Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security

Energy Policy 139 (2020) 111360

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Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security Qing Yang a, Lei Zhang b, *, Shaohui Zou a, Jinsuo Zhang c a

School of Management, Xi’an University of Science and Technology, Xi’an, 710054, PR China School of Management, China University of Mining and Technology, Xuzhou, 221116, PR China c School of Economics and Management, Yan’an University, Yan’an, 716000, PR China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Coal Production capacity Intertemporal optimization Demand uncertainty Deviation degree

As coal is the main energy source in China, coal overcapacity and undercapacity have negative impacts on the economy, environment, and energy security. To reveal the optimal production capacity (OPC), an intertemporal optimization model under uncertainty is established by a combined Dynamic Programming - Influence Factor Scenario Analysis - Monte Carlo method (DP–IF–SA-MC). The OPCs in 1990–2025 are solved to minimize the expected cost to the economy and environment driven by the production capacity (PC) under energy security. In addition, we define, and calculate the PC deviation (PCD), or the deviation of the actual PC (APC) from the OPC. The results show that compared with the APC, the OPC has advantages of more stable development, more timely adjustment, and lower cost, and it provides cost savings of 302.857 billion yuan in 1990–2017. According to PCD, China’s coal industry existed under the risk of overcapacity in 1990–1999 and 2009–2017 and under­ capacity in 2000–2008, and it can experience overcapacity for most of the time without capacity reduction policies. In future, the optimal management of the PC should be based on the OPC and PCD over the next 5–10 years rather than the current capacity utilization rate, and focus on overcapacity management in the long run.

1. Introduction The resource situation in China, which has been described as “rich coal, bits of gas, and poor oil,” reflects the leading position of coal in China’s energy consumption (NDRC et al., 2016; Liu et al., 2018; Ding et al., 2019). Since 1990, China’s coal industry has experienced many large-scale overcapacity and undercapacity episodes that have had negative impacts on not only the economic circumstances of the coal industry but also on the environment, energy security, and national economic and social development (Zhang et al., 2017a,b, 2018; Yang et al., 2018). For example, the overcapacity since 2012 resulted in an idle production capacity (PC) level of 31%, a 46.86% profit margin, and 412,500 unemployed workers in 2015. To solve the problem, the Chinese government has been committed to the optimal management of the coal PC (Yang et al., 2019). For the overcapacity management since 2012, the task of capacity reduction has been clearly established; that is, it was estimated to take three to five years to reduce the PC by 500 million tons from 2016, and the coal PC was re-determined according to the working time of the whole year not exceeding 276 working days. Accordingly, the coal industry had reduced

the PC by 290 million tons at the end of 2016, which resulted in a supply gap of 100.8 million tons (Shi et al., 2018). In order to alleviate the coal supply shortage, the Chinese government required the coal PC to be re-determined based on 330 working days in the whole year and released the new coal PC at the end of 2016. This inconsistency of policies over the year indicates that the gov­ ernment lacked the ability to make a scientific and accurate judgment on the optimal PC of coal (OPCC) and then failed to use a scientific basis for the annual quantity of the capacity reduction. In addition, the coal in­ dustry had been experiencing obvious overcapacity in 2010–2015, while the capacity reduction policy was introduced in 2015. The postregulation outcome was a high costs of resources wastage from the PC reductions; the cost to bring about the PC reduction was 76.850 billion yuan in 2016 (Wang et al., 2019a,b). Therefore, it is important for the optimization management of the PC to explore the OPCC in the future. The research results also can promote the transformation from reactive to proactive management. Unfortunately, there are few studies on the OPCC in the available literature, which makes it difficult to provide support for the manage­ ment of the PC and to ascertain more scientific proxy variables of

* Corresponding author. School of Management, China University of Mining and Technology, Quanshan district, Daxue Road, Xuzhou, 221116, PR China. E-mail addresses: [email protected] (Q. Yang), [email protected] (L. Zhang). https://doi.org/10.1016/j.enpol.2020.111360 Received 1 November 2019; Received in revised form 25 January 2020; Accepted 15 February 2020 Available online 24 February 2020 0301-4215/© 2020 Published by Elsevier Ltd.

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overcapacity in empirical studies (Wang et al., 2018b; Yang and Wu, 2016). In the existing empirical studies, capacity utilization (CU) is a common proxy variable to reflect the degree of overcapacity, but it may not be a good proxy variable, as there is a lack of a reasonable interval for judging the degree of overcapacity, and the reasonable intervals are likely to vary (Shi, 2013). Compared with CU, the positive deviation degree of the actual PC from the OPCC may be a more scientific proxy variable. However, the lack of research on the OPCC makes the data of the proxy variable unavailable. Accordingly, the lack of research on the OPCC makes it difficult to determine the best PC management practices. In addition, although there are abundant theoretical studies on the intertemporal optimization of PC (IOPC) that provide effective refer­ ences for this study, it is necessary to develop a new IOPC suitable for China’s coal industry based on the following particularities of the coal PC. 1) As well as the economic costs, the OPCC should consider the environmental and social costs, and the energy security constraints due to the multiple effects of the coal PC on the economy, environment, and society. 2) It is probably inappropriate to assume that coal demand in future will be random or can be forecast based on the historical trend in the existing IOPC model. This is because the coal demand sequence has an obvious trend rather than a random trend. This trend will not be likely to fluctuate in accordance with the historical trend due to the large transformation in the country’s economic growth, industrial structure, and energy structure (Zhang et al., 2017a,b; Zhang and Qi et al., 2013). Therefore, this study tries to scientifically answer the question of the OPCC in China’s coal industry. First, an IOPC model under uncertain demand in China’s coal industry is established by a combined Dynamic Programming - Influence Factor - Scenario Analysis - Monte Carlo method (IF-SA-MC). Second, to minimize the expected cumulative cost to the economy and environment driven by the PC under the guarantee of energy security, the OPCC in 1990–2025 is solved using the exhaus­ tive method. Third, we analyze the characteristics of the OPCC and the difference between the actual PC of coal (APCC) and the OPCC. Fourth, we define, calculate, and analyze the deviation level of the coal PC (DLCPC) and deviation degree of the coal PC (DDCPC) between the APCC and OPCC in 1990–2025, which provides the basis for PC regu­ lation. Finally, the policy implications for the optimal management of the coal PC are put forward. This study is likely to make the following theoretical and practical contributions. Theoretical contributions. 1) The IOPC model under de­ mand uncertainty in China’s coal industry is built on the basis of the existing IOPC model and the particularity of coal. It can enrich the IOPC model and thereby provide a new model reference for relative research in respect of coal and even other energy sources. 2) The DDCPC to be calculated can provide not only a new proxy variable for the empirical studies but also a new research path, or the research “calculation-causecountermeasure,” which is based on the DDCPC. This makes up for the deficiency of the traditional research of “current situation-cause-coun­ termeasure,” which, according to the discussion in the fourth paragraph of the Introduction, is based on the CU. (2) Practical contributions. 1) Two new indicators for monitoring the status of the coal PC, the OPCC and DDCPC, are put forward, and they can make up for the deficiency of the CU. 2) A new idea for PC regulation can be put forward in future that is based on the DLCPC, and it can overcome the deficiency of PC regu­ lation based on the current CU.

the research objects are expanded from a single area to multiple areas and from a single product to multiple products, and the demand setting is also expanded from certain demand to uncertain demand. 2.1. The IOPC model under certain demand The initial IOPC research usually constructed the IOPC model under certain demand. Neebe and Rao (1986) discussed the decision-making scheme of the optimal PC for a single product to minimize the total cost under the constraint of meeting the demand in a given time range with a certain demand function. Li and Tirupat (1994) extended the IOPC model of a single product to that of multiple products and designed a two-stage heuristic method to solve the model. Rajagopalan’s (1998) IOPC model considered the replacement, expansion, and disposal of the PC as well as the effect of scale economies. Fong and Srinivasan (2011) further expanded the single area research into multiple areas and considered the possibility of imports. 2.2. The IOPC model under uncertain demand The existing research on the IOPC under uncertain demand generally adopts the transformation method, which converts stochastic program­ ming into the deterministic equivalence. The main transformation methods include demand forecasting, demand process definition, and use of the scenario analysis method that refers to the replacement of uncertain demand with demand that is forecast, stochastic process defined, and uses demand scenarios. There are many methods to forecast demand. These are mainly time series, grey prediction, multiple regression, the support vector basis, and the artificial neural network method. They have been widely applied to energy demand forecasting with some advantages and disadvantages (Li et al., 2016; Chen et al., 2017; Wang et al., 2012). Time series modeling is simple and feasible, but it requires the data to be stable. The grey forecasting method is suitable for medium-to long-term forecasts and cannot dynamically reflect the variability of the system (Pao et al., 2012). The multivariate regression method supports its error test, but it is difficult to reflect periodic fluctuations (Algarni et al., 2014). The artificial neural network method can estimate the complex non-linear relationship between demand and its influencing factors, but it cannot accept the reasoning process and theoretical basis (Szoplik, 2015). The stochastic processes of demand mainly comprise the Markov process, geometric Brownian motion process, and the birth and death process. Some researchers define the demand sequence as a single sto­ chastic process. Freidenfelds (1980) assumes demand is subject to the birth and death process to realize the transformation from stochastic to deterministic demand. In addition, some researchers define the demand sequence as a combination of multiple stochastic processes. Bensoussan and Tapiero (1982) model demand as a mixed diffusion/pure jump process and take into account the randomness of the structural floating term. Bean et al. (1992) define the demand sequence as a Brownian motion with a floating term and a death process of semi-Markov birth. Chen et al., 2002, Wang et al. (2018a) and Wang et al. (2019a,b) transform stochastic demand into some scenario of demand. 2.3. Brief comments

2. Literature review

The rich research work on the IOPC model provides important ref­ erences for the construction of the IOPC model in this study, but it still needs be expanded in the following respects. 1) Many parameters in the existing theoretical models are not easy to estimate, which limits their applicable value in various industries and countries. 2) The objective function in the existing research is the minimization of the economic costs, which ignores the environmental and social costs that may be brought about by the fluctuation of the PC of some products. Thus, it limits their applicable value in the industries in which PC fluctuations are likely to produce social and environmental impacts. 3) The method

Although there have been few studies on the IOPC in China’s coal industry, the theoretical research work on the IOPC model can provide useful references for this study. The IOPC model is derived from in­ ventory theory. Effective inventory management is essentially focused on determining the optimal timing and order quantity of inventory purchases by seeking a balance between the storage cost and the required service level (Harris, 1990). Based on this idea, there are rich IOPC models for the flexible application of inventory theory, in which 2

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adopted in the existing research for the transformation from demand uncertainty to certainty assumes that demand has stochastic volatility, or that the future trend of demand can be explained by its historical trend. This limits the applicable value in the industries that do not meet these assumptions.

Table 1 Advantages and disadvantages of PC strategies responding to demand volatility. Name

Strategy

Advantages

Disadvantages

Strategy 1

Maintain much excess capacity without PC exit and undercapacity

Low capacity adjustment cost and shortage cost without undercapacity

Strategy 2

Maintain not enough excess capacity without PC exit and overcapacity Adjust PC frequently with the fluctuation of demand

Low cost of PC adjustment without overcapacity

High storage cost, possible overcapacity, with waste of resources, loss of enterprises, and unemployment The high cost of shortages, possible under capacity, and high risk of energy security High PC adjustment cost, high resource waste, and social operation cost

3. Methodology 3.1. Framework of the coal IOPC model The discussion in the Introduction shows that OPCC decisions are faced with two challenges. 1) One challenge is how to select the OPCC to minimize the economic, social, and environmental costs and ensure energy security under fluctuating demand. 2) The other challenge is how to select the OPCC based on the expectations of the future demand. Therefore, assuming the coal demand is known, the first step is to discuss the OPCC decision to minimize the economic, social, and environmental costs and ensure energy security under fluctuating demand. After relaxing the assumption, the next step is to discuss the OPCC decision under demand uncertainty. Finally, the methods to build and solve the IOPC model are proposed. The framework of coal IOPC model are shown in Fig. 1.

Strategy 3

Low storage cost and shortage cost; no overcapacity and undercapacity

3), it will increase the cost of the PC adjustment. Therefore, faced with demand fluctuations, the OPCC decision-making is to minimize the total cost of the storage, shortage, and exit costs. 3.1.1.2. Environmental cost. The construction of coal PC can destroy land resources by occupying the land (Yu et al., 2014). It is necessary to internalize the environmental cost based on a quantitative estimation of the cost of the land destruction and reclamation (Ding et al., 2019). Thus, the OPCC decision-making is to minimize the total cost after internalizing the environmental cost.

3.1.1. IOPC under known demand fluctuation 3.1.1.1. Economic cost. Faced with demand fluctuation, the PC strate­ gies are roughly divided into the three categories shown in Table 1, and they are superior to the other two in some respects but with the following inevitable problems. If a firm maintains too much excess ca­ pacity (Strategy 1) or too little excess capacity (Strategy 2), this will bring about high storage and shortage costs, respectively. If the PC quantity is adjusted frequently with the fluctuation of demand (Strategy

3.1.1.3. Energy security. As coal is the main energy source in China, shortages in the supply of coal can endanger the national energy security and the stable operation of the society and economy (Proskuryakova,

Fig. 1. Research framework of the coal IOPC model. 3

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2018; Su et al., 2019; Song et al., 2019). Therefore, the minimum coal PC level should be determined to ensure security of the national energy supply.

Table 2 The advantages and disadvantages of the combination methods of the influ­ encing factors and the uncertainty of the factors.

3.1.1.4. The objectives and constraints of the IOPC model under certain demand. To sum up, in the model under certain demand, the objectives are to minimize the total PC costs, which include storage, shortage, exit, and environmental costs, and the constraint condition is that the mini­ mum coal PC can ensure national energy security. 3.1.2. IOPC under demand uncertainty As discussed in Section 2, the IOPC under uncertain demand gener­ ally adopts the transformation method to convert stochastic program­ ming into the deterministic equivalence. The main transformation methods can be summarized into two categories. (1) Forecast the de­ mand based on the evolution law of historical demand such as a time series model or the grey forecasting method. These are not suitable for a coal demand forecast, because the evolution law of coal demand in the past may not be suited to the future coal demand in China as a result of the transformation of the economic growth rate, industrial structure, energy structure, and stronger environmental constraints (Lin and Wang, 2019). (2) Another way is to explore the relationship between the coal demand and its factors and then examine the uncertainties of those factors. The advantages are the stable relationship between the coal demand and its factors for a long time and the fact that full consideration is given to the uncertainty of the future demand factors. This mainly includes the combination of the influencing factor method and scenario analysis method (IF–SA) and the combination of the influencing factor method and Monte Carlo simulation (IF–MC). Their advantages and disadvantages are shown in Table 2. Combined with the two combination methods in Table 2, we build the probability density function of demand (PDFD) to consider the de­ mand uncertainty as shown in Fig. 2. We use a combination of the influencing factors method, scenario analysis, and Monte Carlo simu­ lation (IF-SA-MC) and take the definite integral based on the PDFD to replace the certain demand. The IF-SA-MC method exerts the superior­ ities of the IF-SA and IF-MC methods and overcomes their disadvantages.

Methods

Steps

Advantages

Disadvantages

IF-SA a

Build the econometric equation of influencing factors, set up the scenarios of influencing factors, and estimate the coal demand under each scenario b . Build the econometric equation of influencing factors, estimate the PDFD of each influencing factor based on the historical data, sample N times by the Monte Carlo method according to the estimated PDFDs, calculate the corresponding N quantities demanded by plugging the sampling results of the influencing factors into the econometric equation, and fit the PDFD by N quantities demanded d.

The stable relationship between coal demand and its factors for a long time, and the consideration given to the uncertainty of future demand factors. Based on the Monte Carlo method; there are a number of scenarios of factors with their possibility.

The number of scenarios is small; the possibility of each scenario is not given.

IF- MC

c

The PDFD of each influencing factor is calculated from historical data. However, the evolution law of coal demand in the past may not be suited to the future coal demand in China as a result of the transformation of the economic growth, industrial structure, and energy structure, and stronger environmental constraints e.

Notes. a IF-SA indicates the combination of the influencing factor method and sce­ nario analysis method. b References: Lin, B., Du, Z., 2017. Promoting energy conservation in China’s metallurgy industry. Energy Policy 104, 285–294. c IF-MC indicates the combination of the influencing factor method and Monte Carlo simulation. d References: Peerapat, V., Iain, F., MacGill, A., 2012. Monte Carlo based decision-support tool for assessing generation portfolios in future carbon con­ strained electricity industries. Energy Policy 41, 374–392; Wolderufael, Y., 2010. Bounds test approach to cointegration and causality between nuclear energy consumption and economic growth in India. Energy Policy 38, 52–58. e PDFD indicates the probability density function of demand.

3.1.3. Method Because of the long period of time to achieve a particular coal PC, which involves a process flowing form investment decision-making to production, the OPCC in a certain period does not necessarily equate to the OPCC to minimize the total cost during the PC periods. Therefore, the dynamic programming method is introduced to obtain the OPCC that minimizes the cumulative cost in the period 1990–2015. In addi­ tion, an exhaustive method is used to solve the IOPC model due to its high accuracy and acceptable calculation speed based on only a decision variable, PC, with a small range of values in the IOPC model.

� � 8 1 > < Pt ðYt Þ ¼ min Ct ðYt ; ΔYt Þ þ Pt 1 ðYt 1 Þ ðt ¼ 1990; 1991; :::; 2025Þ 1þi > : Y1 ¼ η1 *Q1;d s:t:Yt � ηmin *Qt;d

3.2. IOPC model in China’s coal industry According to the analyses in Section 3.1, in the IOPC model, the objectives are the minimized cumulative cost including storage, shortage, exit, and environmental costs in 1990–2025, and the constraint condition is that the minimum coal PC can ensure national energy security. In consideration of uncertain coal demand in 2018–2025, the cost function in the IOPC model for the period of 2018–2025 is different from that in 1990–2017. The PDFD is con­ structed by using the IF-SA-MC method, and it is used to replace the deterministic demand. Thus, the IOPC model is constructed, as shown in Equation (1).

(1)

where Yt refers to the quantity of coal PC in the t period, Pt ðYt Þ repre­ sents the minimum cumulative cost from the first period to the t period, and ΔYt refers to the change of PC measured by ΔYt ¼ Yt Yt 1 . Ct ðYt ; ΔYt Þ refers to the total cost function in the t period, and that in 1990–2017 and 2018–2025 are shown in Equation (2) and Equation (3), 1 respectively. i, 1þi , η1 , ηmin , and Qt;d represent the interest rate, discount coefficient, the relationship coefficient between the PC and demand in the t period, the critical point coefficient of the lower limit of the coal PC, and the coal demand in the t period, respectively. When t ¼ 1990; 1991; …; 2018,

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Energy Policy 139 (2020) 111360

Fig. 2. Construction steps of probability density function of coal demand.

8 � < HCðYt Þ þ SC Qt;d Yt �δ Qt;d Ct ðYt ; ΔYt Þ ¼ HCðYt Þ þ SC Qt;d Yt δ Qt;d : þECð ΔYt Þ þ LRCð ΔYt Þ

� Yt � þ LDCðYt Þ; Yt � Zt Yt þ LDCðYt Þ ; Yt < Zt

b is the estimated change of the PC. where Δ Y 3.3.2. Cost function The cost functions in the IOPC model include the exit, storage, shortage, and environmental costs, and their calculation formulas are

(2)

When t ¼ 2019; 2020…; 2025,

8 Z ∞� � > HCðYt Þ þ SC Qt;d Yt δ Qt;d > > > 0 < Z ∞ � � Ct ðYt ; ΔYt Þ ¼ HCðYt Þ þ SC Qt;d Yt δ Qt;d > > > > : 0 � � þLRCð ΔYt Þ f Qt;d dQt;d

� � � Yt þ LDCðYt Þ f Qt;d dQt;d ; Yt � Zt � Yt þ LDCðYt Þ þ ECð ; Yt < Zt

shown in Table 3.

where HCðYt Þ, SCðQt;d Yt Þ, and ECðΔYt Þ represent the cost functions of storage, shortage, and exit, respectively, and δðQt;d Yt Þ represents the Crohneck function, whose value is as shown in Equation (4). Qt;d and Zt refer to the quantity demanded and the remaining PC after the capacity reduction in the t period, respectively. LDCðΔYt Þ and LRCðΔYt Þ represent the land destruction cost and the land reclamation cost of the capacity reduction in the environmental cost, respectively, and fðQt;d Þ represents the PDFD. � � 1; Qt;d Yt > 0 δ Qt;d Yt ¼ (4) 0; Qt;d Yt � 0

3.3.3. PDFD Using the Logarithmic Mean Divisia Index method, coal demand is decomposed into three factors as shown in Equation (8): economic growth, energy intensity, and energy structure. The significant impact of these factors on coal demand has been verified (Yu and Zhu, 2012; Lin and Ouyang, 2014). Qd;e;t Qd;c;t * ¼ GDPt *ENINt *ENSTt Qd;c;t ¼ GDPt * GDPt Qd;e;t

3.3.1. Boundary production function In the IOPC model, the cost functions reflect the relationships of the cost and quantity of the production factors, but its decision variable is the PC. Thus, it is necessary to build the quantitative relationship be­ tween the quantity of production factors and the PC. The relationship can be reflected by the boundary production function (Zhang et al., 2016, 2018). The function is shown in Equation (5), and its steps of derivation and estimation refer to Wang et al. (2019a,b).

2

ðQt;d μt Þ � 1 2σ 2 t f Qt;d ¼ pffiffiffiffiffie σ t 2π

4. Parameter value and data source

b , and b where δ ¼ ln A, b δ is the estimated value of δ, and Y, A, K, L, α β indicate the PC, technical level, capital and labor input, and the esti­ mated values of the capital and labor output elasticity, respectively. Further, let the per capita capital be k, and then k ¼ K/L. It is substituted into Equation (7). The relationship between the PC and the changed quantity of the fixed assets and labor force (ΔK and ΔL) is then shown in Equations (6) and (7), respectively. (6)

bb bb ΔL ¼ ½=ðk β e δ Þ�1=ð α þ βÞ

(7)

(9)

where μ and σ indicate the mean and standard deviation of the coal demand in the t period, respectively.

(5)

b kbβ =ebδ Þ1=ðbα þbβÞ ΔK ¼ ðΔ Y

(8)

where Qd;c;t , GDPt , Qd;e;t , ENINt , and ENSTt represent the coal con­ sumption, gross national product (GDP), energy consumption, energy intensity, and energy structure in the t period, respectively. The PDFD for each year is constructed by the steps in Fig. 2, and the normal PDFD for each year is fitted based on the 2000 coal demand of each year, as shown in Equation (9).

3.3. Special functions in the IOPC model

b b Y ¼ e δ Kbα L β

(3)

ΔYt Þ

4.1. Boundary production function The sample interval selected in the model is 1990–2017. Y’, K, and L are measured by the annual raw coal output (unit: 10,000 tons), the average annual balance of fixed assets (unit: 10,000 yuan), and the average annual number of employees (unit: person) in China’s coal in­ dustry, and their data are derived from the China Coal Industry Yearbook, China’s Industrial Statistics Yearbook, and China’s Statistical Yearbook. In b and b Table 4, α β are estimated as 0.710 and 0.656, respectively.

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Energy Policy 139 (2020) 111360

Table 3 Cost functions in IOPC model. Cost meaning

Components

Measurement

Estimation equation

Exit cost, or the cost driven by the exit of PC

Sunk cost of fixed assets, or FSðEKt Þ

The loss value of fixed assets

FSðEKt Þ ¼ ð1 KPt �NRt =Kt Þ�EKt Where FSt , EKt , KPt NRt represent the sunk cost, the annual average balance of the exit of fixed assets, the original price of fixed assets and the expected net residual value ratio in the t period, respectively.

Staff settlement cost, or PSðELt Þ

The cost to resettle retirees, reemployed persons and terminators a

PSðELt Þ ¼ ðPS1 pr1 þ PS2 pr2 þ PS3 pr3 ÞELt Where ELt refers to the total number of people who need to be resettled in the process of de-capacity; pr1 , pr2 and pr3 refer to the proportion of internal retirees, re-employed persons and terminators in the total resettlement staff, respectively; PS1 , PS2 , and PS3 indicate the resettlement cost of internal retirees, re-employed persons and terminators

The cost to resettle retirees

PS1 ¼ ðCWa1 þ TWb1 SS1 Þt1 Where CW and TW express the average wage of workers in the coal industry and urban workers, respectively; a1 and b1 the proportion of living expenses of retirees and basic wage of workers to the average wage of workers, respectively; SS1 expresses the payment rates of social security and housing accumulation fund from internal retirees; t1 indicates the years of payment during the period of internal retirees.

The cost to re-employed persons

PS2 ¼ ðMWa2 þ TWb1 SS2 þ CWc2 Þt2 Where MW represents the minimum wage; a2 represents the proportion of living expenses to the minimum wage; c2 represents the proportion of training expenses to the average wage; SS2 represents the payment rates of social security and housing accumulation fund from re-employed persons; and t2 represents the average time of job-waiting.

The cost to terminators

CW t3 12 Where t3 refers to the average working year of the dismissed personnel. PS3 ¼

Storage cost, or the cost of maintaining PC

Depreciation amount of fixed assets, or HCðKt Þ

Annual depreciation amount of fixed assets

HCðKt Þ ¼ drKt Where dr refers to the annual depreciation rate of fixed assets, which is calculated by the “average method of years"

Shortage cost, or the economic and social costs brought about by the inability of PC to meet demand

The loss of business opportunities b SCðQt;d Yt Þ

The profit that can be obtained by expanding PC

SCðQt;d Yt Þ ¼ ½CPt *ðssQt;d cumax Yt Þ drKt;os CWt Lt;os �δðssQt;d cumax Yt Þ Where CPt refers to the coal price in t period, cumax refers to the upper limit of CU, ss refers to the self-sufficiency rate of coal, and CWt refers to the average annual wage of workers in the coal industry.

Environmental cost function, or the environmental cost driven by PC construction in coal mines

The cost of land destruction, or LDCðYt Þ

Land requisition price

The cost of reclamation, LRCðYt Zt Þ

The cost of land reclamation arises in the process of decapacity

c

LDCðYt Þ ¼ PL Yt ðSo þ Su Þ=nl Where PL is the price of land requisition; Y is the total coal PC in China; So and Su are the proportion of the land occupied by open pit and underground mines in the process of capacity construction, and are obtain by the following equations; nl indicates the years of use of the land occupied in the process of PC construction. So ¼ η0 ðθob þ θod þ θow Þ Where η0 represents the proportion of open-pit mineral PC to the total PC; θob , θod and θow represent the area of industrial site, the excavation of stope, and waste dump per unit PC in open-pit mines. Su ¼ ð1 η0 Þθub Where θub represents the area of industrial site per unit PC in the underground mine. LRCðYt Zt Þ ¼ αlr ðYt Zt ÞðSo þ Su Þ Where αlr represents the cost of land reclamation per unit area.

Notes. a References: Wang, D., Wang, Y., Song, X., Liu, Y., 2018b. Coal overcapacity in China: multiscale analysis and prediction. Energy Econ. 70, 244–257. b Although shortage cost includes the loss of business opportunities, overtime fees, energy security risk and inflation driven by insufficient coal supply, the shortage cost in this paper only includes the first kind of cost due to the following reasons. 1) The constrain of the lower limit of coal PC in the IOPC model has ensured energy security and no inflation at feasible area; 2) As the coal firm work 330 days/year in “three shifts”, there is little chance to increase output by overtime work. c Xu, X., Gu, X., Wang, Q., Liu, J.P., Wang, J., 2014. Ultimate pit optimization with ecological cost for open pit metal mines. Trans. Nonferrous Met. Soc. China 24, 1531–1537.

4.2. Cost function

and Human Resources and Social Security Bureau (http://www.mohrss. gov.cn). K, CW, and TW in 2018–2025 are estimated by a combination of the influencing factors method and scenario analysis method. MW in 2018–2025 are based on the AR (1) model. Based on the notice on strengthening the management of laid-off workers in state-owned enterprises and the construction of a reemployment service center, the notice on adjusting the policy of the industrial injury insurance premium rate, and the measures for economic compensation for violation and termination of labor contracts, a1 , b1 , SS1 , t1 (unit: year), a2 , SS2 , c2 , t2 (unit: year), and t3 (unit: year) can be determined.

4.2.1. Exit cost function According to the Notice of the State Administration of Taxation on the Follow-up Management of the Cancelled Enterprise Income Tax Examination and Approval Project, NR is 5%. Based on the existing results, pr1 ¼ 0:372, pr2 ¼ 0:126, and pr3 ¼ 0:503(Wang and Zhao et al., 2018; Shen et al., 2016). The data of KP (unit: 10,000 yuan), K (unit: 10,000 yuan), CW (unit: 10,000 yuan/year/person), TW (unit: 10,000 yuan/year/­ person), and MW (unit: 10,000 yuan/year/person) in 1990–2017 are from the China Industrial Statistics Yearbook, China Statistical Yearbook, 6

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Table 4 Parameter estimation results of boundary production function. b α

b β

δ

bε

0.710*** (0.036)

0.656*** (0.161)

-9.695*** (2.663)

Table 6 Energy intensity growth in 2019–2025 under the baseline scenario.

b δ

R2

Fvalue

-9.484 –

0.943

190.880***

Notes: The values in brackets are standard errors; ** and *** denote the sig­ nificance at 5% and 1% levels, respectively.

6.300

2020 2021–2025

6.200 5.800

Government Work Report in 2019 a Center for China and World Economic Research, Tsinghua University b Calculated from the GDP growth in the first half of 2019, whose data are from National Bureau of Statistics World Economic Outlook report c Mean value of GDP forecast interval of Lin and Tan (2017) d

-4.352 -3.471

The mean value of the prediction results of Wang et al. (2018), under which the BS is calculated a. The mean value of the prediction results of BP Energy Outlook (2018) under which the BS is calculated b

4.178

4.3.1. Baseline scenario (BS) settings The BSs and their data sources of the GDP growth, energy intensity growth, and energy structure from 2019 to 2025 are shown in Tables 5–7. These BSs can meet the requirements of the national pol­ icies, such as doubling GDP by 2020 compared with 2010, reducing energy consumption per GDP by 15% compared with 2015, and reducing the proportion of coal consumption in energy consumption to 58% (NDRC et al., 2016; NDRC, 2017). After substituting these BSs into Equation (8), we can obtain the coal demand in the period 2018–2025 under the BS shown in Table 8. At the critical points in time, the coal demand under the BS in 2020 is 3.947 billion tons, which achieves the coal demand target of the 13th Five-Year Plan (NDRC et al., 2016) and is basically consistent with the prediction results of other studies (Lin et al., 2018). With regard to the change trend, coal demand, taking 2020 as the inflection point, shows a slight upward trend and then a slight downward trend, which is consistent with the forecast results of BP (2018).

Table 5 GDP growth in 2019–2025 under the baseline scenario.

2019

2019 2020

4.3. PDFD

4.2.4. Environmental cost function αlr (unit: yuan/hectare) and PL (unit: yuan/hectare) are calculated by the product of αlr in 2015 and the coal average wage index and the product of PL in 2015 and the average selling price index of commercial housing, for which the data are from the China Real Estate Statistics Yearbook. Their data in 2018–2025 can be obtained by the AR (1) model. Using the report on the Mine Geological Environment Protection and Land Reclamation Program, the website of the China Coal Industry Association, the current Index of Construction Land for Coal Industrial Projects, and GB50215–2015, nl (unit: year), η0 , θob (unit: hectare), θod (unit: hectare), θow (unit: hectare), and θub (unit: hectare) can be determined.

Data sources

Data sources

Notes. a References: Wang, D., Nie, R., Long, R., Shi, R., Zhao, Y., 2018a. Scenario prediction of China’s coal production capacity based on system dynamics model. Resour. Conserv. Recycl. 129, 432–442; Chai, J., Du, M., Liang, T., Sun, X.C., Yu, J., Zhang, Z.G., 2019. Coal consumption in china: how to bend down the curve? Energy Econ. 80, 38–47. b References: Xie, H., Wu, L., Zheng, D., 2019. Prediction of China’s energy consumption and coal demand in 2025. J. China coal Ind. 44, 1949–1960. (in Chinese); BP, 2018, BP energy outlook, 2018.https://www.bp.com/content/ dam/bp-country/zhcn/Publications/EO18–China%20 one%20pager–CN.pdf; Chai, J., Du, M., Liang, T., Sun, X.C., Yu, J., Zhang, Z.G., 2019. Coal consumption in china: how to bend down the curve? Energy Econ. 80, 38–47.

4.2.3. Shortage cost function CPt (unit: yuan/ton) in 1990–2017 are calculated by the product of the coal price index and price ratio coefficient or the ratio of the coal price in 2018 to the coal price index, for which the data are obtained from the website of China Coal Resources (http://www.sxcoal.com/) and China Statistics Bureau (http://stats.gov.cn/). The coal prices in 2019–2025 are estimated by a combination of the influencing factors and scenario analysis method. ss is set at 0.90 according to the ss in China of about 90% in the past decade without energy security prob­ lems. Let cumax ¼ 0.9, as the coal industry has been in a state of under­ supply over the past 28 years with the CU of more than 90%.

GDP growth/%

Energy intensity growth/%

2021–2025

4.2.2. Storage cost function k (unit: yuan/person) is calculated by the quotient between the average annual balance of fixed assets and the average annual number of employees in the coal industry, for which the data are both from the China Industrial Statistics Yearbook. According to t4 ¼ 15 (unit: year), dr can be determined.

Year

Year

4.3.2. Multiplier factor Referring to Babonneau et al. (2012) and Duan et al. (2018), based on the normal distribution with a mean of 1.0 and a standard deviation of 0.3, 2000 positive random numbers are produced by the Monte Carlo method, and the random numbers are used as multiplier factors whose frequency distribution histogram is shown in Fig. 3. 4.3.3. Parameter values of PDFD We obtained 2000 possible demand values for each year from 2019 to 2025 by the product of the coal demand under the BS and the multiplier factor, and their mean and standard deviation are regarded as the expectation and standard deviation of the PDFD, as shown in Table 9.

Notes. a References: State Council of China, 2019. 2019 government work report. http://www.gov.cn/zhuanti/2019qglh/2019lhzfgzbg/. (Accessed 8 September 2018). (in Chinese). b References: China and World Economic Research Center, Tsinghua Univer­ sity, 2019. Tsinghua University macroeconomic report: China’s GDP growth will reach 6.3% this year. https://baijiahao.baidu.com/s?id¼163711393660182 8244&wfr ¼ spider& for ¼ pc/(Accessed 9 August 2019). (in Chinese). c References: World Bank, 2018. Global Economic Prospects. https://www. useit. com.cn/thread- 17722-1-1.html. (Accessed 19 August 2019). (in Chinese). d References: Lin, B., Tan, R., 2017. Estimating energy conservation potential in China’s energy intensive industries with rebound effect. J. Clean. Prod. 156, 899–910; Dong, K., Sun, R., Hochman, G., Li, H., 2018. Energy intensity and energy conservation potential in China: A regional comparison perspective. Energy 155, 782–795.

4.4. Others In order to obtain a reasonable value of ηmin , the quotient of the PC and demand are decomposed into Equation (10). Yt Yt yt sst ¼ * ¼ Qt;d yt Qt;d cut

(10)

where Qt;d , Yt , yt , cut , and sst represent the demand, PC, output, CU, and the self-sufficiency rate in the t period, respectively. 7

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Energy Policy 139 (2020) 111360

Table 7 Energy structure and its growth in 2019–2025 under the baseline scenario. Year

Energy structure/%

Energy structure growth/%

Data sources

2019 2020 2021 2022 2023 2024 2025

58.500 58.000 56.818 55.636 54.455 53.273 52.091

0.008 0.009 -0.020 -0.021 -0.021 -0.022 -0.022

13th Five-Year Plan for energy development a The mean value of the prediction results of the Revolutionary Strategy of Energy Production and Consumption (2016–2030), Liu et al. (2018), Energy Outlook (2018) under which the BS is calculated b

Notes. a References: National Development and Reform Commission (NDRC), Na­ tional Energy Administration (NEA), 2016. 13th five year plan for energy development. http://www.nea.gov.cn/135989417_14846217874961n.pdf. (Accessed 29 August 2019). (in Chinese). b References: NDRC, 2017. Energy production and consumption revolution strategy 2016–2030. http://www.ndrc.gov.cn/z cfb/zcfbtz/201704/t2017 0425_845284.html. (Accessed 9 January 2019). (in Chinese); Liu, Q., Lei, Q., Xu, H., Yuan, J., 2018. China’s energy revolution strategy into 2030. Resour. Con­ serv. Recycl. 128, 78–89; BP, 2018, BP energy outlook, 2018. https://www.bp. com/content/dam/bp-country/zh_cn/Publications/EO18—China%20one% 20pager–CN.pdf.

Fig. 3. Frequency distribution histogram of multiplier factor of coal demand.

demand scenarios, the OPCC maintained a lower level in 2019–2021 and began to decline in 2022 and 2024, respectively, which are earlier than that under the BS. In the relatively high and high demand scenarios, the OPCC continued to increase in 2019–2025, and reached 4.32 and 4.50 billion tons, respectively, which are higher than that under the BS in 2025. (2) When the parameters of the exit, shortage, storage, and envi­ ronmental cost function change slightly ( 10%–10%), the OPCCs remain unchanged due to the following reasons. As the exit cost per PC is much larger than the storage or shortage cost per PC, the exit cost per PC will still be larger than the storage or shortage cost per PC despite the slight decrease or rise of the exit, storage, or shortage cost. In addition, as the environmental costs are far lower than the other three types of costs, the OPCC remains un­ changed despite a slight change of the parameters in the envi­ ronmental cost function. (3) When ηmin varies slightly ( 10% 10%), the OPCC remains un­ changed, because its original value is 0.9, and this is still less than 1 even with the slight changes. As for the ηmin being less than 1, the constraint condition does not work, as the OPCC in the original model is higher than the demand.

Therefore, ηmin is equal to the quotient of ssmin and cumax . Over the past 30 years, the coal self-sufficiency rate in China has been from 0.911 to 1.082 without energy security problems. Thus, ssmin can be set as 0.9. Obviously, cumax can be set as 1, and then ηmin is 0.9. The discount factor is calculated based on the interest rates, and the data for this in 1990–2017 are from the website of The People’s Bank of China (http://www.pbc.gov.cn/). The interest rates in 2018–2025 are estimated by the combination of the influencing factors method and scenario analysis method. In addition, all the price parameters above are converted by the CPI based on the 1990 prices. 5. Results and discussion 5.1. Results and sensitivity analyses of IOPC model By solving the coal IOPC model, the OPCCs, their growth, and their annual costs in 1990–2025 are obtained in Table 10. For the sensitivity analyses, we test the change of the OPCC when a certain parameter rises or falls by 5% and 10% and the other parameters remain unchanged.

5.2. Characteristics analyses of OPCC Compared with the APCC, the OPCC in China has the following characteristics. 5.2.1. Compared with the APCC, the OPCC presents a more stable development trend From the standard deviations, minimum, and maximum in Table 12, it can be seen that the OPCC growth has a smaller fluctuation than that of the APCC. For the specific time periods of 1990–1999 and 2014–2017, the fluctuation of the OPCC is significantly more stable than that of the APCC. This is particularly evident in 2014–2017, when the OPCC remained unchanged, but the APCC experienced a significant decline; the decline rate was especially high in 2017 at 12.543%.

(1) The expectation of the PDFD can have a slightly positive impact on the OPCC with slight variations ( 10%–10%), while the standard deviation of the PDFD can have no effect on the OPCC. Referring to Ce (2018), the expectations of the PDFD in different demand scenarios are set by the growth and decline of the de­ mand factors under the BS by 5% and 10% in Table 11. The change of the PDFD and OPCC driven by the change of the expectation in the PDFD are shown in Fig. 4 and Fig. 5, respec­ tively. Compared with the BS, under relatively low and low

Table 9 Expectation, standard deviation of PDFD in 2019–2025 a.

Table 8 Coal demand and its growth in 2019–2025 under the baseline scenario. Year

2019

2020

2021

2022

2023

2024

2025

Coal demand/ billion tons Coal demand growth/%

3.875

3.947

3.931

3.913

3.893

3.872

3.849

1.101

1.874

-0.416

-0.458

-0.502

-0.548

-0.596

Year

2019

2020

2021

2022

2023

2024

2025

Expectation/ billion tons Standard deviation/ billion tons

3.875

3.947

3.931

3.913

3.893

3.872

3.849

1.150

1.162

1.184

1.179

1.174

1.168

1.162

Notes. a PDFD indicates the probability density function of demand. 8

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Energy Policy 139 (2020) 111360

Table 10 The OPCC, its growth, and annual cost in 1990–2025 a. Year

OPCC/billion tons

OPCC growth/%

Annual cost/billion yuan

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

1.112 1.160 1.220 1.280 1.360 1.440 1.460 1.460 1.460 1.460 1.480 1.560 1.720 2.040 2.400 2.800 3.060 3.340 3.380 3.560 3.680 4.020 4.060 4.120 4.120 4.120 4.120 4.120 4.120 4.120 4.160 4.160 4.160 4.160 4.160 4.160

6.115 3.390 4.918 6.250 7.353 1.370 0.000 0.000 0.000 1.351 5.333 8.861 19.767 16.505 16.667 9.286 9.150 1.198 5.325 3.371 9.239 0.995 1.478 0.000 0.000 0.000 0.000 0.000 0.000 0.971 0.000 0.000 0.000 0.000 0.000

4.269 4.183 3.887 3.265 3.047 3.072 3.132 3.184 3.352 3.544 3.669 3.862 4.192 4.905 5.419 6.191 6.926 7.629 7.713 8.670 9.074 9.714 10.042 10.951 11.049 11.813 12.151 12.526 12.930 13.359 13.977 14.472 15.005 15.571 16.170 16.799

Fig. 4. Probability density function of demand in different demand scenarios (2020 and 2025) Notes: BS, SL, SML, SMH, and SH indicate the demand scenarios of the baseline scenario, low demand, relatively low demand, relatively high demand, and high demand, respectively.

Note. a OPCC indicates the optimal production capacity of coal; The annual cost is the present value taking 1990 as benchmark year, and is converted by the CPI index (1990 ¼ 1). Table 11 Scenario setting of the expectation of the coal PDFD (2019–2025). Name

GDP growth compared with BS/%

Energy intensity growth compared with BS/%

Energy structure growth compared with BS/%

BS (BS) Low demand (SL) Relatively low demand (SML) Relatively high demand (SMH) High demand (SH)

0

10

0 10

0

5

5

10 5

5

5

5

10

10

10

Fig. 5. Sensitivity test of the expectation of probability density function in different scenarios Notes: BS, SL, SML, SMH, and SH indicate the demand scenarios of the baseline scenario, low demand, relatively low demand, relatively high demand, and high demand.

Notes: PDFD indicates the probability density function of demand; BS indicates the baseline scenario.

5.2.2. Compared with the APCC, the adjustment of the OPCC is more timely Fig. 6 shows the adjustment of the APCC is later than the OPCC by about two years. In 1993–1995 the OPCC grew at an average rate of 5.035%, whereas the APCC contracted sharply in 1993–1994 and increased in 1995–1997. Obviously, in 1993–1995, the adjustment of the OPCC was earlier than the APCC by two years. In addition, as shown in Fig. 5, during 2000–2003, 2004–2008, 2009–2012, and 2013–2017, the adjustments of the OPCC are earlier than that of the APCC by about two years.

Table 12 Descriptive statistics of the OPCC growth in 1990–2017. Name

Mean

Standard deviation

Min

Max

OPCC growth/% APCC growth/%

5.035 4.477

5.647 8.836

0.000 -12.543

18.605 21.757

Notes: The OPCC and APCC indicate optimal and actual production capacity of coal, respectively.

9

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Energy Policy 139 (2020) 111360

Fig. 6. The OPCC, APCC, and their growth in 1990–2017.

5.2.3. Compared with the cost of the APCC, if developing along the OPCC pathway, the coal industry can save a lot of costs, especially the exit and shortage costs In Fig. 7, the total cost and annual costs of the OPCC are much lower than those of the APCC in 1990–2017. Fig. 8 shows that the cost savings of the OPCC over the APCC mainly come from the exit and shortage costs. The OPCC had no negative growth in 1990–2017 without exit costs. Nevertheless, the exit of the APCC occurred in 1992–1994, 1998–2001, and 2015–2017, which resulted in high exit costs, espe­ cially the 39.183 billion yuan cost in 2017. The OPCC exhibited few shortage costs, whereas the APCC produced high shortage costs in 2001–2008; they exceeded 20 billion yuan in 2004–2007.

Fig. 8. All kinds of cost savings of the OPCC over APCC.

resource allocation. In fact, the government had been committed to PC management, especially the policy of coal capacity reduction, in 1998–2000 and 2015–2017. This caused the APCC to be near the OPCC in the short run, but the DDCPC is still increasing in the long run. Therefore, the previous capacity managements measures are like to have only a limited long-term effect. 2) The APCC in China is higher than the OPCC for most of the time without the capacity reduction policy in 1990–2017. As the average DDCPC was 5.912, and the government mainly implemented its ca­ pacity reduction policy in 1990–2017, it can be inferred that China’s coal industry was at risk of overcapacity for the vast majority of the time without the capacity reduction policy. In addition, as there can be basically the same time length for overcapacity and under­ capacity, the DDCPC was only influenced by the economic cycle. In fact, more overcapacity means that the DDCPC in China may also be driven by other supply factors, such as market mechanism distortion and system distortion, which is in accordance with some existing results (Zhu et al., 2017; Yang et al., 2018, 2019). 3) In 1990–2017 China’s coal industry experienced two rounds of positive DDCPC (1990–1999 and 2009–2017) and one round of negative DDCPC (2000–2008). This implies that there was a risk of overcapacity in1990–1999 and 2009–2017 and undercapacity in 2000–2008.

5.3. Estimation of the DDCPC and design of the PC regulation target 5.3.1. Calculations and characteristics analyses of the DLCPC and DDCPC in past years In order to provide the bases for coal PC management, two concepts of the DLCPC and DDCPC are defined to describe the deviation between the APCC and OPCC. These are calculated by the APCC and OPCC dif­ ference and the proportion of the DLCPC in the OPCC, respectively, as shown in Fig. 9. Several characteristics of the coal PC in China can be found in Fig. 9 and Table 13. 1) On the whole, the DDCPC fluctuates upwards in 1990–2017. In Fig. 9 and Table 13, the DLCPC and DDCPC in 2004–2017 are generally higher than those in 1990–2003. This indicates that the PC devel­ opment is increasingly deviating from the objective of optimizing the

5.3.2. Estimation of the DDCPC and DLCPC in future and target design of proactive PC regulation As there is a long capacity construction cycle of 5–10 years and high costs of exit in the coal industry, the DDCPC and DLCPC in future years

Fig. 7. Annual cost of OPCC and APCC and their difference.

Fig. 9. DLCPC and DDCPC in China in 1990–2017. 10

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Energy Policy 139 (2020) 111360

6.1. The index to monitor the status of the coal PC should change from the CU to the OPCC and DDCPC

Table 13 Descriptive statistics of DDCPC in China. Year

Mean

Standard deviation

Max

Min

1990–2003 2004–2017 1990–2017

6.596 5.228 5.912

16.125 20.034 17.904

34.406 30.887 34.406

-18.896 -26.604 -26.604

In the past, the CU was a common index to monitor the status of the coal PC, but it had some disadvantages. There is a lack of a reasonable interval for judging the degree of overcapacity and undercapacity, and the reasonable interval is likely to vary as the OPCC is likely to corre­ spond to a different CU. However, the OPCC is the PC that minimizes the expected cumulative cost to the economy and environment under en­ ergy security. The DDCPC reflects the deviation degree of the APCC from the OPCC, and its positive and negative directions indicate the risk of overcapacity and undercapacity. Therefore, the OPCC and DDCPC are more scientific and effective indicators to monitor the status of the coal PC.

Notes: The DDCPC indicates the deviation degree of the coal production capacity.

are estimated without the capacity reduction policy to provide the bases for proactive PC management. As for the APCC, since 2016 the government has implemented a policy to strictly control the new coal PC (NDRC et al., 2016). Assuming that this policy will continue to be implemented in 2017–2025, the APCC will remain at 4636.7 million tons in 2017–2025; thus, the DDCPC and DLCPC in 2018–2025 are obtained in Table 13. Table 14 shows the DDCPC and DLCPC are relatively stable in 2018–2025, with the DLCPC at 476.7–516.7 million tons and the DDCPC at 11.459%–12.541%. Therefore, in the next 5–10 years, under the policies to strictly control the new PC, the coal PC will remain in the state of overcapacity, so the objective of the optimal management of the PC is to eliminate this overcapacity. The target design to eliminate overcapacity can vary with the government’s acceptance of the DDCPC. As the DLPCD will be reduced from 516.7 million tons in 2018–2019 to 476.7 million tons in 2020–2025, the upper limit of the PC reduction target will be 476.7 million tons. If the government can accept a 5% or 10% DDCPC, the overcapacity reduction target will be 268.7 and 60.7 million tons, respectively.

6.2. The optimal management of the coal PC should be based on the direction and size of the DDCPC and DLCPC in the next 5–10 years rather than the current CU Due to the PC construction period of 5–10 years, the current CU can only reflect the results of the PC investment 5–10 year ago. Thus, the PC management based on the current CU relates to the post-regulation period, and this brings about very high correction costs, such as the exit cost of 376.851 billion yuan driven by the capacity reduction policy in 2016 (Wang et al., 2019a,b). However, the PC policy based on the DDCPC in the next 5–10 years is forward-looking and promotes a PC that will develop in a way that is consistent with the OPCC, which can save a lot of economic and environmental costs. According to the results in this study, with the help of the policies to strictly control new PC, the coal PC will still be in a state of overcapacity for the next 5–10 years. Thus, if the government can accept a 0%, 5%, or 10% DDCPC, the target for the PC reduction should be 476.7, 268.7, and 60.7 million tons, respectively, in the next 5–10 years.

6. Conclusions and policy implications To reveal the IOPC, an IOPC model under uncertain demand is established by the DP–IF–SA-MC method. The OPCCs in 1990–2025 are solved to minimize the expected cumulative cost to the economy and environment driven by the PC under the guarantee of energy security. In addition, we define, calculate, and analyze the DDCPC and DLCPC. The results show that in 1990–2017, compared with the APCC, the OPCC has the advantages of more stable development, more timely adjustment, and lower cost, and it provides cost savings of 302.857 billion yuan, which are mainly from exit and shortage costs. According to the DDCPC, China’s coal industry existed under the risk of overcapacity in 1990–1999 and 2009–2017 and undercapacity in 2000–2008, and it can experience overcapacity for most of the time without a capacity reduc­ tion policy. Based on the conclusions, the following policy implications are presented.

6.3. The optimal management of China’s coal PC should focus on overcapacity management in the long run According to the results of this study, for the period 1990–2017 the coal DDCPC would have been positive for the vast majority of the time without the capacity reduction policy. Thus, the PC optimization man­ agement should focus on the management of overcapacity. Moreover, when there are more years when the PC is in a state of overcapacity, this means that the overcapacity can be explained not only by negative de­ mand shocks but also by supply-side factors such as long-term mecha­ nism distortion. As it is difficult to improve the supply-side factors by the capacity reduction policy commonly used in the past, the PC optimiza­ tion management should focus on overcapacity management in the long term such as market mechanism improvement and system reform, including more perfect systems of the finance, the fiscal decentraliza­ tion, and the local officials’ political promotion (Gan et al., 2015; Zhu et al., 2017). The following aspects of the long-effect mechanism of optimal management in China’s coal industry need further study. (1) The respective causes of the DLCPC need to be verified according to their data in this paper. (2) Based on the result of (1), possible countermea­ sures should be proposed and their functional mechanisms on the DLCPC should be verified. (3) The optimal capacity policy mix can be obtained based on the results of (1) and (2).

Table 14 DDCPC and DLCPC in 2018–2025. Year

OPCC

APCC

DDCPC

DLCPC

DLCPC under the accept of 5% DDCPC

DLCPC under the accept of 10% DDCPC

2018 2019 2020 2021 2022 2023 2024 2025

41.200 41.200 41.600 41.600 41.600 41.600 41.600 41.600

46.367 46.367 46.367 46.367 46.367 46.367 46.367 46.367

12.541 12.541 11.459 11.459 11.459 11.459 11.459 11.459

5.167 5.167 4.767 4.767 4.767 4.767 4.767 4.767

3.107 3.107 2.687 2.687 2.687 2.687 2.687 2.687

1.047 1.047 0.607 0.607 0.607 0.607 0.607 0.607

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes: DLCPC and DDCPC indicate the deviation level and degree of the coal production capacity, respectively; The OPCC and APCC indicate optimal and actual production capacity of coal, respectively. 11

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Energy Policy 139 (2020) 111360

CRediT authorship contribution statement

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Qing Yang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - original draft, Funding acqui­ sition. Lei Zhang: Data curation, Writing - review & editing, Funding acquisition. Shaohui Zou: Resources, Visualization, Investigation, Project administration, Funding acquisition. Jinsuo Zhang: Writing review & editing, Supervision. Acknowledgments This research is funded by China Postdoctoral Science Foundation (No. 2019M663781), Social Science Youth Foundation of Ministry of Education in China (20YJC630190), Natural Science Foundation of China (No. 71874187), and National Social Science Foundation of China (No. 19BGL183). References Algarni, A.Z., Zubair, S.M., Nizami, J.S., 2014. A regression model for electric-energyconsumption forecasting in Eastern Saudi Arabia. Energy 19, 1043–1049. Babonneau, F., Haurie, A., Loulou, R., Vielle, M., 2012. Combining stochastic optimization and Monte Carlo simulation to deal with uncertainties in climate policy assessment. Environ. Model. Assess. 17, 51–76. Bean, J.C., Higle, J.L., Smith, R.L., 1992. Capacity expansion under stochastic demands. Oper. Res. 40, 210–216. Bensoussan, A., Tapiero, C.S., 1982. Impulsive control in management: Prospects and applications. J. Optim. Theor. Appl. 37, 419–442. BP, 2018. BP energy outlook 2018. https://www.bp.com/content/dam/bp-country/z h_cn/Publications/EO18—China%20one%20pager–CN.pdf. Chen, Z.L., Li, S., Tirupati, D., 2002. A scenario-based stochastic programming approach for technology and capacity planning. Comput. Oper. Res. 29, 781–806. Chen, Y., Peng, X., Chu, Y., Li, W., Wu, Y., Ni, L., Bao, Y., Wang, K., 2017. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Appl. Energy 195, 659–670. Ding, S., Zhang, M., Song, Yan, 2019. Exploring China’s carbon emissions peak for different carbon tax scenarios. Energy Pol. 129, 1245–1252. Duan, H., Mo, J., Fan, Y., Wang, S., 2018. Achieving China’s energy and climate policy targets in 2030 under multiple uncertainties. Energy Econ. 70, 45–60. Fong, C.O., Srinivasan, V., 2011. The Multiregion dynamic capacity expansion problem: an improved heuristic. Manag. Sci. 32, 1140–1152. Freidenfelds, J., 1980. Capacity expansion when demand is a birth-death random process. Oper. Res. 28, 712–721. Gan, C., Zou, J., Wang, J., 2015. Term of local officials, enterprise resource acquisition and excess capacity. China Ind. Econ. 324, 44–56 (in Chinese). Harris, F.W., 1990. How many parts to make at once. Oper. Res. 38, 947–950. Li, S., Tirupati, D., 1994. Dynamic capacity expansion problem with multiple products: technology selection and timing of capacity additions. Oper. Res. 42, 958–976. Li, Y., He, Y., Su, Y., Shu, L., 2016. Forecasting the daily power output of a gridconnected photovoltaic system based on multivariate adaptive regression splines. Appl. Energy 180, 392–401. Lin, B., Ouyang, X., 2014. Energy demand in China: comparison of characteristics between the US and China in rapid urbanization stage. Energy Convers. Manag. 79, 128–139. Lin, B., Tan, R., 2017. Estimating energy conservation potential in China’s energy intensive industries with rebound effect. J. Clean. Prod. 156, 899–910. Lin, B., Wang, Y., 2019. Inconsistency of economic growth and electricity consumption in China: a panel VAR approach. J. Clean. Prod. 229, 144–156. Lin, J., David, F., Lu, H., Lynn, P., Zhou, N., 2018. Has coal use peaked in China: nearterm trends in China’s coal consumption. Energy Pol. 123, 208–2014. Liu, Q., Lei, Q., Xu, H., Yuan, J., 2018. China’s energy revolution strategy into 2030. Resour. Conserv. Recycl. 128, 78–89. National Development and Reform Commission (NDRC), National Energy Administration (NEA), 2016. 13th five year plan for energy development (in Chinese). http://www. nea.gov.cn/135989417_14846217874961n.pdf. (Accessed 29 August 2019).

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