Simulating the sustainable effect of green mining construction policies on coal mining industry of China

Journal of Cleaner Production 226 (2019) 392e406

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Simulating the sustainable effect of green mining construction policies on coal mining industry of China Rui Qi a, Tongyi Liu b, *, Qingxuan Jia c, Li Sun a, Jiangyi Liu a a

School of Economics and Management, China University of Geosciences, Wuhan, 430074, China School of Environment and Natural Resources, Renmin University of China, Beijing, 100872, China c School of Labor and Human Resources, Renmin University of China, Beijing, 100872, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 June 2018 Received in revised form 31 March 2019 Accepted 2 April 2019 Available online 6 April 2019

Green Mining Construction (GMC) is the Chinese national strategy of the environmental regulation of the mining industry to motivate mining enterprises to improve their resource utilization efficiency, protect the environment and harmonize the relationship between enterprises and communities. This paper uses system dynamics (SD) and the Malmquist-Luenberger (ML) Index to model the sustainable effect of the policy instruments of Green Mining Construction, namely, environmental taxes and subsidies. A case study of typical coal mining enterprises in Anhui, China, is provided to demonstrate the application of the proposed model in which the corporate performances of three optional strategies in response to the Green Mining Construction policies are examined. The responsive strategies are categorized as scale expansion, technical innovation and environmental protection. The simulation results show the following: (i) The environmental taxes and subsidies with disparate rates can help enterprises that focus on technical innovation and environmental protection perform better, albeit with a certain degree of lag. (ii) The synergy impact of subsidies and taxes is not significantly more than the separate ones. (iii) Environmental regulations reduce the productivity of mining enterprises, but to varying extents. This negative effect of environmental regulation should be compensated by other measures. This study offers insights to help enterprises select optimal strategies in response to the Green Mining Construction policies and to inform the government of possible sustainable policy designs to promote the Green Mining Construction. The limitations of the model are discussed for further improvements in simulating the effect of environmental regulation. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Green mining construction Policy effect System dynamics Malmquist-Luenberger index Coal mining

1. Introduction Many countries view the mining industry as a key engine of economic development. However, there is a risk that mining operations can cause severe social and environmental problems (UNIDO, 2014). Mining of coal, rare earth, metal, phosphate, etc., has caused serious heavy metal pollution, air pollution, subsidence, and forest degradation in China. Pollution poses serious health risks to the public (Li et al., 2014). Subsidence destroyed the landscape and farmland (Hu et al., 1997). Rapid environmental degradation has annoyed governments and communities in the mining area, so environmental regulation of mining is imminent in many countries. An ambitious mining sustainability strategy, named “Green Mining

* Corresponding author. E-mail address: [email protected] (T. Liu). https://doi.org/10.1016/j.jclepro.2019.04.028 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Construction”, has been adopted by the Chinese government. The government claimed a target of essentially realizing GMC by 2020 (MLR, 2009). By 2014, China had launched 661 green mining pilot enterprises (MLR, 2014). However, it will still be difficult to meet the 2020 target. Pigouvian taxes and subsidies are the most popular environmental regulation instruments (Wiener, 1999). Market-based environmental regulations are more effective over time than the command-and-control policies (Jaffe et al., 2002) but there might be some different preferences in taxes and subsidies (Cherry et al., 2012). The Chinese government is attempting to use a key factor in promoting GMC with these policy instruments. From the recent price-based resource tax (MOF and SAT, 2016) to the document jointly issued by six ministries to promote GMC (MLR et al., 2017), the intensity of environmental regulation has increased. The logic of these institutional designs is that although mining enterprises may response differently to regulations, the disparate rates of taxes

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and subsidies will help those complying with the regulations gain an advantage in performance, which will further incentivize the compliance of other enterprises. However, there is a dearth of research to confirm this effect. The possible effect of environmental regulations has attracted a great deal of attention. Studies have focused on two topics. The first topic is the macroeconomic effect, such as the effect of environmental tax on carbon emissions (Sundar et al., 2016) and national economic growth (Abdullah and Morley, 2014), the effect of the slackness of environmental policy on productivity (Albrizio et al., 2017), etc. The second topic is the microeconomic effect, for instance, how reasonable tax bracket changes the pollution reduction of enterprises (Kuo et al., 2016), the effect on management decisions under environmental regulations of different intensity (Leiter et al., 2011), and the ‘double dividend’ of environmental externality and technical innovation brought by the optimal combination of tax and subsidies (Hattori, 2017), while some also point out a non-statistical significance between the high investment of environmental regulations and plant-level productivity (Becker, 2011), etc. Many national mining environmental regulations have been examined, such as those of Australia (Connor, 2016), Chile (ReyesBozo et al., 2014), Canada (Gibson, 2006), India (Ghose, 2003), Ghana (Hilson and Yakovleva, 2007) and South Africa (Campbell et al., 2017). Policymakers need to provide incentives for mining enterprises to update and improve technology (Arango-Aramburo et al., 2017). It has been noted that governmental subsidies and taxes play an important role in prospecting the environmental
rez, 2016). technology in mining industries (Calvo and Pe The behavioral responses of enterprises to environmental regulations are important (Brouillat and Oltra, 2012). Enterprises have strong learning ability and can timely adjust their operation under environmental regulations (Galloway and Johnson, 2016). Hilson (2000) believed that the improvement of mining environment mainly depends on the actions taken by mining enterprises. Zhao et al. (2017) used system dynamic model to analyze Chinese corporate compliance with governmental carbon reduction labelling policy. However, environmental regulations can also have negative effects, especially on green total factor productivity from the perspective of city (Li and Wu, 2016) and industry (Zhao et al., 2018), which makes policymakers hesitate and enterprises oppose regulation. Based on the above analysis, it is necessary to explore the relationships among policy instruments, corporate behavioral responses, and corporate performance and productivity. Therefore, this study focuses on the following two issues:
Whether environmental regulations give enterprises that comply with GMC better performance than those that do not comply.
Whether environmental regulations hurt the green total factor productivity of the mining industry. This paper use system dynamics (SD) to simulate the response strategies of enterprises to the integrated GMC policy. The separate and synergy impacts of taxes and subsides on corporate performance and productivity are investigated. SD can solve the problem of ‘nonlinearity’ in policy transmission, making it reliable to test policies in a simulated environment. The SD model is widely applied to policy simulation (Matthew et al., 2017), industry analysis (Xu and Szmerekovsky, 2017) and behavior of enterprise (Liu and Ye, 2012). Some researchers have employed SD to simulate the environmental pollution (Yu and Wei, 2012) and production capacity (Wang et al., 2016) of mining industry, but their focus was not environmental regulation. This paper proposes a hybrid model

393

based on SD model and the Malmquist-Luenberger (ML) Index to answer these questions and Fig. 1 shows the overall diagram of this paper. This hybrid model has not yet been applied to environmental regulation of the mining industry, thereby providing some degree of novelty to this work. 2. The methodology of the system dynamics model 2.1. Model assumption The large-scale mining enterprises of China are usually stateowned enterprises, whose operation strategies are significantly influenced by the government. Applications of the GMC pilot by mining enterprises are out of political consideration. Managers who attempt to win the political appreciation from their superiors will actively promote GMC, and others take a wait-and-see attitude because they worry that GMC will affect short-term economic performance. Therefore, if environmental regulation policies can enable GMC enterprises to achieve simultaneous advantages in economic, environmental and social performance, more enterprises will join GMC. Our model aims to examine the sustainable effect of GMC policy by simulating whether typical mining enterprises comply with GMC policy. The mining industry is a very complex system and operational decisions of enterprise include many concepts (O'Regan and Moles, 2006). This model focuses on the reinvestment strategy of revenue to examine whether or not should the enterprises comply with GMC. A corporate reinvestment decision is quite profit-motivated, and other noneconomic factors that can affect it are not considered in this study. According to ‘Opinions on accelerating the construction of green mines implementation’ which is issued by Chinese Ministry of Land and Resources, this new regulation can encourage mining enterprises to make progress mainly in the following three directions: improving resource utilization efficiency, protecting the environment, and promoting enterprisecommunity harmony. Based on these policy targets, this paper assumes that mining enterprises have two complying responsive strategies: reinvesting in technology innovation and reinvesting in pollution control, and one non-complying responsive strategy: reinvesting in scale expansion. Reinvesting in scale expansion can lead to greater production, but it does not comply with GMC, so it will face higher environmental taxes and fewer environmental subsidies. Reinvesting in technological innovation and environmental protection are in line with GMC, so enterprises will benefit from taxes and subsides. However, the focus of this round of GMC policy (MLR et al., 2017) is on environmental administration, so reinvesting in environmental protection has higher subsidies rate and lower tax rates. Enterprise-community harmony is also required in GMC policies, but it does not directly influence enterprise strategies, so this model introduces the social subsystem to examine the impact of different strategies on it. Steps to establish SD model for policy simulation (Zhao et al., 2017) include: establishing casual loops to describe the logical structure of the system, building equations to generate quantitative relationships, simulating and examining the model, and investigating the possible impact of a change in the control variables on the model output. 2.2. System structure 2.2.1. Causal loop diagram The causality diagram reflects the basic feedback relationship of the main agents in the model. The positive and negative feedback relationships in the model are an important basis for the construction of the flowchart. The causality diagram in the economic

394

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Fig. 1. Overall diagram of the research.

(2) Environmental pollution status / Impact factor of ecological income / Coal income /After-tax income of the enterprise / Enterprise reinvestment / Pollution control investment / Emissions reduction of industrial waste1/ Environmental pollution status (Negative)

2.2.2. Stock-and-flow diagram Based on the causality diagram mentioned above, the corresponding flow diagram is specified by the variables refining and the function establishing. The flow diagram and the functional interpretation of the subsystems are given after that (Fig. 3).

Fig. 2. Causal loop of the comprehensive system.

and social subsystem (Fig. 2) mainly shows the five feedback loops focusing on after-tax income and environmental pollution. The comprehensive SD model uses three feedback loops to illustrate the self-enhancing mechanism of after-tax income. (1) After-tax income of the enterprise / Enterprise reinvestment / Research and development investment of the enterprise / Technological level / Coal income / After-tax income of the enterprise (Positive) (2) After-tax income of the enterprise / Enterprise reinvestment / Exploitation and development of resources investment / Coal production / Coal income / After-tax income of the enterprise (Positive) (3) After-tax income of the enterprise / Enterprise reinvestment / Pollution control investment / Environmental pollution status / Impact factor of ecological income / Coal income / After-tax income of the enterprise (Positive) The model uses two feedback loops to illustrate the formation mechanism of environmental pollution. (1) Environmental pollution status / Impact factor of ecological income / Coal income /After-tax income of the enterprise / Enterprise reinvestment / Exploitation and development of resources investment / Coal production / Total discharge of industrial waste / Environmental pollution status (Positive)

2.2.3. The subsystem models The economic subsystem of the mining area, shown in Fig. 4, is the core part of the overall system of the mining area. This subsystem mainly explores the economic development model of mining enterprises by analyzing the main components of after-tax income and the operation behavior of the enterprise. The after-tax income of enterprises affects the per capita disposable income in the mining area, and the pollution has a negative effect on the wellbeing of residents. The government influences the specific decisions of enterprises through relevant environmental economic policies. The economic subsystem of the mining area interacts with the other two subsystems, affecting the sustainability of the total system. The social subsystem (Fig. 5) simulates the useful and harmful impacts of the mining industry on the community. Most large-scale mining enterprises in China are state-owned, which means that they have social responsibilities. In recent decades, conflicts between mining enterprises and communities have rapidly increased, primarily due to environmental pollution and subsidence (Ho and Yang, 2018). Promoting harmony between enterprises and communities has become an important political issue. In research funded by the Chinese Ministry of Land and Resources in 2011, the author introduced a composite indicator to evaluate the potential harmony of the relationship between enterprises and communities (Qi et al., 2014). The basic idea of the indicator is that useful impacts

1 This means the sum of the emissions reduction of noxious waste pollution, the emissions reduction of wastewater pollution and the emissions reduction of exhaust fumes. The stock-and-flow diagram does not appear in this variable because of the adjustment of the emissions reduction rates.

R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

Fig. 3. Stock-and-flow diagram of the mining area.

Fig. 4. The economic subsystem.

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Fig. 5. The social subsystem.

such as improvement in employment and income will promote harmony, while harmful impacts such as subsidence and pollution will decrease harmony (Qi et al., 2014). In this model, the harmonious composite indicator was introduced as a critical criterion to evaluate the corporate social performance. The analytic hierarchy process was employed to weight and aggregate the composite indicator. The environmental subsystem (Fig. 6) reflects the potential externalities of the mining economic subsystem. Mining causes serious environmental pollution, while corporate environmental investment and governmental environmental subsidies will reduce pollution, at least to a certain extent. In this subsystem, the key indicator is the final polluting level, which will further affect the final income of the enterprises and harmony between enterprises and community, making the subsystem interaction more significant and closer to reality. 2.3. Data sources The application of the established SD model was demonstrated

by a case study in Huainan Mining Area, Anhui Province. Huainan Coalfield is the best and largest coalfield in the south of the Yellow River in China, especially in Southeast China. It has 44.4  109 t of long-term reserves and 15.3  109 t of proven reserves, accounting for approximately 71% of Anhui Province and 32% of East China. The research mainly relies on Huainan Mining (Group) Co., Ltd., one of the top 500 enterprises in the Chinese Enterprise Group (ranked 304th in 2016) and the 17 key enterprises in Anhui Province, along with the national 14 M t level coal production bases and six large coal-power bases. The data was collected from the group report Huainan Mining Industry (Group) Co, Ltd, National Green Mine Construction Plan (2013), 2013, HNMIC, The Audit Report of Clean Production (2015), 2015a, HNMIC, Annual Pollutant Emission Reduction Plan and Implementation Measures (2015), 2015b. The field work was conducted in Yue Zhangji Mine Area, one of the 11 most profitable mines in Huainan Mining Group, located at Yue Zhangji town (Fig. 7). As the first 10 million-ton modernization mine in Anhui Province, Yue Zhangji Mine has good green mining construction and has made remarkable achievements in energy conservation,

Fig. 6. The environmental subsystem.

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397

Fig. 7. Location of the study area.

emissions reduction and environmental protection. It is the ideal case to study corporate compliance with GMC policies.

test and the extremum test. The software Vensim PLE 5.1 is used to simulate the model, and the processes are followed Karnopp et al. (2012).

2.4. Model construction The model includes 10 state variables, including the after-tax profits of enterprises, the total amount of solid waste discharge and the statistics of subsiding land, and 16 rate variables, including annual after-tax income, the amount of solid waste generated and the number of technicians added, along with numerous auxiliary variables and constants. The parameters in the model were determined through the economic data of enterprise and the relevant literature (Wang et al., 2018). Parameters were determined by the uniformly distributed random function, analytic hierarchy process, statistics testing method etc. Details can be found in Appendix 1. 2.5. Model validation It is imperative to test and validate the model before simulating the policy impact. The main methods are the main historical data

2.5.1. Main historical data testing The historical data test is the method to detect the feasibility of the model. This paper tests the annual after-tax income of enterprise and coal revenue from the economic subsystem, the total amount of ‘three wastes’ from the environmental subsystem, and the four main variables of the land subsidence statistics from the social subsystem in 2011e2014. The results are shown in Table 1. All the errors are within 5%, indicating that the system model is robust to simulate reality. 2.5.2. Extremum test The extremum test is the method to detect the accuracy of the model. This paper conducts extremum test followed the guide in Karnopp et al. (2012). Specifically, the emission reduction coefficients of solid waste, liquid waste and exhaust gas are set to zero, which means the environmental investment of enterprises is

Table 1 Historical test of the major historical variables. Year After-tax income of enterprise (100 M CNY)

Coal income (100 M CNY)

Total discharge of industrial waste (104 t)

Overall amount of collapse land (km2)

Simulation value Actual value Error (%) Simulation value Actual value Error (%) Simulation value Actual value Error (%) Simulation value Actual value Error (%)

2011

2012

2013

2014

41.423 43.070 3.82 49.102 51.113 3.93 295.65 283.37 4.33 12.59 12.23 2.94

42.441 43.615 2.69 50.309 50.597 0.57 290.67 279.77 3.9 13.97 13.86 0.79

42.378 44.421 4.60 50.235 52.322 3.99 321.95 337.22 4.53 15.14 14.65 3.34

42.678 42.441 0.56 50.590 50.247 0.68 309.03 298.84 3.41 16.21 15.78 2.72

Note: The historical data is collected from the HNMIC, 2015. Annual Performance Report of Yue Zhangji Mine (2012-2015), 2015c.

R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

55

9000

50

6750 104 t

100 M CNY

398

45

2250

40 35 2011

4500

2015

2019

2023

Actual pattern

2027

2031

0 2011

2035

Extremum test

2015

2019

Actual pattern

a Test result of after-tax income

2023

2027

2031

2035

Extremum test

b Test result of environmental pollution status

0.7

Percentage

0.65 0.6 0.55 0.5 0.45

0.4 2011

2015

2019

Actual pattern

2023

2027

2031

2035

Extremum test

c Test result of the harmony composite indicator Fig. 8. Extremum test of the main variables.

not effective. Then the changes of the after-tax income of the enterprise, the environmental pollution status, the harmony composite indicator are examined. Theoretically, the income and harmony indicator will decrease and the pollution will increase. The simulation results of these three variables are shown in Fig. 8. The after-tax income of enterprises (8-a) and the harmony composite indicator (8-c) in the economic subsystem are lower than the real value. The pollution level raises rapidly (8-b) compared to the actual mode of pollution. The extremum test is modelling an ideal (or impossible) scenario. If there is no environmental protect, the emission of pollutants could increase with the scale of the enterprises. In the extremum test, the enterprises whose scale showed an increase trend before they applied the GMC were modelled. Therefore, it is reasonable that the slope in Fig. 8-b is nearly a constant value. The harmonious composite indicator was calculated based on various impact factors of per-capita disposable income, overall amount of collapse land, social security and employment expenditure, and environmental pollution status. Impact factors are calculated by Table Functions. When environmental protection fails and pollution increases, the harmony composite indicator would decrease dramatically during a considerable period. However, this index would not reach zero, since other factors would also play a role for the changes of harmony composite indicator. Throughout the extremum test in Fig. 8, the values of the variables of all the subsystems are in line with our hypothesis, so the model is robust. 3. Simulation results of the system dynamics 3.1. Classification of different responsive strategies of enterprises In a free market economy, enterprises can respond to environmental regulation independently. They can choose freely to comply (or not) with the GMC policies. Enterprise operational strategy is a

complex issue. This model focuses on reinvestment strategies. To simplify the model, reinvestments are classified into three kinds: scale expansion, technical improvement and environmental improvement. According to the different rates on these three kinds, the enterprises have three optional strategies: Scale-Oriented Strategy, Technical-Oriented Strategy, and EnvironmentalOriented Strategy. What should be noted here is the strict financial management institution of Chinese state-owned coal mining group and enterprises. Typically, a group has several mining enterprises and one sales company. Mining enterprises are only in charge of production, so they have no direct income. They can apply funds from the group. The fund includes two parts, the regular part, for covering the regular running cost, and the additional part, for covering the additional cost. The additional fund is no more than 8% of the total fund, according to the financial institution of Huainan Group, however, the managers of mining enterprises have more discretion on how to use it. So this model focuses more on the allocation of this fund. (1) Scale Oriented Strategy (SOS). Most reinvestment of this type of strategy goes into scale expansion, such as purchasing more machines and employing more workers. Accordingly, research and development (R&D) and technological innovation investment and environmental protection investment are both low. This kind of strategy will bear a higher environmental tax burden and enjoy lower government subsidies. It's promoted by government to transform into the latter two styles. (2) Technical Oriented Strategy (TOS). This strategy complies with the requirement of improving resource utilization efficiency in GMC. The investments focus on technological innovation and production efficiency improvement. By investing in technical promotion, it also enhances the

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399

Table 2 Setting of mining strategies of enterprises and environmental policy. Strategy type

Reinvestment rate (on)

SOS TOS EOS

Environmental policy

Scale

Technical

Environmental

Tax rate

Subsidy rate

0.6 þ 0.08 0.6 0.6

0.16 0.16 þ 0.06 0.16 þ 0.02

0.16 0.16 þ 0.02 0.16 þ 0.06

0.06 0.04 0.02

0.02 0.04 0.06

internal potential of overall competitiveness. However, there is still deficiency in environmental protection investment. (3) Environmental Oriented Strategy (EOS). This strategy complies with the requirement of environmental protection in GMC. The investment focuses on environmental protection, obtaining a lower tax burden and higher environmental protection subsidies by reducing pollution emissions, further achieving the unity of economic benefits and environmental benefits.

and ‘the harmony composite indicator’ in the social subsystemdare chosen to analyze and compare the development of enterprises and mining areas under different environmental policies. (1) After-tax income of the enterprise. This critical value can intuitively reflect different environmental policies effect on corporate economic performance. Shown in Fig. 9-a, the after-tax income of SOS under the tax policy has risen faster than the other two types at first, peaking at 2017, after which time after-tax income of TOS and EOS will exceed that of SOS. Fig. 9-b shows that TOS and EOS will attain the same advantage under the subsidies policy in approximately 2018, indicating that lagging of the policy impact and tax is somewhat more effective than subsidies. Before 2027, the after-tax income of TOS is higher than EOS, which reflects opportunity costs during the transformation process. After 2027, EOS will rank first, which reflects a stronger lagging policy impact on EOS. (2) Environmental pollution status. The degree of environmental pollution is an important indicator to evaluate the GMC policy. In Fig. 10-a and 10-b, both environmental pollution status of SOS and TOS shows an initial trend toward rapid increase, gradually slows in the middle and decreases at the end. The ultimate pollution of TOS is slightly lower than SOS, both of which are under two policies, reflecting the effect of reducing investment in expansion. The growth rate of environmental pollution status of EOS will gradually decline in approximately 2021, indicating a more significant impact of environmental policy. Pollution status is lower in the later stage under the subsidies policy, which indicates that the degree of pollution is more sensitive to subsidy tools. The impact of tax policy is not significant enough due to multivariate transmission. (3) Harmonious composite indicator. In Fig. 11, the harmonious indicators of the three different strategies all experience a process of falling, smoothing and ultimately slowly rising. The decline is mainly due to the mitigation of after-tax

Table 2 shows the setting of specific enterprise decision variables and policy variables. The basic reinvestment rate of Yue Zhangji Mine at 2011e2014 on scale expansion, technical improvement, and environmental improvement is about 0.6, 0.16, 0.16 separately. The model hypothesizes that the enterprises who take the SOS strategy will invest the 8% fund into scale expansion. Or they will put it into technical and environmental investment. GMC encourages the enterprises to promote efficiency and environment simultaneously, so the 8% fund is divided into 6% and 2%. Due to the insufficiency of Chinese government information publicity, the accurate disparate rates of environmental tax and subsidies cannot be obtained. The rate of SOS is estimated by the accountants of Huainan Group, based on financial data of group for the first three quarters of 2016. Other rates are estimated based on relevant news reports on GMC. The evidence of these estimations can be found in Appendix 2. Experts from Huainan Group were consulted about the rationality of these data. 3.2. Results of the SD model

60

60

55

55

50

50

100 M CNY

100 M CNY

3.2.1. Results under the single policy The total operating cycle is set to be 25 years with consideration of the long-life cycle of mining enterprises. The starting year is 2015, coincident with the real implementation time-point of the Pigouvian tax on mining in China. Three observational variablesd ‘the after-tax income of enterprise’ in the economic subsystem, ‘environmental pollution status’ in the environmental subsystem,

45 40

45 40 35

35 30 2011

2015 SOS

2019

2023 TOS

2027

2031

EOS

a After-tax income under the tax policy

2035

30 2011

2015 SOS

2019

2023 TOS

2027

2031

2035

EOS

b After-tax income under the subsidy policy

Fig. 9. After-tax income of enterprise under the single policy.

R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

6000

6000

4500

4500 104 t

104 t

400

3000 1500

3000 1500

0 2011

2015

2019

SOS

2023 TOS

2027

2031

0 2011

2035

EOS

2015

2019

SOS

a Environmental pollution status under tax

2023 TOS

2027

2031

2035

EOS

b Environmental pollution status under subsidy

0.7

0.7

0.65

0.65 Percentage

Percentage

Fig. 10. Environmental pollution under the single policy.

0.6 0.55

0.6 0.55

0.5 2011

2015 SOS

2019

2023 TOS

2027

2031

2035

EOS

a Harmonious indicator under tax

0.5 2011

2015 SOS

2019

2023 TOS

2027

2031

2035

EOS

b Harmonious indicator under subsidy

Fig. 11. Harmonious composite indicator under the single policy.

income and the increase in environmental pollution after all the residents in the mining area benefit directly and indirectly from the enterprises. The harmony indicator curves of EOS under tax or subsidy policies both fluctuate in approximately 2023. Additionally, the curve of EOS under subsidy will pick up in approximately 2027, while the effect of tax has a certain lag, indicating that subsidy policy has a better impact on the harmony indicator. The overall harmony levels of EOS under tax and subsidies are significantly higher than those of SOS and TOS, illustrating that the mining enterprises with an increment of environmental investment can achieve a better balance between economic benefits and social benefits.

3.2.2. Results under policy combination In the real world, the combination of taxes and subsidies is always employed by the government, because the policy-makers always believe the synergy impact will be more significant. Therefore, this paper stimulates the impact of policy combination. The simulation results under policy combination in Fig. 12 show similar trends as the results under the single policy. 3.3. Discussion This paper used SD to model the effect of environmental taxes, subsidies and policy combination with disparate rates on mining enterprises performance that comply (or not) with GMC policies and applied it with a case study in typical coal mining enterprises in Huainan. Simulation results confirmed that the enterprises who comply with the policy will benefit from GMC after a certain degree

of lag. These results implied that GMC policies have a sustainable effect on mining enterprises. Even if the original motive for enterprises to apply the GMC pilot is to gain a political reputation, these economic policies will make this choice profitable, that is, in line with market rules. However, it cannot be inferred that these policies can guarantee China will achieve its 2020 goal of GMC. First, the disclosure of operation information about Chinese state-owned enterprises is not standardized and transparent. The comparative advantages of those pilot enterprises are difficult for other enterprises to understand, which will decrease the incentive effect of the policies. More detailed and accurate information on GMC mining corporate economic, environmental and social performance should be provided to the entire mining industry. Second, these policy effects have a lag period of approximately 5 years in our model, which means in the first 5 years of the implementation, enterprise managers who decide to comply with GMC will have a disadvantage in the evaluation system. The managers of Chinese state-owned enterprises are administrative cadres, who are managed by the Party through nomenklatura, evaluation, promotion and rotation institutions. The comparative disadvantage in corporate income at the beginning of the transformation may lower their evaluation results in a GDP-oriented evaluation system and reduce their promotion opportunities. This special management system may hinder the managers choosing compliance strategies. The synergy impact is not significantly better than taxes or subsidies alone, scilicet, the policy combination has half the result with twice the effort. Authorities prefer to use the combination of taxes and subsidies to promote the policy implementation, and they do so now to promote the GMC. The policy implementation of China emphasizes more efficiency. However, based on the simulation results, this paper does not recommend the simultaneous use

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Table 3 New classified mining investment strategies of enterprises and corresponding policies.

60

100 M CNY

55

Types

50 45

SOS TOS

40 35 30 2011

2015

2019

SOS

2023 TOS

2027

2031

2035

COS

EOS

a After-tax income under policy combination EOS

6000

104 t

4500

Code

SOS TOS1 TOS2 TOS3 TOS4 TOS5 TOS6 COS1 COS2 COS3 COS4 COS5 COS6 EOS1 EOS2 EOS3 EOS4 EOS5 EOS6

Investment Rate (on) Expansion

R&D

Environment

0.680 0.600

0.160 0.245 0.240 0.235 0.230 0.225 0.220 0.215 0.210 0.205 0.195 0.190 0.185 0.180 0.175 0.170 0.165 0.160 0.155

0.160 0.155 0.160 0.165 0.170 0.175 0.180 0.185 0.190 0.195 0.205 0.210 0.215 0.220 0.225 0.230 0.235 0.240 0.245

Tax Rate

Subsidy Rate

0.060 0.050

0.020 0.026

0.044

0.032

0.038

0.038

0.032

0.044

0.026

0.050

0.020

0.060

3000 1500 0 2011

2015

2019

SOS

2023 TOS

2027

2031

2035

EOS

b Environmental pollution status under policy combination 0.7 0.65 Percentage

401

0.6 0.55 0.5 2011

2015

2019

SOS

2023 TOS

2027

2031

2035

EOS

c Harmonious indicator under policy combination Fig. 12. Simulation results under policy combination.

of taxes and subsidies. Some research reaches a similar conclusion in the field of technological innovation (Lin et al., 2013), whereas others reach a contrary conclusion on emissions reduction and R&D investment (Li and Yang, 2015). The underlying reasons for this phenomenon are beyond the scope of this paper. 4. Productivity estimation 4.1. Malmquist-Luenberger index and industrial productivity Do environmental regulations decrease the productivity of

mining industry? This is the topic of greatest concern to both supporters and opponents of environmental regulations. Some research argues the Porter Hypothesis, that is, that environmental regulations can induce efficiency and encourage innovation that helps improve productivity (Ambec and Barla, 2006). The Porter Hypothesis has been examined by many researchers, mainly including the impact of different environmental regulations
rate-Marco and Valle
s-Gime
nez, 2015), regional differences (Za (Zhao and Sun, 2016) and industry types (Wang and Shen, 2016). In this paper, the Malmquist-Luenberger (ML) Index is introduced to study the impact of environmental regulation on corporate productivity. Environmental taxes and subsidies use disparate rates to incentivize the pro-environmental behavior of enterprises. The ML index based on the result of the SD model will explore different discriminable functions of the policy combination at different rates. The impacts of policy combination are simulated here because this policy is now employed in GMC in the real world. A new type of enterprise, the Comprehensive Oriented Strategy (COS), is taken into account. COS represents the corporate emphasis on both R&D and environmental investment, with a more balanced ratio floating between 1% and 3% on each investment. The other three enterprises invest in their preferential field with a rate floating between 4% and 9%. Table 3 shows the details of these four new classifications. The ML index considers both the increase in ‘good’ output and the decrease in ‘bad’ output, such as environmental pollution (Chung et al., 1997). In this model, there are x decision units (mining enterprises in our research) use N inputs, x ¼ ðx1 ; x2 /xN Þ2RN þ , to product M good outputs y ¼ ðy1 ; y2 /yM Þ2RM þ , and I bad outputs, b ¼ ðb1 ;b2 /bI Þ2RIþ , so it has: PðxÞ ¼ fðy;bÞ : x can produce ðy;bÞg. The simulation result of SD above can be separated into the input and output indicators needed in ML index calculation according to different investment rates. The input indicators include investment in the exploration and development of resources, R&D investment and protection, and regulatory input. The output indicators include after-tax income of enterprise (desired output) and environmental pollution (undesired output). Therefore, the output-oriented ML index containing undesired output is as follows:

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R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

h 912 i i !t  !tþ1  = 1 þ D 0 xt ; yt ; bt ; bt 1 þ D 0 xt ; yt ; bt ; yt ; bt *h i i t   tþ1  ; ! !  1 þ D 0 xtþ1 ; ytþ1 ; btþ1 ; ytþ1 ; btþ1 1 þ D 0 xtþ1 ; ytþ1 ; btþ1 ; ytþ1 ; btþ1 h

Here, t is the production period.

!t t t t D 0 ðx ; y ; b ; bt Þ and

!tþ1 tþ1 tþ1 tþ1 D 0 ðx ; y ; b ; ytþ1 ; btþ1 Þ are the directional distance !t functions in the current period. D 0 ðxtþ1 ; ytþ1 ; btþ1 ; ytþ1 ; btþ1 Þ tþ1 ! and D 0 ðxt ; yt ; bt ; yt ; bt Þ are the mixed distance functions. The corporate productivity can be observed by the floating relationship between the ML index and 1.

4.2. Results of the ML index The most important function of GMC is to encourage mining enterprises to focus on technical innovation or environmental protection strategies rather than scale expansion. Accordingly, this paper will compare the ML index of SOS to the other three types (TOS, COS and EOS) separately to examine the effect of the policy. Fig. 13 compares results of the SOS and the TOS (1e6). First, the implementation of the GMC will decrease the productivity of these two types of enterprises. In our simulation cycle, the efficiency of the two types of enterprises is usually less than 1. The phenomenon of super efficiency is very short. The average of SOS productivity during the simulation period was 0.928. The average value of the 6 TOSs was 0.929. Second, since the ML index measures green total factor productivity, the curves of the two categories of mining enterprises have high similarity, which indicates that although TOS has improved the technical efficiency of the industry to a certain extent, it has not improved the green total factor productivity of the mining industry. The

(4.1)

environment still limits the sustainable development of the mining industry. Fig. 14 compares the results of the SOS and the EOS (1e6). The implementation of the GMC also has negative impacts on the green total factor productivity of environmental mining enterprises, but those impacts are smaller than the impact on SOS. The average productivity of EOSs during the simulation period was 0.946, significantly higher than 0.928 of SMEs. Some EOSs (EOS1 and EOS2) have a higher super efficiency by 2024, others are higher by 2031. That means the more an enterprise reinvests in environmental protection, the later they obtain an advantage in green total factor productivity. Considering the short-sighted evaluation and promotion system mentioned above, this situation will further hinder managers from choosing environmental strategies. Fig. 15 compares the results of SOS and COS (1e6). CMEs

1.15

1.05 ML index

MLtþ1 t

8 < ¼ h :

0.95

0.85

0.75 2016

2019 SOS

EOS1

2022 EOS2

2025 EOS3

2028

2031

2034

EOS4

EOS5

EOS6

1.15

Fig. 14. Comparison of the ML index between SOS and EOS.

ML Index

1.05

1.15

0.95

1.05

0.75 2016

2019 SOS

TOS1

2022 TOS2

2025 TOS3

2028 TOS4

2031

2034

TOS5

TOS6

ML index

0.85

0.95

0.85

Fig. 13. Comparison of the ML index between SOS and TOS2. 0.75 2016 SOS

2019 COS1

2022 COS2

2025 COS3

2028 COS4

2031 COS5

2

The years in the figure mean the growth rate between the current year and the previous year.

Fig. 15. Comparison of the ML index between SOS and COS.

2034 COS6

R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

represent a more balanced strategy between TOSs and EOSs. Unsurprisingly, their results are also more balanced. COSs have an average of 0.937 for green total factor productivity, which also means a negative impact of GMC on these enterprises. COS6 is in a dominant position throughout almost the entire simulation cycle, except in 2020. 4.3. Discussion The results of the ML index based on SD simulation show that environmental regulation does have a negative impact on the green total factor productivity of all mining enterprises. From that perspective, this model negates the Porter hypothesis. However, the degrees of impact on enterprises with different strategies are not the same. The impacts on the total factor productivity of TOS and SOS are very similar, but the impacts on EOS and COS are small. Since this model uses a great deal of actual coal enterprise operation data, it can be inferred that if industrial productivity is measured by green total factor productivity, environmental pollution is the primary problem that restricts the sustainability of typical coal enterprises in China. For enterprises, if sustainability or green total factor productivity maximization is their final goal, the optimal strategy is not to maximize reinvestment in environmental protection but to balance reinvestment in environmental protection and technological innovation. In particular, considering the assessment of and promotion pressure on state-owned enterprise managers, such a strategy can balance the short-term performance and long-term development of enterprises. It is not a new conclusion that environmental regulation has a negative impact on industry productivity (Zhao et al., 2018). This paper uses this conclusion to call for more supportive policies, not to oppose environmental regulation. First, environmental regulations are not just taxes and subsidies but also other auxiliary policies. To simplify the analysis, our model does not consider the impact of these policies on the system; instead, they will also reduce the negative impact of environmental regulation on productivity. Second, considering the problem of low management efficiency prevalent in Chinese state-owned enterprises, this paper emphasizes that while carrying out environmental regulation, more scientific and transparent management reformation should be introduced to Chinese state-owned enterprises, especially in the evaluation, promotion and rotation system. Improvements in management are critical to the sustainability of the mining industry. Since the primary goal of this paper is to measure green total factor productivity rather than to explore the source decomposition of green total factor productivity, this paper does not decompose the ML index into the technical efficiency change index or the technological progress index. The mine system is complex and uncertain (Dong et al., 2018), which also leads to uncertainty in our model. The uncertainties include indicators selection and data collection. Based on the principle of accessibility, the after-tax income, pollution status and harmony composite indicators were chosen to evaluate corporate economic, environmental and social performance. In the future, more appropriate indicators will be introduced into the model, such as Prime operating revenue increasing rate (PORIR), PM2.5 index and social conflict rate. The credibility of enterprise data is lower than that of government statistics department, so it will be a good solution to improve the scope of the model application to the city level.

403

5. Conclusions This paper proposed an SD approach to simulate mining responsive strategies of enterprises and their performances in the context of combined government environmental regulation (that is, taxes and subsidies) to promote the implementation of Green Mining Construction. Three optional strategies are proposed that mining enterprises can select to comply (or not) with GMC. Application of the model is examined in the case study of the typical coal mining enterprises of China. The simulation results show that environmental taxes and subsidies with disparate rates will help enterprises that choose to comply with GMC to have an advantage in economic, social and environmental performance over enterprises that do not comply. These results imply that GMC has a sustainable effect, albeit with a certain degree of lag. Considering the defective evaluation and management system in Chinese state-owned enterprises, these lags may hinder the implementation of GMC. A further calculation of the MLI based on the SD models shows that the environmental regulation will decrease corporate productivity, no matter what strategies they choose. This result is inconsistent with the Porter Hypothesis. More auxiliary policies should be introduced into GMC, especially scientific and transparent management in state-owned enterprises. There is still much room for improvement in this study. First, the SD model in this paper does not include the political promotion structure of mining managers of enterprises. The managers of Chinese state-owned enterprises have strong political promotion incentives. That means that factors such as political attitudes and social responsibility play an important role in the promotion of state-owned enterprise managers, which will also contribute significantly to the sustainable impact of environmental policies. More of these factors should be considered in future studies. Second, the data employed in this paper are collected from a resource-abundant area. A more comprehensive and effective environmental regulation plan should include evidence from resource-exhausted regions. Third, due to the inherent limitation of variable selection in the simulation of the SD model, the simplification of some factors will reduce the relative accuracy of the model and should be improved in the future. Fourth, due to the insufficiency of Chinese government and state-owned enterprises information publicity, some policy variables, such as the desperate rates of taxes and subsidies are estimated from different informal resources, which decrease the reliability of the model and impede a deep research into the mechanism and threshold effect environmental regulation. Acknowledgements This research was supported by the National Social Science Fund Youth Project (Grant No. 14CKS014), the National Natural Science Foundation of China (Grant No. 718041067), the Humanities and Social Science Fund of Ministry of Education of China (Grant No. 17YJC630028), the Open fund of Mineral Resource Strategy and Policy Research Center of China University of Geosciences (Grant No. H2014008A) and the Fundamental Research Funds for the Central Universities (Grant No. CUG170624). Appendix 1 Residual equations and parameters of level and rate variables in the model are shown as below:

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R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

Model subsystem

Major variables

Type

Basic equation

Units

Parameter sources

Parameter calculation method

Economic subsystem

Technological level

Level

INTEG (Technology value addedTechnology elimination, 0)

/

Cumulative numerical summation

Technology value added

Rate

/

Technology elimination

Rate

IF THEN ELSE (Time  2015, Research and development total investment, Research and development total investment*0.723) Technological level*Technology elimination rate

National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise

Multiplication

After-tax income of enterprise

Rate

Coal income - Tax revenue expenditure of enterprise

CNY

Coal income

Auxiliary

CNY

Coal price

Auxiliary

Resource tax rate

Auxiliary

10000 * Average price of the coal * Coal production * (1- Impact factor of ecological income) 320 * RANDOM UNIFORM (1.26, 1.32, 2) * Impact factor of technological level IF THEN ELSE (Time  2015, {ontrue}, 0.06)

Amount of noxious waste pollution

Level

Amount of wastewater pollution

Level

Amount of exhaust fumes

Level

Environmental subsidy

Level

Emissions of noxious waste pollution

National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise Notice on implementation of coal resource tax reform from the Ministry of Finance Report on Clear Production, Annual Pollutant Emission of enterprise Report on Clear Production, Annual Pollutant Emission of enterprise Report on Clear Production, Annual Pollutant Emission of enterprise Work plan for green mining construction in Anhui province (2017e2025 years) Report on Clear Production, Annual Pollutant Emission of enterprise Summary of pollutant reduction indicators

Report on Clear Production, Annual Pollutant Emission of enterprise Summary of pollutant reduction indicators

Statistics testing method

Report on Clear Production, Annual Pollutant Emission of enterprise Summary of pollutant reduction indicators

Statistics testing method

104 t

Pollutant summation

Summation

/

National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise Summary of fiscal revenue and expand indicators National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise

Selection function

Environmental subsystem

Social subsystem

/

CNY

/

INTEG (Emissions of noxious waste pollution - Emissions reduction of noxious waste pollution, 103.6) INTEG (Emissions of wastewater pollution - Emissions reduction of wastewater pollution, 27.6) INTEG (Emissions of exhaust fumes - Emissions reduction of exhaust fumes, 0.82) INTEG (Environmental subsidy value added, 0)

104 t

Rate

Total discharge of industrial waste*0.7856

104 t

Emissions reduction of noxious waste pollution

Rate

104 t

Emissions of wastewater pollution

Rate

(Environmental subsidy þ Pollution control investment) *Emissions reduction rate of noxious waste pollution Total discharge of industrial waste*0.2086

Emissions reduction of wastewater pollution

Rate

104 t

Emissions of exhaust fumes

Rate

(Environmental subsidy þ Pollution control investment) *Emissions reduction rate of wastewater pollution Total discharge of industrial waste*0.0058

Emissions reduction of exhaust fumes

Rate

104 t

Environmental pollution status

Auxiliary

Environmental subsidy rate

Auxiliary

(Pollution control investment þ Environmental subsidy) *Emissions reduction rate of exhaust fumes Amount of noxious waste pollution þ Amount of wastewater pollution þ Amount of exhaust fumes IF THEN ELSE (Time  2015, {ontrue }, 0.02)

Overall amount of collapse land

Level

INTEG (Amount of collapse land Clean-up area of collapse land, 4.83)

km2

Fiscal surpluses

Level

CNY

Social security and employment expenditure Harmonious composite indicator

Level

INTEG (Fiscal revenue-Fiscal expend, 0) INTEG (Social security and employment expenditure value added, 0) Impact factor of per-capita disposable income * 0.3 þ Impact factor of overall amount of collapse

Auxiliary

104 t

104 t

CNY

104 t

104 t

CNY

/

Selection function

Subtraction

Multiplication

Uniform distribution random function Selection function

Cumulative numerical summation Cumulative numerical summation Cumulative numerical summation Cumulative numerical summation Statistics testing method Enterprise data conversion

Enterprise data conversion

Enterprise data conversion

Cumulative numerical summation Cumulative numerical summation Cumulative numerical summation Analytic hierarchy process

R. Qi et al. / Journal of Cleaner Production 226 (2019) 392e406

405

(continued ) Model subsystem

Major variables

Type

Basic equation land * 0.23 þ Impact factor of Social security and employment expenditure * 0.2 þ Impact factor of environmental pollution status * 0.27 Tax revenue expenditure of enterprise (Resource tax þ Other duties or charges) Innovation subsidy þ Social security and employment expenditure þ Environmental subsidy Coal production*Collapse land rate

Units

Parameter sources

Parameter calculation method

CNY

Summary of fiscal revenue indicators

Summation

CNY

Summary of fiscal expend indicators

Summation

km2

National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise National Green Mine Construction Plan data of enterprise

Enterprise data conversion

Fiscal revenue

Rate

Fiscal expend

Rate

Amount of collapse land

Rate

Clean-up area of collapse land

Rate

Overall amount of collapse land*Land reclamation rate

km2

Social security and employment expenditure value added

Rate

Social security and employment expenditure * Coefficient of expenditure

CNY

Enterprise data conversion Enterprise data conversion

Appendix 2. The estimation process of Table 2

Table A2 Setting of mining strategies of entreprises and environmental policy

There are different ways to determine the rates. For rates in blue box are estimated by accountants of Huainan group based on their long-term work experience. Rate within red box is also testified by the calculated tax rate according to the report (http://www.echina.org/reports-zh/ reports-20140710-zh). Rate in green box is calculated according to report (https://news. smm.cn/news/100735741). Simply, enterprises complying with the GMC can receive a 15% tax relief. Total taxes of mining enterprises should be 30% according to report (http://politics.people.com.cn/n/ 2015/0430/c1001-26928403.html). Therefore, enterprises complying with the GMC total can receive a 4.5% (¼30%*15%) tax relief compared with those non-complying enterprises. It means that EOS for enterprise complying with the GMC should be 1.5% (¼0.06e0.045). In this paper, this tax rate is set as 2%, which is very close to 1.5%. The TOS tax (black box) is calculated as an equidistant tax rate between SOS and EOS.

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