Fossil energy subsidies in China's modern coal chemical industry

Energy Policy 135 (2019) 111015

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Fossil energy subsidies in China’s modern coal chemical industry Yiming Li a, b, Changqing Li b, a, * a b

School of Chemical Engineering, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, PR China School of Economics and Management, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Modern coal chemical Coal-to-liquids Energy subsidy G20 Inventory approach Energy subsidy reform

The issue of whether there is a fossil energy subsidy in China’s modern coal chemical (MCC) sector remains controversial, although domestic coal prices have been liberalized since 2013. To identify potential fossil energy subsidies in the MCC industry, an inventory approach is used in subsidy measurement. Three representative forms of coal consumption subsidization are identified and measured in this paper: feed coal supply at a pref­ erential price, prior access to coal mining rights, and privilege in coalmine M&A (mergers and acquisitions) cases. Using China’s coal-to-liquids (CTL) industry as a case study, we find that the current subsidy helps save 50% of the coal consumption cost of a typical plant, and the total amount of subsidy in the CTL industry will reach 16.4 billion Yuan in 2022. However, according to the results of efficiency, wastefulness and effectiveness tests, 47.03% of the current subsidy in the industry is excessive, leading to overinvestment and energy waste. To compensate for the deficiency of the subsidizing mechanism, we suggest replacing subsidizing channels that rely on mining rights concessions or M&A cases with channels using long-term coal supply contracts that couple contract prices and oil prices.

1. Introduction Coal is China’s dominant energy source. In 2018, coal accounted for 59% of China’s total energy consumption (National Bureau of Statistics, 2019) and half of the world’s coal consumption (BP, 2018). Coal has contributed to China’s rapid economic growth over the past three de­ cades while also causing severe air pollution and enormous carbon emissions (Qi et al., 2016). To control the environmental damage of coal use and to fulfill China’s international commitment to peak carbon emissions in 2030, China has launched a series of policies, including the Air Pollution Control Action Plan, the Energy Development Strategy Action Plan (2014–2020), and the total Energy consumption Control Plan. To gradually reduce dependence on coal and realize “the energy revolution”, the Chinese government has also initiated a series of supply-side reforms, such as boosting the deployment of non-fossil en­ ergy and coal decapacity, as well as a series of consumption-side re­ forms, such as coal caps, decapacity of thermal power, and boosting the development of electric vehicles and hydrogen battery vehicles. Ac­ cording to the “13th Five-Year-Plan (FYP, for the years 2016–2020), China will reduce its share of coal consumption from 64% in 2015 to 58% in 2020, while the share of non-fossil energy consumption will rise

from 12% to 15%. A coal subsidy would offset China’s efforts to reduce its reliance on coal because it would encourage wasteful consumption and increase GHG emissions by reducing the consumption and production costs of coal. Under the planned economy, coal prices were controlled by the government, and the coal subsidies were widespread. In 1984, the subsidy rate for China’s coal industry was as high as 61% (Dixon et al., 1997). With the implementation of the market-oriented reform of coal price in the mid-1980s, the scale of coal subsidies decreased rapidly. By 1995, the subsidy rate had fallen to 29% (Dixon et al., 1997). In the decade since China has further liberalized the price of coal and stopped intervening in the price of thermal coal. By 2008, China’s coal subsidy rate had dropped to 9% (Jiang and Lin, 2014). However, any subsidy rate multiplied by China’s huge consumption of coal has a huge impact. According to the estimation of Jiang and Lin (2014), China’s coal sub­ sidies were still worth 20 billion US dollars in 2010. A new area of research on coal subsidies involves China’s modern coal chemical industry, which refers to a series of technologies to convert coal into liquid fuel, gas or basic chemicals. Compared with traditional coal utilization methods in China (e.g., small-scale power generation, coal-fired heating, low-level coal chemical industry), MCC is considered more energy efficient and promising for pollution control

* Corresponding author. School of Economics and Management, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, PR China. E-mail address: [email protected] (C. Li). https://doi.org/10.1016/j.enpol.2019.111015 Received 6 March 2019; Received in revised form 24 September 2019; Accepted 26 September 2019 Available online 7 October 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.

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Energy Policy 135 (2019) 111015

Abbreviations AACE ASU BOP CCPD CCPUA CCS CTL CTO DCL EPC G20 GHG IC ICL IDC IEA ISBL LPG M&A MCC NAO NDRC OECD PSA

PSE-CSE Producer Support Estimation-Consumer Support Estimation SOE State-Owned Enterprise TPI Total Plant Investment WGS Water Gas Shift Ci The cost of the estimated equipment Ci; ​ ref The cost of the reference equipment F The unit market price of the product FAIPI Fixed Assets Investment Price Index in the estimating period FAIPIref Fixed Assets Investment Price Index in the reference period kðiÞ Cost scaling factor (CSF) M The cost of coal mining PInt The preferential price of “affordable coal” under Yulin Scheme subsidization/the internal price of coal produced by captive mine PRep The pit price of feed coal Q Yield of the MCC plant Coal consumption under a maximal load QMax r Subsidy rate RConv Operating profit of the coal conversion/MCC plant Si The scale of the estimated equipment Si;ref The scale of the reference equipment W Costs other than feed coal cost x The coal consumption per ton of product: : : : : C: : : :

American Association of Cost Engineers Air Separation Unit Balance Of Plant Coal-to-liquids Project Database of China China Coal Processing & Utilization Association Coal Consumption Subsidy Coal-To-Liquids Coal-To-Olefin Direct Coal Liquefaction Engineering, Procurement and Construction Group of Twenty Green House Gas Indirect Cost Indirect Coal Liquefaction Interest During Construction International Energy Agency Inside Battery Limits Liquefied Petroleum Gas Mergers and Acquisitions Modern Coal Chemical National Audit Office of China National Development and Reform Commission of China Organization for Economic Cooperation and Development Pressure Swing Absorb

and can separate high-concentration CO2 that is easy to capture and utilize. To boost the development of the MCC industry, the central and local governments provide subsidizing policies in the areas of R&D in­ vestment, land use, coal resource access, and loans. With this support, the coal consumption of China’s MCC industry has increased rapidly with the booming of the industry. In 2017, China’s coal chemical in­ dustry consumed 67.3 million tons of coal, accounting for 2.5% of China’s total coal consumption (CCPUA, 2018). This number will in­ crease to 146.2 million tons by 2020, accounting for 5% of the country’s coal consumption (CCPUA, 2018). Moreover, due to the immature CCUS technology, enormous CO2 generated by MCC is discharged in situ, posing a serious threat to China’s climate goals. According to the esti­ mation of Greenpeace (2017), by 2020, China’s MCC will produce GHG emissions of 409–792 million tons per year, which is roughly equivalent to the total emissions of Australia or the UK at that time (OECD, 2019). This paper aims to answer the following questions: First, what are the types and mechanisms of existing fossil energy subsidies in China’s MCC industry? Second, what is the scale of CCS in the MCC industry? Third, have the existing subsidies achieved the intended objectives? To answer these questions, the remainder of the paper is organized as follows. Section 2 is a review of the literature on coal subsidies. Section 3 introduces the CCS subsidy disclosure framework based on the inventory approach. Section 4 identifies, documents and analyzes three typical subsidization policies in China’s MCC industry. Section 5 takes the coalto-liquid industry as a case study to measure the scale of CCSs and test their efficiency, effectiveness and wasteful impacts. Section 6 provides recommendations on subsidy redesign and replacement.

Nations Environment Programme and International Energy Agency, 2002). Coal is currently the dirtiest of fossil energies (Dixon et al., 1997); thus, subsidies for coal will significantly increase pollution and GHG emissions (Steenblik and Coroyannakis, 1995). Since the 1990s, scholars have attempted to measure the total scale of coal subsidies and assess the potential positive impact of subsidy reforms on the environment. Larsen and Shah (1992)found that the world’s total scale of coal subsidies reached 51 billion in 1987, accounting for 22.97% of global fossil energy subsidies. If these coal subsidies were removed, global GHG emissions would fall by 9%. Okogu and Birol (1993) selected 5 major coal-consuming countries among OECD countries (accounting for 37% of OECD coal consumption) for examination and found that in 1991, the maximum subsidy for coal producers in these countries was 105 US dollars per ton, and the minimum was 22 US dollars per ton. Once coal subsidies are phased out, GHG emissions could fall by as much as 18.1% in Germany, 5.8% in Britain and 3.7% in Spain. Dixon et al. (1997) found that between 1990 and 1995, the subsidy rate of coal decreased from 57% to 44%. The removal of these coal subsidies in OECD countries alone would reduce global CO2 emissions by 1.5%. de Moor (2001) found that the total amount of coal subsidies in the world reached 53 billion dollars between 1995 and 1998 (de Moor, 2001). The IEA re­ ported that world coal subsidies still reached 40 billion US dollars in 2008, most of which were indirect subsidies, such as electricity price subsidies (IEA et al., 2010). Coady et al. (2015) found that post-tax coal subsidies (including the environmental cost, health cost, crowding out investment in new energy and other externalities that are not included in the price of coal use) worldwide in 2013 reached 2.94 trillion dollars, accounting for approximately 60% of all post-tax subsidies for fossil energy. Since the 2009 G20 summit in Pittsburgh, the IEA, the OECD, the World Bank, the IMF and other international organizations have been tracking and reporting the world’s coal subsidies. For instance, the IEA database shows that world coal subsidies have been increasing in the last five years, from 1.2 billion dollars in 2014 to 3.38 billion dollars in 2018 (IEA, 2019). Coal is the dominant energy source in China, accounting for more than 60% of the country’s total energy consumption. In 2018, China

2. Literature review on coal subsidies Coal consumption subsidies are one category of fossil energy sub­ sidies. According to the OECD, fossil energy subsidies are defined as government actions that lower the purchase price for consumers, raise the selling price for producers, or reduce costs associated with producers and consumers of fossil energies (oil, coal and natural gas, etc.) (United 2

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produced 46.7% of the world’s coal and consumed 50.7% of it (BP, 2018). Any potential energy subsidy multiplied by this large amount of coal production and consumption would have a huge environmental and economic impact, so Chinese coal subsidies are of particular concern to scholars. Larsen and Shah (1992)found that as the second largest fossil energy subsidy country at that time, China’s coal subsidy reached 3.39 billion in 1987, accounting for 24.09% of its total fossil energy subsidies. If these coal subsidies were removed, China’s GHG emissions would be reduced by 10%. Dixon et al. (1997) found that from 1984 to 1995, through a series of coal market reforms including lifting coal price control, allowing private enterprises to run mines, and solving the long-term debt problem of coal trading, China’s subsidy rate for coal was reduced from 61% to 29%. Lin and Jiang (2011) found that in 2007, China’s coal subsidies, mainly for the use of electricity, reached 53.2 billion CNY, accounting for 0.21% of China’s GDP that year. The removal of these subsidies could reduce GHG emissions by 100.45 million tons per year. In 2008, the coal subsidy increased to 87.19 billion CNY(Jiang and Lin, 2014). The OECD began reporting on the scale of China’s coal subsidies in 2006. Data show that in the past 12 years, China’s coal subsidies via budgetary and tax expenditure increased from 0.15 billion Yuan to 5.24 billion Yuan in 2017, a 34-fold increase (OECD, 2017). Existing estimates of fossil energy subsidies are based on two main approaches: the price-gap approach and the inventory approach. The price-gap approach takes the difference between the domestic price (subsidized price) and the international price (open market price) as the basis for energy subsidy measurement (Subsidies and Right, 1999). This approach functions well when there is a mature global market and a uniform international pricing system. The oil subsidy, for instance, can be measured by using the domestic market price minus the CIF price of imported oil. However, the price-gap approach has a very large limita­ tion in coal subsidy measurement. First, because coal is relatively bulky and generally used nearby, international trade of coal accounts for only a small proportion of total consumption, so it is difficult to find a fair reference price (Larsen and Shah, 1992; Steenblik and Coroyannakis, 1995) Second, the price-gap approach can only capture subsidies that directly affect end-use market prices; this approach cannot cover fossil energy subsidies through budgetary, tax expenditure and other non-price channels (Koplow, 2009). To make a more comprehensive estimate of coal subsidies another mainstream approach needs to be implemented: the inventory method. An inventory approach aims to identify, document, and quantify a wide range of government in­ terventions in energy markets utilizing a combination of support de­ livery mechanisms (Kojima and Koplow, 2015). The advantage of this approach lies in its comprehensive coverage of consumer and producer subsidies, so it is widely used in cross-country comparative analysis (OECD, 2013) and nation-level subsidy estimation (Koplow and Martin, 1998). Although researchers and international organizations continue to track China’s coal subsidies, updating the scope and methodology of coal subsidy measurement has never been more urgent. First, in recent years, China’s coal consumption in traditional areas, such as thermal power, has declined, while coal consumption of emerging coal chemical industries, such as coal-to-liquids, has increased significantly (BP, 2018). Previous studies (Jiang and Lin, 2014; Lin and Jiang, 2011) have mainly estimated coal consumption subsidies in the power sector but failed to consider the changes in coal consumption structure caused by the recent development of the coal chemical industry. Second, most existing studies focus on subsidy policies at the central level but pay less attention to local policies. Considering the regional nature of the coal market and the important role of local state-owned enterprises in coal production and consumption, it is necessary to evaluate coal subsidies at both the central and local levels. Third, limited by methods or scopes, existing coal subsidy studies or databases cannot fully reveal all poten­ tial coal subsidies. For example, due to the limitations of the price-gap approach, the IEA’s database (IEA, 2019) only covers consumer

subsidies. The OECD database (OECD, 2017), which uses the inventory approach, only covers budgetary and tax expenditure transfers and ex­ cludes most categories of indirect and implicit subsidies, such as cross-subsidies, preferential access of resources and risk sharing. 3. Framework of subsidy disclosure and measurement 3.1. The decision tree approach for subsidy disclosure and measurement We developed a subsidy disclosure and measurement framework based on an inventory approach. In this framework, policies on the watch list are tested according to the definition of fossil energy subsidy and measured against a baseline case. A decision tree for subsidy disclosure is shown in Fig. 1. The decision tree demonstrates four basic steps of subsidy disclosure and measurement: Step 1. Establish a policy watch list; Step 2. Examine the policy documents on the watch list to disclose coal consumption subsidies; Step 3. Identify the subsidization mechanism; Step 4. Measure the scale of the subsidy. The decision tree covers explicit subsidies (e.g., price controls) and implicit subsidies (e.g., preferential energy resource access) and can complement the gap in existing subsidies in fossil energy reports (e.g., the IEA annual report/database only covers end-use pricerelated subsidies, and the OECD annual report/database only covers budgetary transfers and tax expenditures). This decision tree approach is suitable for scholars, officials and international organizations concerned with fossil fuel subsidies. The approach is based on China’s policy regime. When used in regimes abroad, the selection of watch lists needs to be adjusted for local policy frameworks. Because the approach is designed primarily for the coal chemical industry and the coal mining industry, when applied in other industries, substantial changes should be made to the watch list, mechanism identification and measurement baseline. The four basic steps are as follows. Step 1. Establish a policy watch list The policy watch list of the decision tree approach covers three major coal acquisition channels for China’s modern coal chemical industry: the wholesale and retail market for coal, the primary market for coal mining rights, and the M&A market for coalmines. In the identification of sub­ sidies, policies involving the end-use market are the most conspicuous (especially for researchers using the price-gap approach), while implicit subsidies through the latter two channels are easily neglected. However, surveys have shown that implicit subsidies through mining right access and preferential M&A cases are widespread in coal-producing countries, including China (Koplow et al., 2010), the United States (Sanzillo, 2012), India (Comptroller and Auditor General of India and Union Government, 2012) and Indonesia (Fane, 2012). The policies of these three channels are examined (and the decision tree is divided into three branches) because of their differences in policy areas and potential subsidy mechanisms: subsidizing via the wholesale and retail market may involve coal market regulatory policies (e.g., price control); sub­ sidizing via the primary market of coal mining rights may involve resource management policies (e.g., preferential transfer of mining rights, royalty concessions); and subsidizing via the M&A market of coalmines may involve coal industry policies (e.g., encouraging mergers and acquisitions). The policy watch list covers policies at the central and provincial levels. Local policies are often excluded in the scope of many subsidy studies. However, local governments in China have great autonomy in the management of state-owned mineral resources and the formulation and implementation of industrial policies. After reviewing global sub­ sidy policies, Koplow et al. (2010) determined that the local policies of China and Germany played an important role in subsidizing the energy industry. In addition, the OECD’s database shows that China’s total coal subsidies at the central level are much lower than at the local level (OECD, 2017). Therefore, the effectiveness of research could be 3

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Energy Policy 135 (2019) 111015

Fig. 1. Decision tree for disclosure of coal consumption subsidies in three main coal acquisition channels.

undermined if local-level subsidies are omitted from the scope. In summary, the list covers interventions at the central and provin­ cial levels, referring to three coal consumption channels of the MCC sector. The provincial policy of the list covers those in Shanxi, Xinjiang, Ningxia, Inner Mongolia, Shaanxi and Guizhou because these provinces are the major areas for the deployment of the coal chemical industry (NEA, 2017). The watch list covers policy data for 15 years, from 2004 to 2018.

different customer groups, which is a type of spontaneous business behavior. However, discriminatory pricing by state-owned enterprises or utility departments (e.g., state-owned power stations and grid com­ panies) should be regarded as a cross-subsidy. This element may seem obvious, but it can sometimes be tricky in practice. In the past, for example, the annual coal ordering meeting between power generation enterprises and coal mining enterprises was organized by the National Development and Reform Commission (NDRC). In this ordering meeting, enterprises will sign coal supply contracts together or sepa­ rately, whereby the coal unit price is often lower than the market level. Compared with the way of releasing the guiding price, the role of NDRC in the coal ordering meeting is vague and opaque. At this point, we use a criterion to distinguish whether the government’s actions have sub­ stantially affected the market transaction: if there is no government intervention, can the transaction be possible at the current price? If so, we believe that the current transaction is dominated by spontaneous market behavior; if not, the government’s actions interfere with the transaction, and there is the possibility of subsidies. The second element of coal subsidy identification is the direct cost of coal consumption. To identify whether a policy is a coal subsidy, it is necessary to identify whether this policy reduces the direct use cost of coal consumption of enterprises. The direct cost of coal consumption refers to costs directly related to the acquisition, transportation, and treatment of coal, such as the purchase cost, the transportation cost, the cleaning cost, and the acquisition cost of coal mining rights. The related subsidies may include coal price controls, supply contracts coordinated by the government at preferential prices, the differential pricing of state-

Step 2. Examine the policy documents The decision tree approach provides a logical framework for deter­ mining whether a policy can be identified as a subsidy. In practice, subsidy identification is not only a rigorous technique, but also an art involving researchers’ subjective judgment and experience-related skills (Koplow, 2015). To prevent excessive subjectivity, the researcher must take two steps in subsidy identification: First, the definition of the sub­ sidy must be clear and operational; second, the attitude and principle in subsidy disclosure shall be consistent, clear, and reasonable. The coal consumption subsidy defined in this article refers to any government action that reduces the direct cost of coal consumption by enterprises. This definition includes the following elements: government action and the direct cost of coal consumption. Let us discuss them below. The first element of coal subsidy identification is government action. If no government action involved, it does not qualify as a subsidy. For example, some enterprises will adopt different pricing strategies for 4

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Energy Policy 135 (2019) 111015

owned coal enterprises (cross-subsidy), coal washing equipment in­ vestment funds, and fuel subsidies for coal transportation. The direct cost of coal consumption is the focus because the reduction of the direct cost will have a direct influence on coal consumption by enterprises (via price elasticity), thus affecting the environmental objectives of concern in this paper. However, the coal consumption subsidy defined in this paper does not include increasing the profits of enterprises through non-coal channels, for example, subsidies that reduce the costs of water, elec­ tricity, and land use, direct budgetary transfers to coal using enterprises, investments, loan guarantees, etc. The main reasons to exclude subsidies involving indirect costs are as follows. First, the above indirect subsidies may be widely used in local governments’ subsidies to other industries (e.g., general supporting policies in local development zones and in­ dustrial parks in many areas of China), thus distracting the research focus of the study and eroding the significance of this form of subsidy. Second, unlike changes in coal’s direct costs, the extent to which these reductions in indirect costs can influence business decisions and encourage the use of coal is debatable. Other general questions on the operational aspects of subsidy identification can also be found in the OECD (2009) and Kojima (2016).

Table 1 Structure and elements of CCPD. CTL project database

Tracking all planned, ongoing, operational and terminated projects in China (updated June 7, 2019)

Number of projects Data type

15

Information source

Data use in this paper

Step 3. Identify the subsidy mechanism The decision tree approach provides the basic logic for the identifi­ cation of subsidy mechanisms. Consistent with Steenblik (2002), the subsidy mechanism can be identified based on the following aspects: Is the subsidy transferred from government budgetary and tax expenditure (direct subsidy) or other sectors (indirect subsidy)? Is the subsidy real­ ized in the form of increased income (explicit subsidy) or the exemption of some expenses (implicit subsidy)?

Policy database

Filing policies related to CTL industry (updated July 20, 2019)

Data type

Central and provincial coal mining/MCC/CTL industry planning, central and provincial coal mining/MCC/CTL industry regulatory policies, central and provincial coal resource management regulations, environmental protection regulations, and investment incentives list. NPC, the State Council, NDRC, MEE, MNR, MOF, NAO and their provincial counterparts. Watch list of subsidizing policies.

Information source Data use in this paper

Step 4. Measure the scale of the subsidy

Project investment, process parameters, main equipment selection, operation parameters (partial), basic information (scale, location, investors, upstream and downstream enterprises, etc.). Files: feasibility study report, environmental evaluation report, commercial contract (equipment purchase), financial report of listed company, research institution report, paper, etc. Fieldworks: 4 CTL plants in Inner Mongolia and Shanxi. Interviews: 39 industrial technical experts from Chinese CTL enterprises (Shenhua, Yankuang, Luan, Yitai, Yanchang); CTL Technology R&D institutes (Synfuel China, Stanford Research Institute, Shanghai Yankuang Energy Technology); Coal gasification equipment manufacturers (Shell, U-gas, East China University of Science and Technology); chemical design institutes. Form in Section 4.2.1

subsidizing mechanism and policy clauses. To avoid unnecessary repe­ tition, we introduce the CCS according to the schemes and list the dis­ tribution of these schemes at the local level in Table 2. To estimate the number of each type of subsidizing scheme, quantitative models are also established.

A subsidy is measured against a baseline, which refers to the cost, income or gross profit level of the project when the specific subsidy does not exist. In this paper, the key assumption about the baseline is that a newly established independent coal deep processing project can only acquire feed coal via local coal wholesale or on the retail market at a fair-market pit mouth price if it is not supported by any form of CCS. Other potential acquisition channels for coal, such as mining rights leasing or coalmine M&A, are usually blocked from unsubsidized pro­ jects in practice (Appendix 1) due to local protectionism (Wedeman, 2003; Wright, 2006), corruption (NAO, 2015), and coal local conversion targets (coal industrial policy that requires a certain proportion of coal resources allocated to local coal conversion projects; see Appendix 1).

4.1. Subsidization through wholesale or retail markets One of the typical forms of CCSs is ordering local SOEs to supply coal to MCC projects at a below-market price (Table 3). The provincial areas that this subsidy affected include Xinjiang, Inner Mongolia, and Shaanxi. The most detailed documented description stems from Yulin Gov. [2018] Ref. No.16 (Table 2). For the convenience of the following analysis, we describe this form of CCS as the Yulin Scheme. The transfer originates from the profit loss of local SOEs. Belowmarket prices can benefit subsidized MCC plants by reducing the unit operating costs and improving capacity utilization (Fig. 2). The amount of the transfer equals the margin between the operating profit of a subsidized plant and that of a baseline plant. We assume that an MCC plant will reach its maximal load as long as its revenue can cover the complete cost; otherwise, the MCC plant will operate under a lower load to minimize losses. To consider overhaul and routine maintenance, the maximal load of the MCC plant is set as 80% of its designed capacity. The lower load is half of the maximal load, equivalent to 40% of the designed capacity. Unless it is under overhaul, the plant should not be completely shut down for financial reasons because in practice, this will be unac­ ceptably costly and technologically risky. For an MCC plant, the mini­ mum load to maintain the functional operation of the process varies between 40% and 50%. We first model the operating profit function of an MCC plant. The operating profit (RConv ) is the function of the feed coal price (P) and the yield of the MCC plant (Q):

3.2. CTL project database of China Surveying the distribution of the subsidy requires access to data on feed coal suppliers or sources of China’s current CTL projects. However, we found such data unavailable from any published sources. To collect the industrial data, we developed our own CTL Project Database of China (CCPD) based on 5 years of field investigation, interviews with industrial experts, data mining of internal documents, open access commercial contracts, and industrial survey reports (Table 1). The database includes 15 ICL and DCL projects in operation or under con­ struction in China. The project data attributes include technology de­ tails, finance data, and process and equipment information. 4. Subsidy disclosure and modeling for China’s modern coal chemical industry One of the main objectives of the inventory approach is to disclose, identify and document any subsidizing interventions. At the provincial level, many programs show a consistent pattern in terms of the 5

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Energy Policy 135 (2019) 111015

Table 2 Inventory of coal consumption subsidies in China’s MCC industry. Subsidy Channels

Shanxi (SX) Coalmine M&A Privilege

Policy Info. (Code/ Jurisdiction/ Source)

Representative Policy

SX DOLR., [2005] Ref. No.247 Department of Land and Resources of Shanxi Province http://www.sh anxilr.gov.cn/Ar ticle/ShowArti cle.asp?Arti cleID¼89

Notice on the issuance of Shanxi Province coal enterprise resource conformity and the paid use implementation plan

Encourage provincial key state-owned coal enterprises, local key state-owned coal mines and other large and medium-sized coal deep processing enterprises to pool, merge, participate in, hold shares and purchase local small and mediumsized coal mines.

Notice on the issuance of several regulations for improving the allocation and management of coal resources

For projects whose annual coal consumption is greater than the capacity of the captive coal mine, the supply gap can be filled by local SOEs at preferential price if the coal quality matches. Preferentially approve coal mines construction targeting at the demand of national key construction and demonstration projects in coal power generation, coal-to-gas and coal-to-liquid sectors. Developers of new coal mines must invest in projects that increase the added value of coal products and extend the coal industry chain. Enterprises that have coal mining rights shall be restructured with coal conversion enterprises in accordance with the price standards for administrative allocation of coal resource. The competent department shall give priority to the approval of the coal mine (restructured in this way).

Inner Mongolia (IM) Preferential IM GO, [2013] Coal Supply Ref. No.74 General office of the people’s government of Inner Mongolia http://www. nmg.gov.cn/a rt/2013/8/29/a rt_1686_137646. html Preferential Ordos Gov., Access to [2017] Ref. No. Coal 191 Resource People’s Government of Ordos City htt p://www.ordos. gov.cn/xxgk/in formation/ordo s_xxw52/ msg1020426 0837.html.

Coalmine M&A Privilege

Ningxia (NX) Preferential Access to Coal Resource

Notice on the 13th five-year plan for the development of the coal industry

IM GO, [2015] Ref. No.57 General office of the people’s government of Inner Mongolia http://www. nmg.gov.cn/a rt/2015/9/15/a rt_1686_137953. html

Notice on matters relating to supporting coal conversion enterprises to M&A coal production enterprises.

NX Gov., [2012] Ref. No.125 People’s Government of Ningxia htt

Notice on the issuance of Interim measures for allocation of coal resources for

Table 2 (continued ) Subsidy Channels

Provisions

Coalmine M&A Privilege

Shaanxi (SA) Preferential Coal Supply

To be qualified for coal resource allocation, enterprises shall meet one of the

Preferential Access to Coal Resource

Policy Info. (Code/ Jurisdiction/ Source)

Representative Policy

Provisions

p://news.10jqka. com.cn/2012100 9/c529902591. shtml

industrial construction projects

NX Coal Dev. & Pro. Reg., [2008] Standing member of the people’s congress of Ningxia hui autonomous region htt p://www.nxdrc. gov.cn/in fo/1022/7851.ht m

Regulations on the Exploration Development and Protection of Coal Resources in Ningxia Hui Autonomous Region

following conditions: Projects of coal transformation and comprehensive utilization supported by the state and autonomous regions; Industrial projects with an investment of more than 20 billion Yuan whose feasibility is examined and confirmed by the competent department. The developer of coal resource in East Ningxia shall be large qualified enterprise groups, which are encouraged to cooperate with the enterprises that have allocated coal resources. The coal resources in the East Ningxia coal base give priority to largescale coal chemical industry, coal, electricity and aluminum integration and other coal deep processing projects.

Yulin Gov.[2018] Ref. No.16 People’s Government of Yulin City http://www.yl. gov.cn/site/1/h tml/zwgk/0/1/ 16/20576.htm

Notice on the issuance of several policies to promote the advanced development of the coal chemical industry

SA Gov., [2008] Ref. No.15 People’s Government of Shaanxi Province

Opinions on the implementation of compensated use of coal resources in Shaanxi Province

Using the coal resource reclaimed through coordinated operations and mining right shares held by governments, “affordable coal” is provided to major basic chemical projects and preferential major projects. The price of “affordable coal” includes full cost and reasonable profit. Long-term supply contracts for more than 10 years could be signed according to the actual coal consumption and investment amount of the projects. To apply for coal resources allocation by administrative agreement, a largescale coal (continued on next page)

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Energy Policy 135 (2019) 111015

Table 2 (continued ) Subsidy Channels

Policy Info. (Code/ Jurisdiction/ Source)

Table 2 (continued ) Representative Policy

http://xuewen. cnki.net/cjfd -sxrz200810006. html

Xinjiang (XJ) Preferential Coal Supply

Preferential Access to Coal Resource

Provisions

Subsidy Channels

development project shall also meet the following conditions: coal-tomethanol projects with capacity of 1 million tons or more; coal liquefaction projects of 3 million tons or more; or coal liquefaction projects of 1 million tons or more with independent intellectual property rights. When coal mining rights are transferred through bidding, auction or listing, priority shall be given to the conversion of coal under the same conditions. The coal mined in excess of the needs of the conversion project shall be incorporated into the local commodity coal for unified distribution at the local market price.

XJ Gov., [2011] Ref. No.85 Xinjiang Uygur Autonomous Region Government http s://www.hts.gov. cn/zhaoshangyi nzi/show.php?it emid¼95

Provisions on the administration of paid allocation, exploration, development and transformation of coal resources

XJ Gov., [2011] Ref. No.85 Xinjiang Uygur Autonomous Region Government http s://www.hts.gov. cn/zhaoshangyi nzi/show.php?it emid¼95

Provisions on the administration of paid allocation, exploration, development and transformation of coal resources

Guizhou (GZ) Preferential Access to Coal Resource

Policy Info. (Code/ Jurisdiction/ Source)

Representative Policy

Provisions

projects can have a discount of 60%. GZ GO, [2008] Ref. No.4 General Office of Guizhou Provincial People’s Government htt p://www.gzgtzy. gov.cn/xwzx/t zgg/201707/t20 170703_269 4455.html

Notice on the issuance of opinions on the implementation of the pilot reform on deepening the paid use system of coal resources in Guizhou Province

Projects that are qualified to apply for administrative allocation of coal resource include: key coal utilization projects approved by the state or provincial governments.

Table 3 Summarization of disclosed CCS in China’s MCC industry. Disclosed subsidy scheme

Description

Subsidization mechanism

Classification of subsidies (target/ instrument/ pathway of benefit) (Anthony, 2000)

Preferential Coal Supply

At below-market prices, state-owned coal mining enterprises supply coal to target MCC projects. The price equals the production cost plus appropriate fixed profit. Resource management departments give priority to the transfer of highquality coal resources to target projects.

The windfall rent and a part of absolute rent extracted by stateowned coal mining enterprises are transferred to the target projects.

Input/market transfers/indirect &explicit

Due to the incomplete control of coal resources, the target project extracts part of the absolute rent and differential rent generated by coal mining as its surplus. Due to the complete control of coal resources, the target project extracts all the absolute rent and differential rent generated by coal mining as its surplus.

Input/underpricing of publicly owned or managed assets/ indirect &implicit

Preferential Access to Coal Resource

The coal used in coal converting projects can also be solved by direct supply contract (with local suppliers) and through government coordination and market operation. The exploration and development of coal resources shall adhere to the principle of “allocating resources by projects” and the general requirement of “paying attention to the actual investment and transformation of projects". The pithead conversion rate of coal resources for coal power and coal chemical projects must be above 60%. The local economic development fees for coal power and coal chemical

Coalmine M&A Privilege

RConv ðP; QÞ ¼ ðF

The market administrator uses regulatory instruments to promote target projects to acquire coal resources through the acquisition of coal mines.

ðx � P þ WÞÞ � Q;

Input/ Administrative intervention in M&A cases/indirect &implicit

(formula 1)

where F is the unit market price of the product, e.g., diesel, gasoline, LNG, or olefin; x is the coal consumption per ton of product; and W represents any other cost except feed coal, including the labor, auxiliary material cost, maintenance cost, amortized depreciation, period cost, consumption tax on refined oil and other business taxes. Subsidies of the Yulin Scheme are modeled as follows: � � � 8 Conv Int P ; QMax RConv PRep ; QMax ; RConv PRep ; Q � 0 > : RConv PInt ; QMax RConv PRep ; Max ; RConv PRep ; Q < 0 2 (formula 2) 7

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Energy Policy 135 (2019) 111015

privileged projects can secure their coal source by controlling a captive mine rather than through fragile and fluctuating contracts with local coal suppliers. To avoid companies swindling coal resources in the name of project investment, the amount of allocated coal commonly matches the actual consumption of coal utilizing projects, and the selling of surplus coal is restricted or monitored (Table 2). We identified this form of CCS in policy documents from Xinjiang, Inner Mongolia, Shaanxi, Ningxia, and Guizhou (Table 2). We name this approach the Ordos Scheme because policies in the Ordos area are representative. Preferential access to state-owned resources is identified as a com­ mon type of energy subsidy (The World Bank et al., 2010). Scholars (Koplow et al., 2010; Laan, 2010; Milazzo, 1998; Steenblik, 2002; Stone, 1997) and international organizations (Coady et al., 2015; Stone, 1997; The World Bank et al., 2010) have suggested that uncharged preferential access to nature resources should be regarded as an implicit transfer. The key difference from explicit subsidies is that the implicit transfer endows the industry with benefits by declining to impose socially justifiable costs rather than by transferring value (Bartelmus, 1999). For instance, some governments charge taxes and other applicable fees and recover only a small fraction of the rent generated by fishing, forestry and mining (Fane, 2012). When firms, such as state-owned mining com­ panies, are granted undercharged preferential access to these natural resources, they benefit from a surplus (Stone, 1997). We find hidden subsidies in China’s coal market. The first sources of implicit subsidies include the incomplete cost of coal (i.e., due to lax legal standards) (Mao et al., 2008) on environmental and production safety. The cost of coal mining in China is lower than the full cost after comprehensive consideration of resource recovery, environmental governance and production safety. Mao et al. (2008) found that from 2002 to 2006, factors such as low production safety costs, low envi­ ronmental costs, low resource tax and low land prices in China’s coal industry made the actual cost of coal mines approximately 40% lower than the completed coal cost (Zhang, 2010), thus creating surplus for the exploitation of coal resources (Yang and Chen, 2008). Further evidence of incomplete coal costs is the fact that China’s coal industry has a sig­ nificant amount of absolute rent (the part of the coal industry profit higher than the average profit level of the society) (Gunton, 2004). The absolute rent is often absent in well-functioning coal markets with sound regulatory systems (Anthony, 2000). The second source of implicit subsidies is the differential rent generated by large integrated coal fields with good mining conditions (such as large coalmines in Inner Mongolia, Xinjiang, and Shaanxi). In a fully open and competitive resource market, differential rents are usu­ ally dissipated by the influx of mining enterprises (Comptroller and Auditor General of India and Union Government, 2012). In contrast to the market-oriented mining rights transfer regime (bidding, auction, listing, etc.) applied in small coalfields, the Chinese government adopts a nonmarket transfer regime (so-called administrative transfer) for large, high-quality integrated coalfields. In many countries, nonmarket transfer of resources will lead to corruption and the inefficient allocation of resources (Eilperin, 2012; Libecap, 1984; Sanzillo, 2012; Steenblik, 2002), but China has successfully transformed it into a subsidization tool. By allocating the coal resource to target enterprises at a lower price than the equilibrium level, the differential rent of the high-quality coal field is not dissipated because of the market transfer, such as bidding, but is transferred to the subsidized enterprise in the form of surplus. In some cases, subsidized projects also benefit from royalty conces­ sions. We assume that there is no royalty concession in any leasing process because local resource management agencies have been ordered to avoid any resource owner losses in the last five years. Consistent with the Yulin Scheme, we choose the operating profit gap as the indicator to measure the transfer amount generated by the Ordos Scheme subsidy. In contrast to the baseline plant, the feed coal cost of such subsidized plants is the complete cost of coal produced by captive mines. The complete cost of coal mining includes labor, material, period charge, amortized mining right price, resource tax, environmental

Fig. 2. The gap between the pit-mouth price and the preferential price of “affordable coal”. (The “affordable coal” price ¼ coal producing cost*(1 þ 7.92%); 1 RMB Yuan is equal to 0.1292 Euros and 0.1456 US Dollars (based on the exchange rate on July 12, 2019)).

Where PRep is the pit price of feed coal and PInt is the preferential price of “affordable coal” under the Yulin Scheme subsidization (Table 2). QMax is the coal consumption under a maximal load; QMax represents the 2 minimized coal consumption when the MCC plants experience losses. Substituting formula 1 into formula 2, we can obtain the following: � � 8 x⋅QMax ⋅ PRep PInt ;RConv PRep ;Q � 0 > |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} > > > cost saving ​ effect < Yulin � � Subsidy ¼ ; � � > Conv Rep QMax > R P ; þ x⋅QMax ⋅ PRep PInt ;RConv PRep ;Q < 0 > > 2 : |fflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} load increasing ​ effect

cost saving ​ effect

(formula 3) Where x⋅QMax ⋅ðPRep PInt Þ represents the saved cost of coal consump­ � � tion, i.e., the “cost-saving effect”. RConv PRep ; QMax equals the additional 2 loss when the MCC plant faces losses but continues to operate under the maximal load, i.e., the “load-increasing effect”. 4.2. Subsidization through the primary market of coal mining rights Another typical form of CCS is preferential access to the coal mining right market (Table 3). To support the deployment of certain industries in the area, local governments allocate coal mining rights to downstream projects, such as thermal power stations or MCC plants. Thus, these 8

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Energy Policy 135 (2019) 111015

management funds and other taxes and fees. For convenience of calcu­ lation, this cost is set as a constant. The model of the Ordos Scheme subsidy is as follows:

mergers include industrial regulatory policy rather than independent decision-making of the mine operators. Small coalmines are usually forced to shut down because they cannot meet the unit scale criteria of

� � 8 x⋅QMax ⋅ PRep M ; RConv PRep ; Q � 0 > |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} > > > < resource ​ rent ​ from ​ coalmine � Ordos � � Subsidy ¼ ¼ SubsidyYulin þ x⋅QMax PInt M ; � Conv Rep � QMax Conv Rep Rep > >R P ;Q < 0 þ x⋅QMax ⋅ P M ;R P ; |fflfflfflfflfflfflfflfflfflfflfflfflffl {zfflfflfflfflfflfflfflfflfflfflfflfflffl } > > 2 : limited ​ usufruct ​ effect |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} load increasing ​ effect

8 Conv > < R ðM; QMax Þ SubsidyOrdos ¼

> : RConv ðM; QMax Þ

(formula 5)

resource ​ rent ​ from ​ coalmine

market regulators. The unit scale criteria for coalmines have been constantly revised and expanded, from 90 thousand tons per year in 2005 to 600 thousand tons per year in 2017, causing waves of small and medium coalmines to shut down and await restructuring. Unless these coalmines are restructured to accommodate a larger capacity by merg­ ing or being merged, restarting will not be approved. Every M&A case is reviewed with regard to the restructured capac­ ities, the qualification of the acquirers, etc. The merging cases of pref­ erential downstream projects are approved in order of priority. The Shanxi Scheme is covert and complicated in practice, although its subsidization mechanism is similar to the Ordos Scheme. Down­ stream projects, including some MCC plants, are subsidized because they are preferentially approved to merge upstream coalmines to reduce their feed coal cost. The only difference between the Shanxi Scheme and the Ordos Scheme is that the Shanxi Scheme allows projects to sell coal from its captive coalmine when the operating profit from coal selling is higher than that from coal converting. Because the capacities of coalmines and MCC plants are equal, selling coal from captive mines will reduce the amount of coal available for MCC plants. Thus, the operator must reduce the load of the MCC plant to the minimum so that the amount and profit of coal selling can be maximized. We model the subsidy of the Shanxi Scheme as follows:

� � RConv PRep ; QMax ; RConv PRep ; Q � 0 � � : � Q RConv PRep ; Max ; RConv PRep ; Q < 0 2 (formula 4)

Substituting formula 1 into formula 4, we can obtain the following: where M represents the cost of coal mining. Formula 5 indicates that the subsidy of the Ordos Scheme is equiv­ alent to the resource rent extracted from the captive mine plus the losses due to the “load-increasing effect”. If we subtract formula 3 from for­ mula 5, we find that the Ordos Scheme provides an additional fixed bonus over the Yulin Scheme. This additional bonus equals the profit of the captive mine that supplies feed coal under a fixed internal price. Because this additional profit stems from a limited usufruct of the captive coalmine, we call it the “limited usufruct effect”. 4.3. Subsidization through the M&A market The third type of CCS supports downstream projects merging suitable

� � RConv ðM; QMax Þ RConv PRep ; QMax ; RConv PRep ; Q � 0 � � � � SubsidyShanxi ¼ : � � Q Q Q > : RConv M; Max þ x⋅ Max ⋅ PRep M RConv PRep ; Max ; RConv PRep ; Q < 0 2 2 2 8 > <

coalmines by a series of interventions. The privileged downstream projects have complete property rightsto merged coalmines and thus are not restricted to coal use (Table 3). This is the most significant difference between this form and the Ordos Scheme. A representative document is

(formula 6)

Substitutingformula 1 into formula 6, we can obtain the following: where V represents a call option.

� 0; RConv PRep ; Q � 0 � � � � Conv Rep QMax SubsidyShanxi ¼ x⋅QMax ⋅ PRep M ¼ SubsidyOrdos þ V; ​ V ¼ R ; ; RConv PRep ; Q < 0 ; P > 2 > |fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} : |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl {zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl } resource ​ rent ​ from ​ coalmine 8 > > <

(formula 7)

negative ​ load increasing ​ effect

SX DOLR., [2005] Ref. No.247 (Table 2). Here, we use the Shanxi Scheme to represent this form of CCS. M&A activities under the Shanxi Scheme should be carefully distinguished from voluntary market behaviors because of the substan­ tial role of the local government in the transaction. The drivers of such

Formula 7 indicates that the Shanxi Scheme subsidy equals the resource rent extracted from the captive mine. This subsidy also equals the Ordos Scheme subsidy plus the return from optional coal use. The return of the option equals the losses avoided when the MCC plant faces losses but continues operating under a maximal load and is equivalent to 9

Y. Li and C. Li

Energy Policy 135 (2019) 111015

the negative value of the “load-increasing effect”.

Table 4 Key parameter assumptions for total plant investment estimation of CTL plants with capacities of 1 Mt/a, 2 Mt/a, and 4 Mt/a.

4.4. Case study: coal consumption subsidy in China’s coal-to-liquids industry

Plant capacity, Mt/a

CTL technology is used to produce fuel from coal. The definition of CTL includes indirect liquefaction (ICL) via the Fischer-Tropsch syn­ thesis process (Xiang et al., 2014) and direct liquefaction (DCL) via the hydrogenation and liquefaction processes of water-coal slurries (Comolli et al., 1999; Li et al., 2015). After more than 10 years of demonstration and commercialization, CTL is representative of the deployment scale and technological readi­ ness among MCC technologies. Currently, China has 6 CTL plants in operation with a total capacity of 7.62 million tons per year (Kong et al., 2018) and ranks as the second-leading CTL producer worldwide after South Africa. The expected capacity will reach 13 million tons per year during the 13th five-year plan period (NEA, 2017). By 2022, coal-based fuel produced by CTL plants will replace 6.5% of China’s total oil pro­ duction (BP, 2018). The disclosure and measurement of CCSs in the CTL industry could be of significance to China’s MCC industries. In 2017, the total con­ sumption of China’s CTL industry reached 18.23 million tons, ac­ counting for 27.1% consumed by the MCC industries (CCPUA, 2018). When all the projects under construction are completed and put into operation in 2020, China’s CTL industry will consume 47.17 million tons of feed coal per year, accounting for 32.26% consumed by the MCC industries (CCPUA, 2018).

i

Major subsystems inside battery limits

1.1 1.1.1 1.2 1.2.1 1.2.2 1.3 1.3.1 1.3.2 1.3.3 1.4 1.4.1 1.4.2 1.5 1.5.1

Airseparating system ASU, 104CMH Gasification Coal Prep, t/h Gasifier, t/d WGS & gas cleanup WGS unit, 104CMH Rectisol, 104CMH Claus, Mt/a Synthesis system F-T reactor, Mt/a Refine & Upgrade, Mt/a Off-gas system PSA, 104CMH

1

Equipment purchase of inside battery limited (ISBL) (PC) Construction cost of ISBL (CC) Engineering cost of ISBL (EC)

2 3 4

EPC cost inside battery limits (EPCISBL) Balance of plant (BOP)

4.5. Model specification

5

The subsidy rate (r) is the ratio of the subsidy amount per ton of products to the unit price of those products:

6

Indirect cost (IC)

7

Total plant cost (TPC)

8

Interest during construction (IDC)

9

Total Plant Investment (TPI)

r¼

SubsidyCTL ​ per ​ unit ​ of ​ fuel ; fuel ​ price ​ per ​ unit ​

Where the total CCS for the CTL industry (SubsidyCTL) is aggregated from the Yulin Scheme, the Ordos Scheme and the Shanxi Scheme subsidies. X X X SubsidyCTL ¼ SubsidyYulin þ SubsidyOrdos þ SubsidyShanxi ; j k l j

k

Base unit size

Max unit size

10 150 2000

350 4000

50 70 10

170

50 – –

% of PC % of PC

Factors

1

2

4

CSF

ni

ni

ni

3

6

12

4 6

8 12

14 28

2 2 2

2 2 2

6 4 3

2 1

4 1

8 1

1

1

1

0.5 0.5 0.67 0.67 0.644 0.65 0.65 0.63 0.67 0.7 0.75 0.6 0.65 0.65

80 20

1þ 2þ3 % of EPCISBL % of EPCISBL 4þ 5þ6 % of TPC

15.5 32

10

7þ8

The measurement of subsidy is based on the operating profit of a simulated CTL plant with a capacity of 2 million tons per year. Ac­ cording to formula 1, the operating profit is the difference between the operating income and the complete cost of fuel. The operating income is assumed to be the sales income of the main ICL products, including diesel, gasoline and liquefied petroleum gas (LPG). According to the technological data of 4 operating CTL plants in the CCPD, the production distribution is assumed to be 70% diesel, 20% gasoline and 10% LPG by mass ratio. The complete cost includes the fixed cost and variable cost. The method used to estimate the fixed cost is consistent with Peters et al. (1991), Bibber et al. (2007), Kreutz et al. (2008), Haarlemmer et al. (2014), Bassano et al. (2014), and Albrecht et al. (2017). Using this method, the fixed cost is aggregated from the adjusted EPC costs of 5 major ISBL subsystems, BOP cost and IDC. The key parameter assump­ tions for fixed cost estimation are shown in Table 4. The valuation of EPC cost is based on 13 real equipment contracts stored in the CCPD. When the capacity of the reference equipment is not consistent with the estimated equipment capacity, a cost scaling factor (CSF) is often used (e.g., Haarlemmer et al., 2014; Kreutz et al., 2008; Peters et al., 1991).

l

Where j is the number of CTL projects subsidized in the Yulin Scheme, k is the number of CTL projects subsidized in the Ordos Scheme, and L is the number of CTL projects subsidized in the Shanxi Scheme. The reference price is the pit price of suitable coal in the local whole sale market. Because the pit price removes the long-distance trans­ portation cost in the coal price, sothatit is on the same price measure­ ment basis with the captive coalmines of CTL projects under other subsidizing scheme. According to Zhang (2013), the suitable coal is feed coal with less than 20% ash, more than 25% volatiles, less than 1% sulfur and a heat value between 5200 and 5500. Here, we assume that the contract price of “affordable coal”in the Yulin Scheme equals the internal supply price from captive mines in the Ordos Scheme and Shanxi Scheme, representing the sum of the completed mining costs and the average profit of typical local coal mining enterprises. According to Yulin Gov., [2018] Ref. No.16 (see Table 1), “affordable coal” is pro­ vided to major basic chemical projects and preferential major projects. The price of “affordable coal” includes full cost and reasonable profit. Since there is no specific standard for “reasonable profit” specified in the document, we use the average profit of typical local coal mining en­ terprises as reasonable profit. The average profit of typical local coal mining enterprises is the product of the local pit price and the average sales profit margin of China’s coal mining industry. We set the sales profit margin as 7.92%, which equals to the average performance from 2011 to 2018.

� Ci ¼ Ci; ​ ref �

Si Si;ref

�kðiÞ �

FAIPI ; ​ i ¼ 1; 2; …; 5; FAIPIref

Where Ci; ​ ref refers to the cost of the reference equipment, Si refers to the scale of the estimated equipment, Si;ref refers to the scale of the reference 10

Energy Policy 135 (2019) 111015

Y. Li and C. Li

Fig. 3. Cost compositions among five subsystems. Table 5 Coal consumption data of China’s currently operating CTL plants. Source

Asiachem (2017)

Yao (2016)

Liu (2015)

Kong et al. (2015)

Lu’An (2014)

Shu (2014)

Tang (2014)

Sun et al. (2013)

Zhou et al. (2011)

Process type Scale, Mt/a Coal input, tce/t - Feed coal, tce/t - Power coal, tce/t

ICL 2 4.57 3.49 1.08

ICL 4 5.05 NA NA

ICL 2 5.23 4.07 1.16

DCL – 4.04 3.66 0.38

ICL 1 4.49 3.61 0.88

DCL 1 4.63 3.23 1.4

ICL 0.16 NA 3.48 NA

ICL 1 4.73 3.44a 1.29a

ICL 1 4.3 3.62 0.68

a

Data adapted from Yu (2015).

equipment, kðiÞ refers to the CSF, and FAIPI is the fixed assets invest­ ment price index. We also use a scaling exponent of 0.9 to represent the learning effect in the construction of multiple parallel trains (Kreutz et al., 2008). By comparing the public data of 3 actual CTL investment cases, we determine that the average error of our fixed cost estimation is 10.03%; 16.45% is the maximum error and 5.51% is the minimum error obtained. This accuracy is very close to that of the class 2 level (L: 5% to 15%; H: þ5%–20%) in the cost estimate classification system of AACE (Christensen and Burton, 2005). The result of the TPI estimation is shown in Fig. 3. To reflect the importance of coal in this paper, the variable cost is separated into coal cost and other costs. Coal cost covers the costs of feed coal and power coal. Analyzing data from various studies (Table 5), we set the feed coal consumption per ton of fuel as 3.33 tons and the power coal consumption as 1.43 tons. We assume that the CTL plant is fully powered by its own BOP system without the sale or purchase of heat, electricity, and steam. Thus, the power cost only includes the power coal cost. To consider the price difference between power coal and feed coal, the power coal price is assumed as 70% of the feed coal price. To reflect the substantial energy loss in practice, we assume an additional 25% allowance for power coal consumption, which is consistent with Peters’ recommendation. Key parameter assumptions for variable cost estima­ tion are shown in Table 6. The annual loads of CTL plants are the average statistical capacity utilization rate of China’s CTL industry from 2013 to 2017 and are assumed to be 80% after 2018.

Table 6 Key parameter assumptions for variable cost estimation of CTL plant. Parametera

Scaling factor

Feed coal consumption, ton/ton of fuel output Power coal consumption, ton/ton of fuel output Energy line loss and contingency, % of power coal Power coal price, % of feed coal price Catalyst cost, Yuan/ton of fuel output Byproduct production, % of total fuel production Water consumption, ton/ton of fuel output Water price, Yuan/ton Operating labor for 4 Mt/a of capacity Direct supervisory labor, % of operating labor Average unit wage, million Yuan/year Maintenance cost, % of total plant cost Administration cost, % of operating labor cost Total borrowed capital, % of total plant investment Financial cost, % of total borrowed capital

0.25

Value 3.33 1.43 25 70 237.5 10 13.45 5 1700 15 0.07 11 25 55 5

a Parameters other than coal consumption are referenced from: Peters et al., (1991); Zhou et al. (2011); Tang (2014); Xiang et al. (2014); Liu (2015); Kong et al. (2015); Binbin (2015).

Table 7 Average RCU of China’s CTL industry (2013–2018). Year (n)

Rate of capacity utilization (RCUn)

2013 2014 2015 2016 2017 2018

82.08% 85.86% 67.69% 44% 48.14% 64.8%

4.6. Results 4.6.1. Coal consumption subsidies in the CTL sector The CCS scale in China’s CTL industry is estimated by multiplying the annual subsidy amount of all CTL projects by the rate of capacity 11

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Energy Policy 135 (2019) 111015

Table 8 The subsidizing scheme and coal consumption data of China’s existing CTL projects. Plant

Operating Year

Province

Designed Coal consumption (x*QMax) Mt/a

Captive coalmine or Source of feed coal

Subsidizing scheme

Yufu 200

2021

Guizhou

Yitai IM Yitai 16 Yitai GQB LuAn 180

2020 2009 2018 2018

Inner Mongolia Inner Mongolia Xinjiang Shanxi

YitaiYili Shenhua 400 Yancon 100 Yancon 400 Shenhua DCL Shenhua 18 LuAn 16

2017 2017 2015 2021 2009 2009 2009

Xinjiang Ningxia Shaanxi Shaanxi Inner Mongolia Inner Mongolia Shanxi

Feed coal: 6.66 Power coal: 2.86 Total: 9.52 Total: 0.762 Total: 9.52 Feed coal: 3.33 Power coal: 1.43 Total: 4.76 Total: 19.04 Total: 4.76 Total: 19.04 Total: 4.76 terminated Total: 0.762

Guizhou Hongxi Mining and other Gaoyuan Mine and one other Suancigou Mine Suancigou Mine Rainbow Bay Tianlong Mine Cilinshan Mine and 2 others Shanxi Xiangyuan Huipo Mining Coalmine in East Kanxiang and Aermale Area Meihuajing Mine and 2 others Jinjitan Mine Hongxidun Mine Shangwan Mine Shangwan Mine Tunliu Mine

Shanxi Yulin Ordos Ordos Yulin – Yulin Ordos Ordos Ordos Ordos – – Shanxi

Source: CCPD.

Fig. 4. Distributions of three subsidizing schemes in six representative coal-producing provinces.

utilization (RCU) (see the formula below):

SubsidyCTL ¼

n h�X � i X X X SubsidyYulin þ SubsidyOrdos þ SubsidyShanxi � RCUn : j k l i¼0

j

k

l

Yulin Scheme will come into play when the Yitai Ganquanbao Project begins operating in 2019. The first project subsidized in the Shanxi Scheme will be put into operation in 2021. By 2022, Yulin Scheme subsidies will account for 11.8% of the total CCS in China’s CTL in­ dustry, while Shanxi Scheme subsidies will account for 10.4%. The subsidy rate of CCS depends on the relative values of the coal price and the fuel price. For instance, the rapid increase in the subsidy rate since 2015 is due to oil prices plunging in late 2014, while the domestic coal pithead price changed more moderately. If the market structure of coal and oil does not materially change, one can expect that the subsidy rate will remain at approximately 20% or more in the next few years.

The data on the average RCU from 2013 to 2018 are adjusted from the annual production capacity warning report of key petrochemical products issued by the CCPUA (2018) (see Table 7). RCU after 2018 is set as the normal industry standard, that is, 80% of the designed value. The annual subsidy amount of each scheme is calculated by substituting the actual coal consumption of the project into the corre­ sponding model (Formulas 3, 5, 7). The subsidizing scheme type and coal consumption data of all existing CTL projects in China are shown in Table 8. The distributions of three subsidizing schemes in six representative coal-producing provinces are shown in Fig. 4. In 2018, China’s total CCS on the CTL industry was 6.3 billion Yuan. It will increase to 16.4 billion Yuan by 2022, equivalent to the total investment cost of an ICL plant with an annual capacity of 1 million tons (Fig. 5). At present, the Ordos Scheme is the only CCS form implemented in operating CTL plants. The

4.6.2. Subsidy amount for a simulated ICL plant with 2 million tons/year capacity According to our estimation (Fig. 6), the average operating profit of the baseline plant during 2011–2018 is 246.57 Yuan per ton of fuel. On 12

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Energy Policy 135 (2019) 111015

only 14.05 Yuan per ton of fuel, which is not meaningful. 4.7. Efficiency, wastefulness, and effectiveness tests of identified subsidizing schemes After acknowledging the scale of CCS in MCC, it is important to distinguish inefficient subsidies from efficient subsidies. IEA et al. (2010) introduced a decision tree method to identify and phase out inefficient subsidies (Fig. 7). The method includes the following four phases: a) efficiency test; b) wastefulness test; c) cost effectiveness analysis of alternative policy tools1; and d) cost effectiveness of public funds. Any subsidizing policy that fails to pass these tests should be phased out. In another guide (The World Bank et al., 2010), the method is adjusted. Policy makers can choose to redesign or replace a subsidy rather than phase it out if it fails the latter two tests. According to the decision tree method, we separately test all three channels of CCS. However, the final test is not included because it refers to wide economic decisions (The World Bank et al., 2010), in which the externalities of the CTL industry (e.g., national security) should be dis­ cussed. Because space is limited, the analysis is within a sectoral scope rather than a wide economic scope.

Fig. 5. Total scale of coal consumption subsidies in China’s CTL in­ dustry (2013–2021).

4.7.1. Efficiency test China’s CTL industry has been positioned as a capacity reserve and technology reserve. This positioning suggests that any CCS to CTL should achieve the following two objectives: improve profitability and resilience to attract investment of a sufficient scale and boost the learning-by-doing process of demonstration projects to continuously promote technology. According to the above analysis, notably, the profitability of a CTL plant is sensitive to the international oil price. Therefore, we use the model established in this chapter to test the effi­ ciency of CCSs by estimating the extent to which the interventions improved profitability, robustness against oil price volatility and the rate of capacity utilization. Fig. 8. shows the domestic diesel price level corresponding to the IRR of CTL plants under different subsidy levels. We assume that the do­ mestic pithead coal price is 500 Yuan/ton and that the depreciation period is 20 years. It is not difficult to observe that the three subsidy models have significantly improved the profitability of the plants. In the baseline plant, the break-even point is approximately 6,000 Yuan/ton. The Yulin Scheme and the Ordos Scheme of subsidization can reduce the project’s break-even point to approximately 4,300 Yuan/ton, while the Shanxi Scheme can further reduce the subsidy to 3,000 Yuan/ton. When the oil price is below 6,500 Yuan/ton, the profitability of a plant sub­ sidized in the Shanxi Scheme is obviously the best. Fig. 9 shows the sensitivity of the operating profits of plants with different subsidy levels to oil price fluctuations. The assumed diesel price is 7,273 Yuan/ton, and the coal price is 400 Yuan/ton. The sub­ sidies significantly reduce the sensitivity of project profits to oil price fluctuations. However, CCSs reduce projects’ sensitivity to coal prices, thus lowering the incentive of enterprises to reduce unit coal consumption through technological progress. During the 13th Five-Year Plan period, the state requires current CTL demonstration projects to constantly reduce coal consumption. As a subsidizing policy tool, CCS sets the coal cost of CTL plants approximately equal to a fixed internal price, thus completely eliminating any influence of price fluctuation in the coal market. From the perspective of internal incentives, the existence of the

Fig. 6. Compositions of subsidies in different subsidizing schemes.

average, Yulin Scheme subsidization can save 50.3% of the coal con­ sumption cost for a CTL plant, thus increasing the operating profit by 38.14%–398.61 Yuan/ton. The Ordos Scheme can produce a subsidy of 171.05 Yuan per ton of fuel. It is composed of two parts. The major part is the “cost-saving ef­ fect”, which is identical to that in the Yulin Scheme, accounting for 88.89% of the total subsidy. The remaining part is a “limited usufruct effect”, accounting for 11.11% of the total subsidy. The operating profit generated by the “limited usufruct effect” is only 19 Yuan per ton of fuel and can barely improve the financial performance of the project. The average subsidy of the Shanxi Scheme is 185.1 Yuan per ton of fuel, which is composed of three parts according to the definition. The major part is the “cost-saving effect”, which is consistent with the Yulin Scheme, accounting for 82.14% of the total subsidy. The second part is the “limited usufruct effect”, which is consistent with the Ordos Scheme and accounts for 10.27%. The third part is produced by an option allowing the operator to sell coal from a captive mine, i.e., the “loadreducing effect”; this part accounts for 7.59% of the total subsidy. The execution of the option requires the operator to decrease the load of the CTL plant by half to save approximately 40% of the plant’s maximum coal consumption for sale. Compared with the Ordos Scheme and the Yulin Scheme subsidization, the Shanxi Scheme subsidization will cause a 10% decrease in the capacity utilization rate of a CTL plant during the eight-year observation period and will produce an additional profit of

1 Policy tools and regulatory theories involve a wide range of research fields and are always controversial. Various intervenes (fiscal, financial or regulatory) are implemented in many countries to adjust macro-economy or boost the development of an innovative industry (Wade, 1990). For the types and impacts of policy tools employed in emerging energy, see (Polzin et al., 2015; Harborne and Hendry, 2009; Vallentin, 2008; Jacobsson and Johnson, 2000).

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Fig. 7. Decision tree for identifying and phasing out inefficient subsidies adapted from IEA et al. (2010).

Fig. 9. Sensitivity of the operating profits of CTL plants with different sub­ sidization levels to oil price fluctuations. Fig. 8. IRR improvement of CTL plants subsidized in different schemes.

We use real oil prices between 2011 and 2018 to estimate the average capacity utilization of demonstration projects at different sub­ sidizing levels (Fig. 10). The coal price is assumed to be 500 Yuan/ton. We find a significant increase in the average capacity utilization of plants subsidized in the Yulin Scheme and the Ordos Scheme due to the capacity-increasing effect, but this increase does not occur in the case of the Shanxi Scheme because the operator has an option to sell feed coal if its profit is more attractive.

current forms of CCS may lead to a failure to achieve the upgraded demonstration target during the 13th Five-Year Plan or to achieve it only in documents and reports. The CCS is not specifically used to support R&D activities. However, with regard to demonstration projects, this subsidy is related to tech­ nological improvement. CCS can increase the cumulative learning time of the CTL plant by raising its break-even point. The study of “learning curves” shows that the cumulative operating time is positively corre­ lated with the experience gain of front-line workers and administrators, equipment efficiency enhancement, talent reserve building and contin­ uous process design improvement.

4.7.2. Wastefulness test The wastefulness test usually involves a behavioral economics analysis in which the energy elasticity coefficient is used to estimate the promotion effect of cost reduction on energy use. The energy elasticity 14

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the economic downturn because a significant portion of invested pro­ jects are operated by actors in mining, power, and petroleum industries that are initially induced by excessive subsidies and not qualified in terms of management and technological capabilities. These MCC plants are financially and technologically fragile. When waves of bankruptcy occur, energy waste transforms into economic risk. A series of invest­ ment failures of the China Datang Corporation is an unfortunate but typical example (Greenpeace, 2015). A large number of coal chemical assets were processed at a low price (Yue, 2014), which directly caused losses to the country and enterprises. It is necessary to examine the extent to which CCS is excessive. Here, we define excessive subsidies as certain subsidies that cause the oper­ ating profit of the subsidized project to exceed that of an unsubsidized champion company in the CTL industry worldwide. Under this defini­ tion, excessive subsidies lead to an investment return on a CTL project that is beyond the normal range in the industry, thus artificially enhancing the investment motivation. We select Sasol as a worldwide unsubsidized champion company considering its domination of deployed plants and operating performances. More importantly, very little significant evidence suggests that Sasol has any current subsidy, although it received large direct subsidies from the South African gov­ ernment in the 1980s and 1990s (Davie, 2005). Fig. 11 shows the operating profit data of Sasol, the baseline case and plants subsidized by three identified CCS schemes. The data for Sasol are based on its public annual reports. Differences between Sasol and the simulated plants in terms of coal price, fuel sales price and technology are not taken into account. For plants subsidized in the Ordos Scheme, we find that as much as 47.03% of the total subsidy is excessive in the calculation period. Nearly all subsidies captured in 2011 and 2012 were excessive, which we believe is responsible for the investment surges in the MCC sector during 2009–2011. In 2017, 2018, over subsidization resurfaced and could cause a similar issue in the CTO (coal-to-olefin) sector in the following years. In 2017, the profit increase caused by Shanxi Scheme subsidiza­ tion was excessive, accounting for 919 million Yuan. The number could reach 4.568 billion Yuan annually in 2022 if future energy prices are consistent.

Fig. 10. Average capacity utilization of CTL projects at different subsidizing levels (2011–2018).

4.7.3. Cost effectiveness test of alternative tools The effectiveness test for subsidizing policy requires a comparison of policy effectiveness between the current policy tool and the alternatives. From the subsidy inventory, we find that the CCSs are diversified in form and mechanism, covering all three coal acquisition channels. Thus, we

Fig. 11. Excessive subsidy in current subsidizing schemes.

method is widely used in the residential sector because the elasticity coefficient can be derived directly from price and sales data without the need for a further understanding of the complex mechanisms of subsidy transmission. This method is not applicable in the CTL industry because, according to the analysis in the previous section, the consumption in­ crease generated by CCS is far from the energy waste; the subsidy leads the project to return to a normal capacity utilization rate from the status of production reduction due to loss. In the case of the CTL industry, energy waste is more likely to be induced by distorted investment decisions. Excessive subsidies could increase the investment return to beyond a reasonable level in the in­ dustry, causing the investment willingness of the industry to greatly exceed the needs of the government’s planning and national strategy. Consequently, the CTL industry could be overinvested and locked into a high-carbon path (Yang, 2015). Overinvestment has a precedent in the development of the MCC in­ dustry in China. In 2006, with no industrial-scaled demonstration plants in operation and very little demonstrated financial and technological feasibility (Fan, 2006), nearly 30 CTL projects were in the detailed planning or feasibility study stages in China, with estimated production equivalent to 10% of the country’s oil demand in that year (Dubey, 2011). In 2013–2014, 104 MCC projects were submitted for approval, and only one-fifth of these projects were approved by the central gov­ ernment (Yan, 2014). Furthermore, sectors with overcapacity cannot be sustained under

Fig. 12. Scheme sliding observed in China’s CTL industry (2013–2017). 15

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can test the effectiveness of any current known subsidizing scheme by comparing it with the other two schemes. First, we compare the Yulin Scheme with the Shanxi Scheme and the Ordos Scheme. A critical difference is that the latter two involve the transfer of coal mining rights and the former does not. Notably, the subsidy generated through mining rights transfer is irrevocable and could last throughout the entire life cycle of the project. Thus, the incentive for coal consumption reduction for the operators is perma­ nently eliminated. The Ordos Scheme and the Shanxi Scheme have poorer effectiveness than the Yulin Scheme. However, the Ordos Scheme and the Shanxi Scheme are more enforceable than the Yulin Scheme. Over a long period in China’s coal industry, the enforcement of long-term coal supply contracts could not be guaranteed because of legal deficiencies. When the coal price rises sharply, coal suppliers can easily destroy a contract without bearing substantial consequences. Because the core of the Yulin Scheme sub­ sidization is a long-term coal supply contract, recipients could not avoid the risk of default and price blackmail in contract enforcement. To avoid such problems with enforceability, local governments simply gave away the mining rights to the recipients in this case. This explains why in reality, the proportion of projects subsidized by the Ordos Scheme ac­ counts for 68.6% of all subsidized projects, which is five times higher than that of the Yulin Scheme. We further compare the Shanxi Scheme with the Ordos Scheme. The Shanxi Scheme and the Ordos Scheme have similar performances in subsidizing efficiency according to the analysis in the previous section, but the Ordos Scheme has advantages in the effectiveness test due to better performance in raising the capacity utilization rate of the CTL plant. However, the Shanxi Scheme has a lower enforcement cost than the Ordos Scheme. The difference in the mechanism between the two schemes is that in the Ordos Scheme subsidization, the use of coal pro­ duced from captive mines is limited to the production of synthesis fuel, while the Shanxi Scheme does not contain such a restriction clause. To ensure that all the coal production of captive mines is used to produce synthesis fuel, regulators must make continuous and substantial efforts to monitor the daily operation of the plants subsidized by the Ordos Scheme. Due to information asymmetry, only a very small amount of noncompliance can be identified, although the incidence is very high in practice according to a series of reports (Bi, 2015; Zuo and Gao, 2012). Once restrictions on coal use become widely unenforced, projects subsidized in the Ordos Scheme will reduce the load of CTL plants when necessary, saving a certain amount of coal produced from captive mines for sale rather than delivering it all to produce synthetic fuels. In other words, the Ordos Scheme will slide into the Shanxi Scheme. It is difficult to observe this slide directly. However, due to the existence of a loadreducing effect, a CTL plant subsidized in the Shanxi Scheme will have a 40% lower load than that under the Ordos Scheme subsidization when selling coal is more attractive than producing fuel. Therefore, we can prove that such a slide exists between the schemes by testing the actual capacity utilization rate of the industry. Due to the operating profit estimation model we constructed (pa­ rameters are based on Tables 4 and 6), we are able to simulate the annual load of CTL plants based on the actual capacity of existing plants, types of subsidies, and historical data of coal prices and oil prices. All currently operating CTL projects are nominally subsidized in the Ordos Scheme (Fig. 5). We calculate the overall load of the CTL industry in the scenario of the absence of scheme sliding, which is represented by the light blue line in Fig. 12. We find that with subsidies, plants are always profitable during the sample period, reaching the assumed maximum capacity utilization rate of 80%. In the scenario in which scheme sliding occurred across the entire industry, the capacity utilization rate of the industry dropped to 40% in 2016 and 2017 due to the load-reducing effect of the Shanxi Scheme, as shown by the red line in Fig. 12. We find that the actual capacity utilization rate line of the industry, which is represented by the blue line in Fig. 12, is closer to the scheme-

sliding scenario than the scenario without scheme sliding. This result implies that within the entire CTL industry, plants originally subsidized in the Ordos Scheme eliminated the restriction on utilizing captive mines, sliding to the Shanxi Scheme. This unexpected sliding is costly considering the negative effect on the demonstration process. 5. Conclusion and policy implications According to the estimations of this paper, the current coal con­ sumption subsidy of approximately 24 billion Yuan in China’s modern coal chemical industry has been neglected, leading to a serious under­ estimation of fossil energy subsidies in the coal industry. In 2018 alone, China’s CTL industry received 6.3 billion Yuan in coal subsidies, which is higher than the total amount of coal subsidies reported in some studies (e.g., the IEA and OECD fossil energy subsidy report database). Considering that the coal consumption of the entire modern coal chemical industry is approximately four times that of the CTL industry, the scale of CCS in China’s coal chemical industry may reach 24 billion Yuan. This is 4.6 times the total amount of China’s direct coal subsidies in 2017 (OECD fossil energy subsidy report database). Despite this huge amount, it is necessary to keep CCS in the modern coal chemical industry, which is in the demonstration period. Subsidies can insulate the demonstration plants from harsh competition (with refineries) in the fuel market, allowing them to reduce costs and become competitive via learning-by-doing. We find that at the average coal price and oil price (coal price: 500 Yuan/ton, diesel price: 6,000 Yuan/ton), the IRR of subsidized CTL projects could reach 17.5%–19%, while un­ subsidized projects will suffer from loss. Therefore, we do not propose cancelling the coal consumption subsidies in the modern coal chemical industry during the demonstration period. However, current coal subsidies need to be improved because they are excessive and distort incentives for investment. In 2011, 2012, 2013, 2014, 2017 and 2018, the profit of subsidized CTL projects in China exceeded the normal profit of mature international CTL enterprises. This excessive subsidy reached 919 million Yuan in 2017, which distorted the investment motivation of the market actors. Assuming that diesel and coal prices are in line with 2018 levels, the predicted excessive subsidy in 2022 will rise to 4.568 billion Yuan, which is likely to lead to an enormous money rush into the CTL industry, leaving China’s coal in­ dustry locked in a high-carbon path. Therefore, we suggest that coal consumption subsidies should be linked to the level of oil prices or the profit level of the coal chemical plant. When the oil price rises to make the CTL projects profitable, the subsidized plants will be charged a fee equal to excessive subsidies, which will be injected into the industrial development fund. The money is given back to companies to make up losses from decreased oil prices or to develop technologies that increase coal-utilizing efficiency. Because the current coal consumption subsidies are implicit and in­ direct, they have little direct impact on the energy market and govern­ ment budget. We found that none of the existing coal consumption subsidies in the coal chemical industry transferred from government budgets or tax expenditures, nor did they affect the open market price. Currently, all CCS obtained by the CTL industry are mainly realized through preferential access to coal resources. In 2022, these subsidies will be transferred via preferential coal supply at an under-market price, preferential access to coal resources and coalmine M&A privilege, and the subsidy scale will rise to 16.4 billion Yuan. Among this amount, 1.94 billion will come from the profit transfer of state-owned coal enterprises, and the rest will come from the resource rent extracted from coal min­ ing. According to estimates of the scale of coal consumption, China’s modern coal chemical industry will receive a profit transfer of 6 billion Yuan from the coal mining industry in 2022, accounting for 2.08% of the total profit of the coal mining industry in 2018, which will have little impact on the industry as a whole. The transparency of coal subsidies needs to be strengthened so that they can be measured and monitored. All three forms of coal 16

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consumption subsidies found in the modern coal chemical industry are indirect, and two of them are implicit. One of the subsidies requires that all of the coal preferentially allocated is used for conversion; therefore, the coal chemical sector, not the coal sales sector, will benefit from the subsidy. However, by comparing the empirical data, we find that because it is difficult to externally monitor the coal use of an energy group, 29.4% of the CCS in 2017 was not transferred to the CTL sector but to the coal mining sector of enterprises in the form of coal sales. To better track China’s fossil energy subsidies, we suggest that researchers focus on indirect and implicit subsidies and call on subsidized demon­ stration projects to make information (e.g., the use of coal resources allocated via preferential policies) publicly accessible.

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Declaration of competing interest None. Acknowledgement We thank the anonymous reviewers for their valuable comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.enpol.2019.111015. Funding This paper is supported by the Natural Science Foundation of China (Funding No. 71262022). Role of the funding source The funding source had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. References Albrecht, F.G., K€ onig, D.H., Baucks, N., Dietrich, R.-U., 2017. A standardized methodology for the techno-economic evaluation of alternative fuels – a case study. Fuel 194, 511–526. https://doi.org/10.1016/j.fuel.2016.12.003. Anthony, C., 2000. OECD Work on Defining and Measuring Subsidies in Fisheries. OECD, Paris, France. Asiachem, 2017. Approval of application for water intake permit for Guizhou Bijie’s 2 million ton/year coal-to-liquids project, Ministry of Water Resources of China. http://www.sohu.com/a/155445489_747560. (Accessed 1 March 2019). Bartelmus, P., 1999. Green accounting for a sustainable economy: policy use and analysis of environmental accounts in the Philippines. Ecol. Econ. 29, 155–170. https://doi. org/10.1016/S0921-8009(98)00086-X. Bassano, C., Deiana, P., Girardi, G., 2014. Modeling and economic evaluation of the integration of carbon capture and storage technologies into coal to liquids plants. Fuel 116, 850–860. https://doi.org/10.1016/j.fuel.2013.05.008. Bi, H., 2015. 22 coalmines unauthorized extension in Shaanxi, Yanchang included. http ://www.nbd.com.cn/articles/2015-10-29/957513.html. (Accessed 12 October 2017). Bibber, L.V., Shuster, E., Haslbeck, J., Rutkowski, M., Olsen, S., Kramer, S., 2007. Baseline Technical and Economic Assessment of a Commercial-Scale Fischer–Tropsch Liquids Facility. NETL, Washington, D.C. NETL/DoE-2007/1260. Binbin, Z., 2015. Economic analysis of coal-to-oil projects in China. Coal Process. Compr. Util. 19–26. https://doi.org/10.16200/j.cnki.11-2627/td.2015.08.005. BP, 2018. Global energy outlook. https://www.bp.com/content/dam/bp-country/zh _cn/Publications/2018SRbook.pdf. (Accessed 17 January 2019). CCPUA, 2018. Implementation of coal consumption control and industrial development of modern coal chemical industry in China. http://www.sohu.com/a/2442937 83_697078. (Accessed 25 November 2018). Christensen, P., Burton, D.J., 2005. In: Cost Estimate Classification System – as Applied in Engineering, Procurement, and Construction for the Process Industries. American association of cost engineers international recommended practice No. 18R-97. http://www.aacei.org/technical/rps/18r-97.pdf. (Accessed 2 July 2017). Coady, D., Parry, I., Sears, L., Shang, B., 2015. How Large Are Global Energy Subsidies? International Monetary Fund, Washington, DC.

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