Carbon pricing and terms of trade effects for China and India: A general equilibrium analysis

Economic Modelling 63 (2017) 60–74

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Carbon pricing and terms of trade effects for China and India: A general equilibrium analysis

MARK

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Basanta K. Pradhana, , Joydeep Ghoshb,1, Yun-Fei Yaoc, Qiao-Mei Liangd a

Development Planning Centre (DPC), Institute of Economic Growth (IEG), Delhi 110007, India Institute for Economic Modeling Studies, New Delhi, India c SINOPEC Research Institute of Petroleum Engineering (SRIPE), Beijing 100101, China d School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China b

A R T I C L E I N F O

A BS T RAC T

Keywords: Carbon pricing Terms of trade effects CGE model China India

Using country-specific dynamic computable general equilibrium (CGE) models, this paper estimates carbon prices in China and India, and compares the effects of carbon pricing policies under terms of trade effects. Estimated carbon prices are higher in China due to differences in emission intensity and in the rate of deployment of new technologies in the models. Differences in carbon prices open up the possibility of carbon trading between the two countries to achieve mitigation objectives. Further, under assumptions about different exchange rate regimes and international fossil fuel prices, the effects of carbon pricing policies on the two economies are mostly similar in terms of direction but, expectedly, different in terms of magnitude. Terms of trade effects could exacerbate carbon pricing effects to a greater degree in China as the country is significantly more dependent than India on external trade and investment. Policymakers should factor in terms of trade effects while designing or evaluating carbon pricing policies in the two countries.

1. Introduction The relationship between energy consumption and economic growth has received considerable attention in the literature. It is of particular significance for China and India, as rapid economic growth in these two countries has been accompanied by increasing levels of energy consumption and emissions. The burning of fossil fuels such as coal, oil, and gas emits carbon dioxide (CO2), and CO2 emissions by sectors such as electricity, transportation, cement, and steel contribute significantly to climate change (global warming). Climate change is gradually becoming an important policy issue in the two countries in view of international climate change negotiations, such as the recently concluded Paris Agreement. It aims to hold the increase in global average temperature to well below 2 °C relative to pre-industrial levels, and to pursue efforts to limit the temperature increase to 1.5 °C relative to pre-industrial levels, to prevent catastrophic damage to the planet. China and India are among the top five emitters of CO2 in the world, and must participate in any effort to mitigate global climate change for that effort to succeed. Several countries use carbon pricing (carbon tax) as a tool to achieve emission reduction objectives. This paper estimates carbon prices in China and India and, using country-specific dynamic CGE models, compares the economic and environmental effects of ⁎

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carbon pricing policies. Several empirical studies analyse the effects of carbon pricing policies on China and India. Qi et al. (2016) report that curbing the rise in China's CO2 emissions will require the implementation of carbon pricing, which will need to rise over $25/ton to achieve China's stated goal of peaking CO2 emissions by 2030. Ojha (2008) finds that the negative impact of a carbon tax in India could be reduced if the emissions target is modest and carbon tax revenues are transferred exclusively to the poor. Liang et al. (2007) report that the adverse effects of carbon taxes on China could be alleviated by subsidising the production sector. Fisher-Vanden et al. (1997) report that tradable permits represent a lower-cost method than carbon taxes for stabilising India's emissions, while Weitzel et al. (2015) find that international prices of fossil fuels influence the income distribution effects of climate change mitigation policies in India. In a related study on climate change mitigation in different Asian countries, Calvin et al. (2012) find that Japan and Korea tend to reduce emissions much less than the global average for a given carbon price. The relationship between carbon price (marginal abatement cost) and energy/emission intensity of GDP has been the focus of several studies. Stern et al. (2011) find that under a common percentage cut in emission intensity relative to the business-as-usual (BAU) scenario,

Corresponding author. E-mail addresses: [email protected] (B.K. Pradhan), jghosh87@rediffmail.com (J. Ghosh), [email protected] (Y.-F. Yao), [email protected] (Q.-M. Liang). Director, Institute for Economic Modeling Studies, New Delhi

http://dx.doi.org/10.1016/j.econmod.2017.01.017

0264-9993/ © 2017 Elsevier B.V. All rights reserved.

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countries with higher BAU emission intensities have lower marginal abatement costs. Paltsev et al. (2007) report that for a common percentage cut in emissions without international trade in emission permits, there is roughly an inverse relationship between carbon prices and emission intensities in several developed countries. Further, Wang et al. (2010) report that carbon abatement policies could have spill over effects, while Zhu et al. (2016) find that trade openness can mitigate carbon emissions. Thus, based on the literature, one can conclude that, in general, carbon pricing is associated with economic impacts, and similar carbon prices will affect countries differently, on account of differences in energy/emission intensities. Through changes in the prices of fossil fuels, climate policies could be associated with terms-oftrade effects also. Carbon pricing could lead to higher domestic prices which, in turn, could affect the exchange rate; that is, it could lead to currency appreciation in real terms. This linkage between carbon pricing and terms of trade is reported by McKibbin et al. (2014), which finds that climate policies in the US could reduce investment in the capital-intensive energy sector, which in turn could lower imports of durable goods and strengthen US terms of trade. Similarly, changes in international fossil fuel prices arising out of global abatement levels could also lead to terms of trade effects. Klepper and Peterson (2006) looks at `this aspect from a global carbon abatement perspective. They group all the countries into six regional blocks and point out that there is a linkage between climate policies (carbon pricing) and terms of trade effects among these blocks of countries. In this paper we attempt to compare the effects of carbon policies under different assumptions about terms of trade effects for China and India in a comparative perspective. To our knowledge such an exercise has not been carried out in the literature for these two countries. This exercise assumes particular importance as China and India are significantly dependent on external trade (more importantly fossil fuel trade) and investment, and both countries, being among the largest emitters, have started implementing carbon pricing policies. The main objective of this paper is to estimate the impacts of changes in terms of trade, under carbon pricing policies, on China and on India. To achieve this objective, we use country-specific dynamic CGE models (described later) to compare the effects of carbon pricing on the two countries under different assumptions about exchange rate regimes and international prices of fossil fuels. CGE models have been widely used to analyse carbon pricing policies. These models provide flexibility to analyse terms of trade effects through the incorporation of alternative assumptions about the foreign exchange market and global commodity markets. The alternative assumptions about the foreign exchange market (under carbon pricing) considered in this study are fixed and flexible exchange rate regimes. Similarly, alternative assumptions about global commodity (fossil fuel) markets (under carbon pricing) are analysed by exogenously setting higher and lower world prices of fossil fuels (coal, oil, and natural gas) in the two models (10 per cent higher and lower relative to BAU). Our analysis is focused on estimating the effects on GDP, household income, and CO2 emissions under the above scenarios. In other words, by using CGE models to construct different scenarios, we want to assess to what extent movements in the exchange rate and international prices of fossil fuels influence the effects of carbon pricing policies in the two countries. The results of this exercise suggest that global market factors such as exchange rate movements and international fossil fuel prices could influence the effects of carbon pricing, and that these factors play a particularly important role in the case of China, as the country is much more dependent on external trade and investment than India. The main policy implication of this study is that the two countries should consider these factors while designing carbon pricing policies, keeping in view their respective economic structures. Carbon prices are also estimated separately under different emission targets for the two countries, in view of the significance of carbon pricing for achieving mitigation objectives. The rest of the paper proceeds as follows. Section 2 provides an

Fig. 1. Trends in per capita GDP (PPP, constant 2011 $) of India, China, and the World. Source: World Bank

overview of energy sector policies in China and India. Section 3 describes the models and data. Section 4 discusses the main findings of the paper, and finally Section 5 presents the conclusions and policy implications. 2. Overview of energy sector policies IN China and India We begin this section by discussing trends in economic growth and carbon emissions for China and India. We follow with a presentation of carbon pricing initiatives, and end by outlining some key energy sector policies. In both China and India, growth performance has been remarkable over the past two decades; however, the growth drivers have been different. India's growth has been strongest in the service sector, while China's growth is broad-based, across agriculture, industry, and services (Bosworth and Collins, 2008), and much more energy-intensive than India's. Despite their impressive growth performance in recent years, per capita income in China and India is lower than the world average (Fig. 1). The per capita income was similar until the mid-1990s, but since then China's per capita income increased dramatically relative to India's. China's growth has been fuelled by high levels of capital accumulation, due to high levels of domestic savings and foreign capital inflows. In India, the large public sector deficit has constrained capital accumulation in the economy to some extent, and capital inflows are much lower (in absolute terms) than China's. The shares of FDI in GDP are, however, similar for the two countries. For example, in 2013, the share of FDI in GDP was 10.4 per cent for China and 12.1 per cent for India (Prajakta Patil, 2014). Foreign capital inflows have played a major role in the growth process, and international trade has expanded rapidly over the past two decades due to policy reforms. Between 2005 and 2013, exports expanded at an annual rate of 11 per cent in China and at 10.5 per cent in India, while imports increased at an annual rate of 10 per cent in China and 11 per cent in China (WTO, 2014). However, the composition of exports was quite different. China's exports are concentrated in goods, whereas India's trade has a much larger services component. In recent years, due to the rise in income levels in both countries, carbon emissions have increased rapidly. The past decade saw an average annual increase in CO2 emissions of 2.7 per cent globally. China was the largest emitter (29 per cent of global emissions) and India the fourth-largest (6 per cent of global emissions) (PBL, 2012). China's per capita emissions, much below the world average until 2002 (Fig. 2), increased dramatically afterwards and overtook the global average in 2006. After many decades of rapid fall, China's energy intensity increased between 2002 and 2005, partly for the rapid rise in China's exports. In 2004, exports accounted for 34 per cent of China's GDP; international trade accounted for 23 per cent of its total carbon 61

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(2011) has modelled the projected implications of the introduction of a US$10 carbon price in China nationally (dollars are also in 2010 PPP) by 2020. It finds that this price range, along with other accompanying mitigation policies, may well produce significant emissions reductions by 2035. India has also launched new initiatives to price carbon. The coal cess—introduced in 2010 to finance clean energy technologies— has been progressively increased in recent years. The Perform, Achieve and Trade (PAT) scheme is a permit trading mechanism based on energy intensity targets that require big energy users to improve efficiency by 1–2 per cent per year. While the scheme does not explicitly target emission reductions, reduced energy intensity is likely to impact India's national CO2 emissions. It is expected that up to 6.6 million tons of oil equivalent will be saved due to the scheme over the first three-year cycle (Regan and Mehta, 2012). As mentioned before, energy consumption in China and India has increased rapidly in recent years. China became the largest global energy consumer in 2011 and is the world's second-largest oil consumer after the United States. China is the world's top coal producer, consumer, and importer of coal and accounts for almost half of global consumption (US Energy Information Administration, 2015). India was the world's third-largest coal producer and consumer in 2011 after China and the United States, and the fourth-largest oil consumer and importer in 2012 (Ahn and Graczyk, 2012). We now discuss some key policies related to the fossil fuel sector in the two countries. In both countries, the fossil fuel sector is characterised by government regulation, although in recent years market forces have been increasingly allowed to operate. Price controls, in general, are targeted at two kinds of goods—industrial raw materials (like fossil fuels) and essential commodities (like food items). Price controls in the case of raw materials stabilise input costs for producers and enhance the competitiveness of Chinese products in world markets. However, with increasing dependence on imported fossil fuels, it is becoming increasingly difficult for China to remain competitive globally. In India, similar price controls have played a role in keeping a check on the inflation rate, although it is becoming increasingly difficult to finance these price controls through budgetary sources. Further, India's domestic energy resources are limited, and dependence on imports is likely to increase in the future (Government of India, Planning Commission, 2011). In China, price decontrols and market reforms since the 1990s have integrated its coal market more into the global economy, and its shares in global coal production and consumption grew from less than 20 per cent in the 1970s to over 50 per cent in 2010 (IEA, 2012). Until 2008, China was a net exporter of coal; in 2009, for the first time, it became a net importer of coal and immediately became the world's secondlargest importer after Japan (IEA, 2012). China's vast resources of coal enable it to remain the mainstay of the country's energy industry and have supported the country's massive economic growth over the past decade. China has been the world's leading coal producer and consumer since the early 1980s and accounts for close to half the global coal consumption. However, Chinese production and consumption declined by nearly 3 per cent in 2014, the first decline in the coal industry in 14 years. These trends reflect the economic downturn (particularly in coal-consuming sectors such as steel and cement), slower electricity demand growth, greater hydroelectricity generation, and China's stricter environmental regulations recently imposed on high-polluting industries, including coal. In 2014, coal imports declined as a result of slower economic and electricity consumption growth and of excess domestic supply (US Energy Information Administration, 2015). China reformed the coal tax structure at the end of 2014. The resource tax imposed on coal mining companies shifted the volume-based tax system to a valuebased tax system, and allowed local governments to collect 2–10 per cent of the value of domestic coal sold. China depends more than most other economies on coal for power generation and also uses coal for more, diverse purposes. Non-power coal demand likely has significant

Fig. 2. Trends in per capita CO2 emissions of India, China, and World. Source: World Bank

emissions in 2004 (Wang and Watson, 2008), and more than 27 per cent in 2007 (Su and Ang, 2013). To counter the rapid increase in energy intensity, China's 11th Five Year Plan (2006–2010) set a target to decrease the overall energy intensity of the economy by 20 per cent. India's per capita emissions is much lower than both China's and the world average, and energy intensity has been declining trend. While India's per capita income was comparable to China's until the mid1990s, India's per capita emissions has always been lower than that of China's, reflecting the differences in economic structure. Energy consumption per dollar of GDP (at PPP) of China in 2008 was 0.19kg of oil equivalent, which is higher than that of India (0.10kg of oil equivalent) and lower than the world average (0.20kg of oil equivalent). China's pattern of economic growth—based on export-oriented manufacturing and on reliance on cheap energy and inefficient energy use—is unsustainable; therefore, it must change (Fan et al., 2015). As China's energy sector increasingly becomes market-driven, cheaply priced energy—upon which the expansion of energy-intensive exportoriented industries has relied—will no longer be viable (Pan, 2011). The main sources of growth are shifting from energy-intensive manufacturing to rising productivity and better-quality and competitive goods and services (Fan et al., 2015). These changes in the Chinese economy signify the gradual transition towards a less energy-intensive economic structure. The 12th Five Year Plan of China devotes considerable attention to energy and climate change and establishes a new set of targets and policies for 2011–2015. These targets and policies aim to reduce fossil fuel consumption, promote low-carbon energy sources, and restructure China's economy. As part of its Intended Nationally Determined Contribution (INDC) report submitted to the UNFCCC in 2015, China announced that it will achieve peaking of CO2 emissions around 2030, make best efforts to peak early, and lower emission-intensity of GDP by 60–65 per cent from the 2005 level. Further, the country has pledged to increase the share of non-fossil fuels in primary energy consumption to around 20 per cent and increase the forest stock volume by around 4.5 billion cubic metres relative to the 2005 level. To achieve these targets, China plans to activate a national carbon trading market in 2017. India has also pledged to reduce its carbon footprint of growth by pursuing proactive policies. As part of its INDC, India has committed to reduce the emission intensity of GDP by 33–35 per cent relative to the 2005 level by 2030, increase the share of renewables in the electricity sector (installed capacity) to 40 per cent by 2030, and create an additional carbon sink of 2.5–3 billion tons of CO2-equivalent through additional forest and tree cover by 2030. India's Twelfth Five Year Plan (2012-17) has as one of its key pillars a low-carbon inclusive growth strategy for the economy. Carbon pricing is one of the options being actively pursued by the two countries to check emission levels. China intends to implement a nation-wide carbon pricing scheme. The International Energy Agency

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and gas production, including unconventional resources output, to increase revenue for local and regional governments and to encourage more efficient production of hydrocarbons. The resource tax was raised to 6 per cent late in 2014, although the tax rate was lower for projects using certain enhanced oil recovery techniques or containing high sulphur or heavy oil. As China's oil demand continues to grow, oil imports have increased dramatically over the past decade, and reached a record high in 2014. The oil and gas sector in India continues to be dominated by the public sector, although the share of private companies in the upstream sector is increasing. India's New Exploration Licensing Policy (NELP) is focused at increasing investments in domestic exploration and production activities. However, despite many promising discoveries in NELP blocks, the policy has had limited success in reducing dependence on foreign imports. Also, the policies have not been able to attract oil majors with experience and technical expertise to invest in India. In the downstream sector, the government has introduced certain reforms, including deregulation of petrol prices. However, with marketing companies under government control, prices of petroleum products are set at levels reflective more of consumer concerns and not of markets. In the gas and kerosene sector also, the government exercises control on pricing through the administered pricing mechanism (APM). India's demand for oil and gas has been increasing significantly over the past two decades. Since the liberalisation of the upstream sector and subsequent reforms in the downstream, the oil and gas sector has been more open and competitive than other energy sectors in India. It is open to 100 per cent FDI and a number of private and foreign private companies are actively operating. Introduced in 2001, the India Hydrocarbon Vision (IHV 2025) plan laid out the long-term vision for the oil and gas sector with the objectives of enhancing energy security and promoting a free market and competition within the sector. The IHV 2025 plan confirmed the importance of foreign investment, but also emphasised the critical role of Indian public sector firms. Pricing of India's petroleum products was decided by the government under the APM until it was dismantled in April 2002. The APM was based on a cost of operating capital-plus formula. In the postAPM regime, oil marketing companies (OMC) were allowed to set the price of all fuels, while two strategically important products for the poor, kerosene and LPG, remained regulated. However, two major transportation fuels, gasoline (petrol) and diesel, were still regulated by the government, although no official subsidy was provided. The Indian government announced the deregulation of gasoline prices and the phased deregulation of diesel prices in 2010 but, as of 2012, diesel prices were still regulated and changes in gasoline prices took place only in consultation with the concerned ministry. As a result, the actual selling price (retail price minus taxes and dealer's commission) of those four “sensitive products” is lower than the ex-pump price, based on import/trade parity, that OMCs pay refineries. The difference between the two prices is called “under-recoveries”. As India imports nearly 80 per cent of its crude demand, international crude prices influence under-recoveries to OMCs. The actual retail prices of petroleum products for Indian consumers are not low even by international comparison, and are even higher than in Canada and the United States. This is due to the high taxation on petroleum products levied by both central and state governments, comprising 37 per cent of retail price in the case of gasoline in 2012 in Delhi, and 16 per cent for diesel. In 2010, India spent about 25 per cent of its total import bill on oil, and international oil price shocks and exchange rate movements have significant macro implications for the country. India's natural gas prices are regulated and set at different levels for gas originating from different producers. Gas from fields allocated to public sector firms by the government is sold under prices set by the government under the APM. Joint-venture gas producers are paid based on a formula pegged loosely to international prices but, de facto, the government maintains close oversight of price adjustments.

influences on coal prices in China. Increased coal prices, along with government-fixed electricity prices, make coal-fired power unprofitable in China and have sometimes resulted in power shortages in the country. The Chinese government encourages electricity producers to acquire coal mines so that they can use the profits from the mining sector to cross-subsidise their losses from electricity generation (Yang et al., 2012). This is similar to the reverse auction for coal mines for the power sector, where power companies bid for the lowest price for electricity given a price for coal. This process is beneficial to electricity consumers at the cost of coal-bearing areas. In India, the government enjoys a monopoly in producing coal, as most of the production is from government-controlled mines. Coal is the primary source of energy in India, and two state-owned companies, representing a share of almost 90 per cent, maintain a near-monopoly over India's coal production (Ahn and Graczyk, 2012). The policy for captive mining was introduced in 1993 for both private and public sector companies. This opened the coal sector to private investment, although the progress made in most captive coal blocks allotted by the government has not been promising. Coal production has been dismal for reasons such as land acquisition and rehabilitation issues, environmental clearances, terms of mining leases, hurdles for setting up of user industries, and transportation bottlenecks. Therefore, although India has the fifth-largest reserves of coal in the world, it is not able to meet its own domestic demand, and coal imports have been rising in recent years. However, recently, policies have been changed for increasing coal production and private participation through bidding process. In 2000, coal prices were completely deregulated, and coal producers allowed to set their own prices based on an escalation formula under a cost-plus approach. The greatest challenge posed by India's pricing system is the disparity with international coal prices. Coal produced domestically is priced at a discount to imported thermal coal and, because of the large price differential, consumers are reluctant to buy imported coal. Recently, the government has introduced the auction route for coal blocks, rather than administrative allocation, which was vitiated with corruption allegations. China's natural gas sector is dominated by a few national oil and gas corporations as a result of previous oil and gas industry development policies. Price controls, restricted access to mineral rights, and rationed markets have reduced commercial interest in developing natural gas resources in China. Imported natural gas is also subject to price control. Natural gas importers are required to sell at government-set domestic prices, which are typically below cost. Although natural gas production and use is rapidly increasing in China, the fuel comprised only 5 per cent of the country's total primary energy consumption in 2012. To meet projected long-term demand increases, China is expected to continue importing natural gas in the form of LNG and from a number of new and proposed import pipelines from neighbouring countries. China's natural gas prices, similar to retail oil prices, are regulated by the National Development and Reform Commission (NDRC) and have been kept below international market rates. The NDRC implemented a new system linking gas prices more closely to higher international oil prices to increase investment in the natural gas sector, make the pricing system transparent and responsive to market fluctuations, and make domestic natural gas competitive with other fuels and imported gas. In China, the oil sector is also subject to price controls. Producers are more affected by oil price shocks than consumers because consumption commodities are under strict price control (Tang et al., 2010). In 2009, the Chinese government launched a fuel tax. It also initiated reform of the domestic product pricing mechanism, to (1) tie retail oil product prices more closely to international crude oil markets (US Energy Information Administration, 2015); (2) ensure better profit margins for refiners, who must sell fuel at regulated prices; and (3) reduce energy intensity, which resulted from lower consumer prices and higher demand. In 2011, China levied an ad valorem resource tax of 5 per cent on all oil 63

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input and the fixed fossil fuel resource. In both models, imperfect substitution is assumed in case of electricity from different sources. Household income comprises income from labour, capital, and transfers from government and the rest of the world. Household income is spent on consumption, savings, and income taxes. Household consumption is modelled using a Cobb-Douglas function in the China model, and by using a linear expenditure system (LES) in the India model. The main source of enterprise income is the return on capital in both models. A part of enterprise income (net of taxes) is transferred to households, and the rest is retained as enterprise saving. Government income is from various taxes (tariffs, indirect taxes, income taxes) and from the rest of the world. Government expenditure includes government consumption, transfers to households and enterprises, and subsidies. Government consumption is fixed in both models. The difference between government income and expenditure forms government saving. There is imperfect substitutability between imports and domestic output (Armington). Domestically supplied commodities are composed of domestic and imported commodities. As for exports, the models use a constant elasticity transformation (CET) function to allocate total domestic output between exports and domestic sales. Investment in the China model is characterised in two ways— circulating capital investment and fixed investment. Adjusting total fixed investment with corresponding sectoral shares, fixed investment supplied to each sector is identified, the aggregate of which serves to clear the capital market. Further, multiplying the fixed investment supplied to each sector with the corresponding composition matrix, fixed investment supplied by each sector is identified, which serves to clear the commodity market. In the India model, sectoral investment demand serves to clear the commodity market, and the aggregate of sectoral investment demands serves to clear the capital market. Regarding model closure, both models assume that government consumption is exogenous, while government saving is endogenous. For the current account balance, both models assume fixed foreign saving and flexible exchange rate. For the savings-investment balance, both models assume savings-driven closures. In the China model, capital is fixed and freely mobile among sectors, while in the case of the labour market, the wage rate is fixed and labour supply adjusts to clear the labour market. In the India model, full employment of capital and labour is assumed, and factor prices adjust to clear the factor markets. In short, the models have similar features, and capture the economic structure of the respective countries in significant detail. The baseline (BAU) scenarios are created by assuming growth in total factor productivity, labour force, government consumption expenditure, and aggregate investment. The other assumptions are related to energy efficiency, technological developments in renewables, and international prices of fossil fuels. An energy efficiency growth rate is assumed to capture future gains in energy efficiency. Future technological developments in the renewable electricity sectors are modelled by assuming efficiency growth in these sectors. Changes in the international prices of fossil fuels are modelled keeping in view future price projections of these commodities. The main source of data for the China model is a social accounting matrix (SAM) based on the 2007 input-output table (Department of National Account of National Bureau of Statistics PR China, 2009). The exogenous parameters are based on related research and own adjustments. The main source of data for the India model is the SAM for 2003-04 developed by Ojha et al. (2009), whose income distribution block is based on the SAM constructed by Pradhan et al. (2006). The main difference between the two SAMs is the decomposition of the electricity sector into three separate sub-sectors—hydro, nuclear, and non-hydro—in the SAM developed by Ojha. The non-hydro energy sector includes thermal, wind, and solar electricity. However, given India's energy mix, thermal is the main constituent of this group. Two modifications were done in the non-hydro sector for the purpose of this study. The first modification pertains to the disaggregation of non-

However, re-gasified LNG is priced based on different supply contracts, long- and short-term supplies, and spot prices. Short-term supplies and spot cargos carry substantially higher prices than domestically produced gas and long-term supply contracts for LNG. In practice, India's Gas Utilisation Policy negates the right of NELP producers to sell gas on a purely commercial basis. The government allocates gas and gives the fertiliser, LPG, and power sectors priority. Thus, the above discussion leads to two main conclusions regarding the fossil fuel sector in China and India. First, both countries view the fossil fuel sector as vital for achieving development objectives, while pursuing options like carbon pricing to reduce dependence on fossil fuels. And second, in both countries, the fossil fuel sector is increasingly being exposed to global market factors. This implies that carbon pricing policies may or may not have the desired impacts on trade, investment, emission levels, energy mix, etc. As mentioned before, this study aims to understand to what extent the effects of carbon pricing policies in the two countries might be affected by global market factors such as exchange rate movements and international fossil fuel prices.

3. Models and data Both the China and India models2 are climate policy-focused, pricedriven, recursive, dynamic CGE models with disaggregated energy sectors. The key feature of the models is the possibility of substitution between fossil and non-fossil energy sources in production due to changes in relative prices. Carbon pricing could lead to a shift from fossil energy sources to non-fossil energy sources, as it makes fossil fuels more expensive than non-fossil energy sources. Further, carbon pricing could also lead to less energy-intensive methods of production in the economy. Both models are single-country models, and the respective economies are represented in greater detail compared to global models, which typically have more aggregated structures of the two economies. There are 24 sectors in the China model and 18 sectors in the India model (see Appendix I, II, and III for the lists of sectors and model descriptions, respectively). Further, in both models, there are two household groups (rural and urban), enterprises, and the government. The production structures of both models are similar. Labour, capital, energy, and other intermediate inputs combine through a nested constant elasticity of substitution (CES)/Cobb-Douglas/ Leontief function structure to form sectoral output. The top level of the nest is a Leontief combination of aggregate intermediate inputs and the energy-value-added composite in both models. At the next level, labour combines with the capital-energy composite through a CES function to form the energy-value-added composite in the China model. In the India model, energy combines with aggregate value added to form the energy-value-added composite. At the third level, the capital-energy composite is formed by using capital and energy through a CES function in the China model. At the fourth level, the energy composite is formed by combining electricity with the non-electric energy input in both models. Finally, the non-electric energy input is a Cobb-Douglas aggregate of coal, crude oil, and oil products in the China model and a CES aggregate in the India model. In the China model, the petroleum refining sector has a different production structure than the other sectors. Since crude oil is the most important raw material in the petroleum refining process, it is taken out of the non-electric energy composite and placed at the top level, following the MIT-EPPA model (Paltsev et al., 2005). In both models, the fossil fuel sector is modelled differently than the other sectors to capture the effects of resource (fossil fuels) scarcity in the economy. In case of the fossil fuel sector, the top nest is a CES-aggregate of value added-aggregate intermediate 2 See Liang et al. (2009) and Pradhan and Ghosh (2012a) for detailed descriptions of the China and India models, respectively. Concise versions of the models are given in Appendix II and III, respectively.

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hydro into thermal and other renewables. The second modification pertains to the creation of a carbon capture and storage (CCS) sector (from thermal electricity sector) that is similar to the thermal electricity sector, but is less efficient. 4. Results The findings of the study are presented in this section. The first part of this section discusses the estimation of carbon prices for the two countries, and the second part analyses the effects of a cap-and-trade type of carbon mitigation policy under different assumptions about exchange rate regimes and international fossil fuel prices. It is to be noted that the carbon price in this paper refers to a single national policy for each country. Carbon pricing mechanisms (such as China's carbon market pilot) in both countries are at a preliminary stage, and no attempt has been made to incorporate these prices into the models.

Fig. 3. Carbon prices for different levels of abatement.

pricing is a low-hanging fruit for India in the medium run compared to China. In the long run, however, we find that carbon prices for India are higher than China's. In the case of India, carbon prices increase at an increasing rate (convex to the origin) until 2035-40, but thereafter increase at a decreasing rate (concave to the origin), thus signifying the transition towards renewables. This result is reported also by Morris et al. (2008). This study finds, using the EPPA model (Paltsev et al., 2005), that the marginal abatement cost curves are flatter for later time periods (2050) compared to earlier time periods in the case of the United States, Europe, and Japan, due to the introduction of new energy technologies. The transition towards renewables implies lower emission intensities and higher abatement costs for India. The introduction of new technologies in the China model takes place at a much faster rate, and therefore relatively higher carbon prices are observed in the medium run. In fact, BAU emissions peak in 2030 in the China model, reflecting the very rapid introduction of new technologies. In the case of India, the introduction of new technologies takes place at a slower rate, and emissions increase throughout the entire time horizon, although at a slower rate in the later time periods. Often, China is referred to as the ‘dragon’, and India as the ‘elephant’, and these descriptions are also reflected in the introduction of new energy technologies in the two models. China has significantly higher technological and financial resources than India, and can deploy new technologies at a much faster rate than India. In interpreting the carbon pricing results, it must be kept in mind that although BAU emissions for China are much higher than India's (more than double) in all time periods, carbon prices depend upon emission intensities and the pace of introduction of technology and not on total emissions. For India, the carbon price increases because it becomes increasingly difficult to find additional abatement opportunities. If the introduction of clean technology is accelerated in the India model, which is a

4.1. Estimation of carbon prices Both China and India are taking measures to price carbon. The price of carbon indicates the cost of emission abatement for the economy. Carbon prices (Table 1) were estimated for three different levels (10 per cent, 30 per cent, and 50 per cent) of abatement relative to the baseline. Business-as-usual CO2 emissions increase to about 5.7 billion tons in 2050 in the case of India, and peak at 13.8 billion tons in 2030 in the case of China and decline thereafter. The CO2 emission profiles (Table 1) are based on GDP growth projections of the two economies until 2050, as well as modelling assumptions. The price of carbon is affected by energy/emission intensity, domestic and foreign energy prices/policies, modelling assumptions (such as the rate of introduction of renewables), etc. Further, global carbon abatement policies could affect a country's abatement costs through changes in energy prices (Morris et al., 2008; Klepper and Peterson, 2006). However, we do not consider this aspect in our analysis. Fig. 3 presents the estimated carbon prices for China and India for different time periods and levels of abatement. In general, the carbon price increases over time for India, but is almost constant for China. The carbon price is higher for higher abatement levels for both countries, as the CES formulation—used to model the substitution possibilities between fossil and non-fossil energy sources—has the property that substitution becomes more difficult as one moves farther from the benchmark data (Morris et al., 2008). Carbon prices for China are higher than India's in the medium run, but lower than India's in the long run, indicating that India has more opportunities for abatement in the medium run than China. In other words, it is less costly for India to abate in the medium run than for China. The implication is that carbon

Table 1 CO2 emissions (million tons) in BAU and carbon prices ($/ton CO2) for different levels of abatement. 2015

2020

2025

2030

2035

2040

2045

2050

CO2 emissions BAU IND

2,620

3,186

3,661

4,090

4,496

4,916

5,300

5,689

CO2 emissions BAU CHN

9,640

11,812

12,798

13,765

13,684

13,388

12,861

12,322

10 per cent reduction in emissions relative to BAU 7.26 12.46 CO2 price IND CO2 price CHN 36.75 24.56

20.08 24.24

30.86 21.83

45.82 28.41

66.40 35.31

83.49 41.57

103.04 42.37

30 per cent reduction in emissions relative to BAU CO2 price IND 23.66 36.58 CO2 price CHN 112.67 90.20

54.47 91.00

78.48 84.10

110.03 91.49

151.38 98.39

184.14 104.33

219.52 99.67

50 per cent reduction in emissions relative to BAU CO2 price IND 57.30 83.22 CO2 price CHN 289.86 281.52

117.81 304.79

161.13 287.62

212.90 286.49

275.37 297.89

320.10 318.91

364.49 325.49

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possibility in the future, the carbon price may not rise so fast or to such an extent. In the case of China, the carbon price is almost constant over time for different abatement levels for two reasons. First, because of the investment-driven economic growth pattern, the negative impacts of mitigation activities in previous periods led to a reduction in investment, which led to a decrease in later periods in economic growth, energy consumption, and carbon emissions. Therefore, the carbon price does not need to increase much to achieve the same abatement in later periods. Second, as mentioned before, there has been very rapid introduction of clean technologies in the China model. The above discussion, thus, leads us to conclude that in the short/medium run, China will have to bear higher costs than India to reduce emissions but, in the long run, India will have to bear higher costs than China. As pointed out earlier, China's economic structure is much more energyintensive than India's and, therefore, it will be more costly for China to move to a lower energy-intensive economic structure in the short/ medium run. Once the transition to an energy-efficient economic structure has taken place, it will be relatively less costly for China to lower emissions. Carbon prices are higher for higher levels of emission reductions for both countries. In the case of China, carbon prices are significantly higher for the 50-per-cent abatement level relative to the other two abatement levels, implying that deep cuts in emissions are likely to be very costly for the economy. Again, this is a reflection of the energy-intensive structure of the Chinese economy. The estimated carbon prices must be viewed with caution, given the rather strong model assumptions (like fixed world energy prices) used to estimate them. As Morris et al. (2008) points out, “any particular set of MACs can at best only provide a rough approximation of the marginal abatement cost in a particular country”. Despite the shortcomings of the approach we have followed, the estimated carbon prices could provide a basis for a discussion on mitigation efforts by two of the biggest emerging emitters in the world.

Table 2 Effects of climate policy under different exchange rate regimes on GDP growth (per cent). Period

2015–20 2020–25 2025–30 2030–35 2035–40 2040–45 2045–50

BAU

CDC flexible

CDC fixed

IND

CHN

IND

CHN

IND

CHN

8.3 7.4 6.9 6.6 6.4 4.7 4.4

7.0 5.2 4.5 4.1 3.7 3.1 2.8

8.3 7.4 6.8 6.5 6.3 4.3 2.9

6.9 5.5 5.3 4.5 3.6 2.6 1.7

8.1 7.2 6.6 6.2 5.9 3.7 2.0

6.7 4.2 3.8 3.7 3.4 2.0 0.6

the DART model to model the CDC regime. Carbon pricing policies, in general, are associated with high domestic energy prices and, thus, currency appreciation in real terms. Both China and India intervene in their respective foreign exchange markets to protect domestic producers and consumers against currency volatility. Therefore, analysing the effects of a stronger currency due to climate policies vis-à-vis the effects of climate policies in its absence assumes significance from a policy perspective. In particular, this analysis could help to understand to what extent climate policies could impact trade competitiveness of the two countries. Further, both countries are significantly dependent on energy imports. As China is significantly dependent on energy-intensive exports, changes in the international prices of fossil fuels (due to various factors, including climate policies adopted by other countries) could also play a crucial role in determining the effects of climate policies for the two countries. Therefore, we compare how exchange rate regimes and international prices of fossil fuels influence the effects of climate policy on each country. Table 2 shows the effects of different exchange rate regimes on GDP growth in the two countries. Growth in GDP is lower under the fixed exchange rate regime relative to the flexible exchange rate regime in both countries. Under the fixed exchange rate regime, investment growth slows down significantly relative to the flexible exchange rate regime, and leads to lower GDP growth in the fixed exchange rate regime. The slowdown in investment growth is due to lower foreign savings (capital inflows). Exports are relatively higher under the fixed exchange rate scenario, and positively impact the current account balance and, therefore, lower foreign capital inflows are required to balance the current account, and capital inflows are lower under the fixed exchange rate regime in both countries. The fixed exchange rate favours exports of sectors like agriculture and services in the case of India. An increase in exports of these commodities leads to higher income (lower income loss) for Indian households. Fig. 4 shows the effects on household disposable income. It is observed in the case of India that the adverse effects on household income are lower under the fixed exchange rate regime than in the flexible exchange rate regime. While a fixed exchange rate favours

4.2. Carbon pricing and terms of trade effects In this section, the economic and environmental effects of carbon pricing (cap-and-trade type of system, called “common but differentiated convergence” or “CDC”; see Höhne et al., 2006) are analysed under different assumptions about exchange rate regimes and world prices of fossil fuels for the two countries. The different exchange rate regimes considered (by changing model closure rules) are fixed and flexible exchange rate regimes, while the assumptions regarding the exogenously set world prices of fossil fuels (coal, oil, and natural gas) are 10 per cent higher and 10 per cent lower, relative to the BAU. In the fixed exchange rate regime, the real exchange rate is held fixed, while foreign savings (investments) is allowed to vary. In the flexible exchange rate regime, foreign savings is held fixed and the exchange rate adjusts to clear the foreign exchange market. The climate policy regime (CDC) considered here takes into account the 2 °C target for global warming, mentioned earlier in the paper. The CDC approach assumes that per capita emissions of all countries converge in the long run, but developing countries start their convergence trajectory only after reaching a certain threshold (see Johansson et al., 2014). The CDC regime is implemented in the two models by imposing a carbon tax and through adjustments in capital flows (foreign savings/investments) simultaneously. In the CDC regime, international capital flows take place because of trade in emission allowances, and such flows are modelled as foreign savings/investments in our models. The value of such flows for a particular year is calculated as the difference between the emission allowance and emissions (under carbon tax), multiplied by the corresponding carbon tax for that year. The data on carbon tax rates and emission allowances were obtained from a global CGE model called DART (Klepper et al.,. 2003; see Appendix I for carbon tax rates (carbon prices) and emission allowances for selected years). It is to be noted that we do not model carbon trading explicitly in our models. We exogenously set carbon prices and emission allowances obtained from

Fig. 4. Effects of exchange rate regimes on household incomes.

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Indian households, a flexible exchange rate favours Chinese households. This is due to differences in economic structure of the two economies. Capital inflows are lower under the fixed exchange rate regime than in the flexible exchange rate regime, and lower capital inflows/remittances hurt the Chinese economy to a much greater extent than the Indian economy. The importance of foreign capital inflows/remittances for China is well documented in the literature (UNCTAD, 2005). Lower capital inflows seem to affect the Chinese economy in general, and households are relatively worse off. Thus, a fixed exchange rate regime affects the two countries in different ways on account of different economic structures. India is a consumptiondriven economy, and a fixed exchange rate stabilises prices and drives exports of less energy-intensive sectors like agriculture and services. Since agriculture and services are major employment providers, Indian households are better off under the fixed exchange rate regime. However, in the case of China, a fixed exchange rate regime lowers capital inflows/remittances, and leads to significant loss in household income relative to the flexible exchange rate regime. A flexible exchange rate, on the other hand, leads to relatively higher capital inflows and positively impacts household incomes in China. In general, income loss increases over time due to increase in the carbon price in both countries. Regarding CO2 emissions (Fig. 5), we find that emissions are marginally higher in the flexible exchange rate regime in the short run and marginally lower in the long run, relative to the fixed exchange rate regime, for both countries. Carbon dioxide emissions peak around 2025 in the case of China, while in the case of India emissions peak around 2040, under both exchange rate regimes. The significant reduction in emissions under climate policy in China in the long run (after 2025) is a reflection of the lower abatement costs for China in the long run which, in turn, is the result of introduction of clean technologies in the China model. Higher world prices (exogenous) of fossil fuels slow down GDP growth in China while there is negligible effect in the case of India (Table 3). Higher world energy prices will lower the marginal abatement cost and, thus, alleviate the negative shock from climate policy for achieving the same degree of mitigation. Higher international fossil fuel prices hurt Indian households more than Chinese households, but lower world prices hurt Chinese households more than Indian households (Fig. 6). China is an export-oriented economy, and lower world prices of fossil fuels, which reflect lower demand in international markets, lead to lower GDP growth and household income than when world prices are higher. In comparison, Indian households are relatively better off when world prices of fossil fuels are lower, because India is a consumption-driven economy, and lower prices (especially oil prices) stimulate demand in the economy. Regarding CO2 emissions (Fig. 7), we find that emissions are marginally lower under higher international fossil fuel prices, for both countries. Thus, the results reveal that both exchange rates and international

Table 3 Effects of climate policy under different world prices of fossil fuels on GDP growth (per cent). Period

2015–20 2020–25 2025–30 2030–35 2035–40 2040–45 2045–50

CDC flexible

CDC flexible with higher world price

CDC flexible with lower world price

IND

CHN

IND

CHN

IND

CHN

8.3 7.4 6.8 6.5 6.3 4.3 2.9

6.9 5.5 5.3 4.5 3.6 2.6 1.7

8.3 7.4 6.8 6.5 6.3 4.3 2.9

6.7 4.2 3.8 3.8 3.5 2.1 0.7

8.3 7.3 6.8 6.5 6.3 4.3 2.9

6.7 4.1 3.7 3.7 3.4 1.9 0.4

Fig. 6. Effects of international fossil fuel prices on household incomes.

Fig. 7. Effects of international fossil fuel prices on CO2 emissions.

fossil fuel prices could potentially influence the effects of carbon pricing policies. While exchange rate assumptions were found to influence carbon pricing effects on GDP for both countries, assumptions about international fossil fuel prices influenced carbon pricing effects on GDP mainly for China. However, carbon pricing effects on household income were influenced by both exchange rate regimes as well as international fossil fuel prices. A fixed exchange rate favours Indian households under carbon policy while a flexible exchange rate favours Chinese households. Both exchange rate movements and international fossil fuel prices, under climate policy, are particularly relevant for China, as it is much more dependent on foreign trade and investment than India. Terms-of-trade effects, under climate policy, have marginal effects on CO2 emissions for both countries. Thus, carbon pricing could potentially hurt China's trade competitiveness (and thus economic growth) much more than India's if there is not enough flexibility in the exchange rate or international fossil fuel prices are lower (due to less international demand). In general, however, both countries are affected by carbon pricing in the same way (direction) under the different assumptions about exchange rate regimes and international fossil fuel prices, as climate policy leads to higher fossil fuel prices in both

Fig. 5. Effects of exchange rate regimes on CO2 emissions.

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China as it is significantly more dependent on external trade and investment than India. Specifically, a fixed exchange rate and lower world prices of fossil fuels could exacerbate the effects of carbon pricing in China. China needs to focus on exchange rate flexibility and on domestic consumption rather than exports to counter terms of trade effects. However, in the case of India, a stable exchange rate and lower world prices reduce the adverse effects of carbon pricing. The main policy implication of this paper is that global market factors like exchange rate movements and international fossil fuel prices could influence the effects of carbon pricing policies in China and India, and each country will have to counter these factors keeping in view their respective economic structures. The terms of trade effects make evaluation of carbon pricing policies more complex, and could potentially impact the mitigation and development objectives of the two countries. Policymakers should therefore factor in terms of trade effects while designing/evaluating carbon pricing policies. The linkages between carbon pricing and investment and trade policies could be a fertile area of further research.

countries and adversely affect economic growth and household income. 5. Conclusions and policy implications China and India are growing economies with rising levels of carbon emissions, and their participation is extremely important for the success of global climate change mitigation efforts. Both countries are considering policies to mitigate the carbon footprint of economic growth and, therefore, estimates of abatement costs are extremely valuable from a policy perspective. The results show that abatement costs are almost constant over time for China but increase significantly in the case of India. The introduction of renewables in the energy mix plays an important role in determining the abatement cost. Carbon prices are higher for China compared to India in the short/medium run, but lower in the long run, due to the assumption of much higher growth of new energy technologies in the China model compared to the India model. Differences in carbon prices open up the possibility of carbon trading between China and India. The results further reveal that the effects of carbon pricing under different assumptions about exchange rate regimes and international fossil fuel prices on the two economies are similar in terms of direction but different in terms of magnitude. Terms of trade effects could exacerbate carbon pricing effects to a greater degree in the case of

Acknowledgements We are very thankful to all the Anonymous Referees and the Editor for their useful comments.

Appendix I See Appendix Tables A.1 and A.2. Appendix II A.2 Description of the basic structure of the China CGE model (based on Liang et al. 2009) A.2.1 Production module The production module specifies the production activity in each sector. Each sector produces only one commodity with no joint production. The inputs in each sector include labour, capital, energy and other intermediate inputs (Fig. A1), following a five-level nested CES function. At the top level of the production nest, a composite output is produced supposing a Leontief function (see Eq. A.1) using different intermediate inputs along with the capital-energy-labour composition.

⎛ Z1, i Z 2, i Xi = min ⎜ , ,⋯, ⎝ α1, i α2, i

Z n, i KEL i ⎞ , ⎟ αn, i αkel, i ⎠

(A.1)

Where,

Xi ~total output of sector i Zj, i ~intermediate input of commodity j in sector i KEL i ~composite capital-energy-labour input in sector i αj, i ~the direct requirement of sector i on sector j for per unit output of sector i αkel, i ~the direct requirement of sector i on capital-energy-labour composition for per unit output of sector i . At the second level, this model using the structure of (K/E)/L which consists of a capital-energy composition and labour. Since the energy and capital are quasi-complementary; while the substitute elasticity between capital and labour, and that between energy and labour is larger. The input of capital or energy usually accompanies the substitute for labour [50]. The production function at this level is shown as Eq. A.2.

Table A.1 Carbon prices and emission allowances for the climate policy regime (CDC) obtained from the DART model.

Carbon price ($ per ton of CO2) – India (B3) Carbon price ($ per ton of CO2) – China Emission allowance (million tons of CO2) – India Emission allowance (million tons of CO2) – China BAU emissions (million tons of CO2) – India BAU emissions (million tons of CO2) – China

2015

2020

2025

2030

2035

2040

2045

2050

1.9 1.9 2,316 8,360 2,449 9,640

6.8 6.8 2,944 9,704 3,011 11,812

34.7 34.7 3,589 10,563 3,481 12,798

71.1 71.1 4,233 9,207 3,898 13,765

107.2 107.2 3,997 7,851 4,294 13,684

145.7 145.7 3,762 6,495 4,705 13,388

222.4 222.4 3,526 5,139 5,079 12,861

440.8 440.8 3,290 3,784 5,458 12,322

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Table A.2 Model sectors. Sectors China

India

Agriculture, iron and steel, building materials, chemical, non-ferrous metals, paper, food, textile, clothing, wood, metalwork, machinery, other heavy industries, construction, transportation, service, water, coking, gas production and supply, coal mining, crude oil products, natural gas products, petroleum refining, and electricity production (consisting of eight sectors: coal, gas, petroleum, hydro, nuclear, biomass, solar, wind) and supply Agriculture, coal, oil, gas, manufacturing I (food and beverages, textiles, wood, minerals), manufacturing II (paper, fertilisers, cement, iron and steel, aluminium, chemicals), manufacturing III (plant and machinery), oil products, electricity (consisting of six sectors: thermal, CCS, hydro, nuclear, biomass, other renewables), construction, road transport, rail/sea/air transport, and other services

1

KEL i = AKEL, i (αKE , i KEi ρKEL, i + (1 − αKE , i )L i ρKEL, i ) ρKEL, i

(A.2)

Where,

KEi ~composite capital-energy input of sector i L i ~ labour input of sector i AKEL, i ~shift parameter in CES function (capital-energy-labour) αKE , i ~CES share parameter of capital-energy composition in capital-energy-labour composition ρKEL, i ~ substitution parameter in capital-energy-labour aggregate function At the third level, the energy composition enters in a CES structure with the capital to produce capital-energy composition, which consists of electricity input and fossil fuel composition at the fourth level. This model distinguishes between the electricity and fossil fuel, since the most of the electricity is a secondary energy which generated by consuming fossil fuels, the substitution elasticity between electricity and fossil fuels should be smaller than those within fossil fuels[50]. At the lowest level, the fossil fuel composition includes coal, crude oil, petroleum and natural gas, following Cobb-Douglas[69] function (see Eq. A.3).

Fossili = AFossil, i ⋅ ∏ FoFfe, i βFoF , fe, i (A.3)

fe

Where,

FoFfe, i ~ input of fossil fuel fe of sector i Afossil, i ~ shift parameter in CES function (fossil fuel composition) βFoF , fe, i ~ share parameter of fossil fuel fe in fossil fuel composition in sector i Some exceptions here are the production function for the petroleum refining sector, agriculture sector, fossil energy sector and electricity sector. Referring to the assumption in MIT-EPPA model [41], since crude oil is used directly as raw material in the petroleum refining process, then in the production function of this sector, crude oil placed in the top level instead of entering the fossil fuel composition at the lowest level. Land or reserve resources are all necessary input during the production of agriculture sector or fossil energy sectors, i.e. the requirement for such resources should be reflected with other inputs composition following CES function at the first level, and inputs composition consisting of the intermediate inputs and capital-energy-labour composition follows, then at the following level, the production function is same as others sectors. In the production function of electricity sector, sectoral output is constitutive of wind, solar, and stable generation following CES function as shown in Fig. A1.

Total output of electricity sector

Wind

Solar

Stable Generation

Value-Resource Intermediate inputs Value-Resource Intermediate inputs

Resource Value-added

Capital

Labor

Resource Value-added

Capital

Labor

Coal Petroleum Natural gas Hydro Nuclear

Value-Resource Intermediate inputs Energy or Resource

Value-added

Capital Fig. A1. Production structure of the electricity sector.

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A.2.2 Income and expenditure moduleHousehold income and expenditure. The household is endowed with the supplies of the factors of production, the services of which may be sold or leased to firms, i.e. the labour income and profit distribution from enterprises. After paying household income tax and receiving various transfers from government, enterprises, and overseas, the household get disposable income. In each period, the household spends on saving which is obtained by multiplying household disposable income with marginal saving tendency; and consumes various goods which is described as a LES (see Eq. A.4).

CDhi, h =

clesi, h⋅(1 − mpsh )⋅YDh PQi

(A.4)

Where,

CDhi, h ~consumption of commodity i by household h PQi ~composite price of commodity i (imports and domestic products) YDh ~ disposable income of household h clesi, h ~consumption share of commodity i in the total consumption of household h mpsh ~marginal saving rate of household h

Enterprise income and expenditure The enterprise income mainly comes from the return on capital. Besides paying the enterprise income tax, distributing the profit to the enterprises from ROW and the households, and receiving transfer from government, the enterprise spends on enterprise saving (see Eq. A.5). (A.5)

EnSavt = (1 − eh )⋅[(1 − etax )⋅YKt − SBTt⋅ERt ] + GtoEt⋅PIndext Where,

EnSav ~ enterprise saving ER ~ exchange rate GtoE ~ government's transfer payment to enterprise YK ~ total capital income PIndex ~ GDP price deflator SBT ~ capital income distributed to enterprises from ROW eh ~ share of total distributed profits from enterprises to households etax ~enterprise income tax Government income and expenditure The income source of government comes from the various tax and transfers from other countries/regions (ROW). Government outgoing includes government consumption, transfers to households and enterprises, export rebate, and government saving (see Eq. A.6).And government consumption is described by the LES function (see Eq. A.7).

∑i PQi⋅GDi + GovSav + GtoH ⋅PIndex + GtoE⋅PIndex = IndTax + Tariff − ExSub + TotHTax + etax⋅YK + WtoG⋅ER

(A.6)

GDi = glesi⋅GdTot

(A.7)

Where,

GDi ~ government consumption of good i GovSav ~ government saving IndTax ~ total indirect tax Tariff ~ total tariff ExSub ~total export rebate TotHTax ~ total household income tax WtoG ~transfers to government from ROW Gdtot ~total government consumption glesi ~ share of good i in total government consumption A.2.3 Foreign trade module In this study we adopt the Armington assumption, and assume that goods are imperfect substitutes for each other. The Armington composite good composed of the domestic good and imports following a CES function (see Eqs. A.8 and A.9).

Qi = AQ, i ⋅[αM , i⋅Mi

ρQ, i

+ (1 − αM , i )⋅Di

ρQ, i 1/ ρ ] Q, i

(A.8)

⎤σQ, i

Mi ⎡ αM , i PD =⎢ ⋅ i⎥ Di ⎣ 1 − αM , i PMi ⎦

(A.9)

Where,

Qi ~ domestic sale of Armington composite good i Mi ~ import of sector i 70

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Di ~domestic good i sold domestically PDi ~ price of good i produced and sold domestically PMi ~ domestic price of import good i AQ, i ~ shift parameter in Armington function (CES) ρQ, i ~ substitution parameter in Armington function for import αM , i ~ share parameter in Armington function (CES) σQ, i ~ substitution elasticity between import and domestic production As for exports, a CET function is adopted to allocate total domestic output between exports and domestic sales (see Eqs. A.10 and A.11).

Xi = AEx, i ⋅[αEx, i⋅Ei

ρEx, i

+ (1 − αEx, i )⋅Di

ρEx, i 1/ ρ ] Ex, i

(A.10)

⎤σEx, i

⎡ 1 − αEx, i PEi Ei ⎥ =⎢ ⋅ Di ⎣ αEx, i PDi ⎦

(A.11)

Where,

Ei ~ export of good i PEi ~ domestic price of export good i AEX , i ~ shift parameter in transformation function (CET) ρEx, i ~ substitution parameter in CET function for export αEX , i ~ share parameter in transformation function (CES) σEX , i ~ substitution elasticity between export and domestic sales A.2.4 Investment module Total investment in the model is characterised in two ways: circulating capital investment and fixed investment. Adjusting total fixed investment with corresponding sectoral shares to identify the fixed investment supplied to each sector (see Eq. A.12). And then multiplying the fixed investment supplied to each sector with the composition matrix of fixed capital to get fixed capital investment supplied by each sector. Major functions in this sub-module are shown by Eqs. A.13-A.14.

Dki⋅Pki = μi ⋅FxdInv Pki =

(A.12)

∑ sf j,i ⋅PQj

(A.13)

j

IDi =

∑ sfi,j ⋅Dkj

(A.14)

j

Where,

Dki ~fixed capital investment supplied to sector i Pki ~price of fixed capital in sector i FxdInv ~total fixed capital investment IDi ~fixed capital investment supplied by sector i μi ~share of sector i in total fixed capital investment sfi, j ~composition matrix coefficient of fixed capital for sector j from investment good i A.2.5 Model closure Model closure identifies the borderline of the model by differentiating the exogenous and endogenous variables. This model considers three principles of closure, namely: government budget balance, foreign trade balance, and invest-saving balance. For the government budget balance, government consumption is exogenous, while government saving is endogenous. For the foreign trade balance, foreign saving is exogenous, and the exchange rate is endogenous. For the invest-saving balance, this model is saving-driving. The adopted principle follows “neoclassical closure”, and assumes that all the saving is transformed into investment, and that total investment equals total saving endogenously. A.2.6 Market clearing Market clearing describes the equilibrium conditions in a CGE model. In this model, only commodity and capital markets are cleared. The clearance of the commodity market requires that the gross supply of a commodity must equal the gross demand for that commodity (see Eq. A.15). So Armington composite good equals the sum of intermediate demand and various final demands (including household consumption, government consumption, fixed and circulating capital investment demand).

Qi = Inti +

∑ CDhi,h + GDi + IDi + Dsti

(A.15)

h

Where,

Inti ~ supply of intermediate goods by sector i Dsti ~ stock change of good i This model assumes that the capital market could achieve full sufficient adjustment under external shock. The supply of capital is set 71

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exogenously; the allocation of which are adjusted among sectors according to the sectoral return of equity. Market clearing means that the total capital demand equals the exogenous total supply of capital. For the labour market, here we referred to the assumption of Glomsrød and Wei[49], assuming that there will be excess supply in the labour market within the current time horizon. Therefore in the model there is no clearance of labour market, but assumes that the wage rate will keep rigid, also there exists involuntary unemployment in the labour market, and labour supply has infinite elasticity. Appendix-III A.3 Description of the basic structure of the India CGE model (based on Pradhan and Ghosh, 2012a) A.3.1 Production module The production module consists of labour, capital, energy and other intermediate inputs combining through a nested CES/Leontief function structure to form sectoral output. In case of the fossil fuel sectors (coal, oil and gas), the top nest is a CES aggregation (see Eq. B.1) of capital– labour–aggregate intermediate input composite and the fixed fossil fuel resource (part of sectoral capital; captures the limited availability of fossil fuels in the economy).

QX (AF )= alphaa (AF )*(deltaa (AF ) *QVAINTA (AF )−rhoa (AF ) + (1−deltaa (AF ))*QRES (RES , AF )−rhoa (AF ) )−1/rhoa (AF )

(B.1)

Where, QX(AF) is output of fossil fuel sector; alphaa (AF) shift parameter for CES production function for fixed resource sectors (between fixed resource and value added intermediate aggregate); deltaa(AF) share parameter for CES function for fixed resource sectors (between fixed resource and value added intermediate aggregate); QVAINTA(AF) is value added and intermediate input aggregate composite; rhoa (AF) CES function exponent for fixed resource sectors (between fixed resource and value added intermediate aggregate); QRES(RES,AF) is quantity of fixed resource. The capital–labour–intermediate composite (see Eq. B.2) is a Leontief function of the capital–labour composite and aggregate intermediate input.

⎛ QINT1, AF QINT2, AF QVAINTA (AF ) = min ⎜ , , ⋯, α2, AF ⎝ α1, AF

⎞ QINTn, AF QVA , α AF ⎟ αn, AF QVA, AF ⎠

(B.2)

Where, QVAINTA (AF) is capital-labour-intermediate composite bundle for fossil fuel sector AF; QINTj,AF is intermediate input j for the fossil fuel sector AF; QVAAF is aggregate value added for fossil fuel sector AF; αj,AF is the direct requirement of sector AF on sector j per unit output of sector AF; αQVA,AF is the direct requirement of sector AF on capital-labour composite per unit output of sector i. The capital–labour composite (aggregate value added) is in turn a CES aggregation of capital and labour (see Eq. B.3).

QVA

(AF ) = alphava

(AF ) *(deltava

(F ,

AF )*QF (F ,

AF )−rhova (AF )+(1 – deltava

(F , AF )*

QF (F ,

AF )−rhova (AF ) ))−1/rhova (AF )

(B.3)

Where, QVA (AF) is quantity of aggregate value added; alphava (AF) shift parameter for CES function (between labour and capital); deltava (F, AF) share parameter for CES function (between labour and capital); QF (F, AF) quantity demanded of factor F from sector A; rhova (AF) is the CES production function exponent for value added In the case of non-fossil fuel sectors the top nest is a Leontief function of aggregate intermediate input and energy–capital–labour composite (see Eq. B.4).

⎛ QINT1, ANF QINT2, ANF QX (ANF ) = min ⎜ , , ⋯, α2, ANF ⎝ α1, ANF

QINTn, ANF αn, ANF

,

⎞

QVAENANF ⎟ αQVAEN , ANF

⎠

(B.4)

Where, QX (ANF) is output of non-fossil fuel sector; QINT j, ANF is intermediate input j for the non-fossil fuel sector ANF; QVAEN (ANF) is value added and energy composite for the non-fossil fuel sector; αj,ANF is the direct requirement of sector ANF on sector j per unit output of sector ANF; αQVAEN,ANF is the direct requirement of sector ANF on energy-capital-labour composite per unit output of sector ANF. The energy–capital–labour composite is a CES function of the energy composite and capital–labour composite (see Eq. B.5). −

QVAEN (ANF ) = alphavaen1(ANF )*(deltavaen1(ANF )*QVA (ANF )−rhovaen1(ANF ) + (1−deltavaen1(ANF ))*QEN (ANF )−rhovaen1(ANF ) )

1 rhovaen1(ANF )

(B.5)

Where, QVAEN (ANF) is QVAEN (ANF) is value added and energy composite for the non-fossil fuel sector; alphavaen1 (ANF) is shift parameter for CES function (between aggregate energy and aggregate value added); deltavaen1 (ANF) is share parameter for CES function (between aggregate energy and aggregate value added); QVA (ANF) is quantity of aggregate value added; rhovaen1 (ANF) is CES production function exponent (between aggregate energy and aggregate value added); QEN (ANF) is quantity of aggregate energy. Similarly, the energy composite (QEN (ANF)) is a CES function of the non-electric composite and electric composite. The non-electric composite is a CES aggregation of coal, oil, gas, and oil products. The electric composite is a CES aggregation of renewable electricity composite and nonrenewable electricity composite. The renewable electricity composite is a CES aggregation of hydro, nuclear, wind/solar and biomass electricity while the non-renewable electricity composite is a CES aggregation of thermal and CCS electricity. The capital–labour composite is a CES function of capital and labour. A.3.2 Income and expenditure moduleHousehold income and expenditure. Households maximise utility subject to income and prices, and the household demand for commodities is modelled through the LES as shown in Eq. B.6.

PQ (C )*QH (C ,

H ) = PQ (C )*gammam

(C ,

H ) + betam (C ,

H )*(EH

(H ) −

∑ PQ C

(C )*gammam

(C , H )) (B.6)

Where, PQ(C) composite commodity price for C; QH(C, H) is quantity consumed of commodity C by household H; gammam (C, H) is per capita subsistence consumption of marketed commodity C for household H; betam (C, H) is marginal share of household consumption spending on commodity C; EH(H) is household consumption expenditure.

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Household income comprises income derived from labour and capital and transfers from the government and the rest of the world (Eq. B.7).

YI

(H ) =

∑ YIF

(H , F )+ trnsfr

(H ,′GOVT ′)*CPI + trnsfr

(H ,′ROW ′)*EXR (B.7)

F

Where, YI (H) is income of household H; YIF (H, F) is income of household H from factor F; trnsfr (H,'GOVT') is transfers from government to household H; CPI is the consumer price index (numeraire); trnsfr (H,'ROW') is transfers from rest of the world to household H; EXR is the exchange rate.Government income and expenditure. Government expenditure is on the consumption of goods and services, and transfers to households and enterprises (Eq. B.8).

EG = ∑ PQ (C )*QG (C ,′GOVT ′)+ ∑ trnsfr C

(H ,′GOVT ′)*CPI + trnsfr

(′PVTENT ′, ′

GOVT ′)*CPI (B.8)

H

Where, EG is government expenditure; PQ(C) is composite price of commodity C; QG(C,'GOVT') is government consumption of commodity C; trnsfr (H,'GOVT') is transfers from government to household H; trnsfr ('PVTENT',' GOVT') is transfers from government to private enterprises. Government income is from taxes (direct and indirect), capital, public and private enterprises, and the rest of the world. Indirect taxes include excise duty (production tax), import and export tariffs, sales, stamp, service, and other indirect taxes, including carbon tax (Eq. B.9).

YG = ∑ tins 0(H )*YI (H )+ ∑ indtax (A ,′ texc′)*PX (A)*QX (A)+ ∑ indtax (C ,′tm′)*pwm 0(C )*QM (C )*EXR + ∑ indtax (C ,′tsal′)*PQ (C )*QQ (C ) H

A

C

C

+ ∑ indtax (C ,′tstm′)*PQ (C )*QQ (C )+ ∑ indtax (C ,′toth′)*PQ (C )*QQ (C )+ ∑ indtax (C ,′tser ′)*PQ (C )*QQ (C ) C

C

+ ∑ indtax (C ,′te′)*pwe 0(C )*QE (C )*EXR +YIF (′GOVT ′, ′CAP′) + C

C

∑ YRES (′GOVT ′,RES )+trnsfr (′GOVT ′, ′PVTENT ′)*CPI RES

+trnsfr (′GOVT ′, ′ROW ′)*EXR − ∑ indtax (A,′tsub′)*PX (A)*QX (A)+ A

∑ pcarbon*emmfactor (emm )*QQ (emm )−QNEINT (′COAL′, ′ECCS′) emm

(B.9)

*emmfactor (′coal′)*pcarbon

Where, YG is government income; tins0 (H) is effective income tax rate paid by household H; YI(H) is income of household H; indtax (A,'texc') is effective excise duty rate; PX(A) is producer price for sector A; QX(A) is output of sector A; indtax (C,'tm') is import duty on commodity C; pwm0(C) is world import price for commodity C; QM(C) is quantity of imports of commodity C; EXR is the exchange rate; indtax (C,'tsal') is sales tax on commodity C; indtax (C,'tstm') is stamp duty on commodity C; indtax (C,'toth') is other taxes on commodity C; indtax (C,'tser') is service tax on commodity C; indtax (C,'te') is export tax on commodity C; PQ(C) is composite price of commodity C; QQ(C) is quantity of composite commodity C; pwe0(C) is world export price of commodity C; QE(C) is quantity of exports of commodity C; YIF('GOVT','CAP') is government income from capital; YRES ('GOVT',RES) is government income from fixed resources; trnsfr ('GOVT','PVTENT') is transfers from private enterprises to government; trnsfr ('GOVT','ROW') is transfers from rest of the world to government; indtax (A,'tsub') is effective production subsidy rate for sector A; pcarbon is the carbon price/tax; emmfactor (emm) is the emission factor associated with emitting sector emm; QQ (emm) is quantity of composite commodity for sector emm; QNEINT ('COAL','ECCS') is the quantity of intermediate input of coal in the ECCS sector. A.3.3 Foreign trade module We adopt the Armington specification (Eq. B.10), and assume that domestically produced and imported goods are imperfect substitutes for each other. The Armington composite good is composed of the domestic good and imports following a CES function.

QQ (C ) = alphaq (C ) *(deltaq (C ) * QM (C )−rhoq (C ) +(1−deltaq (C )) *QD (C )−rhoq (C ) )−1/rhoq (C )

(B.10)

Where, QQ(C) is the quantity of composite good C; alphaq(C) is Armington function shift parameter for commodity C; deltaq(C) is Armington function share parameter for commodity C; QM(C) is quantity of imports of commodity C; rhoq(C) is Armington function exponent for commodity C; QD(C) is quantity of domestic supply of domestic output. The import demand function (B.11), obtained from above, is shown below.

⎛ PDD(C) ⎞ ⎛ deltaq(C) ⎞ QM(C) = QD(C)*( ⎜ ⎟*⎜ ⎟ )1/(1+rhoq(C)) ⎝ PM(C) ⎠ ⎝ (1 − deltaq(C) ⎠

(B.11)

Where, QM(C) is quantity of imports of commodity C; QD(C) is quantity of domestic supply of domestic output; PDD(C) is price of domestically produced commodity C; PM(C) is import price of commodity C; deltaq(C) is Armington function share parameter for commodity C; rhoq(C) is Armington function exponent for commodity C. As for exports, a CET function (B.12) is adopted to allocate total domestic output between exports and domestic sales.

QX (C ) = alphat (C ) *(deltat (C )* QE (C )rhot (C ) +(1−deltat (C ))*QD (C )rhot (C ) )1/ rhot (C )

(B.12)

Where, QX(C) is domestic production of commodity C; alphat (C) is CET function shift parameter for commodity C; deltat (C) is CET function share parameter for commodity C; QE(C) is quantity of exports of commodity C; QD(C) is quantity of domestic supply of domestic output; rhot(C) is CET function exponent for commodity C. The export supply function (B.13), based on the above is shown below.

⎛ PDD(C) ⎞ ⎛ deltat(C) ⎞ QE(C) = QD(C)*( ⎜ ⎟*⎜ ⎟ )1/(rhot(C)−1) ⎝ PE(C) ⎠ ⎝ (1 − deltat(C) ⎠

(B.13)

Where, QE(C) is quantity of exports of commodity C; QD(C) is quantity of domestic supply of domestic output; PDD(C) is price of domestically produced commodity C; PE(C) is export price of commodity C; deltat (C) is CET function share parameter for commodity C; rhot(C) is CET function exponent for commodity C.

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A.3.4 Model closure Regarding model closure, the main assumptions are exogenous government consumption; fixed foreign saving and flexible exchange rate and saving-driven closure. Further, full employment of capital and labour is assumed, and factor prices adjust to clear the factor markets. A.3.5 Market clearing Market clearing describes the equilibrium conditions in a CGE model. In the model market clearing refers to clearing of factor and commodity markets. The market equilibrium equation for factor markets is shown below (B.14). The supply of a factor equals the sum of demands from all sectors.

QFS (F ) = ∑ QF (F , A)

(B.14)

A

Where, QFS (F) is the aggregate supply of factor (labour and capital); QF (F, A) is the quantity of factor F used by sector A. The market equilibrium equation for commodity markets is shown below (B.15). The quantity of composite good equals the sum of intermediate demand from sectors, household consumption demand, government consumption demand, and investment demand.

QQ (C ) = ∑ QINT (C , A) + ∑ QH (C , H ) + QG (C ,′ GOVT ′) + QINV (C ) A

(B.15)

H

Where, QQ(C) is quantity of composite good; QINT(C,A) is intermediate demand for commodity C from sector A; QH(C) is quantity of household consumption of good C; QG(C, ‘GOVT’) is quantity of government consumption of commodity C; QINV(C) is quantity of investment demand for commodity C.

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