climate Change (UNFCCC) (2013): Annex I Party GHG Inventory Submissions. Report for industrial processe. 28

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Figure 5 gives an overview of the production volumes of these four non-metallic mineral products across countries.31 Russia and Japan are the countries with the highest volume of minerals production in absolute terms reaching more than 70 million tons. In Ukraine, the production volume of mineral products amounts to about 19 million tons. All 21 countries presented in Figure 5 produce clinker which is the product with the highest share of production in all countries except from the Netherlands. Shares of clinker production reach from 30 percent in the Netherlands to nearly 90 percent in Denmark and Ireland.

31 Due to the lack of production data in several countries (e.g. confidentiality issues) the sample is reduced to 22 countries.

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Figure 5: Structural characteristics of the minerals industry across countries (2007)

a) Output composition in absolute terms

Source: DIW ECON b) Output composition in relative terms

Source: DIW ECON

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In Ukraine, the share of clinker production is about 60 percent. With the exception of the Netherlands all countries produce lime. Soda ash is only produced in 12 countries (i.e. the Netherlands, Portugal, Romania, United Kingdom, Ukraine, Poland, France, Germany, Italy, Russia, and Japan) and is the product with the lowest share of production in all countries. In Ukraine, the production of clinker is followed by the production of lime (26%), glass (6%) and soda ash (5%).

In order to assess whether the production structure in the minerals sector has a measurable influence on the level of efficiency, several variables potentially influencing efficiency are considered in the estimation. These variables include the volumes of production (in absolute and relative terms) of different non-metallic mineral products as well as GDP per capita and energy intensity (as presented in section 1.3.4). The time period considered is from 1997 to 2007.

Our estimation results do not show a significant relationship between the production structure of the minerals industry and efficiency. This is the case both if volumes of production in absolute as well as in relative terms are considered. We only find some weak relationships between single products (e.g. soda ash) and technical efficiency, but not of the entire production structure of the sector.32 This leads to the conclusion that in case of the minerals industry, differences in the technical efficiency levels cannot be explained by differences in the production structure across countries. The estimated efficiency scores do therefore not need to be adjusted for country specific structural characteristics.

1.4.5 Summary and implications for the minerals industry in Ukraine

The international benchmark analysis of the minerals industry has placed Ukraine among the countries with poor performance in terms of technical efficiency. A closer look at the production structure of the minerals industries across countries has not given evidence that structural characteristics need to be considered when comparing efficiency levels across countries.

32 Alternative approaches such as the standardization of the volumes of production by the number of employees or total revenues in the minerals sector do not show a measurable influence on technical efficiency either. 31

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Given Ukraine‟s current level of technical efficiency in the minerals industry of 0.53 (as estimated based on the benchmarking approach in section 1.4.3), Ukraine has an efficiency improvement potential of about 47 percent. In terms of GHG emissions, this corresponds to a savings potential of up to 11 Mt of CO2 equivalents.33

1.5 Benchmarking the chemical industry

This section describes the results from applying the international benchmarking approach to the chemical and chemical products industry. The focus of interest is again on the relationship between the inputs used in the production processes in different countries (i.e. labour, capital and energy) and the respective outputs in terms of gross output and greenhouse gas (GHG) emissions. For ease of notation the chemicals and chemical products industry is henceforth referred to as chemicals industry.

1.5.1 Database

The two major sources providing data on the chemicals industry in different countries are:

 The World Input Output Database (WIOD)34, and

 The United Nations Framework Convention on Climate Change (UNFCCC)35.

The chosen country sample includes 17 European Union (EU) countries and 10 non-EU countries (as listed in Table 3) for which the following information is available:

 GHG emissions (in thousand tonnes of CO2 equivalent, source: UNFCCC36),

 Fuel combustion (in TJ, source: UNFCCC37),

 Gross output (in millions of US dollars, source: WIOD 2012),

 Number of persons employed (in thousand persons, source: WIOD 2012),

 Real fixed capital stock (in millions of US dollars, source: WIOD 2012),

33 As exemplary calculated for the year 2007. 34 http://www.wiod.org/ 35the National Inventory Submissions 2013, http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/7 383.php 36 For countries not included in UNFCCC database and for the USA, data from WIOD was taken 37 For countries not included in UNFCCC database and for the USA, data from WIOD was taken 32

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 GDP per capita in Purchasing Power Parities (PPP) (in international US Dollar, source: WEO 201338)

 Total primary energy consumption per dollar of GDP (energy intensity) (Source: EIA 201339)

Since the most recent information for all countries is available for 2007, this year is chosen as base year for the benchmarking analysis. Ukraine is included in all sources except from WIOD. The missing data on gross output, persons employed and capital stock is therefore taken from national statistics.

1.5.2 The chemicals industry in selected countries

Table 3 gives a first impression of the performance of different countries in the chemicals industry. Due to the lack of production data there is only one column for sustainable performance (column I) and one for economic viability (column II). The third column (III) contains a ratio on emissions per energy use. For ease of comparison, the three top performers in each column are shaded in grey. With respect to the sustainability indicator Austria, Italy and Sweden show top performance while France, Korea and Turkey are the best countries with regards to economic viability. The lowest levels of emissions per energy used are in Germany, Korea and the USA.

38 IMF World Economic Outlook Database (WEO) 2013. http://www.imf.org/external/ns/cs.aspx?id=28. 39 U.S. Energy Information Administration (EIA). International Energy Statistics. http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=90&pid=44&aid=8 33

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Table 3: Comparison of capital and emission intensities across countries, chemicals industry (2007)

Emissions per Revenue per capital Emissions per revenue stock energy use 2007 (tons of CO2-eq per Tons of CO2-eq per thousand US-$) (US-$ per US-$) TJ Energy (I) (II) (III) Australia 0.85 0.90 0.13 Austria 0.17 1.76 0.09 Belgium 0.30 1.67 0.09 Brazil 0.41 0.79 0.07 Canada 0.41 2.11 0.10 Czech Republic 1.65 1.23 0.10 Finland 0.40 1.79 0.18 France 0.19 3.96 0.08 Germany 0.21 1.80 0.06 Greece 0.44 1.76 0.10 Hungary 1.06 0.48 0.18 India 0.80 0.92 0.10 Italy 0.16 1.33 0.08 Japan 0.22 0.81 0.07 Korea 0.18 2.53 0.05 Netherlands 0.43 2.09 0.11 Poland 0.80 2.21 0.20 Portugal 0.52 1.58 0.10 Romania 2.96 1.82 0.13 Russia 1.45 1.38 0.18 Slovakia 1.32 2.21 0.12 Spain 0.23 1.60 0.07 Sweden 0.09 1.85 0.07 Turkey 0.18 3.09 0.12 Ukraine 1.83 1.06 0.11 United Kingdom 0.25 1.31 0.09 United States of America 0.38 1.84 0.05

Source: DIW ECON based on wiod.org, Worldsteel Association, UNFCCC, State Statistics Service of Ukraine

The comparison of the different indicators allows identifying the leaders in each respective category, but not for deriving conclusions about the overall performance of a country with respect to efficiency, i.e. about the best combination of sustainability and economic viability. This is the objective of the benchmarking approach to be carried out in section 1.5.3.

1.5.3 Efficiency benchmarking

The efficiency benchmarking of the chemicals industry is based on output-oriented efficiency measures of technical efficiency (see Box 1). All efficiency estimates are given as indices ranging from zero to one, with one indicating best performance. 34

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Figure 6 shows the estimated technical efficiency levels of the chemicals industry in selected countries for the year 2007. Separate results for scale efficiency and overall efficiency (technical & scale efficiency) are shown in the Appendix A.

Figure 6: Technical efficiency levels of chemicals industries in selected countries (2007)

Source: DIW ECON

The results with respect to technical efficiency can be summarized as follows:

 In 11 out of the 27 countries in the sample (Austria, Finland, France, Greece, Italy, Japan, Romania, Slovakia, Sweden, Turkey and the United States) the chemicals industry operates technically efficient. These countries determine the technology frontier of the chemicals industry in an international comparison.

 In the remaining countries (i.e. Australia, Belgium, Brazil, Canada, Czech Republic, Germany, Hungary, India, Korea, the Netherlands, Poland, Portugal, Russia, Spain, Ukraine and the United Kingdom) the chemicals industry is technically inefficient. There are several countries that seem to reach the maximum score of 1 in the figure (e.g. Hungary, Korea, Portugal), but in real terms only reach scores around 0.98 and 0.99. Since nearly all countries reach a high level of efficiency even these small differences matter for differentiating between technically efficient and inefficient countries. 35

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 Due the small variation in the level of technical efficiency across countries (i.e. with the exception of India all countries have a score above 0.9), Ukraine belongs to the lower third of the countries despite its high (in absolute terms) efficiency score of 0.95.

Productivity measurement over time In order to assess the change of productivity over time a special productivity index is used. It measures the technical and productivity changes over time and can be explicitly decomposed into a measure of efficiency change and the rate of technological progress. Applying this index to our model, an improvement of total productivity of on average 1.7 percent per year is estimated for the period 1995 to 2007. This change is driven by a reduction in efficiency of on average 0.9 percent per year and by a technological progress of on average 2.6 percent per year.

1.5.4 Adjustment for structural characteristics

This section takes a closer look at structural characteristics in the chemicals industries across countries. We want to assess whether differences in efficiency levels may be attributable to differences in the production structure of the chemicals industries. If that is the case, the efficiency scores estimated in section 1.5.3 would need to be adjusted for country- specific structural characteristics.

The classification by UNFCCC for chemical products includes the following products: Ammonia, Nitric Acid, Adipic Acid, Carbide, Carbon Black, Ethylene, Dichloroethylene, Styrene and Methanol.40 Since many of these products have similar production processes (i.e. the products are highly correlated in statistically terms), we cannot include all of them in the estimation. This would lead to an incorrect (i.e. biased) quantification of the impacts on technical efficiency. We therefore choose the following two products as representative products for the chemicals sector:

 Ammonia

 Carbide

These two products are considered best representatives for the chemicals industry since they are i) correlated with the majority of other chemicals listed above (i.e. the production of

40United Nations Framework Convention on Climate Change (UNFCCC) (2013): Annex I Party GHG Inventory Submissions. Sectoral report for industrial processes. 36

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ammonia is highly related to the production of other chemical products) and ii) not correlated with each other. In addition to that, ammonia is produced in all of the countries in the sample and therefore represents a central element of chemical production.

Ammonia is a basic chemical and one of the most commonly produced industrial chemicals. About 80 percent of the ammonia produced by industry is used as fertilizer in agriculture. 41 The rest is used as a refrigerant gas, for purification of water supplies, and in the manufacture of plastics, explosives, textiles, pesticides, dyes and other chemicals. The main greenhouse gas emitted from ammonia production is CO2.42

Carbide is a chemical compound composed of carbon and a less electronegative element. The production of carbide generates emissions of CO2, methane and sulfur oxide. Silicon carbide is a significant artificial abrasive. It is produced from silica sand or quartz and petroleum coke. Calcium carbide is used in the production of acetylene and as a reductant in electric arc furnaces (see section 1.3.4). It is made from calcium carbonate (limestone) and carbon-containing reductant (petroleum coke).43

Figure 7 gives an overview of the production volumes of ammonia and carbide in selected countries.44 In absolute terms, Russia and the United States are the biggest producers of ammonia and carbide with production volumes of more than 10 million tons (see Figure 6 a)). Greece and Hungary are the countries with the lowest volumes of production of ammonia and carbide amounting to less than 0.3 million tons. All countries in the sample produce ammonia which also accounts for the biggest share of production (compared to carbide) in all countries. Carbide is only produced in six of the countries in our sample (i.e. Austria, Poland, Russia, Slovakia, Ukraine and the United States). With a production volume of about 0.1 million tons, Slovakia is the biggest producer of carbide in the country comparison. In Ukraine total production of ammonia and carbide amounts to about 5.2 million tons with ammonia accounting for more than 5.1 million tons of production and carbide for less than 0.05 million tons.

41 http://www.health.ny.gov/. 42 Intergovernmental Panel on Climate Change (IPCC) (2006): Guidelines for National Greenhouse Gas Inventories. Chapter 8: Reporting Guidance and Tables. 43 IPCC (2006), see footnote 39. 44 Due to the lack of production data in several countries (e.g. confidentiality issues) the sample is reduced to 15 countries (Greece, Hungary, Czech Republic, Slovakia, Austria, Italy, Romania, Belgium, UK, France, Poland, Canada, Ukraine, USA, Russia). 37

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Figure 7: Structural characteristics of the chemicals industry in selected countries (2007)

a) Production of ammonia and carbide in absolute terms

Source: DIW ECON

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b) Per capita production of ammonia and carbide

Source: DIW ECON In order to account for size effects (i.e. on average higher volumes of production in bigger countries), we divide the production of ammonia and carbide by persons employed in the chemical sector and use these per capita production variables for the estimation.45 As can be seen in Figure 7 b) Slovakia, Canada and Ukraine are the leader countries in terms of per capita ammonia production, with more than 25 tons of production per person employed in the chemicals industry. Slovakia also has the highest per capita production of carbide reaching about 8 tons per person employed in the chemicals industry. These per capita production volumes are used as an approximation for the relative importance of ammonia (and carbide) production across countries. That is, high volumes of per capita production of ammonia can be interpreted as an indication for a focus of production on ammonia in that country. This is the case for example in Canada and in Ukraine. In the United States in contrast, per capita volumes of ammonia production are relatively low, implying that the focus of production in the chemicals sector is not on ammonia.

The time period considered for our analysis is 1995 to 2007. The estimation yields the following results:

45 Since we only have two products, using shares of production (as done for the metal and minerals industry) is not feasible in this case. 39

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 A negative influence of ammonia production (per capita) on technical efficiency

 A positive influence of carbide production (per capita) on technical efficiency

These results imply that a focus of production on ammonia – a highly energy and emission intensive product – goes to the disadvantage of technical efficiency while the production of less energy demanding products such as carbide is positively related to efficiency. Due to this statistically measurable influence of the production structure on efficiency, structural characteristics need to be considered when comparing efficiency levels across countries.

Figure 8 shows the technical efficiency scores adjusted for country-specific structural characteristics. The green bars represent the efficiency scores as estimated by the international benchmark approach (see also Figure 6). The red marks represent the adjusted efficiency scores for each country. In case of Ukraine, the adjustment for structural characteristics in the chemicals industry (i.e. taking into account the focus on ammonia production) leads to an increase in the efficiency score from 0.94 to 0.95 (see Figure 8). In terms of efficiency improvement these results imply that given the specific production structure of its chemicals industry, Ukraine has an efficiency improvement potential of 5 percent46.

46 Maximum value of technical efficiency minus adjusted efficiency score (1 – 0.95 = 0.05). 40

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Figure 8: Technical efficiency levels adjusted for structural characteristics

Source: DIW ECON

1.5.5 Summary and implications for the chemicals industry in Ukraine

The international benchmarking analysis of the chemicals industry has placed Ukraine at the lower third in terms of technical efficiency performance. A closer look at the production structure of the chemicals industry across countries has given evidence that structural characteristics need to be considered for an international comparison of efficiency levels

Given Ukraine‟s specific production structure in the chemicals industry, i.e. its focus on production of ammonia, the estimation results imply an emission reduction potential of 5 percent through technological improvement. In absolute terms, this corresponds to a GHG emission reduction potential of up to 1 Mt of CO2 equivalents.47

1.6 Conclusion

The international benchmarking approach used in our sectoral economic analysis takes into account two main aspects of industrial production processes: i) economic viability and ii)

47 As exemplary calculated for the year 2007. 41

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environmental sustainability. It allows not only to identify the countries with a good performance in single aspects of efficiency such as emission intensity or profitability, but also to determine the countries with the best combination of economic viability and sustainability. Both aspects are combined in the economic concept of efficiency, which can be measured for a specific industry in different countries and then used for comparison.

The efficiency-based international benchmarking approach is applied to the metal, non- metallic minerals and chemicals industry. It is used to determine a technological yardstick for each sector which then is applied to the Ukrainian context to quantify the GHG emission reduction potential in each sector in Ukraine. The specific production structure of the sectors plays an important role in that context. Our estimation results show large emission reduction potentials in the three sectors in Ukraine, ranging from 5 percent in the chemicals industry, over 25-30 percent in the metals industry up to 47 percent in the minerals industry.

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2. Short analytical papers on economic analysis of policy options to support low carbon policies

2.1 Towards a low carbon growth strategy for Ukraine

This section includes the executive summary of the Policy Paper No. 2 “Towards a low carbon growth strategy for Ukraine”. The paper is included as Appendix C-2 to this Final Report.

Ukraine is one of the most energy-intensive economies in Europe. New impulses are needed to overcome traditional production structures that are no longer efficient and unsustainable in social and environmental terms. This paper identifies the key areas in terms of potential to reduce greenhouse gas (GHG) emissions and derives the corresponding policy actions needed to foster low carbon growth.

We argue that growth can only be sustained by fundamentally shifting the Ukrainian economy away from its current carbon-intensive path to a form of growth that is less dependent on the heavy use of natural resources, especially coal.

For a successful transition to low carbon growth, government intervention is indispensable. Firstly, private investments into clean technologies can only be induced by increasing the cost of emitting GHG, for example through carbon pricing or the introduction of an Emission Trading System (ETS). Secondly, the Ukrainian government needs to promote research and development into innovative clean technologies. Thirdly, in order to profit from possible financial assistance and technology transfers from abroad, the Ukrainian government may need to enter into further international commitments and guarantee a more ambitious reduction target in the level of emissions.

The main obstacles for a transition to low carbon growth in Ukraine are the lack of diversification of the economy, heavy reliance on expensive fossil-fuel imports, outdated and inefficient production capacities and unsustainably high subsidies in energy pricing. This implies a dire need for the government to increase competition, introduce market-based prices and to improve energy efficiency across all sectors. 43

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The sectors with the greatest potential in terms of emission reductions are the industrial sector, the energy sector including energy resources as well as electricity and heat production, the transport sector and the residential sector. Promising sectoral policies include the modernization of the capital stock in the industrial sectors, the liberalization of the energy market, deregulations in the heating and electricity sectors as well as improved heat containment in residential buildings and the introduction of fuel taxes for private transport.

2.2 Assessing the innovation potential in Ukraine

This section includes the executive summary of the Technical paper No. 1 “Assessing the innovation potential in Ukraine”. The paper is included as Appendix C-3 to this Final Report.

Innovations are crucial for shifting from a conventional to a sustainable low-carbon economic growth trajectory. This technical paper assesses the current innovation potential in Ukraine and infers to what extent innovations contribute to economic growth.

We analyse input and output factors of innovation in Ukraine over the recent past and across different economic sectors:

 We find that expenditures in R&D have fallen at a rate of 7% between 2005 and 2011. Thus spending in R&D measured as a share of GDP decreases from 0.14% in 2005 to 0.08% in 2011.

 Sectoral R&D funding fluctuates heavily over time and has declined across all industrial sectors except for food processing between 2009 and 2011.

 Despite decreasing expenditure in R&D, patent applications and registrations have increased from 2009 onwards. This is due to the fact that the share of foreign patent holders increased over the last years.

 Innovative outputs show high volatility and sensitivity to business cycle downturns. The share of innovative industrial production has contracted sharply by 40% between 2007 and 2009 and has still not reached the pre-crisis levels by 2011.

 The innovation intensity of output varies widely between industrial sectors, with some small sectors showing substantial innovative capacity.

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When comparing Ukraine with other benchmark countries we find that Ukraine is placed in the middle field regarding the expenditure in R&D, spending a larger share of GDP on R&D than Poland, Belarus or Kazakhstan. Compared to Russia, however, Ukraine would need to increase R&D expenditure by a factor of 1.5. Measuring innovation by the number of patents per capita Ukraine currently takes a place in the lower half among the benchmark group, having 3 times less patented inventions per year, than Belarus or Russia. The difference to the advanced economies such as the EU and the United States is even larger.

Ukraine spends little R&D per patent. Most advanced economies spend more resources per patent. In this regard, a low level of spending per patent may not be so much a signal of efficiency, but rather an indication that the registered patent is not the result of prolonged domestic innovative research.

We evaluate the capacity of patented technology to induce economic growth and conclude that growth of Ukrainian GDP was driven much less by R&D than in other nations, implying that Ukraine‟s domestic capacity for a growth path driven by technological innovation is still limited at the moment.

We conclude that the switch of the Ukrainian economy towards a low carbon economic growth trajectory will require initially large transfers of technology from abroad. This should be accompanied by efforts to increase domestic research capacity.

2.3 Policy options for LCD in industry

2.3.1 Introduction

In 2008, direct GHG emissions made by industrial enterprises comprised more than 30% of total emissions in Ukraine, not including indirect emissions through industrial electricity use. Therefore, an analysis of potentials for emission reductions in industry should be an important part of a low-carbon development plan for Ukraine.

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In the following we aim to review the existing policy proposals for future development in Ukraine‟s industry. We proceed as follows. First, we present the official GHG emission forecast and list the policy goals announced by the government. Then, we compare these options with the other recent proposals. In particular, we take the NERA report (NERA Economic Consulting, 2012) as the most comprehensive reference for our analysis.

The analysis shows that the current policies of Ukraine are not well coordinated and do not rely strongly enough on exploring the existing high potentials for energy saving in industry. The conclusions offered in this chapter are preliminary though; the issue has to be analysed in more detail based on sound empirical modelling, which is envisaged for the course of the current project.

2.3.2 Status quo and the planned policies in industry

At present, the expectations and plans about the development of the Ukrainian industry, its future energy demand and corresponding GHG emissions are very contradictory between different governmental bodies.

The latest official forecast of GHG emissions, which was published in 2009, envisaged that the emissions from industrial enterprises would rise – over the period 2010-2017 they would grow with the average rate of 5.7-6.0% per year, depending on the scenario (“3rd, 4th and 5th National Communication of Ukraine to the UNFCCC”, 2009). According to the National Communications, if cost-effective emission reduction measures are undertaken by industrial enterprises, the potential emission reduction would reach 18.6 million tons of CO2- equivalents. If advanced measures, which would specifically target emission reduction, are undertaken, the potential for emission reduction would add another 4.9 million tons of CO2- equivalents (see Figure 9).

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Figure 9: GHG Emissions by the Ukrainian industry, million tons of CO2-equivalents

160

No emission reduction 140 measures undertaken

120 Cost-effective emission reduction measures 100 undertaken

80 Advanced measures aimed on emission reduction undertaken 60 1990 2000 2005 2010 2015 2020

Source: “3rd, 4th and 5th National Communication of Ukraine to the UNFCCC.” (2009). p. 181.

In early 2009, the Ministry of Industrial Policy of Ukraine adopted “The Programme for Energy Efficiency and Energy Saving in Industry until 2017”48. The Programme aims to bring the energy intensity of industrial processes to the standards of developed countries, particularly the EU.49 The Ministry recognises that the successful achievement of this goal would considerably help to secure energy independence of the country and to decrease imports of fuels50. It is expected that substantial investments into energy efficiency will be made by industrial enterprises from their own funds or will be facilitated by bank credits; resources of the state budget would only cover the relevant research and development activities. The energy demand by industrial sectors and the corresponding energy saving goals are presented in Table 4 .

According to the Programme for Energy Efficiency, upon implementation of the envisaged measures, the demand for primary energy by the three key emitting sectors – metallurgy, machine building and chemistry – would grow with an annual rate of 0.93%. Evidently, the development of the Programme has not been coordinated with the team of experts, who were responsible for the preparation of the National Communications to the UNFCCC, where even the most optimistic scenario of future industrial emissions does not rely on an assumption of such high energy efficiency improvement in industry.

48 The Programme entered into force with the Order of the Minister of Industrial Policy of Ukraine No.152 from February 25, 2009. 49 At present, energy intensity of Ukraine‟s industry is 0.5 kg of oil equivalent per USD. This is 2.6 times more than the international standards (0.21 kg per USD) Source: “The Programme for Energy Efficiency and Energy Saving in Industry until 2017” (p.4) High energy intensity is often claimed to be one of the reasons for the economic crisis in the 1990s. 50 In 2010, energy import dependency ration was almost 40%. Source: OECD/IEA (2012). 47

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Table 4: Programme for Energy Efficiency and Energy Saving in Industry (2009-2017), selected numbers

Share Energy CAGR Planned Energy intensity, in demand, of investments, kg of oil equiv. / UAH output million energy billion UAH tons of oil demand equiv. 2007 2010 2015 2017 over Total R&D (status (goal) (goal) (goal) 2010-

quo) 2017

Non-fuel 52% 48.6 0.39 0.27 0.90% 61.3 0.5 mining & metallurgy

Machine 23% 4.6 0.08 0.069 0.06 0.06 5.26% 56.2 5.8 building

Chemistry 16% 12.5 0.33 0.29 0.25 0.24 -1.50% 5.7 0.5

Light 9% n.a. 0.1 0.01 industry & wood processing

Total 100% 0.93%1 123.4 6.9

1 Not including light and wood processing industries. Source: Programme for Energy Efficiency and Energy Saving in Industry until 2017. Output shares and CAGR – own calculations.

A later official document – “The Revised Energy Strategy of Ukraine for the Period until 2030”, which was made public in 2012, – stipulates that over the next ten years the demand for energy by industry over 2010-2017 will change as follows: the demand for natural gas will decrease by 1.60% per year while industry demand for coal will grow by 1.4% per year. Total energy demand will decrease by 1.2% per year in the same period. Some detailed information is presented in Table 5. In the framework of the Energy Strategy, the Ministry of Energy and Coal Industry plans extensive investments, 694 billion UAH over 2010-2020, into new electricity generating capacities (most of new power plants will be based on coal combustion) and new coal extraction capacities in order to meet the increasing energy demand for electricity and coal by the industry. The forecast about the future energy demand is based on about 30% to 35% energy efficiency improvement. Although the Energy Strategy recognises an important role of energy efficiency and emphasises the need for a comprehensive Programme of measures to

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achieve energy-efficiency goals, it does not go much further than that. While trying to secure energy independency of Ukraine, the Ministry of Energy and Coal Industry urges the industrial enterprises to use domestic coal and to withdraw from usage of gas, which is imported from Russia. The coal mining sector is expected to develop very quickly: according to the Energy Strategy, the yearly volumes of coal extraction should grow by 40-50% till 2030, whereas its consumption by industry will grow by nearly 50%.

Table 5: Demand for energy in industry: Official forecast

Forecast – basic scenario51 CAGR of Energy 2010 energy saving demand potential (real 2015 2020 2025 2030 data) over 2010- used for the 2017 forecast

Electricity 97.6 111.0 120.4 131.0 139.0 +2.1% -7% demand, billion KWt/h

Demand for 21.3 19.6 18.2 18.1 18.3 -1.6% -53% natural gas, billion m3

Demand for 3.1 3.3 3.6 4.1 4.6 1.4% n.a. industrial coal, million tons

Total energy 18390 17247 -1.2% not 30-35% demand, including million tons of oil electricity equivalent -0.01% including electicity Source: “The Revised Energy Strategy of Ukraine for the Period until 2030.” Oil equivalent and CAGR – own calculations.

Based on official regulatory documents, developed by different Ministries, we have different expectations with respect to future dynamics of direct GHG emissions and the demand for energy by industrial enterprises; Figure 10 depicts the results. Whereas the Ministry of Environmental Protection foresees extensive fuel combustion in industry and the corresponding growth of GHG emissions by 5.77% per year, the Ministry of Energy and Coal Policy relies on low growth of energy demand by the industry. After some recalculations for

51 Under the basis scenario, the Energy Strategy of Ukraine assumes average GDP growth at 5% up till 2030 of economic development; in pessimistic scenario, about 3.8% average yearly GDP growth is envisaged. At the same time, according to IMF, the most realistic rate for average yearly GDP growth in 2014-2018 is 3.5%. The economic growth of the last few years also does not allow for better than pessimistic view on the economy, if classified according the assumptions in the Energy Strategy. 49

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the Ministry‟s original set of data, we obtain that the corresponding emissions, expressed in CO2 equivalents, would decrease yearly by almost 1%; the demand for energy would remain stable, although the demand for fuels would decrease as well. At the same time, the Ministry of Industrial Policy expects 1% increase yearly in demand for energy, if expressed in oil equivalents.

Figure 10: Direct GHG emissions and the demand for energy by industrial enterprises, as foreseen by different ministries. Yearly expected percentage changes over 2010-2017.

Source: Own calculations.

First of all, the evident inconsistencies, described above, may indicate an absence of well organised policy coordination between the governmental bodies. As we see, the development of the industrial policy in Ukraine is currently coordinated neither with the energy policy nor with the environmental policy52. This calls for development of a comprehensive inter-sectoral strategy for low carbon growth in Ukraine, which must become a foundation and an instrument for any sub-programme, which would be later on elaborated by sectoral Ministries or lower-ranking sub-sectoral agencies.

52 The industrial policy is under responsibility of the Ministry of Industrial Policy of Ukraine; the energy policy is conducted by the Ministry of Energy and Coal Industry of Ukraine; the environmental policy the area of the Ministry of Ecology and Natural Resources of Ukraine. 50

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2.3.3 Potential for emission reduction in industry: Assessments from the literature

According to the recently published NERA report, there is a high potential for cost-effective emission reduction in the Ukrainian industry (NERA Economic Consulting, 2012). Under the assumption of static energy-intensity (status quo), there is a potential for “a reduction of over 30% of the industrial emissions that would be implied by the static intensity projection for

2030” (NERA Economic Consulting, 2012, p.65). This comprises about 65 Mt CO2

abatement, out of which 51 Mt CO2 is cost-effective. If the government undertakes enhanced

policy measures, the abatements could reach 77 Mt CO2, out of which 72 Mt CO2 is cost- effective. If recalculated into percentage changes of emissions, NERA‟s result implies that industrial emissions can grow by +2.53% to +4.14%. The growth rate of emissions depends on the policy measure that is undertaken. The detailed estimations are presented in Table 3.

Table 6: NERA estimations of GHG emissions from industry until 2030. Yearly % change.

Cost-effective All potential measures measures undertaken undertaken

Planned policies scenario +4.14% +2.96%

Enhance policies scenario +3.44% +2.53% Source: NERA Economic Consulting (2012) and “3rd, 4th and 5th National Communication of Ukraine to UNFCCC” (2009). Own calculations.

Relying on projections from the table, we may view the planned policies of the Ministry of Industrial Policy and expectations about the future energy demand of the Ministry of Energy and Coal Industry as relatively optimistic. A comprehensive empirical analysis, based on realistic expectation about economic development of Ukraine and industrial growth in particular, is urgently needed in order to develop a broad policy package.

2.3.4 What can the government do for the low carbon development?

Except many obstacles on the way to implementation of governmental strategies (financial, lack of effective energy management systems and techniques skills, etc.), the absence of policy coordination, wrong estimation of the status-quo platform, and little apprehension of potential growth impulses, which may arise from cost-effective investments into green technologies, are some of the reasons for the breakdown of the industrial strategies of the

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government even before their implementation starts. With better policy coordination and with some additional political efforts towards energy saving, the allocation of funds between different energy-related projects would become more effective and the Ukrainian industry would converge to the low carbon development path. The economy would benefit from saved energy costs, higher and sustainable growth and higher employment.

A green growth strategy for Ukraine should include a comprehensive set of energy efficiency measures, which would benefit Ukraine while inducing growth and employment. A thorough analysis of measures for such a programme will be done on the basis of sound empirical modelling, which is envisaged for the course of the current project.

Within the set of energy efficiency policies consisting of fiscal, financial, market and informational policies, and measures to be implemented by the government some of the most relevant are:

 Gradual abolishment of energy subsidies , especially for natural gas and heat to move retail gas and heating tariffs to full cost recovery, complemented by targeted programs to support low-income households.

 Introduction of obligatory minimum energy efficiency requirements for new and refurbished buildings as well as other energy intensive goods (i.e. household appliances, cross sector production equipment like electrical engines, pumps, compressed air systems etc.)

 Introduction of efficient carbon taxes (raising the level of the current non-sufficient low rates of the existing CO2-tax) or of a GHG emission trading system covering major GHG emitters.

 Tax relieves implementation of most energy efficient technologies (above minimum energy efficiency standards)

 Implementing economic incentives for use of renewable heat etc.

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3. Impact assessment of identified policies and measures and development of BAU scenarios until 2020 and 2050

3.1 How to assess the potential of identified policies for low-carbon economic growth in Ukraine

Assessing the long-term impact of policies and investment proposals aiming at stimulating carbon-neutral economic growth requires the use of complex economic models. In particular, two issues are relevant. First, the model needs to reproduce the behaviour of all relevant players in the economy (i.e. different firms, private households, the government etc.). Second, the model must be based on a sound database which captures the structure and flow of all income and expenditures in the economy, all relevant interdependencies between different activities that arise from the use of intermediate inputs in the production processes of other activities, as well as relevant data on Greenhouse Gas (GHG) emissions by relevant types such as emissions from fuel combustion or process-based emissions.

A tool capable to meet these requirements is a multi-sector Computable General Equilibrium (CGE) model. It is based on a microeconomic representation of the behaviour of different economic activities (production sectors), private and public households as well as the external sector. CGE models cover all product and factor markets in an economy and are hence able to trace changes in supply, demand as well as prices of different commodities and production factors. Because this also includes all relevant types of primary and secondary energy, a CGE model is also capable to simulate Greenhouse Gas (GHG) emissions from fuel combustion and other industrial processes.

In the following section, we describe the key features of a CGE model that we develop to assess the potential for carbon-neutral economic growth in Ukraine. Apart from the key features of the model we will describe how it will be used for simulating different growth scenarios.

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3.2 Model description

3.2.1 Building blocks

The CGE model for Ukraine includes private households, different production activities, the government as well as the external sector (the rest of the world). These institutions exchange commodities and primary input factors (labour and capital) on separate markets. The model is dynamic and the simulations cover the time period 2011-2050. The overall setup of the model is as follows:

 Private households earn income (remuneration) for labour and capital that they provide to the production sectors and receive transfers from the government. They spend this income for consumption of domestic and imported goods and services as well as for savings.

 Production activity in the economy is represented by 23 different sectors (covering agriculture, mining, manufacturing, utilities, transport and services). They employ labour and capital and use intermediate inputs to produce their specific output. Production sectors export (a part of) their output and/or sell it on domestic markets, where the total demand is represented by the intermediate demand (by production sectors) and final demand (by private households, gross capital formation or the government).

 The government receives income from its capital endowment as well as tax revenue (e.g. from import duties). It uses this income to finance subsidies and transfer payments to private households and public investments as well as for providing public goods and services.

 The rest of the world represents an aggregate of all countries, which export to or import from Ukraine or which interact with Ukraine in financial transactions. It hence absorbs exports from Ukraine and provides imports in return. It also provides investment opportunities abroad to private households and owns some Ukrainian assets.

This setup requires considering the following markets:

 Factor markets for labour and for capital (the so-called primary input factors):

 Supply is determined by the factor endowments (changing over time) of households and the government;

 Demand is derived from production activities.

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 Commodity markets for each of the goods and services that are produced and/or consumed in the country:

 Supply is given by the output of the respective industries + imports;

 Demand is derived from intermediate and final consumption (including export).

 Market for investment goods:

 Supply of domestic investment goods is provided by two sources: a separate activity that uses various commodities as inputs for new capital formation as well as by government issuing bonds in order to finance the budget deficit.

 Investment demand is matched by the corresponding amount of public and private savings which include savings by Ukrainian residents and foreigners (where the latter is consistent with Ukraine‟s current account deficit).

 Foreign exchange market:

 Supply is given by export earnings and well as by purchases of Ukrainian assets by foreigners;

 Demand is derived from the demand for imported intermediate and final goods and services as well as from the demand for overseas investments.

3.2.2 Production technology Production of goods and services is modelled by specific production functions. For each activity, they determine the amounts of different inputs necessary for producing a given amount of output. Technically, all production functions are of the Constant Elasticity of Substitution (CES) type.53 Since the substitutability of specific input factors can be rather different, they are clustered into different nests (inside so-called nested production functions). Essentially, the structure is as follows:

 Non-energy intermediate inputs (as well as feed stocks) cannot be substituted by other input factors (Leontief-type technology, elasticity of substitution is zero)

 Aggregate energy (coal, gas, petroleum products, electricity and heat) and value added inputs (labour and capital) cannot be substituted by one another or by non-

53 CES is a standard form of production and demand functions in microeconomic theory. It includes all types of functions where the degree of substitution of different inputs remains constant. Common types of CES functions are Cobb-Douglas (elasticity of substitution equals one) and Leontief (elasticity of substitution equals zero). The general form for a CES production function with two 1 factors (L and K) is Q=F(∝ 퐾푟 + 1−∝ 퐿푟 )푟 , where Q denotes output, F factor productivity and ∝ and 1 r are exogenous parameters. The constant elasticity of substitution is given by 푠 = . 1−푟 55

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energy intermediate inputs (Leontief-type technology, elasticity of substitution is zero)

 Energy inputs (coal, gas, oil products, electricity and heat) can – to some degree – be substituted with one another (elasticity of substitution above zero). However, possibilities for substitution differ by activity.

 Production in agriculture and mining (coal, gas and oil) also requires the use of natural resources (i.e. arable land and energy resources, respectively). These are modelled as non-tradable input factors with exogenous supply.

The typical structure of production is manufacturing is shown in the figure below.

Figure 11: Schematic representation of a multi-level CES production function

Domestically sold Exported

Sector X output

Non-energy goods Energy & value-added mix

Products of Sector 1 . . . Products of Sector N Energy inputs Value added (primary factors)

Domestic Imported Domestic Imported Energy . . . Energy Labour Capital good1 good K Source: DIW ECON

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3.2.3 Power sector

In order to allow formulation of different scenarios for the development of the power sector until 2050, the supply of electricity is simulated by a separate model – the electricity supply model. The model builds on a detailed representation of different generation technologies (i.e. coal- and gas-fired thermal power plants, hydropower plants, nuclear power plants as well as renewables), which include information on the currently installed capacity, availability factors, the corresponding capital and running costs for the existing capacities as well as investment costs for capacity extension. The key source of the technology-specific assumption is the MACCTool report by Thomson Reuters (see Appendix B-1). The projected costs change over time, reflecting changes in fuel prices as well as in technologies per se.

The objective of the electricity supply model is to compute the dispatch of generation capacity that satisfies the projected demand for electricity in Ukraine (represented by a load curve for every future year), subject to available generation capacities. The model however does not consider the regional distribution and the issue of availability of transmission capacity.

The dispatch is not based on the minimum-cost approach, but is rather based on a set of rules that depict the actual situation in the electricity sectors of Ukraine. The generation technologies are separated into “must-run” and “flexible”. Must-run generation technologies run during the whole year with average available capacity. Must-run technologies include gas and coal CHPs, nuclear power plants as well as solar and wind power plants. Flexible technologies include coal TPPs, hydro power plants, as well as pumped storage and biomass capacities. These technologies are used to cover the peak demand.

Eventually, the electricity-sector model yields estimates of costs of electricity generation for the given future level of electricity demand. This includes the costs of investing into new generating capacities (conventional and renewable) if that is required to match the demand. These results are embedded into the CGE model by passing the cost structure to the CGE model and reading electricity demand from the CGE model (the iteration between models is not automatic).

In order to allow for the perfect match between the electricity cost structure in the CGE model and in the electricity supply model, the production technology for electricity in the CGE model is different in every year. This is achieved by setting all substitution elasticities in the 57

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production function to 0 and scaling the share parameters according to the results of the electricity supply model. Right angles in the substitution nests in the Figure 12 below symbolize that the substitution elasticities are set to zero. Heat is treated as a by-product of coal- and gas-fired CHPs. All costs of this joint production are assumed to be covered by the electricity costs.

Figure 12: Schematic representation of the CES production function electricity sector

Electricity Heat

Domestically sold Exported

Output

Non-energy products Energy and factors

Energy products

Value added (primary factors)

. . . Products of Sector N Products of Sector 1 Products of Sector N Labour Capital Products of Sector 1 . . . Source: DIW ECON

3.2.4 Heat supply sector

The activity of the heat supply sector describes all heat provision that is not covered by the CHPs. Heat supply in Ukraine is currently almost entirely gas-based. The installed capacity of boiler houses exceeds 120 thousand GCal / hour. The biggest part of this capacity is provided by large boiler houses which can produce more than 100 GCal per hour, but there are also many smaller installations.

One key problem related to the heat supply sector is a high share of worn-out equipment and holey transmission lines. It means that there is a need to make massive investments in the sector in the nearest future. Another problem is posed by the high gas prices, and the need (stimulated by the conditions of the IMF financial support program) to reduce the gas and heat subsidies to the households. The latter factor makes a shift in the heat sector from the 58

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gas-fired capacity to the coal-fired capacity a very probable future scenario (foreseen e.g. in the 2014-2015 State modernization program for the heat sector).

These two problems mean that the current composition of inputs used in the heat supply sector (in other words, the production technology) is likely to change dramatically in the nearest future. For this reason, our modelling approach for this sector is similar to the approach we take for the power sector. We set up a simple model of the heat sector. We assume that the currently used gas-fired capacity will be refurbished over the next years, while any new capacity that has to be built to cover the increased heat demand will be coal- fired.

Given the initial heat demand forecast for the period until 2050, the heat model provides the required input composition, including capital, labour, fuel and other costs. Similar to the approach in the power sector, we let the production technology for heat in the CGE model be different across years and make it correspond to the results of the heat model. This is achieved by setting all substitution elasticities in the production function to 0 and scaling the share parameters according to the results of the heat supply model.

3.2.5 Taxes and subsidies

The coverage of taxes and subsidies on products and production factors in the model is not very detailed. For the direct factor taxes, sector-specific tax rates are calibrated from the input-output data and the same rates are applied to both, labour and capital income. For the indirect taxes, product-specific rates are applied to the total domestic supply. These rates are also calibrated based on the input-output table.

Because of their huge importance for the public budget, special attention is paid in the model to the gas and heat subsidies. The size of these subsidies in the benchmark year 2011 is roughly 7% of GDP. The public budget subsidises the use of gas by households and by the electricity and heat sectors. The result of these subsidies is that the households pay a price for gas that is way below (6-7 times lower) the price paid by the industry. The price that the public heat providers pay for gas is also twice lower than the market price. The IMF financial support program requires that these subsidies are gradually abolished.

In the model, we allow the simulation of scenarios with higher gas and heat prices for the users that now pay the subsidized price. To this end, we make the subsidy rates depend on 59

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the exogenously set tariff rates that the households and the public heat providers pay. These tariff rates can be adjusted (e.g. gradually increased, as foreseen by the IMF program) and the required subsidy rates will adjust accordingly. Also the public budget in the model will react to the reduction of subsidies – the public deficit will be reduced.

3.2.6 GHG emissions

In addition to production of physical output, the CGE model also covers the three relevant types of GHG emissions: fuel combustion, fugitive emissions (e.g. in mining activities), as well as emissions from industrial processes (e.g. in the chemical industry or in waste management):

 GHG emissions from fuel combustion are directly related to the amount of energy inputs used by different production activities as well as by households (in TJ). The fuel types are coal, gas, and refined oil products.

 As depicted in Figure 11, substitution between different types of fuel is possible. Significant movements in relative prices of energy inputs may thus induce producers to choose a different type of fuel for their production capacities. In the same manner, the households may also change their demand pattern (for example, the choice of fuel used for decentralized heating, or car use). Such decisions can have a significant impact on the GHG emissions (consider, e.g., substitution of coal through gas).

 Fugitive GHG emissions and GHG emissions from the industrial processes are directly related to the production levels (real output) of the respective activities. The emissions, the source of which is not fully specified in the UNFCCC data, are assigned to the public sector and are kept constant over time in the model.

3.2.7 Rest of the world

Ukraine is modelled as a small open economy, which does not have an impact on the world prices, but can export or import any required amount of any commodity. The world prices (in particular, the prices for the main export goods, such as raw oil, metals, etc) are thus taken as given.

Imports of every type of goods are modelled as imperfect substitutes to the domestically produced variants of the same type of goods (this is the so-called Armington assumption). It

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means that the market price on the domestic market for any product is a composite of the price of domestic producers and the import price (combined using a CES function). Similarly, the exports are modelled as being slightly different from the variants of the product sold on the domestic market. The gross output of any production sector is transformed into domestically sold varieties and exported varieties using a CET transformation function.

The model also reports payments and receipts of residents in Ukraine in their transactions with the rest of the world (for example, due to investments abroad).

3.2.8 Behavioural setup and equilibrium

The bottom-line of the behavioural setup is that all institutions adjust their behaviour in response to prices. On all markets, prices are determined by market clearing. This means that prices on each market adjust until the quantity of supplied factors, goods or services equals the corresponding level of demand. The different institutions in the model take these prices as given and behave as follows:

 Activities demand different goods and services (so-called intermediate demand) as well as labour and capital for producing their output (as determined by their respective production function). Profits are determined by revenues from selling final output less costs of all necessary inputs. Activities are assumed to choose their demand for different inputs in order to maximise their profits.

 Private households derive their wellbeing (so-called “utility”) from final consumption (i.e., the more they consume, the better off they are). They spend a given fraction of their income for savings as well as to maximise their utility subject to the available income (which is mainly given by earnings from labour and capital endowments);

 The government keeps the real value of public service provision constant. Therefore, government income has to adjust depending on changes in commodity and factor prices.

The equilibrium of the model is a set of prices and quantities (i.e. levels of demand) such that:

 All factor and commodity markets clear;

 Private households realise maximum utility given their specific household income;

 Activities realise maximum profits given their specific technologies; and

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 Expenditures of all institutions do not exceed the income of the respective institution (so-called hard budget constraint).

3.2.9 Dynamic elements

As mentioned above, the model is dynamic, that is, it produces equilibrium solutions (i.e. values of input and output variables) for each year in the simulation period 2011-2050. The equilibria for different years are linked by the changes of labour force and capital stock as well as by changes of specific exogenous parameters that determine world prices, factor productivity or energy efficiency. In line with standard theory of economic growth, this setting implies that economic growth is driven by the following factors:

 Population growth/decline: The available labour force in each year changes in line with the overall level of population growth (which is negative in Ukraine). This is appropriate as long as labour market rigidities such as unemployment or demographic changes are not explicitly modelled.

 Capital accumulation: By assumption, a constant share of existing capital – the Gross Fixed Capital Stock – depreciates every year while new capital is created from private and public investment. The sources of investment are foreign and domestic savings.

 Productivity growth. Due to exogenous technological progress, the productivity of labour and capital can grow over time as well as at different levels for specific activities.

While the rates of population growth and depreciation are simply exogenous parameters, the modelling of investment decisions is more complex. We model investments – or gross fixed capital formation – as being responsive to changes in incentives. In particular, investment demand in a given sector increases if the return from a given amount of capital exceeds the costs of building up that amount of capital through investments (capital formation).54 In the opposite case, once the ratio decreases, demand for investments declines as well. In addition, the model also allows for incremental increases of investments under specific policies such as green technology rollout.

54 This ratio of return to capital over costs of capital formation – or more generally, market value over replacement value of capital – is known as Tobin's q. 62

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3.2.10 Model closure

The reaction of households and firms to changes of economic conditions in the CGE model is determined by specific behavioural assumptions. In particular, households maximize utility subject to their available income and firms maximize profits under given technologies constraints. In addition, the complete model specification also requires formulating behavioural rules for the government and the exchange of goods, services and financial assets between Ukraine and the rest of the world. These rules are typically referred to as model closure. In terms of modelling, this requires specifying which macroeconomic indicators will be fixed at target levels and which are allowed to freely adjust.

Generally, the economy-wide (general equilibrium) nature of the model requires that all monetary flows in the economy are taken into account, all markets clear, and all expenditures match with corresponding income sources. This implies that certain macroeconomic identities in the model must hold at all times. On the aggregate level, it requires that the sum of domestic investment and investments abroad must equal the sum of public and private savings. In turn, this implies that at least one of these components (public and private savings, domestic investment, and the current account balance) must be free to adjust to changing economic developments.

In the CGE model for Ukraine, we need to reflect the current macroeconomic situation, in which the public budget is characterized by a large deficit, and the external trade balance is negative. As far as the state budget is concerned, the major source of the deficit is given by the huge energy (gas and heat) subsidies. In the future, these subsidies need to be reduced, because this situation is unsustainable. The tariffs for gas and heat will have to gradually approach the corresponding market prices and the subsidies will be reduced.

A suitable model closure in this situation encompasses an endogenous budget balance that reacts to the changes in the subsidy rates. Accordingly, the real value of government expenditure will also be determined by the model. The private savings depend directly on household income and thus, also adjust. Hence, public and private savings are endogenously given by the model solution.

As far as the links with the global economy are concerned, the model is based on the small open economy assumption. This implies that all goods and services as well as financial assets can be bought from and sold to world markets in unlimited number at given (fixed) 63

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prices. However, the market clearing condition for foreign currency requires that the balance of payments holds. Trade deficit must be matched by the inflow of foreign capital (purchase of domestic assets by foreigners). This latter amount (the current account deficit) is fixed in the model.

3.2.11 Database

The model builds on an extensive database. Main data sources are:

 National Accounts of Ukraine for 2011. (Source: State Statistic Service of Ukraine, 2013, Kyiv.)

 Ukrainian Input-Output Table at Consumer Prices for 2011. (Source: State Statistic Service of Ukraine, 2013, Kyiv.)

 Energy Balance of Ukraine for 2011. (Source: State Statistic Service of Ukraine, 2013, Kyiv)

 GHG emissions inventory of Ukraine for 2011. (Source: National GHG Inventory report of Ukraine for 1990-2012. Submitted to the Secretariat of the UNFCCC on April 12, 2014. Retrieved from http://unfccc.int)

 Relevant publications by the IMF, the State Statistic Service of Ukraine (the official statistical institute), and others, including:

 for GDP outlook till 2019 – IMF. (2014). World Economic Outlook Database. May 2014.

 for producers„ price index – State Statistic Service of Ukraine. (Different years.) Producer Price Indices. Retrieved from http://www.ukrstat.gov.ua/

The sources listed above are not always entirely consistent. Therefore, we had to make a choice of the source at the instances when a conflict was identified. In particular, we used the following rules:

 The total macroeconomic indicators, such as GDP, value added, tax revenue, exports, imports, total output, final consumption, etc., are taken directly from the national accounts (NA).

 The input-output table (IOT) is used to split the totals in the NA into different activities. In this way the split by activity/product is carried out for the following data pieces:

 Intermediate product use 64

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 Value added

 Total output

 Taxes on products and activities

 Final consumption (households, government, investment)

 Imports and exports

The model database is thus linked to the official statistics of Ukraine and the key economic indicators coincide. The sectoral figures for value-added in 2011 are reproduced by the model with the same high precision (Figure 13).

Figure 13: Gross value added by key sectors in Ukraine (2011)

600,000

500,000

400,000

300,000 mlnUAH

200,000

100,000

0 Agriculture Mining Manufacturing Utilities Transport Services

Source: UkrStat.

An important output of the model is the amount of GHG emissions by activity. We included the information on the emissions from fuel combustion and from industrial processes (including agriculture and waste management) as reported in the GHG emissions inventory of Ukraine for 2011. For the economic activities, for which no information from UNFCCC is available, estimations were made based on the additional information from the Energy

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Balance of Ukraine. The resulting database which lists all 23 production activities is reported in Table 7.

The model described in this section will be used to assess the impact of specific policy measures and technologies aimed at stimulating low carbon growth in Ukraine. It will also form the basis for the development of BAU scenarios until 2020 and 2050 as presented below.

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Table 7: Energy use and GHG emissions by activity

Wood, Extraction Textiles Coal Extraction Other Food and paper, Coke oven Agriculture of natural and Oil refining extraction of crude oil mining beverages pulp, and products gas furniture print

Energy consumption (2011, in TJ) Liquid Fuels 57190 110 31 27 902 1412 9 56 529 12791 Solid Fuels 1175 84385 1183 2380 26 585 10908 3 Gaseous Fuels 22963 2530 4372 8583 25890 55693 405 7390 7248 Biomass 1907 884 542 1065 13 6064 13 2495 234 Other Fuels 38 92 24 46 2 1949 0 1 16 2312

GHG Emissions (CO2e in Gg) A. Fuel Combustion 5594 4823 247 483 1614 3570 26 472 643 1444 Liquid Fuels 4186 8 2 2 67 106 1 4 39 875 Solid Fuels 117 4667 110 222 2 54 603 0 Gaseous Fuels 1274 140 242 475 1436 3090 22 410 402 Biomass * 14 2 1 2 0 11 0 5 0 Other Fuels 3 7 2 3 0 141 0 0 1 167 B. Fugitive Emissions from Fuels 19596 4 3 460 397 C. Emissions from Industrial Processes 36299 TOTAL Emissions (CO2e in Gg) 41893 24419 251 486 1614 3570 26 472 1104 1841 * CO2 emissions from combustion of biomass fuels are not included in the total CO2 emissions from fuel combustion (UNFCCC Website).

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Table 7 (continued): Energy use and GHG emissions by activity Natural gas Water and Metal Machinery Other Heat and Chemical Construc- Public production waste Minerals produc- and equip- manufactu- hot water s tion electricity and manage- tion ment ring supply distribu- ment tion Energy consumption (2011, in TJ) Liquid Fuels 177 190 3515 182 518 797 1768 74 1130 40 Solid Fuels 940 30693 330042 233 13422 258 822536 823 207 Gaseous Fuels 135364 47018 209440 16315 2381 184151 7684 281916 281 Biomass 439 3606 5 150 155 3528 3 Other Fuels 1661 0 4 3 0 2 1509 19 32 0

GHG Emissions (CO2e in Gg) A. Fuel Combustion 7716 5494 44071 941 1291 216 88194 432 15785 38 Liquid Fuels 12 14 269 13 38 59 136 5 87 3 Solid Fuels 73 2864 32181 22 1252 24 77696 78 19 Gaseous Fuels 7510 2609 11620 905 132 10201 426 15617 16 Biomass * 1 7 0 0 0 7 0 Other Fuels 120 0 0 0 0 0 154 1 3 0 B. Fugitive Emissions from 28608 Fuels C. Emissions from Industrial 10726 10983 26527 11234 Processes TOTAL Emissions (CO2e in 18442 16476 70598 941 1291 216 88194 29041 15785 11272 Gg) * CO2 emissions from combustion of biomass fuels are not included in the total CO2 emissions from fuel combustion (UNFCCC Website).

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Table 7 (continued): Energy use and GHG emissions by activity

Gas Non- Other Total Private Transport pipeline specified Total services industries Households transport by sectors

Energy consumption (2011, in TJ) Liquid Fuels 421498 2209 505155 14765 2648 522568 Solid Fuels 16690 1316490 27573 3167 1347229 Gaseous Fuels 100804 56044 1176472 598886 12150 1787507 Biomass 4088 25190 12947 329 38466 Other Fuels 544 8254 8254

GHG Emissions (CO2e in Gg) A. Fuel Combustion 30997 5675 4999 224765 36940 1176 262881 Liquid Fuels 30997 161 37084 910 199 38193 Solid Fuels 1658 121644 2705 299 124648 Gaseous Fuels 5675 3109 65312 33227 674 99213 Biomass * 31 81 98 2 181 Other Fuels 39 644 1 645 B. Fugitive Emissions from Fuels 3 49072 49072 C. Emissions from Industrial Processes 726 96495 96495 TOTAL Emissions (CO2e in Gg) 30997 5678 5725 370332 36940 1176 408448 * CO2 emissions from combustion of biomass fuels are not included in the total CO2 emissions from fuel combustion (UNFCCC Website). Source: UNFCCC, Energy Balance of Ukraine, DIW ECON calculations.

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3.3 Scenario analyses

The model described in Section 3.2 will be used to simulate the impact of specific policy measures and/or technology changes aiming at stimulating low-carbon growth in Ukraine. To this end, the impact of policies and technology change has to be assessed against a robust benchmark which captures the hypothetical development of the economy in the absence of such changes (henceforth referred to as Business as Usual, BAU). In this section, we discuss the key assumptions underlying the development path for Ukraine‟s economy under the BAU scenario (section 3.3.1), present the results of the BAU scenario (section 3.3.2) and elaborate on the different channels through which technological change and low-carbon growth policies can be implemented (section 0).

3.3.1 The Business-as-Usual (BAU) Scenario

The business-as-usual (BAU) scenario describes a hypothetical development path of the economy which the model reproduces under the assumption that there will be no substantial policy interventions aiming at green growth facilitation. It is not to be understood as a forecast (i.e. most realistic development) of the economy but rather as a benchmark for comparing the results of different policy scenarios and a motivation for policy action.

In the CGE model, all key economic variables, such as GDP, output and value added by activity etc, are endogenous. That is, they are determined inside the model and cannot be fixed at exogenously given values. Instead, replicating a given development path – e.g. the forecasts of the IMF until 2019 – requires modifying the parameters of the model that influence economic development, such as productivity growth (by activity), tax and subsidy rates, or world prices.

3.3.1.1 Growth assumptions As mentioned above, there are three main sources of growth: population growth (exogenous), capital accumulation (partly exogenous) and productivity growth (exogenous).

A particular challenge is the trend in population, which has been decreasing since the mid- 1990s. The National Institute of Demography forecasts a continuous population decline at an

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average rate of 0.4% per year. In fact, this implies that changes in population will have a negative impact of economic growth. In other words, economic growth needs to be generated from other sources, in particular capital accumulation (investments) and productivity growth. Hence, the development of the overall economy as well as of individual activities will be mainly reflected in activity-specific changes of labour and capital productivity. For the BAU scenario, these parameter values will have to be adjusted so as to meet historical developments.

For the first years of the simulation period (2011 - 2019), some target values for Ukraine are determined by the current forecast of the IMF (IMF Country Report No. 14/106, May 2014). This forecast assumes that Ukraine will manage to implement a package of reforms that will stabilize its macroeconomic situation. The model parameters for the first years are adjusted so as to replicate the IMF forecast.

Table 8: GDP growth 2012-2019

Year GDP growth, % 2012 0.3 2013 0.0 2014 -5.0 2015 2.0 2016 4.0 2017 4.0 2018 4.5 2019 4.5

Source: IMF Country Report No. 14/106, May 2014

For later years, there are no reliable projections on annual GDP growth available. Consequently, productivity levels cannot be adjusted to meet a given development path. Instead, we use conservative assumptions on further productivity growth, based on the historical data in the growth period 2000-2008.

Altogether, we assume the following developments of labour and capital productivity under the BAU scenario:

 Agriculture:

 Average productivity growth rate 2.2% in 2012-2019

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 Average productivity growth rate 4.0% in 2020-2050

 Manufacturing industries:

 Average productivity growth rate 1.9% in 2012-2019

 Average productivity growth rate 3.5% in 2020-2050

 Mining:

 Average productivity growth rate 1.1% in 2012-2019

 Average productivity growth rate 2.0% in 2020-2050

 Transport

 Average productivity growth rate 2.2% in 2012-2019

 Average productivity growth rate 4.0% in 2020-2050

 Service sector

 Average productivity growth rate 2.2% in 2012-2019

 Average productivity growth rate 4.0% in 2020-2050

3.3.1.2 GHG emission intensity and energy efficiency This section contains the calculations representing the deliverable agreed upon in the contract extension: differentiated development paths of energy efficiency for several selected energy intensive industries of the agricultural, energy and manufacturing sectors of the Ukrainian economy.

Despite a very steep decline of the emissions intensity over the last 15 years, Ukraine remains one of the most emissions and energy intensive countries of the world; its emissions intensity of GDP is still more than three times higher than average over OECD Europe (see Figure 14). Most of these emissions stem from fuel combustion. This indicates a significant potential for further reduction of emissions and energy intensity of GDP.

Low Carbon Growth in Ukraine Final Report

Figure 14: GHG emission intensity in Ukraine and OECD Europe (1990-2010)

3.0

2.5

2.0

1.5

adjusted -

1.0 PPP

0.5

0.0

tCO2e/GDP in 2005 constant prices and constant2005and pricesin tCO2e/GDP

1992 1993 1995 1996 1997 1999 2000 2001 2003 2004 2007 2008 1990 1991 1994 1998 2002 2005 2006 2009 2010

OECD Europe Ukraine

Source: OECD55 and PWT 7.156; own calculations

The intentions to decrease energy intensity of GDP have been repeatedly declared by different development programs in Ukraine. The issue is recognised as a strategically important and of the highest priority.57

Energy intensity is measured by relating the amount of energy inputs in a given activity to the respective production volume. In the CGE model of Ukraine, this ratio can be controlled by specific parameters.58 To reflect assumed increases in energy efficiency, these parameter values must be adjusted over time. In the BAU scenario, we base our assumption about the development of the energy efficiency of different production sectors on the historical trends extracted from the statistics provided by the national Agency for Statistics as well as by the IEA.

55 Emission data were retrieved from http://www.oecd.org/statistics/; OECD refers to the UNFCCC as the primary source of the data. 56 Heston, A., Summers, R., and Aten, B. (July 2012). Penn World Table Version 7.1. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. 57 According to the recently revised “Energy Strategy of Ukraine”, Ukraine should reduce energy intensity of GDP by almost 60% by 2030. According to “The State Programme for Stirring-up the Economy” (Decree of the Cabinet of Ministers, No.187 from February 27, 2013), the energy intensity of GDP in Ukraine has to decrease by 1.5-3% per year by 2014. The Government announced its intention “to enhance the economic incentives for energy saving”. The same Programme envisages revision of subsidies to coal mining enterprises and optimization of coal mining sector. 58 More specifically, these parameters define the required volumes of energy input per unit of output for each activity. 73

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Figure 15 shows the development of energy intensity (defined as primary energy input relative to real output) in the aggregated sectors between 2002 and 2012. We interpret these data as showing a downward trend in primary energy intensity in the recent years and we use an average rate of change to extrapolate this trend into the future. Thus, we assume for the BAU scenario that energy efficiency of sectoral output increases:

 by 4.0% p.a. in agriculture,

 by 4.0% p.a. in mining,

 by 3.0% p.a. in manufacturing,

 by 6.0% p.a. in services.

Figure 15: Primary energy intensity of sectoral output in BAU (TJ per mln UAH in constant prices of 2001)

(a) mining, manufacturing, transport

0.25 Manufacturing (historical) Mining (historical) Manufacturing (projection) Mining (projection) Transport (historical) Transport (projection) 0.2

0.15

0.1

0.05

0 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON calculations using data from IEA and UkrStat.

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(b) agriculture and services

0.04

Agriculture (historical) Services (historical) 0.03 Agriculture (projection) Services (projection)

0.03

0.02

0.01

0.00 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON calculations using data from IEA and UkrStat.

In addition to primary energy efficiency, an important issue is also the efficient use of electricity and heat. For producing an estimate of the corresponding future trend we used the historical electricity demand data provided by UkrStat. Figure 16 illustrates these data for the years characterized by economic growth in Ukraine. Again, these downward trends in electricity intensity are extrapolated into the future. In the BAU scenario, we thus assume electricity efficiency in manufacturing to improve at an average rate of 2% p.a, in services at 1% p.a. and in agriculture at 4% p.a. We assume also that these rates are not preserved forever, but that there is smooth reduction of the rate of change over time (at 3% p.a.).

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Figure 16: Electricity intensity of sectoral output in BAU (GWh per mln UAH in constant prices of 2001)

(a) manufacturing and mining

0.4

Manufacturing and mining (historical) 0.35 Manufacturing and mining (projection)

0.3

0.25

0.2

0.15

0.1

0.05

0 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

(b) agriculture

0.07

Agriculture (historical) Agriculture (projection) 0.06

0.05

0.04

0.03

0.02

0.01

0 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON calculations using data from IEA and UkrStat.

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0.13

Services (historical) Services (projection) 0.12

0.11

0.1

0.09

0.08

0.07

0.06 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON calculations using data from IEA and UkrStat.

3.3.1.3 Power sector development For the electricity sector, the assumptions that determine future developments in the CGE model are adjusted such that they are consistent with the findings of the sector-specific model of the power sector. In particular, the production function in the CGE model is calibrated such that the costs of producing electricity coincide with the findings of the power sector model.

The BAU scenario of the power model reflects an increase in the electricity demand as well as an increase in the renewables capacity. In particular, the total electricity demand increases from 199 TWh in 2013 to 314 TWh in 2030 and 468 TWh in 2050. This increase in demand (together with the need to refurbish existing capacities) requires investments in additional generation capacity (Figure 17), estimated at a total of 2400 bln UAH (at constant

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prices of 2011). This investment amount is huge, two times higher than the GDP level of 2011. However, it is required to match the electricity need of a growing Ukrainian economy.

Figure 17: Installed generation capacity in the BAU scenario

45 2013 2030 2050 40

15 Installed capacity, Installed capacity, GW 10

0 Nuclear Hydro Coal Gas PV+Wind+Biomass

Source: DIW ECON

According to the BAU scenario, in 2030, 48% of the electricity will be produced by nuclear power plants, while the remaining parts will be coal (30%), gas (8%), hydropower (9%), and other sources, such as solar, wind and biomass (5%). In 2050, the share of solar and wind power plants will increase to 13%, and the shares of conventional sources will slightly decrease (Figure 18).

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Figure 18: Fuel mix of produced electricity (in percent of total generation, BAU scenario)

100%

90%

80%

70% PV+Wind+Biomass 60% Hydro 50% Gas 40% Coal 30% Nuclear

20%

10%

0% 2013 2030 2050

Source: DIW ECON

3.3.1.4 Heat sector development As mentioned in the model description, the evolution of the heat sector is also modelled using results from an external model. The purpose of the heat sector model is to simulate different scenarios of capacity development and calculate corresponding investment costs and the resulting structure of the production inputs (fuel, capital, labour, other inputs).

In the BAU scenario, we assume that new capacity is 50% coal-fired and 50% gas-fired. We further assume that the existing gas-fired capacity that is actively used can be refurbished. Given the low utilization rates of the worn-out gas-fired capacity, the currently installed capacity of 120000 GCal/hour will not be sufficient to satisfy heat demand in 2050. Our estimation is that the capacity of 30000 GCal/hour that is used 40% of the total time in the year is enough to satisfy the current demand (105000 TCal per year). Using the IEA data on energy use, we can infer that the major part of this available capacity (27500 GCal/hour) is gas-fired. This is the capacity that will be refurbished in the BAU scenario.

The assumed amount of additional coal-fired capacity that is required in the BAU scenario stems from the estimate of demand increase that is roughly the same as the increase in 79

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electricity use: 200% increase between 2011 and 2050. This additional demand can be satisfied by installing a coal-fired capacity of 65 TCal per hour.

The corresponding investment costs for refurbishment and new capital amounts to 4000 mln UAH per year between 2015 and 2050, or a total of about 150 bln UAH.

Figure 19: Fuel mix of produced heat (in percent of total generation, BAU scenario)

100%

90%

80%

70%

60%

50% gas coal 40%

30%

20%

10%

0% 2015 2020 2025 2030 2035 2040 2045 2050

Source: DIW ECON

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3.3.1.5 Gas import price The price of imported gas is an important driver of economic development in Ukraine, as suggested by the recent history. For the BAU scenario, we follow the assumptions of the IMF country report for Ukraine for the period until 2019. For later years, we take the IEA long-term trend for the imported gas in Europe as a basis.

Figure 20: Assumed development of the import price (in real terms) for natural gas (USD per thousand cubic meters)

440 Ukraine imports (IEA and IMF), $ per tcm 420 Europe imports (IEA), $ per tcm 400

380

360

340

320

300 2011 2016 2021 2026 2031 2036 2041 2046

Source: DIW ECON based on IEA (2013) and IMF Country Report for Ukraine (2014)

3.3.1.6 IMF support program The conditions of the IMF support program for Ukraine that was agreed upon in May 2014 are very detailed and are likely to be implemented in the full extent. They relate the provision of funds to the obligation of the Ukrainian government to conduct a painful but necessary reform in the energy sector. The majority of existing subsidies on energy must be abolished. As a result, the deficit of public budget must be brought back to a sustainable level.

We assume that the IMF program is implemented as agreed. According to the published degrees of the Ukrainian government, the regulated gas tariffs for the households will rise by 56% in 2014, by 40% in 2015, and by further 20% in the years 2016 and 2017. For the heat generating companies, the tariffs for gas will rise by 40% in 2015, and by further 20% in the

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years 2016 and 2017. After that, the tariffs will follow the pace of the import price described above.

3.3.2 Results of the Business-as-Usual scenario

The BAU scenario corresponds to a rather restrained picture of the economic development in Ukraine until 2050. Given the poor economic performance of Ukraine in the recent years, the resulting long-term rate of growth of 4.5% in the period 2015-2050 is not over-optimistic. The model‟s staring year is 2011, that is why we have to reproduce the major economic slump in 2014, which is currently estimated to cost 5% of GDP. Figure 21 illustrates this dynamics in the GDP growth.

Figure 21: Real GDP growth rates in BAU scenario

6.0

4.0

2.0

0.0 2011 2016 2021 2026 2031 2036 2041 2046

-2.0 Real GDP growth rate, % growth rate, GDP Real

-4.0

-6.0

Source: DIW ECON (model results)

On the sectoral level, the developments are mainly following our assumptions about the rates of total factor productivity growth (subsection 3.3.1.1). Figure 22 and Figure 23 show that the service sector and the agricultural sector will be the main contributors to economic growth over the next 40 years and will further grow in importance. 82

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Figure 22: Growth of sectoral gross value added in the BAU scenario

6000

5000

4000 Other Services Transport

3000 Utilities Manufacturing Mining Real GVA, bln UAH bln GVA, Real 2000 Agriculture

1000

0 2011 2016 2021 2026 2031 2036 2041 2046

Source: DIW ECON (model results)

Figure 23: Structure of gross value added in the BAU scenario

100%

90%

80%

70% Other Services 60% Transport

50% Utilities Manufacturing 40%

Mining Real GVA, bln UAH bln GVA, Real 30% Agriculture

20%

10%

0% 2011 2016 2021 2026 2031 2036 2041 2046

Source: DIW ECON (model results)

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Structural changes in the economy, as well as our assumptions with respect to the energy efficiency improvements and the developments in the power and heat sector lead to substantial changes in GHG emission intensity of the economy, which falls at an average rate of 1.5% p.a. in the period 2011-2050.

Figure 24: GHG emissions intensity of GDP in the BAU scenario (kt CO2eq per bln UAH in prices of 2011)

350

300

250

200

150

100 GHG emission intensity of GDPof intensity emissionGHG 50

0 2011 2016 2021 2026 2031 2036 2041 2046

Source: DIW ECON (model results)

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The total amount of GHG emissions in the BAU scenario is however rising due to the growth of the economy (Figure 25).

Figure 25: GHG emissions in the BAU scenario (kt CO2eq)

1200000

1000000

800000

600000

400000 GHG emissions, Gg CO2eq.Gg emissions,GHG 200000

0 2011 2016 2021 2026 2031 2036 2041 2046

Source: DIW ECON (model results)

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3.3.3 The Energy-Efficient-Investments (EEI) scenario

The objective of the EEI scenario is to simulate the impact of energy-efficient technologies as well as of the corresponding investment expenditures. In this section, we elaborate on how these were modelled in the CGE model for Ukraine and present the corresponding results.

3.3.3.1 Impact of energy-efficient technologies As explained in section 3.2.2, production technologies are modelled as specific production functions that determine the amount of different inputs – such as labour, capital, energy etc. – needed to produce a given volume of output. Changes in technology imply changes in the underlying parameter values of these production functions. For example, a shift to energy efficient technologies in a certain activity can be modelled by reducing the parameter values for required energy input per unit of output in this activity. Obviously, the critical issue is to determine by how much these parameters are to be reduced. This is discussed below in section 3.3.3.3.

3.3.3.2 Investments in energy-efficient technologies In addition to the impact on production, a shift to energy-efficient technologies can require additional capital expenditures (i.e. additional investments). In the CGE model, production technologies are part of the national capital stock – the so-called Gross Fixed Capital. As explained in section 3.2.9, investments in gross fixed capital are modelled as being responsive to changes in incentives, which are determined by the return to capital relative to the costs of capital formation. In this way, the model yields optimal investment levels at given levels of product and factor prices. To the extent that energy-efficient technologies are the most-profitable investment option – mainly, due to cost savings – the shift to such technologies is part of the regular gross fixed capital formation.59 However, the focus of the different components of the energy-efficient scenario is on assessing the impact of additional investments in energy-efficient technologies that accrue on top of what is economically optimal. Shifting to such technologies implies additional, so-called incremental investments. This is modelled by exogenously adding the respective volumes of incremental investments to the volume of gross fixed capital formation as determined by the model. To the extent that these additional investment volumes have an impact on prices (i.e. the return to installed

59 In fact, this is what happens in the BAU scenario where energy efficiency increases as described in section 3.3.1.2. 86

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capital and the costs of new capital formation), this also captures crowding out effects since the level of gross fixed capital formation is responsive to changes in relative prices.

3.3.3.3 Efficient energy use and the costs of incremental investments Technically, modelling the impact of energy-efficient technologies and the corresponding, incremental investments as described before is straightforward. Of crucial importance, however, is the specification of parameter changes which should be based on empirical assessments of corresponding energy efficiency potentials and incremental investment volumes.

In the CGE model for Ukraine, energy efficiency potentials and incremental investments are mainly based on the results provided by Thomson Reuters (2013) (in the framework of this project) on CO2 mitigation potentials and abatement costs in Ukraine under alternative scenario assumptions (a baseline scenario and a low-carbon scenario). For some missing sectors, we made use of similar calculations provided earlier by NERA (2012). The potential abatement and the corresponding marginal abatement costs for each model sector are presented in Table 9. The detailed list of all measures for each sector is included in Appendix B-2.

In addition to this information, we also use the results of our own efficiency analyses for the metal industry, the non-metallic mineral products industry (i.e. production of clinker, lime, glass and soda ash) and the chemical industry in Ukraine as presented in Chapter 1.

These data are used to assess energy use reduction potential for each production activity in the economy. Furthermore, the reported abatement costs can be used to estimate the volume of incremental investments (i.e. the additional capital expenditures). In the following we explain the corresponding parametric assumptions for different sectors.

The energy efficiency improvements in the EEI scenario are calibrated for each sector such as to match the abatement potential for combustion activities reported in Table 9. These improvements come on top of the baseline energy intensity trends of the BAU scenario (described above in Section 3.3.1.2).

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Table 9: GHG abatement volume and incremental investments in Ukraine by activity

Emissions reduction in 2050 Marginal Abatement measures by sector and Source of (compared to BAU, abatement cost (€ type: cost data in million tons per ton CO2e) CO2e) Agriculture (process) 39.3 63 TR Coal extraction (process) 5.7 36 TR Oil refining (combustion) 0.1 61 NERA Chemistry (combustion) 4.6 45 TR Minerals production (combustion) 2.4 94 TR Metallurgy (combustion) 33.7 31 TR Heat use in buildings (combustion) 8.9 100 DIW econ Heat and hot water supply (combustion) 12.6 325 TR Waste management (process) 15.6 53 TR Transport (combustion) 16.3 179 NERA Source: DIW ECON based on Thomson Reuters (2013) and NERA (2012)

For manufacturing, the energy efficiency assumptions for two key energy-intensive industries of metals and minerals production are specified individually. The rates of change there are assumed to be slower than in the rest of manufacturing. Important efficiency improvements are assumed for the supply side (local boilers and networks) as well as demand side (insulation of residential and office buildings) of heating. Finally, fuel efficiency measures in freight and public transport are included in the EEI scenario.

Specifically, we assume that the incremental investments in energy efficiency lead to a decrease of primary energy intensity:

 by 1.0% p.a. in metals production,

 by 1.5% p.a. in minerals production,

 by 3.0% p.a. in food industry, textiles industry, machinery and equipment, and other manufacturing.

 by 2% p.a. in heat supply

 by 2% p.a. in heat demand by residential sector (households) and service sector

 by 1.0% p.a. in public and freight transport

These developments are illustrated by the following figures.

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Figure 26: Primary energy intensity of sectoral output in manufacturing in the EEI scenario (TJ per mln UAH in constant prices of 2001)

0.25 Manufacturing (historical) Metals production (projection EEI) 0.20 Minerals production (projection EEI) Other manufacturing (projection EEI) 0.15

0.10

0.05

0.00 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON

Figure 27: Primary energy intensity of sectoral output in transport in the EEI scenario (TJ per mln UAH in constant prices of 2001)

0.25 Transport (historical) Transport (projection EEI) 0.20

0.15

0.10

0.05

0.00 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON

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Figure 28: Primary energy intensity of sectoral output in heat supply in the EEI scenario (TJ per mln UAH in constant prices of 2001)

1.20

1.00

0.80

0.60

Heat supply (historical) 0.40 Heat supply (projection)

0.20

0.00 2002 2007 2012 2017 2022 2027 2032 2037 2042 2047

Source: DIW ECON

The corresponding investment volumes are calculated based on the achieved abatement and the abatement costs as specified in Table 9. As the abatement volume increases with time, we aggregate the required total investment by a net present value, which is specified in Figure 29.

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Figure 29: Composition of the required energy efficiency investments (total net present value, in bln UAH of 2011)

70 +20 60

50 +11

40 +73

30 +29

10 +13 0 Measures in Measures in heat Measures in Measures in All measures industry networks housing transport

Source: DIW ECON

3.3.4 Results of the Energy-Efficient-Investments (EEI) scenario

The EEI scenario has a clear positive impact on the macroeconomic indicators such as GDP and household consumption (Figure 30). The predicted GDP in the year 2050 in the GREEN scenario is 7.4% higher than in BAU. The impact on households‟ consumption is slightly larger; it is 8.4% higher than in BAU. These improvements do not come at the cost of higher environmental burden. CO2 emissions are projected to be 7.8% lower than under BAU.

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Figure 30: Dynamics of key indicators (EEI relative to BAU scenario in each year)

10% Real GDP Real consumption Total emissions 8% 6% 4% 2% 0% 2011 2016 2021 2026 2031 2036 2041 2046 -2% -4% -6% -8% -10%

Source: DIW ECON

It is worthwhile to study these key impacts in more detail. Figure 31 and Figure 32 decompose the total GDP and emission effects into the effects of different packages of green investments:

 Energy-saving measures in industry;

 Modernization of heating networks;

 Increasing energy efficiency of buildings;

 Increasing energy efficiency of transport.

To put the effect into the current perspective, the indicators are compared to the levels of 2011. On the one hand, the direction of predicted effects is rather intuitive. On the other hand, the relative magnitude of the effects of different packages could only be quantified using a comprehensive computational model, like our CGE model.

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Figure 31: Decomposition of the total GDP impact of the EEI scenario in 2050 (in % relative to the baseline level of 2011)

40% +1% +4% 35% +3% 30% +28%

25%

20% +36%

15%

10%

0% Measures in Measures in heat Measures in Measures in All measures industry networks housing transport

Source: DIW ECON Figure 32: Decomposition of the total emissions impact of the EEI scenario in 2050 (in % relative to the baseline level of 2011)

-5%

-10% -21%

-12% -15% -3% -1%

-20% -5%

-25% Measures in Measures in heat Measures in Measures in All measures industry networks housing transport

Source: DIW ECON

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Looking at the effects of individual investment packages, we see that improvement of energy efficiency in manufacturing has the most important effect on both, the economy and the GHG emission level. Substantial effect is also produced by the measures in the heat supply sector, where the investments are directed to reducing currently huge heat losses. However, due to limited feedback effects from these investments, the macroeconomic impact is not as large as for the measures in the manufacturing sector.

Figure 33 shows the total impact of the GREEN scenario on the gross value added in different sectors.

Figure 33: Sectoral GVA impacts of the EEI scenario (difference in GVA between EEI and BAU in 2050 relative to the level of 2011, %)

140%

120%

100%

80%

60%

40%

20%

-20%

-40% Agriculture Mining Manufacturing Construction Utilities Transport Services

Source: DIW ECON

The negative effect on mining is due to the reduce energy (mainly, coal) use by industry and heat supply. The positive effects on manufacturing and construction are the largest and correspond to the results in Figure 31.

Note that the improvement of energy efficiency is costly. It requires investments by firms in the corresponding sectors that go beyond “normal” investment levels. Thus, these can only be expected to be implemented if they lead to cost savings and additional profits in the future. To assess the chances for such additional contributions to realize, the impact of the proposed measures on different manufacturing industries must be further assessed. 94

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Figure 34: Profitability of economic sectors in the EEI scenario (net present value of incremental profits, as compared to BAU)

-5

-10 NPV of incremental profits, bln bln profits, of UAH incremental NPV

-15 Agriculture Mining Manufacturing Construction Utilities Transport Services

Source: DIW ECON

Figure 34 reports the value of discounted profit flows for aggregate sectors. The measures in heating and transport are relatively most expensive and they lead to negative profits. It means that the implementation of these measures will need additional incentives provided the government. The increase of heating tariffs for the households (in accordance to the IMF program requirement) is already included in our calculations. The additional incentives may include on the one hand, public subsidies, but on the other hand also the use of market mechanisms like CO2 taxes.

The measures in manufacturing seem to generate enough cost saving for the firms in the sector. It is still worthwhile to look at this in a bit more detail.

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Figure 35: Profitability of key manufacturing sectors in the partial EEI scenario with only manufacturing measures (net present value of incremental profits, as compared to BAU)

1.2

1.0

0.8

0.6

0.4

0.2

NPV of incremental profits, bln profits, bln of incremental UAH NPV 0.0 Food and Chemicals Minerals Metal Machinery and Other beverages production equipment manufacturing

Source: DIW ECON

Figure 35 reports the value of discounted profit flows for individual manufacturing sectors in a partial scenario, where only the measures in manufacturing are simulated. In fact, the values are positive for all sectors. Key investments are carried out in the metallurgy, chemicals and minerals sectors. These are also the manufacturing sectors with the largest GHG emissions in the benchmark year 2011.

Positive returns in the manufacturing sectors mean that these sectors can be expected to invest in the energy-efficiency measures without that additional motivation will be necessary. However, positive profits also suggest that these sectors could potentially contribute more to energy efficiency through additional investments. Moreover, if parts of these profits in manufacturing can be re-distributed towards utilities – the sector that accounts for the strongest losses under the EEI scenario – this could yield a more balanced and thus, preferable overall policy impact.

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4. Short-term, ad hoc expertise and consulting to decision makers and key stakeholders for current topics on the political agenda

4.1 Introduction

The ad hoc expertise was provided on the topic of the impact of Doha Amendment for Ukraine. This analysis was carried out in October 2013 and therefore does not take into account the recent developments, including IMF growth forecast as shown in Table 8.

The international climate negotiations in Doha at the end of 2012 adopted an amendment to the Kyoto Protocol regulating the second commitment period (CP2) from 2013 to 2020. In particular, emission allowances at the national level will be capped based on average emissions from 2008 to 2010. For Ukraine, this corresponds to emission allowances of about 3.1 billion tons of CO2 equivalents or 0.39 billion tons per year (on average). The actual GHG emissions in Ukraine and available assigned amount units (AAUs) for CP2 based on the Doha Amendment are depicted in Figure 36.

Low Carbon Growth in Ukraine Final Report

Figure 36: GHG Emissions in Ukraine and available AAUs for CP2 based on Doha Amendment

Source: UNFCCC (2013), own calculations.

Policy makers in Ukraine worry that this cap on emissions will effectively limit the possibilities for future growth of GDP. Thus, they currently consider not to ratify the Doha Amendment.

In July 2013, DIW ECON put forward arguments in favour of ratification of the Doha Amendment emphasising that it would be beneficial for Ukraine not to opt out (DIW ECON, 2013). This section provides additional evidence that with some political efforts, the CP2 emission target for Ukraine is feasible and economically reasonable. Thus, we recommend to Ukrainian policy makers to ratify the CP2 Doha amendment.

4.2 The challenge

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national GHG emissions will exceed only the allowed amount of emissions induced by the CP2-cap. More precisely, given the Status Quo, we estimate that the Ukrainian GHG emissions on average will exceed the CP2-cap by just 1.3% per year during the commitment period 2013-2020. However, if Ukraine manages to fully utilise its estimated abatement potential at the cost level of up to 40 Euros per ton as estimated by NERA, Ukrainian GHG emissions will clearly remain on average by 6.3% per year below the CP2-cap. The corresponding estimations are illustrated in Figure 37 below.

Figure 37: Available AAUs for CP2 (Doha Amendment) and estimated GHG Emissions in Ukraine

500

400

300

200 MtCO2e Status Quo: DIW econ

100 Abatement potential (@ costs of <40 Euro/t of CO2e)

Availalbe AAUs under CP2

2008 2009 2011 2012 2014 2015 2016 2017 2019 2020 2010 2013 2018

Source: UNFCCC (2013), own calculations

4.3 Analysis

The analysis of future GHG emissions rests on two key assumptions:

 The expected yearly levels of GDP growth until 2020; and

 The intensity of GHG emissions per unit of GDP until 2020.

4.3.1 Expected yearly levels of GDP growth until 2020

Expectations on future economic growth in Ukraine until 2020 have been strongly revised during the past two years. For example, as of April 2011, the IMF expected average growth 99

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rates of at least 4% for Ukraine (IMF, 2011). Recently, however, this optimistic projection has been substantially revised. In its most recent outlook as of October 2013, the IMF expects growth rates of only 0.4% for 2013 and less than two percent until 2018 (IMF, 2013). Assuming a rather high level of GDP growth of 3% for 2019 and 2020, the revised GDP forecast implies that GDP grows from 2011 to 2020 by an annual average of only 1.7% (as compared to 4.2% based on the previous forecast). The initial and revised outlooks are presented in Figure 38.

Consequently, the significant drop in expected growth translates into lower expected amounts of GHG emissions relative to the forecasts of previous years.

Figure 38: Expected yearly levels of GDP until 2020: Outlook of 2011 vs. outlook 2013.

(in constant 2007 national currency units)

1200

1000

800

600 Hryvna

400 billion

200

2006 2007 2008 2011 2012 2013 2014 2017 2018 2019 2020 2009 2010 2015 2016 WEO, April 2011 WEO, October 2013

Source: IMF, World Economic Outlook (WEO).

4.3.2 The intensity of GHG emissions per GPD until 2020

At present, Ukraine remains one of the most emission intensive countries of the world with emissions per unit of GDP more than three times higher than average level in OECD Europe. Hence, there is still a significant potential for further reductions of Ukraine‟s emission intensity. Following the report by NERA (2012), the assumed development of GHG emissions per unit of GDP would be as follows: 100

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 Under Status Quo (SQ) developments, NERA expects the ratio of GHG emissions per unit of GDP (the carbon intensity of GDP) to decrease by an average of -1.5% per year between 2011 and 2020.

 In its Enhanced Policies (EP) scenario, which considers utilisation of all abatement potential at costs of less than 40 Euros per ton, NERA expects the ratio of GHG emissions per GDP to decrease by an average of -2.9% per year over 2011-2020.

4.3.3 Expected national GHG emissions: new adjusted estimations

Applying the expected development of emission intensity to the revised GDP forecast of October 2013, the expected total Ukrainian emissions are adjusted downwards, as already presented in Figure 37.

As a result, under Status Quo developments (when no additional policy efforts are made and current institutions continue as they are now) national GHG emissions will be only slightly higher than AAUs imposed by the CP2: on average, national emissions exceed the CP2-cap by 1.3% per year during 2013-2020. Nonetheless, with some political efforts (i.e. implementation of current plans to reform the wholesale electricity market) it would be easy to get below the CP2-cap. Moreover, if Ukraine manages to fully utilise its estimated abatement potential at costs of up to 40 Euros per ton, national emissions will clearly remain below the CP2-cap between 2013 and 2020 (by 6.3% on average per year).

4.3.4 Costs of reaching the 2012 Doha Amendment

According to the adjusted emissions expectations under Status Quo scenario, the cumulated GHG emissions (when summed up over 2014-2020), will exceed the 2012 Doha Amendment allowances by total amount of 38 MtCO2e. These emissions are represented by the shaded area in Figure 4 (which actually depicts an enlarged view of the Figure 2). In order to reduce the national emissions by this amount, some additional political efforts and investments, which depart from the Status Quo toward the Enhanced Policies scenario, would be needed.

Multiplying the obtained emissions amount by the maximal price at 40 Euro per ton CO2e (NERA, 2012), results in 1.53 billion Euro of total investments, which will be needed over the next seven years in order to keep to the newly imposed emission cap. This implies that with additional investments into technological modernisation of on average 219 million Euro (or

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0.2% of GDP) per year until 2020 the requirements of the 2012 Doha Amendment could be achieved by Ukraine.

Figure 39: Abatements needed to keep with requirements of the Doha Amendment

450

MtCO2e 400

350

2011 2012 2013 2014 2017 2018 2019 2020 2015 2016

Status Quo: DIW econ Availalbe AAUs under CP2

Source: DIW ECON

4.4 Conclusions

The presented analysis shows that the emission target imposed by 2012 Doha Amendment for the second commitment period from 2013 to 2020 is achievable with some additional political efforts and economically reasonable for Ukraine. Requiring yearly investments of 0.2% of GDP, the new target is achievable even under the current limitations in financial markets or difficult situation in Ukraine‟s state budget. The target will not even trigger serious structural changes within the economy. On the other side, the resulting improving overall energy efficiency of the economy and reducing emission intensity of GDP would promote growth in targeted sectors and increase competitiveness of domestic products and services. Additional benefits, listed by DIW ECON (2013), emphasise the obtained conclusions and strongly favour the ratification of the 2012 Doha Amendment by the Ukrainian side.

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5. Revised concept of a low carbon development plan of Ukraine: From Stabilisation to Sustainable Economic Growth

This section includes the executive summary of the Policy Paper No. 6 “From Stabilisation to Sustainable Economic Growth”. The paper is included as Appendix C- 8 to this Final Report.

The IMF rescue programme to Ukraine‟s interim government has reinforced economic stability in Ukraine. However, the fundamental structure of the Ukrainian economy remains flawed. A strong reliance on industry exports coupled with a pronounced dependence on energy imports jeopardizes Ukrainian long-term growth prospects and makes it vulnerable to external shocks. Thus, structural policies ought to prioritise economic diversification to achieve sustainable economic growth.

Abolishing energy subsidies and liberalising energy markets are the key conditions adherent to the IMF loans. However, they are also expected to incur a wave of energy price shocks and thus, economic losses. Hence, the economy risks being locked in a viscous cycle where stabilization requires price shocks which in turn undermine economic growth and – eventually – stabilisation. The focus of this paper is to demonstrate how this cycle can be broken. In the medium-term, energy prices shocks can initiate technological adjustment, yielding productivity gains and energy efficiency. Under favourable circumstances, incipient losses are not only set off but also allow for economic growth. In Ukraine, however, conditions for such a technological adjustment process towards lower energy intensity have not been established yet. Rather, a technological frontier gap and pronounced capital depreciation prevail. Protectionist measures by the government invalidated the market pressure to invest in energy efficiency and capital stock modernization like of the manufacturing industry. Accordingly, reversing the trends of capital depreciation and high energy intensity in manufacturing, improving market structures in the energy sector and ameliorating the economy-wide business environment can provide Ukraine with vast potential for economic growth. This is the focus of this paper.

Above all, initiating capital stock modernisation as well as diversification requires investments. In turn, kick-starting the investments requires the government to act along two lines of action: 103

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The first task is to improve the country‟s investment climate. The corresponding need for reforms in areas such as strengthening of property rights and fighting corruption and red-tape is well understood, and the key measures have been widely discussed already. However, we acknowledge that even when the government succeeds in improving the country‟s investment climate, quick acceleration of investment activities is highly unlikely since trust and confidence of investors can only be build up over a considerable period of time. However, neither the economic nor the political situation in Ukraine allow to wait.

Accordingly, the second task is initiating public support to private investment in all relevant areas including modernisation of capital stock in manufacturing, infrastructure or buildings. As public budgets will not be able to provide the necessary funds, the government needs to secure funding through international donors willing to support economic reconstruction in Ukraine. In fact, by establishing improvements in energy efficiency as key condition for such loans, the government can achieve a win-win situation:

 As argued before, reducing energy intensity is key to re-vitalize the national economy during times of rising energy prices. Hence, a benefit to Ukraine.

 Reducing primary energy consumption as well as carbon emissions is a common objective of international donors such as World Bank, EBRD, European Investment Bank etc. Hence, using donors‟ funds to reduce energy intensity is a clear benefit to those organisations as well.

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6. Findings from the economic assessment of a domestic ETS and possibilities for linking the ETS in Ukraine with other FSU countries

This section includes the executive summary of the Policy Paper No. 5 “MRV and linking a potential ETS in Ukraine with other systems”. The paper is included as Appendix C-7 to this Final Report.

Some of the fundamentals of linking an ETS outlined in this paper are therefore essential for cooperation in international climate change framework as well as for the implementation of national climate change and energy policies and strategies.

There are at least two main fundamentals for both linking and international cooperation to ensure achieving the goals and targets of national strategies and policies:

 Credibility, and

 Stringency of targets.

The creation of a reliable system of monitoring, reporting and verification by the government is essential for these fundamentals. MRV is key for:

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 Ensuring greater transparency, accuracy and compatibility of information with regard to climate change, energy efficiency etc. in order to identify good practice, foster a learning process, and allow for international benchmarking

 Recognition and visibility of mitigation achievements

 Attribution of quantified impacts to policies

 Accounting national and international progress

 Identification of gaps and international support needs

 Creation of access to international public and private finance.

The government should create frame conditions and rules which would allow the business sector to benefit from international climate change cooperation and from the advantages of linking an ETS with other similar systems, as well as from implementing national policies. A reliable MRV system is a crucial element for that.

 The current MRV system in place in Ukraine needs to be substantially improved not only for a potential ETS but also for monitoring implementation of other GHG reduction policies and measures like the carbon tax or the Energy Strategy 2030.

 The MRV system should always be as robust and ambitious as feasible in order to be most useful for domestic purposes of MRV, but also to address international requirements at the same time. To establish two parallel systems for domestic and international purposes would be highly inefficient.

 Respective MRV guidelines and rules should be in accordance with guidelines and rules of major potential emission trading partner countries.

 As an MRV system is quite complex it needs strong coordination capacity between national and subnational entities. Co-ordination and communication between regulators, industry and verifiers through workshops or permanent working group proved to be very helpful.

 Due to the fact that a future ETS linking may generate advantages as well of disadvantages both should be analysed in advance before making a decision on linking.

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DIW ECON (2012c): Benchmarking for sustainable and economically viable technology options – The case of the metal industry in Ukraine. Low Carbon Ukraine, Policy Paper No. 1.

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Appendix A

Figure A1: Overall efficiency levels (technical efficiency & scale efficiency) of metal industries in selected countries (in 2007)

1.0

0.8

0.6

0.4

0.2

0.0

Source: DIW ECON Figure A2: Scale efficiency levels of metal industries in selected countries (in 2007)

1.0

0.8

0.6

0.4

0.2

0.0

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Figure A3: Overall efficiency levels of minerals industries in selected countries (in 2007)

Source: DIW ECON Figure A4: Scale efficiency levels of minerals industries in selected countries (in 2007)

Source: DIW ECON

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Figure A5: Overall efficiency levels of chemicals industries in selected countries (in 2007)

Source: DIW ECON

Figure A6: Scale efficiency levels of chemicals industries in selected countries (in 2007)

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Appendix B-1

This Appendix contains detailed assumption used in the construction of the power model.

Macroeconomic assumptions

Variable Value Source

Discount factor 8% across all years Thomson Reuters MACTool Report Gas price 411.22 $/1000 m³ in 2013, Thomson Reuters MACTool growing with 1% across all Report, own assumptions for years development Gas energy content 36.24 MBTU/1000 m³ across Key World Energy Statistics all years (IEA) (heat content of Russian gas) Coal price 101.18 $/t SKE in 2013, Coal Study (Opitz DIW growing with 0.5% across all ECON), Thomson Reuters years MACTool Report, own assumptions for development Coal energy content 27.77 MBTU/t SKE across all A.G. Energiebilanzen years Nuclear fuel price 0.66 $c/kWh, growing with Royal Academy of 0.5% across all years Engineering Load data 2010 detailed load data has Ukrstat been fitted by 12% growth and 2.5% peak shaving to fit the official Ukrainian data.

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Technology specific assumptions Gas CHP old Variable Value Source Installed capacity 4300 MW in 2013, gradual UNCS (email), Energy shutdown until 2020 Strategy, Thomson Reuters (replacement through MACTool Report, gasCHP_new) Assumption by DIW ECON Availability factor 70% flat across all years Assumption by DIW ECON (due to calibration, could be up to 92% from TR); further adjustment for seasonal influence (during summer 20% of available capacity, during winter 100% of available capacity, hours of the year are split even between summer and winter) Capital cost 0 $/kW across all years Due to shut down and replacement with gasCHP_new O&M Cost 72.5 $/kW and 0 $/kWh Thomson Reuters MACTool across all years Report Lifetime Shut down Assumption by DIW ECON (based on Thomson Reuters MACTool Report and Energy Strategy) Retrofit capital cost - No retrofit due to shut down and replacement Retrofit lifetime - No retrofit due to shut down and replacement Efficiency 30% across all years UNCS, Mitsubishi Heavy Industries (data on efficiency of old gas turbines)

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Gas CHP new (Natural Gas Combined Cycle CHP) Variable Value Source Installed capacity 0 MW in 2013, gradual Assumption by DIW ECON construction of 6300 MW (based on Thomson Reuters from 2014 to 2030, flat MACTool Report and Energy afterwards Strategy) Availability factor 70% flat across all years Assumption by DIW ECON (due to calibration, could be up to 92% from TR); further adjustment for seasonal influence (during summer 20% of available capacity, during winter 100% of available capacity, hours of the year are split even between summer and winter Capital cost 1100 $/kW across all years Thomson Reuters MACTool Report O&M Cost 72.5 $/kW and 0 $/kWh Thomson Reuters MACTool across all years Report Lifetime 25 Thomson Reuters MACTool Report Retrofit capital cost 770 $/kW across all years Assumption by DIW ECON (70% of capital cost) Retrofit lifetime 20 Assumption by DIW ECON Efficiency 60% across all years Siemens (maximum efficiency for NGCC)

Nuclear old Variable Value Source Installed capacity 14000 MW in 2013, 10000 Energy Strategy, MW retrofitted in the first 10 Assumptions by DIW ECON years (2014 – 2023) and

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remaining 4000 MW decommissioned until 2030. Then 10000 MW flat until 2050 Availability factor 74% in 2013, gradual UNCS, Thomson Reuters increase to 77% in 2030, flat MACTool Report afterwards Capital cost 0 $/kW across all years No new capacity installed O&M Cost 1.67 $c/kWh across all years IEA (Projected Cost of Generating Electricity), no cost for waste disposal and decommissioning accurately included Lifetime 50 Thomson Reuters MACTool Report Retrofit capital cost 2800 $/kW across all years Assumption by DIW ECON (70% of capital cost for nuclear_new) Retrofit lifetime 20 Energy Strategy

Nuclear new Variable Value Source Installed capacity 0 in 2013, then gradual Scenario depended variable construction of new capacity, depended on exogenous demand growth Availability factor 89% flat across all years National Renewable Energy Laboratory Capital cost 4000 $/kW across all years Thomson Reuters MACTool Report O&M Cost 1.67 $c/kWh across all years IEA (Projected Cost of Generating Electricity), no cost for waste disposal and

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decommissioning accurately included Lifetime 50 Thomson Reuters MACTool Report Retrofit capital cost 2800 $/kW across all years Assumption by DIW ECON (70% of capital cost) Retrofit lifetime 20 Energy Strategy

Coal old Variable Value Source Installed capacity 17200 MW in 2013, gradual Energy Strategy, shutdown of 12000 MW and Assumptions by DIW ECON continuous retrofit of the remaining 5200 MW until 2030, flat afterwards Availability factor 75% in 2013, gradual Assumption by DIW ECON increase to 80% in 2030, flat (due to bad condition of afterwards Ukrainian coal TPPs, see Energy Strategy) Capital cost 0 $/kW across all years No new capacity installed O&M Cost 37.8 $/kW and 0.00447 Energy Information $/kWh across all years Administration Lifetime 40 Thomson Reuters MACTool Report Retrofit capital cost 1500 $/kW across all years Energy Strategy Retrofit lifetime 20 Assumption by DIW ECON Efficiency 24% across all years Assumption by DIW ECON (due to age and bad condition of Ukrainian coal TPPs, see Energy Strategy and UNCS)

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Coal new (Ultra Super Critical Coal TPP) Variable Value Source Installed capacity 0 in 2013, then gradual Scenario depended variable construction of new capacity, depended on exogenous demand growth Availability factor 85% across all years Natio b nal Renewable Energy Laboratory Capital cost 2250 $/kW across all years Thomson Reuters MACTool Report O&M Cost 37.8 $/kW and 0.00447 Energy Information $/kWh across all years Administration Lifetime 40 Thomson Reuters MACTool Report Retrofit capital cost 1500 $/kW across all years Energy Strategy Retrofit lifetime 20 Assumption by DIW ECON Efficiency 50% across all years World Coal Association

Coal CHP Variable Value Source Installed capacity 4800 MW across all years, UNCS, Assumption by DIW continuous retrofit ECON Availability factor 75% in 2013, gradual Assumption by DIW ECON increase to 80% in 2030, flat (due to bad condition of afterwards Ukrainian coal TPPs, see Energy Strategy) Capital cost 0 $/kW across all years No new capacity installed O&M Cost 37.8 $/kW and 0.00447 Energy Information $/kWh across all years Administration Lifetime 40 Thomson Reuters MACTool Report Retrofit capital cost 1500 $/kW across all years Energy Strategy

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Retrofit lifetime 20 Assumption by DIW ECON Efficiency 24% across all years Assumption by DIW ECON (due to age and bad condition of Ukrainian coal TPPs, see Energy Strategy and UNCS)

Large hydro old Variable Value Source Installed capacity 4754 MW all retrofitted in the Energy Strategy first 10 years (2014-2023) Availability factor 31% in 2013, then gradual Thomson Reuters MACTool increase to 60% in 2030 Report, Assumption by DIW ECON (TR only gives capacity factors) Capital cost 0 $/kW across all years No new capacity installed O&M Cost 85 $/kW in 2013, then Thomson Reuters MACTool gradual decline to 60 $/kW in Report 2030, flat afterwards Lifetime 50 Assumption by DIW ECON (based on past information about hydro plants) Retrofit capital cost 128 $/kW across all years Energy Strategy (5 bln. UAH of investments until 2023 to ensure retrofit) Retrofit lifetime 40 Energy Strategy

Large hydro new Variable Value Source Installed capacity 0 MW in 2013, then gradual Energy Strategy construction of 1046 MW until 2030, flat afterwards Availability factor 55% in 2013, then gradual Assumption by DIW ECON

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increase to 60% in 2030, flat (Thomson Reuters MACTool afterwards Report only gives capacity factors) Capital cost 6250 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 4000 Report $/kW until 2030, flat afterwards O&M Cost 85 $/kW in 2013, then Thomson Reuters MACTool gradual decline to 60 $/kW in Report 2030, flat afterwards Lifetime 30 Thomson Reuters MACTool Report Retrofit capital cost 4375 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 2800 (70% of capital cost) $/kW until 2030, flat afterwards Retrofit lifetime 30 Energy Strategy

Small hydro Variable Value Source Installed capacity 90 MW in 2013, then gradual Thomson Reuters MACTool extension to 600 MW until Report 2030, flat afterwards Availability factor 55% across all years Thomson Reuters MACTool Report Capital cost 5000 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 3000 Report, Assumption by DIW $/kW until 2030, flat ECON afterwards. CAPEX for 2013 assumed to be the same as 2014 O&M Cost 56 $/kW in 2013, then Thomson Reuters MACTool gradual decline to 45 $/kW in Report

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2030, flat afterwards Lifetime 30 Thomson Reuters MACTool Report Retrofit capital cost 3500 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 2100 (70% of capital cost) $/kW until 2030, flat afterwards Retrofit lifetime 20 Assumption by DIW ECON

Pumped storage Variable Value Source Installed capacity 900 MW in 2013, then Energy Strategy gradual extension to 4700 MW until 2030, flat afterwards Availability factor 80% across all years Assumption by DIW ECON (TR only gives capacity factor) Capital cost 1550 $/kW across all years, Thomson Reuters MACTool CAPEX for 2013 assumed to Report, Assumption by DIW be the same as 2014 ECON O&M Cost 5 $/kW across all years, Thomson Reuters MACTool Report Lifetime 40 Thomson Reuters MACTool Report Retrofit capital cost 1085 $/kW across all years Assumption by DIW ECON (70% of capital cost) Retrofit lifetime 20 Assumption by DIW ECON

Large solar PV Variable Value Source Installed capacity 600 MW in 2013, then Thomson Reuters MACTool gradual extension to 1500 Report, UNCS

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MW until 2030, flat afterwards Availability factor 15% across all years Thomson Reuters MACTool Report Capital cost 3840 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 1900 Report, UNCS, Assumption $/kW until 2030, flat by DIW ECON afterwards, 2013 CAPEX based on UNCS (subcontractor) data O&M Cost 0.01 $/kWh across all years Thomson Reuters MACTool Report Lifetime 25 Thomson Reuters MACTool Report Retrofit capital cost 3456 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 1710 (90% of capital cost) $/kW until 2030, flat afterwards Retrofit lifetime 25 Assumption by DIW ECON (whole new panels)

Residential solar PV Variable Value Source Installed capacity 0 MW in 2013, then gradual Thomson Reuters MACTool construction of 459 MW until Report 2030, then gradual extension to 1000 MW until 2050 Availability factor 15% across all years Thomson Reuters MACTool Report Capital cost 5000 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 1500 Report $/kW until 2038, flat afterwards

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O&M Cost 0.01 $/kWh across all years Thomson Reuters MACTool Report Lifetime 25 Thomson Reuters MACTool Report Retrofit capital cost 4500 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 1350 (90% of capital cost) $/kW until 2038, flat afterwards Retrofit lifetime 25 Assumption by DIW ECON (whole new panels)

Wind Variable Value Source Installed capacity 263 MW in 2013, then UNCS, Scenario depended gradual construction of new variable capacity, depended on exogenous demand growth Availability factor 26% across all years Thomson Reuters MACTool Report Capital cost 2920 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 2100 Report, UNCS, Assumption $/kW until 2030, flat by DIW ECON afterwards, 2013 CAPEX based on UNCS (subcontractor) data O&M Cost 71 $/kW across all years Thomson Reuters MACTool Report Lifetime 25 Thomson Reuters MACTool Report Retrofit capital cost 2044 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 1470 (70% of capital cost) $/kW until 2030, flat afterwards

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Retrofit lifetime 20 Assumption by DIW ECON

Biomass Variable Value Source Installed capacity 0 MW in 2013, then gradual Scenario depended variable construction of new capacity, depended on exogenous demand growth Availability factor 76% in 2013, then gradual Thomson Reuters MACTool increase to 83% in 2030, flat Report afterwards Capital cost 6000 $/kW in 2013, then Thomson Reuters MACTool gradual decrease to 4500 Report $/kW until 2030, flat afterwards O&M Cost 0.05 $/kWh across all years Thomson Reuters MACTool Report Lifetime 20 Thomson Reuters MACTool Report Retrofit capital cost 4200 $/kW in 2013, then Assumption by DIW ECON gradual decrease to 3150 (70% of capital cost) $/kW until 2030, flat afterwards Retrofit lifetime 20 Assumption by DIW ECON

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Appendix B-2

This Appendix contains the list of measures used by Thomson Reuters Point Carbon (2013) to calculate the abatement potential in different sectors of the Ukrainian economy.

Measures in coal mining (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Coal Mine Methane – Ventilation Air 2 52 Machine (VAM) Coal Mine Methane – Combined 2 61 Heat and Power (CHP) Coal Mining Energy Efficiency 740 32

Measures in manufacturing (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Aluminum Scrap Recycling 40 37 Ammonia Energy Efficiency 3 17 Ammonia Electrolysis 58 76 Dry Cement Process 5 24 Wet Cement Process (Slag) 153 93 Shale Gas in Cement 65 19 Direct Reduction Iron 24 309 Iron Ore Production Energy 1 46 Efficiency Steel Rolling/Casting Energy 26 10 Efficiency Steel Continuous Rolling 2 158 Steel Electric Arc Furnace (EAF) 4 114 Lime Production Energy Efficiency 122 5 Paper Production Energy Efficiency 8 1 (Boiler Replacement)

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Measures in waste management (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Landfill Gas Power (LFG) 3 38 Clean Municipal Solid Waste Power 45 6 (MSW) Waste Water Utilization for Power 29 18 Segregate Colloids Utilization for 11 57 Power (Food Waste Biogas) Replace Obsolete Municipal Waste 12 4 Facilities (Pumping Systems) Composting 1 3 Biodegradable Plastics 2 160 Increased Recycling 68 24 Modern Materials Recycling 12 2 Waste Production Limits 215 215

Measures in buildings and heat supply (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Draught Proofing 6 91 Wall Insulation 16 57 Windows Energy Efficiency 33 31 Boiler Upgrades 11 84 Heat Pumps 294 24 Heat Network Optimization 35 66 Heat Network Boiler Upgrade 420 14 Water Energy Efficiency 18 64

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Measures in agriculture and other land use (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Biogas Plants - Cattle 5 100 Biogas Plants - Swine 13 24 Concentrated Fodder - Cattle 143 2 Reduction of Cows – Increased Milk 19 8 Production Ionophores in Cattle Ration 0 13 Zeolites in Cattle Ration 19 18 Sunflower Seeds in Cattle Ration 304 31 Crop Rotation 271 16 Extensive to Intensive Agriculture 9 27 Processes Nitrification Inhibitors in Corn 25 12 Production Erosion Prevention Measures 156 9 Preservation of Degraded Lands 36 3 Wetlands Renewal 0 13 Straw Combustion 304 31 Organic Farming 81 14 LULUCF – Organic Fertilizer Use 15 301 LULUCF – No Till Techniques 1 239 LULUCF - Afforestation 35 15

Measures in transport (2014-2054)

Action MAC Value in $/ton Emissions Reduction (2014- 2054) in millions tons CO2e Gas Pipeline Modernization 1078 25 Gas Transport Modernization 687 43 NG Pressure Reduction -366 6 Vehicle Energy Efficiency 921 188 Biofuels 199 133 City Transport Electrification 8230 0

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Railway Electrification 34 6 Source: Thomson Reuters Point Carbon (2013)

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Low Carbon Growth in Ukraine Final Report Appendix C-1: Green Growth Policy Paper No. 1

Appendix C-1 Benchmarking for sustainable and economically viable technology options

Low Carbon Ukraine - Policy Paper No. 1 (December 2012)

Low Carbon Growth in Ukraine Final Report Appendix C-1: Green Growth Policy Paper No. 1

Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Benchmarking for sustainable and economically viable technology options

The case of the metal industry in Ukraine

Low Carbon Ukraine - Policy Paper No. 1 (December 2012)

Project “Capacity Building for Low Carbon Growth in Ukraine”

Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Contact: DIW econ GmbH Dr. Lars Handrich Mohrenstraße 58 10117 Berlin Germany

Phone +49.30.20 60 972 - 0 Fax +49.30.20 60 972 - 99

[email protected] www.diw-econ.de

iii Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Table of contents

Executive Summary ...... iv 1. Introduction ...... 1 2. The benchmarking approach ...... 1 3. Benchmarking for the Ukrainian metal industry ...... 3 3.1 Database ...... 3 3.2 Benchmarking methodology ...... 4 3.3 Benchmarking results ...... 6 3.4 Implications for the metal industry of the Ukraine ...... 8 4. Conclusions and outlook ...... 14 Appendix ...... 15

iv Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Executive Summary

Detailed sectoral analysis is an important component of the search for green growth potentials in the economy of the Ukraine. The focus of the present paper is on developing a general approach for sectoral analysis that takes into account economic viability as well as environmental sustainability.

We present an international benchmarking approach that is based on the economic concept of efficiency. The yardstick for comparing the performance of the economic sectors in different countries is given by:

 High levels of desired outputs such as production volumes (in physical unites) or revenues (values),

 Low levels of undesired outputs like emissions or pollution, and

 Low levels of factor inputs like labour or energy use, or production costs.

As a first example, we apply our efficiency-based international benchmarking approach to the metal industry. The procedure is used to identify the relevant technological yardsticks that could be applied in the case of the Ukraine. The results show large potential for reducing greenhouse gas emissions in the Ukrainian metal industry. The full realisation of this potential would allow abating 52 Mt per year in terms of CO2 equivalent. The assessment of the corresponding investment need will be the subject of our consecutive analysis.

v Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

1. Introduction

Developing a low-carbon growth strategy requires an understanding of the mitigation potential in the most relevant sectors of an economy. The focus of the present paper is on developing a general approach for sector-specific analysis of mitigation potentials that takes into account economic viability as well as environmental sustainability. In this paper we apply the suggested methodology to the analysis of the metal industry in Ukraine.

In the following we present a benchmarking approach that is based on the economic concept of efficiency. In particular, it explicitly takes into account that production processes have undesired outputs, the amount of which should be minimized. In this first application we take greenhouse gas emissions as an example of such undesired outputs. The approach, however, can be applied for different industrial sectors and different kinds of adverse environmental impacts.

2. The benchmarking approach

The key objective of our benchmarking approach is to identify technology options for a given industrial sector to best combine sustainability and economic viability. The yardstick for this comparison is balanced combinations of:

 High levels of desired outputs such as production volumes (in physical unites) or revenues (values),

 Low levels of undesired outputs like emissions or pollution, and

 Low levels of factor inputs like labour or energy use, or production costs.

The focus of the benchmarking approach is on technologies that are actually used in practice, while theoretical solutions and technologies that are not yet implemented are not considered. Thus, only technically as well as economically feasible and viable solutions are considered as benchmarks.

1 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1 and price levels (see Box 1). For practical applications, however, such a comparison is strongly limited by the availability of data and relevant information. In particular, micro-level benchmarking of different installations or companies is rather difficult and requires access to private and often confidential information. Alternatively, one can benchmark the same industry across different countries. In this way, detailed company- or even installation- specific information is compensated by aggregate information from a range of countries, which is more easily available. Such an international benchmarking allows for identifying those countries where the most efficient technologies are used.

Box 2: The concept of economic efficiency

Efficiency is an economic concept which describes the optimal use of production factors in production processes. In economic terms, efficiency is evaluated as the relationship between the quantities of primary factor inputs such as labour, capital or energy (henceforth inputs) and the specific goods such as steel, chemicals or food (henceforth outputs) which are produced from these inputs. It is typically defined as either:

 The lowest-possible amount of inputs for the production of a given set of outputs (input- oriented efficiency); or

 The highest-possible level of outputs that can be produced from at given set of inputs (output-oriented efficiency).

Modern efficiency measurement starts by decomposing overall economic efficiency levels into several subcomponents that can be measured separately:

 Technical efficiency describes the ability of a firm to obtain optimal combinations of input and output quantities;

 Scale efficiency describes the ability of a firm to produce at optimal combinations of input and output quantities while optimising all scale economies; and

 Price efficiency is the most restrictive criterion which also reflects the ability of a firm to combine inputs and outputs in optimal proportions, given their respective price levels.

In this analysis, efficiency will be measured in terms of technical efficiency and scale efficiency.

2 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

3. Benchmarking the metal industry in Ukraine

In this section we illustrate our benchmarking approach for the case of the metal industry in Ukraine. To do so, we compare the relationship between the inputs used in the production processes in different countries (i.e. labour, capital and energy) and the respective outputs in terms of metal products and greenhouse gas (GHG) emissions.

3.1 Database

The available data on the metal industry in different countries stems from four key sources:

 the World Input Output Database (WIOD) which has been compiled by a consortium of scientific organizations with financial support of the European Union60,

 the Steel Statistical Yearbook of the World Steel Association61,

 the United States Geological Survey Mineral Resources Program62, and

 the United Nations Framework Convention on Climate Change (UNFCCC)63.

In those datasets, different industries are defined in accordance with the ISIC standard of the United Nations Statistics Division. The metal industry is described as “Manufacturing of basic metals” and “Manufacturing of fabricated metals”.64 This includes the production of pig iron, crude steel (primary or secondary fusion), precious and non-ferrous metals as well as metal casting and the production of fabricated metal products.65

With respect to the countries that are included in the international benchmarking exercise, our intention was to primarily cover technological leaders in the relevant sector. Our database covers 27 EU countries and 13 other major countries in the world for which the following information is available:

 GHG emissions (in thousand tonnes of CO2 equivalent, source: wiod.org)66,

 Energy Use, Emission Relevant (in TJ, source: wiod.org),

60 http://www.wiod.org/ 61 http://www.worldsteel.org/ 62 http://minerals.usgs.gov/ 63 National Inventory Submissions 2012, http://unfccc.int/national_reports/annex_i_ghg_inventories/ 64 ISIC Rev 3.1 sectors 27 and 28. 65 Note that the analysis does not include the mining of metal ore and the production of coke. 66 UNFCCC for Luxembourg and Ukraine 3 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

 Gross Output (in millions of US dollars, source: wiod.org),

 Number of persons employed (in thousand persons, source: wiod.org),

 Real fixed capital stock (in millions of US dollars, source: wiod.org),

 Total production of pig iron (in thousand metric tonnes, source: World Steel Association),

 Production of crude steel (in total and by production process, all in thousand metric tonnes, source: World Steel Association),

 Production of non-ferrous metals (aluminium and ferroalloys, in thousand metric tonnes, source: United States Geological Survey Mineral Resources Program)

For the present analysis 22 OECD countries and 3 non OECD countries are included in the data base (as listed in Table 1 below). The most recent information for all countries is available for the year 2007 which is the base year for the benchmarking exercise.67

Ukraine is included in all sources except of WIOD. While the levels of gross output and employment are available from national statistics, there is no reliable information on capital stocks in the metal industry. Hence, the country can only partially be considered in the following benchmarking analysis.

3.2 Benchmarking methodology

Table 1 gives a first impression on the performance of the metal industry in the different countries. The first two columns refer to the sustainability (emissions per output), the third and fourth column to economic viability (output per capital input) of the production processes in the different countries. For ease of comparison, the three top performers in each column are shaded in grey. With respect to sustainability indicators (columns i and ii), Italy, Slovenia, Luxembourg and Brazil show top performance, while Finland, Hungary, Korea, Brazil, Poland and Slovakia are the top countries with respect to economic viability (columns iii and iv). Finally, the table highlights the performance of the metal industry in Ukraine with respect to sustainability (columns i and ii).68

67 In fact, 2007 is a good choice for a base year since it is the last year before the start of the global economic crises. 68 Note that values for columns iii and iv are not available for Ukraine since there is no reliable estimate on capital stocks. 4 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Table 10: Comparison of capital and emissions intensities across counties, year 2007

Emissions per Volume of Emissions per volume of Revenue per production per revenue production capital stock capital stock 2007

(tons of CO2-e (tons of CO2-e (tons of metal per thousand per ton of metal product per US-$) product) (US-$ per US-$) thousand US $) (i) (ii) (iii) (iv) Australia 0.43 2.01 2.19 0.47 Austria 0.34 0.96 2.79 1.00 Belgium 0.22 0.69 2.65 0.86 Brazil 0.37 0.48 1.92 1.47 Canada 0.36 1.16 3.43 1.06 Czech Republic 0.47 1.06 2.45 1.09 Finland 0.23 0.87 4.03 1.08 France 0.15 0.70 2.61 0.56 Germany 0.21 0.81 3.08 0.79 Greece 0.13 0.64 2.89 0.60 Hungary 0.32 0.90 3.91 1.41 India 0.77 1.22 1.70 1.07 Italy 0.10 0.54 1.75 0.32 Japan 0.27 0.69 1.34 0.52 Korea 0.34 0.87 3.71 1.45 Luxembourg 0.13 0.25 2.06 1.04 Netherlands 0.20 0.56 2.90 1.05 Poland 0.58 1.18 3.31 1.62 Slovakia 0.71 0.93 3.04 2.31 Slovenia 0.09 0.85 3.12 0.34 Spain 0.13 0.65 2.16 0.43 Sweden 0.15 0.66 2.82 0.63 Taiwan 0.38 0.87 1.83 0.80 United Kingdom 0.32 1.14 2.77 0.79 United States 0.25 1.07 3.09 0.72 Ukraine 2.90 1.12 Source: DIW econ based on wiod.org, World Steel Association, UNFCCC, and the State Statistics Service of Ukraine

5 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

However, the comparison of different indicators does not yet allow for deriving overall conclusions. In fact, it must be emphasized that:

 First, output can be measured as value or in physical quantities (i.e. in millions of Dollars or in tons of output). However, metal industries produce a range of different products such as pig iron, crude steel, non-ferrous metals (aluminium and ferroalloys) based on different production processes. Hence, a meaningful output measure must consider the relevant structural characteristics.

 Second, the costs of production (inputs) not only include capital but also other key inputs such as labour and energy.

 Third, the isolated comparison of different indicators does allow for identifying the leaders in each respective category, but not necessarily for identifying those countries that perform relatively well in all categories. However, the objective of our benchmarking analysis is to identify the best combinations of both, sustainability as well as economic viability.

To identify the countries that realize the most-efficient combinations of inputs and outputs (that is, the most efficient technologies) we employ a specific, empirical estimation technique, the Data Envelopment Analysis (DEA). This is a well-established methodology for estimating different efficiency measures as described in Box 1 above based on a large variety of different input and output measures. For benchmarking metal industries, we consider the use of capital, labour and energy as key inputs and differentiate outputs into pig iron, crude steel69and non-ferrous metals. We also differentiate between primary and secondary production of crude steel as they represent different production chains and differ strongly by energy consumption and GHG emissions.

3.3 Benchmarking results

Our benchmarking analysis of the metal industry is based on output-oriented efficiency measures of technical efficiency und scale efficiency (see Box 1). In other words, we identify the countries that operate at an efficient scale and are able to produce the highest volumes of outputs with the lowest levels of emissions from a given set of inputs (output-oriented

69 Due to data limitations, the production of other steel products like rolled product, pipes, tracks etc. cannot be considered in this analysis. However, since these production processes cause relatively low levels of emissions as compared to those from pig iron and crude steel production, this does not significantly distort the results of the present analysis. 6 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1 efficiency). All efficiency estimates are given as indices ranging from zero to one, which indicates the best performance. For example, a technical efficiency score of one for a given country indicates that in no other country within our sample the metal industry produces more outputs from the same combination of inputs. Likewise, a technical efficiency score below one suggests that at least in one other country the metal industry is capable to produce higher outputs from the same inputs. Similarly, a scale efficiency score equal to one indicates that the country‟s metal industry is producing at efficient scale while a score of less than one indicates that other countries are better in utilizing scale economies.

Figure 40: Overall efficiency levels (technical efficiency & scale efficiency) of metal industries in selected countries (in 2007)

1,0

0,8

0,6

0,4

0,2

0,0

Source: DIW econ

Overall efficiency levels (technical efficiency & scale efficiency) of metal industries in the selected countries are summarised in Figure 1. Separate results for technical and scale efficiency are shown in the appendix. The overall result is as follows:

 In 12 out of the 25 countries in the sample (Australia, Belgium, Canada, Finland, Greece, Hungary, Korea, Luxembourg, the Netherlands, Slovakia, Slovenia and Spain) the metal industry operates fully efficient. These countries determine the technology frontier of the international metal industry in 2007.

7 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

 Among the inefficient countries, two different subgroups can be identified based on the additional results for technical efficiency and scale efficiency as shown in the Appendix:

 in Austria, Brazil, Germany, India, Italy, Japan, Poland and the United States, metal industries are technically efficient but operate at too large scale (that is, underutilization of available production capacities), while

 in the Czech Republic, France, Sweden, Taiwan and in the United Kingdom, the metal industry suffers also from technical inefficiency.

For all countries where the metal industry is found to be inefficient, the analysis provides insights for possible improvements. For example:

 The overall efficiency level for the Germany metal industry is estimated at 67% due to operations at an inefficient scale. Likewise, overall efficiency of the Brazilian metal industry equals 82% due to inefficient scale of operations. In fact, this suggests that the German and the Brazilian metal industry could produce the same output at only 67% and 82%, respectively, of its current scale (i.e., its current input levels).

 The overall efficiency level for the metal industry in the Czech Republic is estimated at 85%, caused by technical inefficiency (technical efficiency score of 0.98, see Appendix) and inefficient scale of operations (scale efficiency score of 0.87 , see Appendix). This suggests that:

 the industry could produce the same output at only 87% of its current scale (input levels), and

 given operations at an efficient scale (input levels), output could be increased by 2% (=1-0.98).

3.4 Implications for the metal industry of the Ukraine

Due to missing information on the capital stock of the Ukrainian metal industry, it is not possible to directly include Ukraine in the benchmarking analysis presented above. However, results and implications for Ukraine‟s metal industry can be derived by identifying countries with comparable structural characteristics and then focussing on the results and implications for these countries.

The most relevant structural characteristics of metal industries across the different countries are shown in Figure 41. With the exemption of Slovenia, Greece and Luxembourg, all

8 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1 countries under consideration produce pig iron as well as crude steel (Figure 41 a). The share of pig iron in total production of iron and steel in the Ukraine (45%) is close to the average share over all countries (34%). A second characteristic of the metal industry in Ukraine is a relatively large share of aluminium and ferroalloys (3%). In fact, of the remaining 25 countries, only Australia, Brazil, Canada, India and the United States produce higher volumes of non-ferrous metals than Ukraine, while Luxembourg, Hungary, the Czech Republic, Austria, Belgium and Taiwan have no or almost no production. Hence, based on output composition the metal industry of Ukraine is not comparable to Slovenia, Greece, Luxembourg, Hungary, the Czech Republic, Austria, Belgium and Taiwan and most similar to Australia, Canada, Brazil, India and the United States.

9 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Figure 41: Structural characteristics of the metal industry in different countries

c) Output composition

140

120

100

60 million tons million

Pig Iron Crude Steel Aluminium & Ferroalloys

Source: DIW econ * Complete data for Japan omitted for better representation. Full data: 87 million tons of pig iron, 120 million tons of crude steel, 0.9 million tons of non-ferrous metals. b) Structure of crude steel production processes

100%

80%

60%

40%

20%

Oxygen Blown Converter and Open Hearth Furnace Electric Furnace

Source: DIW econ

10 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

The second relevant structural characteristic of metal industries is the structure of crude steel production (Figure 41 b). Overall, two main processes can be distinguished. Oxygen blown converters (OBCs) and open hearth furnaces (OHFs) are methods of primary steelmaking in which pig iron is made into crude steel70 while electric arc furnaces (EFs) are mostly used for secondary steelmaking based on metal scrap71. Obviously, all countries where pig iron is produced also use OBCs as primary steelmaker, however at different levels72. Here, Ukraine stands out with 96% primary steel production based on OBCs and OHFs. With respect to GHG emissions, this is of particular relevance since primary steelmaking based on OBCs and in particular OHFs is more energy- and thus, emission-intensive than secondary steelmaking based on EFs. Hence, a metal industry that is structurally similar to that of the Ukraine should also be focussed on primary steel making. As can be seen in Figure 41 b, the countries with similarly high share of OBCs are the Netherlands, Slovakia, Austria, the Czech Republic and to a lesser extent Australia, Belgium, Brazil, Finland, Germany, Hungary, Japan, Sweden and the United Kingdom.

The results of this comparison are summarised in the following Table 2. Obviously, for four countries in the sample the metal industries are similar to that of the Ukraine by output composition as well as by crude steel production processes:

 Australia,

 Brazil,

 the Netherlands, and

 Slovakia.

The industries in Australia, the Netherlands and Slovakia operate at the highest efficiency levels (see Figure ), whereas Brazil is technically efficient but operates at too large scale. These countries can be taken as peer countries for identifying sustainable and economically viable technology options for the Ukrainian metal industry. To a lesser extent, this is also the case for Austria, Canada, Finland, India and the United States. However, the industries in these countries are all not fully comparable. In Canada, India and the United States, the share of EFs in the production of crude steel is much higher than in the mentioned peer countries. In Austria and Finland, the share of energy-intensive productions of non-ferrous metals is too low.

70 http://en.wikipedia.org/wiki/Basic_oxygen_steelmaking 71 http://en.wikipedia.org/wiki/Electric_arc_furnace 72 Nevertheless, pig iron is also traded internationally. 11 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Table 11: Comparing the metal industry of the Ukraine with other countries

Countries with a structurally similar metal industry by… Efficiency score (technical efficiency) … crude steel … output composition production process

Australia X x 1.00 Austria X 1.00 Belgium x 1.00 Brazil X x 1.00 Canada X 1.00 Czech Republic X 0.98 Finland x x 1.00 France x 0.98 Germany x 1.00 Greece 1.00 Hungary x 1.00 India X 1.00 Italy 1.00 Japan x 1.00 Korea 1.00 Luxembourg 1.00 Netherlands x X 1.00 Poland 1.00 Slovakia x X 1.00 Slovenia 1.00 Spain 1.00 Sweden x x 0.99 Taiwan 0.98 United Kingdom x x 0.95 United States X 1.00

Key: Strong similarity

Slight similarity Source: DIW econ

Having identified the relevant peers for the metal industry in Ukraine, we will next take a closer look at the performance indicators of these countries. For convenience, Figure 3

12 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1 presents the indicators of emissions intensity and economic viability. The reversed direction of bars for emissions intensity illustrates that emissions are an undesired output.

Figure 42: Comparison of Ukraine with peer countries

2 US US per $ US $

per ton per

- 2

CO 1 maximum reduction

potential (60%) tons

2 Ukraine Brazil Netherlands Slovakia

Emissions per volume of production Revenue per capital stock

Source: DIW econ

What can be inferred from these values for the GHG emissions reduction potential of the Ukrainian metal industry? Although the respective emission values for the Netherlands and Slovakia are higher than the Brazilian, the two countries operate at a fully efficient level whereas Brazil operates at too large scale. Nevertheless Brazil is technically efficient and possesses of the comparable share of non-ferrous metals production, mainly aluminium production. As for the year 2007, the difference in technological level between the metal sectors in the two countries corresponds to roughly 60% higher emissions per unit of output in Ukraine. Related to the emissions level of 2007, the full realisation of this potential (which obviously requires very substantial investment expenditures) would allow abating 52 Mt per year in terms of CO2 equivalent.

13 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

4. Conclusions and outlook

The benchmarking approach presented in this paper takes into account two main aspects of the industrial production processes: economic viability and environmental sustainability. Both aspects are combined in the economic concept of efficiency, which can be measured for a given industry in different countries and used for comparison. The general approach is applicable to different industries.

The presented analysis should be extended at a later stage to include several important aspects. First, an assessment of the investment need corresponding to the adoption of the identified benchmark technologies should be carried out. Second, the analysis should be extended to include the dynamic perspective, the development of the technology frontier over time.

14 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

Appendix

Figure A1: Technical efficiency levels of metal industries in selected countries (in 2007)

1,0

0,8

0,6

0,4

0,2

0,0

Source: DIW econ Figure A2: Scale efficiency levels of metal industries in selected countries (in 2007)

1,0

0,8

0,6

0,4

0,2

0,0

Source: DIW econ 15 Benchmarking for sustainable and economically viable technology options The case of the metal industry in Ukraine Green Growth Policy Paper No. 1

16 Low Carbon Growth in Ukraine Final Report Appendix C-2: Green Growth Policy Paper No. 2

Appendix C-2 Towards a low carbon growth strategy for Ukraine

Low Carbon Ukraine - Policy Paper No. 2 (April 2013)

Low Carbon Growth in Ukraine Final Report Appendix C-2: Green Growth Policy Paper No. 2

Low Carbon Growth in Ukraine Final Report Appendix C-1: Green Growth Policy Paper No. 1

Towards a low carbon growth strategy for Ukraine

Key policy steps

Low Carbon Ukraine - Policy Paper No. 2 (April 2013)

Project “Capacity Building for Low Carbon Growth in Ukraine”

Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Contact: DIW econ GmbH Dr. Lars Handrich Mohrenstraße 58 10117 Berlin Germany

Phone +49.30.20 60 972 - 0 Fax +49.30.20 60 972 - 99

[email protected] www.diw-econ.de

ii Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Table of Contents

Executive Summary ...... iv 1. Introduction ...... 1 2. The structure of the Ukrainian economy ...... 1 2.1 Ukraine‟s economic structure and pattern of trade ...... 1 2.2 Ukraine‟s carbon record ...... 4 3. Towards low carbon growth in Ukraine ...... 7 3.1 Framework conditions for low carbon growth ...... 7 3.1.1 The role of the Ukrainian government ...... 7 3.1.2 The role of International Cooperation ...... 9 3.2 A sectoral policy mix ...... 10 3.2.1 Industrial Sector ...... 10 3.2.2 Energy Sector ...... 11 3.2.3 Transport Sector ...... 14 3.2.4 Residential Sector ...... 15 4. Policy Recommendations ...... 15 Literature ...... 17

iii Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Executive Summary

iv Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

1. Introduction

From 2000 until the beginning of the economic crisis in 2008, the Ukrainian economy had been growing significantly, however at the expense of excessive depletion of natural resources and degradation of the ecosystem. Due to the heavy reliance on cheap energy, especially coal, and the production of steel, the economic structure of Ukraine is highly carbon intensive. Today, Ukrainian policy makers are facing the challenge to decrease GHG emissions without restricting economic activity. But how can the Ukrainian government tackle emission reductions and climate change while at the same time maintaining further growth, even in the short run?

This paper discusses the policy framework needed to enable sustainable investments in clean technologies. We outline concrete steps and recommendations for the Ukrainian government in the uptake of a low carbon growth strategy.

The paper is structured as follows. Section 2 provides a brief overview of the general economic structure, pattern of trade and carbon intensity in Ukraine and identifies the economic sectors with the greatest potential for emission reductions. Section 3 discusses the framework conditions needed for the successful implementation of a low carbon strategy and outlines a specific policy mix aimed at the different economic sectors identified in section 2. Section 4 concludes with a summary of the main policy recommendations established throughout this paper.

2. The structure of the Ukrainian economy

2.1 Ukraine’s economic structure and pattern of trade

Although Ukraine‟s economy had been growing significantly until the crisis in 2008/2009, this is not a blueprint for future development. The significant growth in the years before the crisis relied to a considerable extent on the abundant availability of rather cheap energy resources. The pre-crisis economic model relied heavily on cheap energy imports mainly from Russia and high export volumes of ferrous and non-ferrous metals (see Tables 1 and 2).

1 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Ferrous and nonferrous metals account for a third of total exports while the import share of mineral products (including mineral fuels such as oil and gas) is even more than one third of total imports. With a negative trade balance the current economic model triggers high trade and current account deficits and will not be sustainable anymore.

Table 1: Ukraine’s export structure (2010-2012)

2010 2011 2012

In million USD Agricultural products 9,935 12,804 17,881 Mineral products (incl. mineral fuels) 6,237 9,608 6,945 Chemicals 4,658 6,980 6,763 Timber and wood products 1,768 2,184 2,193 Industrial goods 1,309 1,621 1,543 Ferrous and nonferrous metals 17,333 22,114 18,890 Machinery and equipment 9,183 11,895 13,284 Other (incl. informal trade) 1,768 2,212 2,313 Total (million USD) 52,191 69,418 69,812

% of total Agricultural products 19.0 18.4 25.6 Mineral products (incl. mineral fuels) 11.9 13.8 9.9 Chemicals 8.9 10.1 9.7 Timber and wood products 3.4 3.1 3.1 Industrial goods 2.5 2.3 2.2 Ferrous and nonferrous metals 33.4 31.9 27.1 Machinery and equipment 17.5 17.1 19.0 Other (incl. informal trade) 3.3 3.2 3.3 Total (%) 100 100 100

Source: National Bank of Ukraine (2013), own calculations

2 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Table 2: Ukraine’s import structure (2010- 2012)

2010 2011 2012 In million USD Agricultural products 5,762 6,346 7,520 Mineral products (incl. mineral fuels) 20,707 29,396 27,077 Chemicals 10,524 12,961 13,519 Timber and wood products 2,000 2,230 2,182 Industrial goods 3,355 3,508 4,465 Ferrous and nonferrous metals 4,128 5,695 5,238 Machinery and equipment 12,689 20,018 22,433 Other (incl. informal trade) 1,414 5,516 7,870 Total (million USD) 60,579 85,670 90,304

% of total Agricultural products 9.7 7.4 8.3 Mineral products (incl. mineral fuels) 34.3 34.3 30.0 Chemicals 17.4 15.1 15.0 Timber and wood products 3.3 2.6 2.4 Industrial goods 5.5 4.1 4.9 Ferrous and nonferrous metals 6.8 6.6 5.8 Machinery and equipment 20.7 23.4 24.8 Other (incl. informal trade) 2.3 6.4 8.7 Total (%) 100 100 100

Source: National Bank of Ukraine (2013), own calculations

Relying on energy intensive production technologies, Ukraine‟s heavy industry also causes high emission rates of GHG. For instance, steelmaking in Ukraine requires four times more energy in Ukraine than in China (OECD 2012). In a separate paper, we estimate the annual potential of reducing GHG emissions for the Ukrainian metal industry to reach a level of 52

Mt per year in terms of CO2 equivalent if switching to efficiency based technologies available today (see DIW econ 2012c).

The Ukrainian economy is lacking competition and transparency in the energy sector, especially in the gas market, causing rent seeking behaviour and economic inefficiencies. The funds spent on subsidies in the energy sectors place a heavy strain on the government budget, decrease the funds available for other policy measures and increase the tax burden.

A serious drawback is the focus of economic investments on the recommissioning of already existing but outdated production capacities, thereby impeding the real increment in more efficient production capacities and technologies. Furthermore, innovations – essential for 3 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

sustainable growth – do not play an important role as a growth determinant in Ukraine. The growth of Ukrainian GDP is driven much less by Research and Development than in other nations (see Technical Paper, DIW econ 201373).

In the past five years, economic growth in Ukraine slowed down significantly. This is mainly due to the following reasons:

 A declining demand for Ukrainian exports. In particular China has switched in recent years from a net-importer to a net-exporter of ferrous and non-ferrous metals by building up its own production capacities.

 The gas crisis: Since 2005 import prices for Russian gas have been increasing significantly. Today Ukraine is paying one of the highest gas prices in Europe.

 The economic and financial crisis in 2008/2009 and the political turmoil in the Mediterranean and North Africa (MENA) region have led to a further drop in demand for Ukrainian exports.

 Ukraine is lacking behind with regards to foreign direct investments; the investment environment is rather poor and has significantly deteriorated in the recent past. The combination of widespread corruption and weak investor protection with a flawed judiciary has left Ukraine 152nd out of 183 countries in the World Bank‟s most recent Ease of Doing Business ranking.

 Furthermore, the Ukrainian economy suffered repeated financial bottlenecks.

In conclusion, the present Ukrainian economic model is exhausted and will not generate any further economic growth. Instead, new reform impulses are needed to overcome traditional production structures that are no longer efficient, internationally not competitive, and unsustainable in environmental and social terms. Ukraine needs a structural change in order to achieve a realigned, sustainable growth path.

2.2 Ukraine’s carbon record

Along with increasing international efforts to reduce GHG emissions, the average carbon intensity of GDP has been falling in the EU-15 countries, the United States as well as in China (EBRD 2011). Even previous transition economies in Europe and Asia, for example Poland (World Bank 2011) and Kazakhstan (DIW econ 2012a), have drafted and

73 DIW econ (2013): “Assessing the innovation potential in Ukraine – Recent track record and implications for low-carbon development”. Low Carbon Ukraine – Technical Paper No.1. 4 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2 implemented ambitious programmes to decrease the carbon content of their GDP. Ukraine on the contrary lags far behind these international developments.

With an aggregate GHG emission of 383 million metric tons of CO2 equivalent (UNFCC 2012a)74, Ukraine is placed among the twenty countries with the highest emissions worldwide

(European Commission 2011). Table 3 shows a comparison between CO2 emissions from energy use (per unit of GDP) in Ukraine and other previous transition economies.

Table 3: Carbon intensity in selected Eurasian economies75, 2010 Ukraine Kazakhstan Russia Poland Carbon intensity

(kg CO2 / $ of 3.03 2.44 1.82 0.79 GDP)

Source: own calculations

As illustrated in Table 3, Ukraine needs more CO2 emissions to produce one unit of GDP than other countries in the region. This is all the more remarkable given the fact that Kazakhstan and Russia are sizeable producers of oil and natural gas, which usually is connected with high emissions from extraction operations. The carbon intensity of Ukrainian exports is far above that of all other countries in Europe, East Asia and the Commonwealth of Independent States (CIS) and is more than six times higher than world average (EBRD 2011).

The sectoral distribution of emissions in Ukraine is shown in Table 4. This is a preliminary account, splitting total GHG emissions in Ukraine according to the major emitting sectors of its economy. While a more detailed investigation of these sectors will be provided during the course of the current project “Capacity Building for Low Carbon Growth”, this overview offers a useful starting point.

More than half of total Ukrainian emissions are accounted for by fuel combustion in industries and industrial processes as well as by the generation of electricity and heat production.

74 Value for 2010, excluding emissions from land use and land use change (LULUCF) (UNFCCC 2012a). 75 Carbon Intensity for 2010 in metric tons of Carbon Dioxide from energy use per thousand U.S. Dollars of GDP, 2005 prices and market exchange rates (EIA 2011). 5 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

Among the industrial sectors, the majority of emissions (17.8 percent) stems from the metal industry, followed by the chemical industry with 3.9 percent.

The extraction, transport and processing of oil, natural gas and coal constitute another major source of emissions (13.3 percent). The transport and residential sector account for 10.4 percent of total emissions each. Out of the emissions in the transport sector, 75 percent are attributable to the use of road vehicles, corresponding to 7.7 percent of total emissions.

Table 4: Green house gas emissions76 in Ukraine by sector, 2010

CO2 equivalent (in Percent of Sector 1000 metric total tonnes) emissions

Industrial processes and fuel combustion in industry 105 510 27.5 Industry: metals 68 278 17.8 Industry: chemical products 14 896 3.9 Industry: mineral products 9 323 2.4 Industry: other 5 743 3.4 Electricity and heat production 94 370 24.6 Production, transport and processing of energy resources (oil, gas & coal) 50 842 13.3 Energy: natural gas fugitive emissions 19 814 5.2 Energy: coal mining 19 675 5.1 Energy: other 11 353 3.0 Transport sector 40 025 10.4 Transport: road vehicles 29 431 7.7 Residential sector 39 921 10.4 Other (incl. agriculture, waste) 52 513 13.7 Total 383 182 100.0

Source: UNFCCC 2012a, own calculations

76 All Greenhouse Gases included, emissions from LULUCF excluded. 6 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

3. Towards low carbon growth in Ukraine

The alternative to the current carbon intensive growth in Ukraine is the adoption of an innovative low carbon growth77 strategy. Low carbon growth strategies comprise two broad sets of policies: (i) framework policies and (ii) sectoral policies. The implementation of such policies necessarily implies the decoupling of energy consumption and GHG emissions from economic development (Low Carbon Growth). Climate policies should be not regarded as a burden to economic development but rather as a chance for further sustainable growth opportunities. In fact, greening policies can induce many positive development outcomes such as enhanced productivity and innovation, the creation of new jobs and markets as well as fiscal revenue generation. There are first signs that this awareness is starting to be reflected in Ukrainian policies. Ukraine has already undertaken some first measures to green its economy (MEP 2006). One example is the discussion of the introduction of an Emission Trading System (ETS), possibly preceded by a carbon tax.

3.1 Framework conditions for low carbon growth

3.1.1 The role of the Ukrainian government

For a successful transition to low carbon growth, government intervention is indispensable. The main task for the government consists in redirecting market forces towards cleaner production and investment. This is achieved by narrowing the cost gap between brown and green technologies, by pricing in the costs of GHG emissions. Such measures include the phasing out of unsustainable brown subsidies, reforming policies, redirecting public investments and enforcing market-based mechanisms. As soon as the cost of brown technologies exceeds the cost of clean technologies, investments in specific abatement technologies will be more attractive yielding positive returns for investors.

The two major instruments for the government to price in GHG emissions in order to redirect market forces towards low carbon investments are a carbon tax or the introduction of an Emission Trading System (ETS).78

77 In general, low carbon growth refers to conventional economic growth at increased efficiency with respect to GHG emissions and the use of natural resources (DIW econ 2012). 78 A more detailed analysis of these instruments and their implementation possibilities is to follow in a further policy paper in the course of the current DIW econ project “Capacity Building for Low Carbon Growth in Ukraine”. 7 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

 A tax on carbon emissions increases the cost of carbon intensive production and thereby provides a financial incentive for firms and private investors to reduce GHG emissions. A tax may institutionally be easy to administer in Ukraine, but determining the optimal tax rate is not a trivial matter. Too low tax rates may not trigger any significant cuts in GHG- emissions, while prohibitive tax rates endanger industrial competitiveness (NECU 2011). In general, it is hard to reach a specific reduction target by using a tax on carbon.

 At present, the Ukrainian government plans to introduce a cap-and-trade system for CO2 emission certificates. Trading systems, such as the European Union ETS, are based on setting a maximum ceiling on emissions. Enterprises that emit less GHG than their cap can sell unused emission allowances to firms that are likely to emit more carbon. Such a carbon market does not require any further environmental standards to be enforced since firms will seek to emit less GHG to avoid having to purchase more allowances. It does, however, require sound economic judgment in setting the level of the cap (DIW econ 2012b) and needs additional efforts to set up a market place for CO2.

Apart from increasing the relative price of dirty to clean technologies, innovation is crucial for the realisation of low carbon growth. As Aghion et al. (2009) point out, “governments (...) need to influence not only the allocation of production between clean and dirty activities, but also the allocation of research and development between clean and dirty innovation.” In the absence of government intervention to redirect technological development towards clean innovations, innovations tend to be biased towards already existing dirty technologies (Aghion et al. 2009). Hence an optimal policy combines carbon pricing (or carbon permits) with strong support in clean-innovation R&D. In practice, these subsidies in R&D can be financed through the receipts form carbon pricing.

In Ukraine, research and development is not sufficiently developed. From 2005 to 2011, spending in R&D measured as a share of GDP decreased from 0.14 per cent to 0.08 per cent (DIW econ 2012).79 However, Ukraine has a comprehensive network of higher education institutes80 and possesses a rather well developed industrial base capable of manufacturing complex machinery. These factors, in theory, form a sufficient basis for the

79 For a more detailed discussion on the innovation potential of the Ukrainian economy see DIW econ (2013): “Assessing the innovation potential in Ukraine – Recent track record and implications for low- carbon development”. Low Carbon Ukraine – Technical Paper No.1. 80 For example, Ukraine outscores other countries in the region, as well as France and the United Kingdom, on the 2011 education index compiled by the United Nations Development Programme (UNDP), which takes literacy and schooling years into account (UNDP 2011). 8 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

development of domestic research in clean technologies. Hence there is a strong need for the government to engage in the promotion of domestic research and development. Possible measures are the promotion of cutting-edge scientific research in natural and economic sciences and specific training aimed at developing new skill sets needed for green jobs. This may be supported by administering specific research subsidies81. Policies should also encourage the cooperation between enterprises, think tanks and universities in the framework of a National System of Innovation (OECD 1997).

» To encourage private investment in clean technologies, public intervention is indispensable. Public policies should combine measures that increase the relative cost of dirty to clean technologies, e.g. through carbon pricing or the introduction of an ETS, with direct subsidies in clean-innovation R&D.

3.1.2 The role of International Cooperation

In many sectors, private investment may be limited to financial constraints and may depend on technology82 transfers from abroad. Fortunately there exist several possibilities of external assistance (OECD 2012), for example within the framework of the UNFCCC or international organisations. Since European, North-American and Asian companies currently operate at the technology frontier with respect to GHG emission reductions, the Ukrainian economy may profit significantly from technological spill over effects from abroad.

Some of the publically supported channels include:

 The Global Environmental Facility (GEF), mandated by the Conference of the Parties to the UNFCCC to support technology diffusion in developing and transition economies. Projects include renewable energy, energy efficiency, sustainable transport, and innovative financing initiatives (GEF 2010);

 The Framework for Implementing Agreements, administered by the OECD and the International Energy Agency (IEA), to share research on breakthrough energy-related technologies and to assist with deployment programmes in member and non-member states (IEA 2003);

81 Domestic research subsidies for green technology may, apart from accumulating domestic knowledge, also lower the final price of green products, or decrease their time to market. This in itself may have positive effects on the uptake of these technologies and thus on climate change mitigation (Acemoglu et al. 2012). 82 The term technology does not only refer to equipment, but also includes know-how and organisational methods suited to cutting emissions associated with particular economic activities (IPCC 2000). 9 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2

 Technical assistance provided by foreign governments or the European Union, i.e. the EU project PROMITHEAS aimed at fostering cooperation among research institutes and universities of the Black Sea littoral states with a focus on renewable energies;

 Multilateral financial institutions such as the European Bank for Reconstruction and Development (EBRD) or the World Bank may give access to large scale loans. The World Bank has been active in financing energy efficiency investments in the building sector of Eastern European countries through targeted loans to its governments (World Bank 2008). Similarly, the EBRD has been financing projects within its Ukrainian Energy Efficiency Programmes (UKEEP).

To fully gain access to financial and technological transfers from abroad, a number of steps are necessary. Firstly, the potentials of specific technologies to reduce GHG emissions as well as their economic cost need to be assessed for all economic sectors within the framework of Technology Needs Assessments (TNAs). Secondly, Ukraine may need to enter into further commitments with multilateral financial institutions or foreign governments. Such commitments may involve a firm assurance by the Ukrainian government to promote low carbon growth policies and to set an ambitious reduction target in the level of GHG emissions.

» In order to gain access to financial assistance and technology transfers from abroad, Ukraine may need to enter into further international commitments and to guarantee a more ambitious target in the level of emission reductions.

3.2 A sectoral policy mix

In the following section we discuss sector specific policy measures to be applied in the emission-intensive sectors identified in Section 2.1. Following the structure of Table 4, these sectors include the industry, energy, transport and residential sector and are listed in decreasing order in terms of their share of total GHG emissions.

3.2.1 Industrial Sector

As illustrated in Table 4, industrial processes and fuel combustion in industry constitute the highest share of emissions in Ukraine (27.5%). This is due to the fact that manufacturing activity in Ukraine is dominated by energy-intensive steel production. The key reason for the energy intensity is an ageing capital stock with outdated and inefficient production technologies that remain in use across the entire region. Smelting one tonne of steel in

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obsolete open-heart furnaces in Ukraine consumes almost four times more energy than smelting one tonne of steel in the European Union countries or China (OECD 2012).

Hence the industrial sector and in particular the metal industry present much potential for the reduction of GHG emissions.83 This however can only be achieved if the capital stock, especially outdated infrastructure and production capacities, is modernized. Promising energy efficiency measures include the improvement of energy management systems, heat recovery, the replacement of electric apparatus, improved cooling systems as well as the replacement of furnaces and kilns in the metals and cement industries (UKEEP 2012).

In order to provide incentives to implement such technologies, regulatory standards for production technologies should be tightened and coupled with reinforced penalties for their violation.

» The main requirement for emission reductions in the industrial sector is the improvement of energy efficiency. This implies the modernization of the capital stock, especially of deteriorate infrastructure and outdated production capacities.

3.2.2 Energy Sector

In order to put Ukraine on a low carbon growth path, much effort has to be directed into the energy sector. The Ukrainian energy sector, especially the gas market, is characterized by “dirty” technologies, high levels of inefficiencies and corruption, rent-seeking behaviour, and misleading incentives by distorted prices.

3.2.2.1 Energy Resources: Oil, Natural Gas and Coal Emissions in the energy sector mainly arise from the production, processing and transport of oil, natural gas and coal. The bulk of emissions (78 percent) originate in equal parts from the transport of natural gas and the mining of solid fuels, primarily coal. Both of these activities offer substantial scope for the reduction of GHG emissions, for example trough the modernisation and optimization of the gas transport system. Particularly promising measures

83 For a detailed discussion of low carbon growth potentials in the metal industry, see DIW econ (2012c): “Benchmarking for sustainable and economically viable technology options – The case of the metal industry in Ukraine”. Green Growth Policy Paper No. 2. 11 Towards a low carbon growth strategy for Ukraine: Key policy steps Green Growth Policy Paper No. 2 include the reduction in greenhouse gas methane emitted from leakages in natural gas pipelines (NECU 2010).

Apart from investments in infrastructure, the key of reducing GHG emissions in the energy sector rests on encouraging market based competition. Specifically, we propose such efforts to include:

 Phasing out direct subsidies to coal mining (Handrich, Pavel and Naumenko 2009) and phasing out economically unfeasibly low tariffs for natural gas consumers. Currently, Ukraine‟s direct and indirect energy subsidies are the world‟s 8th largest in terms of economic value (UNEP 2008). This has become a significant strain on the national budget, a liability in relations with Russia as far as gas is concerned, and a major deterrent to energy efficiency (Opitz 2010);

 Restructuring of the gas sector through transparent regulation by independent authorities, unbundling and demonopolisation in the gas transport and distribution as well as in the oil and gas extraction sectors (Pavel and Poltavets 2005; Aslund and Paskhaver 2010).

These policy measures will lead to an increase in the price of fossil energy, as advocated in Section 3.1.1. Industries that rely on the input of fossil fuels will become less competitive, while non-energy intensive industries, such as the agricultural sector, could profit. This brings about the need for structural change and diversification of the Ukrainian economy. In an international perspective, this will lead to changes in the comparative advantage of Ukraine. Steel and other heavy industries will display decreasing volumes of production, leading to a change in the pattern of foreign trade.

Apart from the structural change, Ukraine needs a stable and reliable macroeconomic policy permitting firms to predict the change of energy prices. Only if policy makers ensure in a credible way that the induced changes of relative energy prices are permanent, firms will make long term investments into energy efficiency and clean technologies.

» The key to reduce GHG emissions in the energy sector lies in promoting market based competition. This involves phasing out direct subsidies to coal mining, phasing out unfeasible low tariffs for natural gas as well as the demonopolisation of the gas transport sector.

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3.2.2.2 Electricity and Heat Production The overwhelming majority of heat in Ukraine (97 percent) is produced by burning natural gas. Natural gas also plays an important role in electricity generation with 8 percent of power stemming from this source. The most important fuel for electricity production is domestic coal (36 percent). Renewable energy sources, mainly large hydro stations, only generate 7 percent of electricity (IEA 2009).

The electricity and heat production sector is the largest fuel consumer in Ukraine after the industrial sector. Emissions from the production of electricity and communal heat account for about 25 percent of all GHG emissions in Ukraine (see Table 4). These emissions can broadly be divided into two categories: (i) Emissions deriving from the combustion of fossil fuels used for heat generation, (ii) emissions deriving from technical losses in heat and electricity generation and distribution.

Emissions from losses in heat and electricity generation and transmission hinge to a large degree on the state of current infrastructure and equipment (Pavel 2007). Both the natural gas transmission and distribution infrastructure as well as the storage and gas-fired units for district heating systems are in dire need for upgrade and renovation (IEA 2012).

The principal way to compel electricity companies and district heating providers to invest in infrastructure improvements lies in liberalising utility tariff rates and improving regulation (Pavel and Chukhai 2005). There is a strong need for removing subsidies for private gas consumption and district heating systems and to adjust residential electricity tariffs to cost- reflective levels (IEA 2012). These must include the capital and replacement costs.84

With respect to renewable energies, investments in wind power, biomass and photovoltaic installations have experienced an increase recently (Trypolska 2012). Generous feed-in- tariffs for solar and wind energy generation and an obligation for state companies to connect new units to the grid, have given a boost to Ukraine‟s renewable energy development (IEA 2012). However, quantities are yet too small to account for an aggregate impact.

In order to further reduce Ukraine‟s dependence on fossil fuels, we propose to:

84 To minimise the social impact of these reforms, the tariff increases may be carried out in a stepwise procedure (Opitz, Dodonov and Pfaffenberger 2004).

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 Maintain the current system of feed-in tariffs guarantying access for electricity generated from renewable sources to the national grid;

 Clarify land rights, permit renewable energy installations for private households and increase the capacity of the grid to include green electricity;

 Support the construction of small biomass cogeneration plants in agricultural areas through preferential loans in order to decrease emissions from heat production and agricultural waste.

» Emission reductions in the heat and electricity sector are subject to the modernization of the gas transmission system and the renovation of depreciated heating equipment. These investments in infrastructure can only be induced if subsidies for gas consumption are eliminated and electricity tariffs are increased to full cost-recovery levels.

3.2.3 Transport Sector

Sectors with comparatively low carbon intensity, such as the transport sector, provide great opportunities for emission reductions. With emissions accounting for 11 percent of total emissions in Ukraine, the energy intensity of transport in Ukraine is lower than that of the EU- 27 average (UNECE 2010). However, this may change in future since incomes are growing and car ownership is increasing (Ernst & Young 2012). The challenge for policy makers consists in reducing present emissions and detaining future emissions from a potentially growing sector.

Measures such as the successive implementation of fuel efficiency standards and fuel taxes in the private transport sector may provide a starting point. These could be complemented by investments in public transport modes and the modernisation of the railway, urban tram and bus systems. With respect to road transport, improvements can be achieved by investments in better road infrastructure and by supporting the implementation of electric, hybrid and fuel cell electric vehicles (DIW econ 2011). However, more research on transport capacities in Ukraine is needed to determine the ecological and efficiency gains of these policy options.

» Promising measures in the transport sector include the adoption of fuel efficiency standards and taxes, the improvement of road infrastructure and the modernization and expansion of the public transport system.

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3.2.4 Residential Sector

The residential sector offers great scope for improvement in energy efficiency, especially in the private household sector. The increase of electricity and utility tariffs to cost covering levels advocated in section 3.2.2.2 will give residents the incentives to save energy (Meissner, Naumenko and Radeke 2012). They may start to engage in energy efficiency investments such as heat containment in buildings through insulation of walls, windows and roofs. This does not only improve energy efficiency, but also reduces Ukraine‟s dependence on Russian oil and gas imports and creates jobs in the construction and handicraft sector.

A mix of measures that have been discussed in neighbouring countries with similar residential structures may prove fruitful (Opitz 2003):

 Enforce the deployment of metering and heat controls to supply residents with the tools to control and reduce their energy consumption;

 Improve the framework for the operation of Energy Service and Performance Companies;

 Sharpen the definition of residential property rights and support the formation of resident associations as methods to share investment outlays and risks;

 Advance the provision of financial support to households for energy efficiency improvements, possibly through co-financing or support from multilateral financial institutions (see section 3.1.2)

» With respect to the residential sector, we advocate giving private residents the incentives and the tools to invest in heat containment and other energy efficiency measures.

4. Policy Recommendations

This paper discussed the policy framework needed to enable investments in clean technologies and outlined concrete steps to assist the Ukrainian government with the implementation of a low carbon growth strategy. Low carbon growth strategies comprise two broad sets of policies: (i) framework policies and (ii) sectoral policies. Both of them call for a central role of the Ukrainian government.

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There is a dire need for the Ukrainian government to implement an effective regulatory framework that increases competition and strengthens the efficiency of markets. Ideally, public policy should combine measures that increase the relative cost of dirty to clean technologies, for example through carbon pricing or the introduction of an ETS, with direct subsidies into clean-innovation R&D.

With respect to sectoral policies, we recommend a specific policy mix aimed at the economic sectors with the greatest potential for emission reductions. The most promising sectors identified in this paper are the industrial sector, the energy sector including energy resources and electricity and heat production, as well as the transport and residential sector. 1. The main requirement for emission reductions in the industrial sector is the improvement of energy efficiency. This implies the modernization of the capital stock, especially of deteriorate infrastructure and outdated production capacities. 2. The key to reduce GHG emissions in the energy sector lies in promoting market based competition. This involves phasing out direct subsidies to coal mining, phasing out unfeasible low tariffs for natural gas as well as the demonopolisation of the gas transport sector. 3. Emission reductions in the heat and electricity sector are subject to the modernization of the gas transmission system and the renovation of depreciated heating equipment. These investments in infrastructure can only be induced if subsidies for gas consumption are eliminated and electricity tariffs are increased to full cost-recovery levels. 4. Promising measures in the transport sector include the adoption of fuel efficiency standards and taxes, the improvement of road infrastructure and the modernization and expansion of the public transport system. 5. With respect to the residential sector, we advocate giving private residents the incentives and the tools to invest in heat containment and other energy efficiency measures.

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