The Integrated Effect of Carbon Emissions Trading and Pollution

Home , Carbon emission trading, Emissions trading

Article The Integrated Eﬀect of Carbon Emissions Trading and Pollution Rights Trading for Power Enterprises—A Case Study of Chongqing

Shengxian Ge 1, Xianyu Yu 1,2,* , Dequn Zhou 1,2 and Xiuzhi Sang 1 1 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing 211106, China; [email protected] (S.G.); [email protected] (D.Z.); [email protected] (X.S.) 2 Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing 211106, China * Correspondence: [email protected]

Received: 6 May 2019; Accepted: 28 May 2019; Published: 1 June 2019

Abstract: To control growing environmental problems, the pollution rights trading (PRT) center was established in Jiaxing in 2007, and China officially joined the carbon emission reduction market (NCET) in 2011. Since power enterprises are the main participants in the NCET market and PRT market, the integrated effect of the NCET market and PRT market on power enterprise profit and the regional environment is one of the major issues that needs to be taken into consideration. Based on system dynamics (SD) theory, we propose an NCET-PRT simulation model for power enterprises in Chongqing. Through analyzing parameters of carbon trading price, free ratio, and emission trading prices, 12 different simulation scenarios are configured for sensitivity analysis. Based on the simulation results, the following observations can be obtained: (1) NCET and PRT can effectively promote the performance of enterprises’ carbon emissions reduction and regional pollutant emission reduction but will have a minor negative impact on the industrial economy at the same time; (2) The trading mechanism is interactive; if the carbon emissions trading (NCET) mechanism is implemented separately, the emission of pollutants will be reduced significantly. However, the implementation of pollution rights trading (PRT) alone cannot significantly reduce CO2 emissions; (3) At an appropriate level, NCET and PRT can be enhanced to achieve a maximum emissions reduction effect at a minimum economic cost.

Keywords: pollution rights trading (PRT); nationwide carbon emissions trading (NCET); system dynamics (SD); power enterprise

1. Introduction In recent years, with the development of Chinese industry and the growth of consumer levels, the economy has grown rapidly, and behind this economic development, the greenhouse eﬀect and environmental pollution have been an unneglectable problem; the emission reduction of the electrical industry is its most critical part. Although the proportion of coal consumption in China has been decreasing in recent years, China will still use a large amount of coal due to the scarcity of environmental resources in the future, which will inevitably lead to the emission of carbon dioxide and other pollutants. To control carbon emissions and pollution emissions, the Chinese Government has implemented pollution rights trading (PRT) policies and carbon emissions trading (CET) policies to alleviate environmental pressure and reduce pollutant emissions. In 2011, the Chinese government issued the “notice on the implementation of pilot carbon emission trading” and approved the four municipalities directly under the central government to carry out carbon emission trading pilots [1].

Sustainability 2019, 11, 3099; doi:10.3390/su11113099 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 3099 2 of 17

In the carbon emission reduction program of the 13th five year plan, it is clearly proposed that the ultra-low emissions from coal power should reach the level of gas and electricity [2]. By the end of 2018, China had opened eight pilot projects, with Fujian adding new ones, gradually moving towards a nationwide carbon trading policy [3]. For pollutant rights trading (PRT), China’s first emission trading center was established by Jiaxing in 2007, marking the gradual institutionalization, standardization, and internationalization of China’s emission trading [4]. In Chongqing, the pollution rights trading (PRT) was launched in 2009, and the paid use and trading of pollutant discharge rights were fully implemented in 2015. With the establishment of the CET market and PRT market, the effectiveness and design of CET and PRT have inevitably become the focus of attention. Since the adoption of the Kyoto protocol, many scholars and experts have paid attention to this hot topic. Galinis et al. [5] described a CGE model to study the impact of different transaction scenarios on Lithuania’s economy and environment. Then, in 2005, the European Union established the European Union Emission Trading Scheme (EU ETS). In the process of research, Stern [6] stated that the carbon trading mechanism is better than the carbon tax policy. After that, Linares et al. [7] constructed a market equilibrium model to address emissions permits and tradable green certification solutions, allowing every company to address the electricity, carbon, and green certification markets simultaneously. To study whether the carbon trading mechanism can achieve a good emission reduction effect, Keohane [8] concluded, through research, that emission reduction targets are always guaranteed in the carbon trading scheme on the condition that the total emission limit is well set. Although many countries and regions are currently implementing carbon trading policies, different countries and regions have different marginal carbon emission reduction costs under their respective emission reduction commitments (Yun, [9]). After 2011, due to China’s voluntary participation, many Chinese scholars also began to study the situation of trading mechanisms in China. Cui et al. [10] built a trans-provincial emission rights trading model, which was divided into no carbon, covering pilot, and covering the whole region for analysis and discussion. Li et al. [11] suggested that ETS is an effective strategy for CO2 reduction, and the free quota ratio should be gradually reduced in ETS. With the official launch of China’s national unified carbon trading system (ETS) in 2017, based on the multi-agent model, Tang et al. [12] introduced an auction mechanism in ETS. As for how to allocate the carbon emissions quota, Zhang and Hao [13] studied the carbon emission quota allocation of China in 2020 and made the allocation scheme reach the Pareto optimality with the consideration of carbon emission reduction capacity, responsibility, and potential. In terms of the efficiency of carbon trading mechanism, Zhao et al. [14] analyzed China’s four major carbon trading markets and determined that China’s current carbon trading market only achieved a weak efficiency, but the expansion of the market can improve that efficiency. Concerning the efficiency of carbon emission reduction, Li et al. [15] believe that the expansion of carbon finance trading and the market mechanism of carbon finance can improve the efficiency of carbon emission reduction. Zhou et al. [16] proposed a method for carbon emission license allocation combining data envelopment analysis (DEA), analytic hierarchy process (AHP), and principal component analysis (PCA). Since the European Union Emission Trading Scheme (EU ETS) has gradually become mature, Ralf Martin et al. [17] summarized the research of scholars and the data sheets provided by some EU countries and enterprises, believing that carbon trading has a great impact on the economy, and the effects of emission reduction in the second stage is relatively obvious. At the same time, they agreed that innovation could make the EU ETS more dynamically efficient and that renewable energy obligations and feed-in tariffs for electricity generation could drive innovation more than carbon trading. Concerning the emission of pollutants, from the perspective of pollution rights trading, as far back as 1997, students at an American school played the game of comparing the cost of reducing pollution to the cost of getting an emissions permit (Nugent, [18]). Chen et al. [19] explained the theory of property rights and put forward the overall planning of the definition of emission rights, emission rights transactions, and emission rights reorganization. Lu [20] interviewed businesspeople to study the actual performance of emission trading on SO2 emission, and unexpectedly found that the sulfur Sustainability 2019, 11, 3099 3 of 17 dioxide emission trading plan of Taiyuan seemed ineffective. However, based on the theory of system dynamics, Liao et al. [21] established the pollutant trading model in China and found that the impact of trading policies on the total emission control of SO has increased steadily since 2011. The establishment of the equilibrium model of the power network shows that the total emissions do not exceed the total emission permit (Li and Liu, [22]). In terms of the impact of sulfur dioxide emission rights trading on average emission reduction costs (APAC) and marginal emission reduction costs (MPAC), (Tu and Shen [23]) found through a calculation that trading policy can effectively reduce APAC and MPAC. With the help of the CGE model, Ma et al. [24] found, through simulation, that only a few regions in China can meet the national SO2 emissions standard under the existing SO2 trading mechanism. From the perspective of China’s energy, to reduce the emission of pollutants, China’s energy structure needs to be transformed from primary energy, such as wood and coal, to renewable energy. Zhao et al. [25] believe that economic development or the development of the non-agricultural labor market or the accumulation of human capital can help accelerate the process of rural energy transformation in China. Further, Tomas Baležentis and Dalia Štreimikiene,˙ [26] think that the transformation of the energy mix needs to be guided by financial incentives to maintain China’s efficient energy production and consumption. From the Perspective of Pollution Sources, Chen et al. [27] found, through research, that China’s coal consumption is gradually declining, while India’s coal consumption is gradually increasing. International scrutiny and support for India’s emissions should be strengthened to link environmental pressures. Changes in the productivity index (LPI), which is related to energy and pollutant variables, are increasing from the southeast coast to the west inland, so Wu et al. [28] propose that the government needs to strengthen environmental regulations in the west. According to the power industry, several scholars have explored the carbon trading policies of the power industry. Chappin et al. [29] studied the impact of CO2 emissions trading (CET) on power company decisions in the context of oligopolies. Based on the carbon emission trading theory, the development potential of the power industry is analyzed in three aspects (Chu et al. [30]). Zhang et al. [31] investigated the market mechanisms of carbon emission trading in the power industry. Huang et al. [32] found that carbon trading has limited incentives for long-term investment in coal-fired power companies. For companies that implement carbon trading under the EU trading system, we find that enterprises with carbon emission allowances have higher profits than those without carbon emission allowances, and enterprises with high carbon emissions face higher carbon risks and higher expected benefits (Oestreich and Tsiakas [33]). As far as we know, the existing literature mainly focuses on the impact of a single emission reduction trading mechanism (CET or PRT) on the economy and environment; there are few integrated analyses of CET and PRT for the power industry. Therefore, we think it is a problem worth studying. Kahrl et al. [34] concluded that although the trading policy has been implemented, the carbon dioxide emission of the power industry is expected to double in the next ten years with the increasing demand for electricity. Moreover, power enterprises are also the most important sources of pollutant emissions according to the data of the China Environment Statistical Yearbook (CESY) [35]. Thus, this study aims to explore the integrated efficiency of CET and PRT for the power enterprise. Motivated by the simulation research method of the emission control market in many recent studies (see, e.g., [12,16]), this paper will adopt the system dynamics (SD) simulation method to study the CET market and PRT market of Chongqing. The combined effect of the CET market and PRT market in Chongqing on the industrial economy of power, and its effect on the environment, will be analyzed. The policy suggestions of promoting CET and PRT will also be discussed. In Section2, we analyze the logic of the SD model and establish a causal loop diagram and stock-flow diagram. Based on a qualitative analysis combined with quantitative formulas, the parameters and variables are explained. In Section3, we first test the validity of the model; then, under different circumstances, the impact of transaction mechanism on corporate profit and the regional environment was analyzed, and optimization experiments were conducted to simulate the optimal situation. In Section4, based on the simulation data, three simulation results are obtained, and reasonable suggestions are put forward for pollution rights trading and Sustainability 2019, 11, 3099 4 of 17

Sustainabilitycarbon emission 2019, 11 trading, x FOR PEER in the REVIEW power industry, to make the trading mechanism more effective4 in of the 17 power industry. 2. Modelling Methodology 2. Modelling Methodology 2.1. Description of the Problem 2.1. Description of the Problem With the official launch of the carbon emission trading system, Chongqing has become the only Withcity in the western official China launch to of participate the carbon in emission the joint tradingconstruction system, of Chongqingthe national hascarbon become market, the onlyand itcity has in taken western the China lead toin participateChina’s carbon in the trading joint construction market. As of the the only national western carbon city market, participating and it has in carbontaken the trading, lead in the China’s analysis carbon of its tradingtrading market.impact can As provide the only valuable western experience city participating for western in carbon cities andtrading, provide the analysisfavorable of support its trading for further impact national can provide policy valuable release. experience In this paper, for westernAnylogic cities software and provideis used to favorable establish support the dynamic for further model national of the policycombined release. system In thisof carbon paper, trading Anylogic and software pollution is rightsused to trading establish of theChongqing dynamic coal model and of power the combined enterprises, system which of carboninclude trading five main and parts: pollution industrial rights powertrading economy, of Chongqing corporate coal andprofit, power carbon enterprises, trading, emission which include trading, five and main environment. parts: industrial The increase power ofeconomy, the industrial corporate power profit, economy carbon trading, promotes emission the gr trading,owth of and enterprises, environment. while The the increase increase of theof corporateindustrial powerprofits economyleads to the promotes increase the of growth pollutants of enterprises, and carbon while emissions. the increase Under of corporatethe influence profits of theleads implementation to the increase ofof pollutants trading policies, and carbon it also emissions. promotes Under environmental the influence governance, of the implementation thus jointly of affectingtrading policies, the industrial it also promotespower economy environmental in two aspects. governance, The thusspecific jointly logic aff frameecting diagram the industrial is shown power in Figureeconomy 1. in two aspects. The specific logic frame diagram is shown in Figure1.

Figure 1. Logic block diagram. Figure 1. Logic block diagram. The economic growth of the power industry increases the profit of enterprises through the role of subsidies,The economic and the increase growth ofof profitthe power will promote industry enterprises increases the to expandprofit of scale, enterprises thus increasing through thethe totalrole of subsidies, and the increase of profit will promote enterprises to expand scale, thus increasing the coal consumption of enterprises, thus leading to an increase of CO2 and pollutant emissions. At this point,total coal the twoconsumption transaction of policies enterprises, and the thus three leading aspects to ofan environmental increase of CO governance2 and pollutant jointly emissions. affect the Ateconomic this point, situation. the two transaction policies and the three aspects of environmental governance jointly affect the economic situation. 2.2. Software Introduction 2.2. Software Introduction AnyLogic is a widely used tool for the modeling and simulation of discrete, system dynamics, multi-agent,AnyLogic and is hybrida widely systems. used tool Based for the on themodeling latest complexand simulation system of design discrete, methodology, system dynamics, it is the multi-agent,first tool to introduce and hybrid the systems. UML language Based on into the the late fieldst complex of model system simulation. design methodology, Compared with it is other the firstsimulation tool to software,introduce it the has UML unique language advantages into th ine system field of dynamics, model simulation. such as convenient Compared dragging with other of variablessimulation and software, parameters, it has convenient unique advantages writing of in relations, system anddynamics, having such a database as convenient interface. dragging Due to the of powerfulvariables and integration parameters, ability, convenient chart output writing ability, of relations, and experimental and having analysis a database ability interface. of Anylogic, Due we to thecan powerful use the system integration dynamics ability, module chart ofoutput the Anylogic ability, and software experimental to conduct analysis complex ability dynamic of Anylogic, system wesimulations can use andthe system study the dynamics model mechanism. module of Inthe this Anylogic paper, wesoftware first used to conduct Anylogic complex software dynamic to draw systema causal simulations loop diagram and in study the systemthe model dynamics mechanism. section, In this followed paper, by we the first drawing used Anylogic of the stock software flow todiagram draw a and causal the loop demarcation diagram in of the the system subsystem. dynami Finally,cs section, after followed validity verification,by the drawing a comparative of the stock flow diagram and the demarcation of the subsystem. Finally, after validity verification, a comparative operation experiment and optimization experiment were established in the experimental section to complete the model construction and analysis in this paper. Sustainability 2019, 11, 3099 5 of 17 operation experiment and optimization experiment were established in the experimental section to complete the model construction and analysis in this paper. Sustainability 2019, 11, x FOR PEER REVIEW 5 of 17 2.3. Causal Circuit Diagram 2.3. Causal Circuit Diagram According to the transaction mechanism and model frame diagram, the causal circuit diagram of According to the transaction mechanism and model frame diagram, the causal circuit diagram the model is constructed by using Anylogic software, as shown in Figure2. of the model is constructed by using Anylogic software, as shown in Figure 2.

Figure 2. Causal circuit diagram. Figure 2. Causal circuit diagram. There are six major feedbacks in the causal loop diagram, three of which are negative, and three There are six major feedbacks in the causal loop diagram, three of which are negative, and three of which are positive. of which are positive. Industrial power economy (+) Government fixed investment (+) Corporate profits (+) Industrial power economy→→ (+) Government fixed investment→→ (+) Corporate profits→ →(+) Industrial power economy Industrial power economy The development of the economy enables the government to invest more capital to support The development of the economy enables the government to invest more capital to support enterprises, which can increase their profits and ultimately promote economic growth. enterprises, which can increase their profits and ultimately promote economic growth. 1. CO emissions (+) Carbon emission trading ( ) Corporate profits ( ) Science and 2 → → − → − technology1. CO2 emissions investment→ (+)(+ )Carbon CO emissions emission trading→ (−) Corporate profits→ (−) Science and → 2 Increasedtechnology carbon investment dioxide→ (+) emissions CO2 emissions promote the implementation of carbon trading policies, which will have a certain impact on the profits of enterprises, resulting in slow regional economic Increased carbon dioxide emissions promote the implementation of carbon trading policies, growth and reduced investment in science and technology, thus slowing down the reduction of carbon which will have a certain impact on the profits of enterprises, resulting in slow regional economic dioxide emissions. growth and reduced investment in science and technology, thus slowing down the reduction of 2. CO emissions (+) Environmental governance (+) Industrial power economy (+) carbon dioxide2 emissions.→ → → Science and technology investment ( ) CO emissions/ ( ) Coal consumption per unit of electricity → − 2 − → (2. ) CoalCO2 demand emissions→( )(+) CO Environmentalemissions governance→ (+) Industrial power economy→ (+) Science − → − 2 Carbonand technology dioxide investment emissions would→ (−) CO increase2 emissions/ the cost (− of) Coal environmental consumption governance, per unit of which electricity can be→ incorporated(−) Coal intodemand the industrial→ (−) CO power2 emissions income, which can increase the country’s science and technology investment,Carbon improve dioxide energyemissions effi ciency,would therebyincrease reducing the cost unitof environmental coal carbon and governance, carbon emissions; which can on the be otherincorporated hand, it canintoalso the decreaseindustrial the power number income, of units which needed can for coalincrease power the generation, country’sreducing science coaland demand,technology and investment, thereby reducing improve carbon energy dioxide efficiency, emissions. thereby reducing unit coal carbon and carbon 3. Pollutant emissions (+) Environmental governance (+) Industrial power economy (+) emissions; on the other hand,→ it can also decrease the number→ of units needed for coal power→ Science and technology investment ( ) Coal consumption per unit of electricity ( ) Coal demand generation, reducing coal demand,→ and− thereby reducing carbon dioxide emissions.→ − → ( ) Pollutant emissions − 3. ThePollutant emission emissions of pollutants→ (+) hasEnvironmental increased the governance cost of environmental→ (+) Industrial governance, power thus economy contributing→(+) to industrialScience power’sand technology economic investment growth.→ The(−) state Coal has consumption increased its per investment unit of electricity fund in science→ (−) Coal and demand→ (−) Pollutant emissions The emission of pollutants has increased the cost of environmental governance, thus contributing to industrial power’s economic growth. The state has increased its investment fund in science and technology, and technological progress has reduced the amount of coal required for each unit of power generation, thus reducing the number of pollutants discharged. Sustainability 2019, 11, 3099 6 of 17

Sustainability 2019, 11, x FOR PEER REVIEW 6 of 17 technology, and technological progress has reduced the amount of coal required for each unit of power 4.generation, Pollutant thus emissions reducing→ the (+) number Pollution of pollutants rights trading discharged.→ (−) Corporate profits→ (−) Industrial power4. Pollutant economy emissions→ (−) Science(+) Pollutionand technology rights tradinginvestment(→) Corporate(+) Coal consumption profits ( )per Industrial unit of → → − → − powerelectricity economy→ (+)( Pollutant) Science emissions and technology investment (+) Coal consumption per unit of → − → electricity (+) Pollutant emissions The emission→ of pollutants promotes the implementation of pollution rights trading policies, The emission of pollutants promotes the implementation of pollution rights trading policies, which which correspondingly reduces the profits of enterprises, leads to the slow growth of the industrial correspondingly reduces the profits of enterprises, leads to the slow growth of the industrial power power economy, and reduces the investment in science and technology, thus slowing down the economy, and reduces the investment in science and technology, thus slowing down the reduction rate reduction rate of pollutant emissions. of pollutant emissions. 5. Carbon5. Carbon and andpollutant pollutant emissions emissions→ (+)( +Trade) Trade policy policy→ (−() )Corporate Corporate profits profits→ ((−)) Industrial Industrial → → − → − powerpower economy economy( →) Total (−) Total electricity electricity( →) Power (−) Power generation generation( →) Coal (−) Coal demand demand( )→ Carbon(−) Carbon and → − → − → − → − pollutantand pollutant emissions emissions TheThe emissions of of carbon and poll pollutantsutants promote the implementation of trading policies, which hurthurt corporate profits profits and inhibit industrial power’spower’s economic growth. The The slow slow growth growth of of total electricityelectricity use use reduces reduces the demand demand for for coal coal burning burning and and ultimately ultimately reduces reduces carbon dioxide emission. 2.4. Stock Flow Diagram 2.4. Stock Flow Diagram According to the causal loop diagram, the stock flow diagram of coal power enterprises under the According to the causal loop diagram, the stock flow diagram of coal power enterprises under integration of carbon trading and emission trading is established, as shown in Figure3. the integration of carbon trading and emission trading is established, as shown in Figure 3.

Figure 3. Coal power enterprise stock flow chart. Figure 3. Coal power enterprise stock flow chart. This model is mainly divided into three subsystems: carbon trading subsystem (Left), economic subsystemThis model (Middle), is mainly and pollutantdivided into trading three subsystem subsystems: (Right). carbon Under trading the subsystem joint action (Left), of the economic three, the subsystemdevelopment (Middle), situation and and pollutant regional trading environment subsystem of the (R poweright). Under industry the are joint studied. action Inof thethe processthree, the of developmentmodel construction, situation all theand data regional required environment were taken of from the power the references industry of are China studied. low-carbon In the industryprocess ofnetwork, model Nationalconstruction, Development all the data and required Reform were Commission taken from (NDRC), the references China carbon of China trading low-carbon network, industryChongqing network, carbon National trading centre, Development the clean and energy Reform network, Commission etc. (NDRC), China carbon trading network, Chongqing carbon trading centre, the clean energy network, etc. 2.5. Main Parameters and Variables 2.5. Main Parameters and Variables In this paper, the entire power industry in Chongqing is selected. The enterprise profit, coal demand,In this and paper, other the variables entire arepower derived industry from in the Ch annualongqing report is selected. of the Chongqing The enterprise city. Theprofit, system coal demand, and other variables are derived from the annual report of the Chongqing city. The system dynamics model is mainly divided into parameters and variables. Variables can be divided into endogenous variables and exogenous variables. Sustainability 2019, 11, 3099 7 of 17 dynamics model is mainly divided into parameters and variables. Variables can be divided into endogenous variables and exogenous variables. The exogenous variables of the model are mainly obtained by referring to databases, literatures, official websites, etc., mainly including the industrial power economy, carbon emission coefficient, investment coefficient, government fixed investment coefficient, carbon quota free ratio, carbon trading price, pollution emission ratio of free quota, pollutant trading price, etc. The carbon trading price and pollutant trading price come from China’s low carbon industry net. The endogenous variables of the model mainly include regional economic growth, fixed government investment, carbon emission costs, pollution rights trading costs, scientific and technological investment, pollutant trading volume, and carbon trading volume. Based on the qualitative analysis, the formula is obtained according to the variable relationship in the system dynamics model (SD). The main variables and parameters are shown in Tables1 and2.

Table 1. Variables used in our model.

Variable Computational formula Unit (0.152 G fixed investment – Enterprise carbon emission cost + × Corporate profits Generating income – Coal cost – Pollutant emission cost – Million yuan Pollutant removal P pollutant trading 1.1)/100 × × (Corporate profits 0.15 + Environmental governance Growth in the economy × Million yuan investment – 0.158 G fixed investment)/100 × G fixed investment Industrial power economy G investment coefficient/100 Million yuan × Income variation coefficient Economic increment / Industrial power economy —— Technology investment impact Industrial power economy 0.062/100 Million yuan × Total electricity (EC growth rate + Population growth rate + × Growth in the electricity 2.5 Income variation coefficient) (1 – Total electricity / M kw h/year × × · 15,000,000)/100 – (3406/Technology investment impact Investment Coal reduction × T/kw h coefficient1) Coal consumption unit electricity · × – (C emissions per unit coal (3000/Technology investment Carbon reduction × Tons/ton impact Investment coefficient2)) × Power generation Total electricity (1 + Line lose ratio + Auxiliary power rate)/100 M kw h × · Coal demand Power generation Coal consumption unit electricity/100 Mt × C emissions per unit coal Coal demand A carbon coefficient CO emissions × × Mt 2 / 100 Free C quota CO emissions Free C quota ratio/100 Mt 2 × R total quota –1 Total quota Rate of change/100 Mt × × Corporate carbon trading volumes (Total quota – Free C quota) / 100 Mt (CO emissions – Free C quota – Corporate carbon trading Punish carbon emissions 2 Mt volumes)/100 (Corporate carbon trading volumes P carbon trading + Punish Enterprise carbon emission cost × Million yuan carbon emissions P carbon penalty)/100 × P carbon penalty 4 P carbon trading CNY/ton × P coal Table function CNY/ton Coal cost P coal Coal demand/100 Million yuan × Generating income Power generation P electricity/100 Million yuan × Pollutant production Coal demand (SO coefficient + NOx coefficient)/100 Mt × 2 Free P volumes Pollutant emissions Free P quota ratio Mt × Punish pollutions Pollutant emissions – Free P volumes Mt Pollutant trading volumes Pollutant emissions Pollutant trading ratio Mt × (Pollutant trading volumes P pollutant trading + Punish Pollutant emission cost × Million yuan pollutions P pollutant penalty)/100 × P pollutant penalty 4 P pollutant trading CNY/ton × Environmental governance (0.0258 CO emissions + Pollutant emissions 16)/100 Million yuan investment × 2 × Pollutant removal (Investment coefficient3 Environmental governance effect)/100 Mt × Pollutant emissions Pollutant production – Pollutant removal Mt

Here, we mainly choose two variables for a detailed explanation. The calculation of carbon emissions is the carbon emission per unit of standard coal multiplied by the coal demand of the enterprise and then multiplied by an actual carbon emission coeﬃcient. Emissions of pollutants are the Sustainability 2019, 11, 3099 8 of 17

sum of SO2 emissions per unit of coal and NOx emissions per unit of coal multiplied by the demand for coal and then subtracted by the amount of reduction imposed by the government. In recent years, China has been very strict in the control of heavy polluting enterprises, so the government’s role has made the emission of pollutants decline.

Table 2. Main parameters of the model.

Parameter Value Unit Data Source P carbon trading 40 CNY/ton Low-carbon industrial network P pollutant trading 1600 CNY/ton Chongqing bureau of ecological environment SO2 coefficient 0.0085 Tons/ton Chongqing bureau of ecological environment NOx coefficient 0.0074 Tons/ton Chongqing bureau of ecological environment Free P quota ratio 0.4 —— National pollution emission reduction policy G investment coefficient 0.6499 —— References [36] Population growth rate 0.0089 —— National Bureau of statistics Auxiliary power rate 0.0552 —— State Grid Corporation of China Line lose ratio 0.0605 —— State Grid Corporation of China Chongqing development and reform Rate of change 0.0413 —— commission Free C quota ratio 0.5 —— National carbon emission reduction policy

In Table2, all the parameters are from the o fficial website of the Chinese government, and the carbon trading price is from the low-carbon industrial network of China. To make the simulation more obvious, the initial price of carbon trading is set at 40 yuan/ton. Since only SO2 and NOX are traded in Chongqing, we set their prices as the product of their transaction prices and the corresponding transaction proportion. The emission coefficient of SO2 and NOX is based on the emission data of unit standard coal in Chongqing. The proportion of the free quota is based on existing national policies and literature. The proportion of fixed government investment is set by reference [36]. The line loss rate and power utilization rate are based on the actual situation in China and the standard data published by the state grid. The change rate of the total quota comes from the Chongqing development and reform commission, and its setting is based on previous average data. In this model, we use some abbreviations to make the model cleaner, as shown in Table3.

Table 3. Abbreviation and full name.

Abbreviation The Full Name G fixed investment Government fixed investment Free C quota ratio Free carbon quota ratio Free P quota ratio Free pollutant quota ratio R total quota Rate of change of total quota P carbon penalty Carbon penalty price P coal Coal price Free P volumes Free Pollutant volumes P pollutant penalty Pollutant penalty price P carbon trading Carbon trading price P pollutant trading Pollutant trading price EC growth rate Rate of growth in electricity consumption C emissions per unit coal Carbon emissions per unit coal A carbon coefficient Actual carbon emissions coefficient

3. The Empirical Research

3.1. Validation Test In order to match the simulation results with the actual situation, the validity of the model was tested. Three variables, coal demand, power generation and pollutant emissions, were selected in Sustainability 2019, 11, 3099 9 of 17 this paper. The fitting value from 2008 to 2017 was obtained through simulation and then compared with the real value to verify the effectiveness of the model. Specific data are shown in Table4 below. The results show that the error of each variable in each year is less than 5%, which is acceptable. The empirical results show that the system dynamics model constructed in this paper can fully reflect the economic situation, corporate profits, and the relationship between variables in Chongqing.

Table 4. Simulated values and real values.

Coal Demand Power Generation Pollutant Emissions Year The The The The The The Error Error Error Fitting Real Fitting Real Fitting Real Ratio Ratio Ratio Value Value Value Value Value Value 2012 761.579 767.98 0.833% 548.02 550.55 0.460% 7.009 7.0086 0.006% − − 2013 676.224 673.11 0.867% 605.580 593.67 2.006% 6.843 6.89 0.682% − 2014 942.196 925.53 2.704% 662.212 674.99 1.893% 8.365 8.5 1.588% − − 2015 1,092.44 1074.77 3.045% 717.589 750.37 4.369% 7.567 7.8 2.987% − − 2016 1,361.48 1358.02 2.204% 769.420 801.78 4.036% 4.305 4.49 4.120% − − 2017 1,321.82 1315.15 2.920% 811.046 840.63 3.159% 3.355 3.5 4.143% − −

3.2. Simulation Scenario Design To explore the impact of different carbon trading and pollution rights trading on Chongqing’s industrial power economy and corporate profits, the model simulates the situation in 2012–2025 with a step size of one year. The study set up six scenarios, including basic scenario (BASE), carbon trading price scenario (A1–A3), free carbon quota proportion scenario (B1–B3), pollution rights trading price scenario (C1–C3), free emission trading quota proportion scenario (D1–D3), pollution rights trading, and carbon emission trading integration mechanism scenario (E1–E3). In the carbon trading scenario, there is no pollution rights trading. According to the average carbon trading price in Chongqing in recent years, the trading price is set at 30, 50, and 60 yuan/ton; Under the scenario of free carbon quota proportion, the quota proportion is set as 0.4, 0.6, and 0.8, respectively. Under the circumstance of pollutant emission trading, according to the transaction prices of pollutants in Chongqing environmental protection bureau and other regions, the transaction prices of pollutants are set as 1500, 2000, and 2500 yuan/ton, and the proportion of the free quota is 0.3, 0.55, and 0.8, respectively. The specific numerical settings are shown in Table5.

Table 5. Simulation scenario setting.

Carbon Pollutant Carbon Free Pollutant Free Simulation Scenario Project Trading Price Trading Price Quota Ratio Quota Ratio (yuan/ton) (yuan/ton) The basic situation BASE 0 0 0 0 A1 30 0.5 1600 0.6 Different carbon trading price A2 50 0.5 1600 0.6 A3 70 0.5 1600 0.6 B1 40 0.4 1600 0.6 Different free carbon quotas B2 40 0.6 1600 0.6 B3 40 0.8 1600 0.6 C1 40 0.5 1500 0.6 Different pollutants trading C2 40 0.5 2000 0.6 price C3 40 0.5 2500 0.6 D1 40 0.5 1600 0.3 Different free pollutant quotas D2 40 0.5 1600 0.55 D3 40 0.5 1600 0.8 E1 30 0.8 1500 0.3 Two trading mechanism E2 50 0.6 2000 0.55 combination E3 70 0.4 2500 0.8 Sustainability 2019, 11, x FOR PEER REVIEW 10 of 17

Sustainabilitymechanism2019, 11 , 3099 E2 50 0.6 2000 0.55 10 of 17 combination E3 70 0.4 2500 0.8

3.3. Simulation Analysis 3.3.1. The Impact of Carbon Emissions Trading and Pollution Rights Trading 3.3.1. The Impact of Carbon Emissions Trading and Pollution Rights Trading The carbon trading policy of Chongqing began to be implemented in 2014, and the pollution The carbon trading policy of Chongqing began to be implemented in 2014, and the pollution rights trading policy was implemented in 2010. Through the simulation, we imitate the situation of rights trading policy was implemented in 2010. Through the simulation, we imitate the situation of 2012–2025. We can see clearly from Figure4, along with the implementation of policy, that carbon 2012–2025. We can see clearly from Figure 4, along with the implementation of policy, that carbon dioxide emissions and pollutants emissions are signiﬁcantly reduced, but at the same time, the industry dioxide emissions and pollutants emissions are significantly reduced, but at the same time, the economy also fell. industry economy also fell.

Figure 4. The impact of trading mechanisms. Figure 4. The impact of trading mechanisms. In carbon trading, if the price is 40 yuan per ton (NCET), compared with not implementing the tradingIn carbon mechanism, trading, industrial if the price power’s is 40 profitsyuan per fell ton by (NCET), 18.56% in compared 2025, and with carbon not dioxideimplementing emissions the felltrading by 9.19%. mechanism, Further, industrial when the power’s pollutant profits price fell was by 18.56% 1600 yuan in 2025, per tonand (PRT), carbon compared dioxide emissions with the unimplementedfell by 9.19%. Further, trading when mechanism, the pollutant the profit price of was enterprises 1600 yuan decreased per ton by(PRT), 3.1%, compared and the pollutantwith the emissionsunimplemented decreased trading by mechanism, 8.55%. We werethe profit surprised of enterprises to find that decreased the impact by 3.1%, of emission and the rightspollutant on corporateemissions profitsdecreased is slightly by 8.55%. smaller We than were that surprised of carbon to emissionfind that trading, the impact but atof the emission same time, rights it hason almostcorporate the profits same e isffect slightly of emission smaller reduction than that asof carboncarbon emissionemission trading. trading, but at the same time, it has almost the same effect of emission reduction as carbon emission trading. 3.3.2. The Interaction of Trading Mechanisms 3.3.2.To The explore Interaction the impact of Trading of diff Mechanismserent carbon trading policies on pollutant emissions and Corporate profits,To weexplore simulated the twoimpact scenarios: of different a carbon carbon trading trading price scenario policies (A1-A3) on pollutant and free emissions a carbon quota and ratioCorporate scenario profits, (B1-B3). we simulated two scenarios: a carbon trading price scenario (A1-A3) and free a carbon1. Diquotafferent ratio carbon scenario trading (B1-B3). prices 1. Different carbon trading prices From A1 toto A3,A3, thethe carboncarbon tradingtrading priceprice increasesincreases gradually, whichwhich meansmeans thethe transactiontransaction costcost increasesincreases gradually.gradually. As can be seen from Figure 55,, whenwhen thethe carboncarbon tradingtrading priceprice keepskeeps changing,changing, thethe pollutant will will also also change. change. The The simulation simulation forecast forecast is that is that in 2025, in 2025, when when the carbon the carbon trading trading price priceis 40 isyuan 40 yuan per ton, per ton,the thepollutant pollutant emission emission will will be be35,540 35,540 tons, tons, and and the the enterprise enterprise profit profit will be 33,828.13374 million yuan. At At this this point, point, the the carbon carbon prices prices of of 30, 30, 50, 50, and and 70 70 will will be be set, respectively, compared with the benchmark of 40 yuan, that is, when the price is changed to –25%, 25%, and 75%, respectively, thethe pollutantpollutant emissionsemissions willwill bebe 43,000,43,000, 26,050 and 8180 tons, respectively, whichwhich willwill have then decreased by –20.9%, 26.7%, and 76.9%, respectively.respectively. At this time, corporate profitsprofits will be 35,590.00, 31,863.52,31,863.52, and and 28,416.70 28,416.70 million million yuan, yuan, respectively, respectively, which which will will have have decreased decreased by –5.2%, by –5.2%, 5.8%, and5.8%, 15.9%, and 15.9%, respectively. respectively. When the When carbon the price carbon rises, pr theice emissionrises, theof emission pollutants of willpollutants also decrease, will also as the emission of pollutants is highly correlated with carbon trading. This result shows that the carbon Sustainability 2019, 11, x FOR PEER REVIEW 11 of 17

Sustainabilitydecrease, 2019as the,, 11, , 3099emissionx FOR PEER of REVIEW pollutants is highly correlated with carbon trading. This result 1111 shows of of 17 that the carbon trading mechanism is necessarily linked with pollutant emissions, and the same is decrease,true for corporate as the emission profits of(Figure pollutants 6). is highly correlated with carbon trading. This result shows tradingthat the mechanismcarbon trading is necessarily mechanism linked is necessarily with pollutant linked emissions, with pollutant and the emissions, same is true and for the corporate same is proﬁtstrue for (Figure corporate6). profits (Figure 6).

Figure 5. The impact of carbon trading prices on pollutants. Figure 5. The impact of carbon trading prices on pollutants. Figure 5. The impact of carbon trading prices on pollutants.

Figure 6. The impact of carbon trading prices on Corporate profits. Figure 6. The impact of carbon trading prices on Corporate profits. 2. Different free carbon quota ratio 2. Different freeFigure carbon 6. The quota impact ratio of carbon trading prices on Corporate profits. FigureFigure7 7shows shows the the influence influence of of the the di differentfferent proportion proportion of of the the free free carbon carbon quota quota on on pollutant pollutant emission.emission.2. Different FromFrom free B1B1 to tocarbon B3,B3, the the quota proportion proportion ratio of of free free carbon carbon quota quota increases increases gradually. gradually. The The simulation simulation resultsresultsFigure show show 7 thatshows that when when the the influence the proportion proportion of the of freedifferentof free carbon ca prrbon quotaoportion quota changes, ofchanges, the the free pollutantthe carb pollutanton quota emissions emissions on pollutant will also will change.emission.also change. When From When the B1 proportion to the B3, proportion the ofproportion free of carbon free of carbon quotafree carbon isquota 0.4, 0.6,quotais 0.4, and increases0.6, 0.8, and respectively, 0.8, gradually. respectively, the The growth simulationthe rategrowth of pollutantresultsrate of show pollutant emissions that whenemissions is 5.22%,the proportionis 5.13%,–5.22%, and 5.13%,of 15.22%,free andcarbonrespectively. 15.22%, quota respectively. changes, The above the The datapollutant above show dataemissions that, show with willthat, the − continuousalsowith change. the continuous growth When ofthe growth the proportion proportion of the of proportion offree free carbon carbon of quotafr quota,ee carbon is the0.4, emission quota,0.6, and the reduction0.8, emission respectively, e ffreductionect of the pollutants growtheffect of willratepollutants graduallyof pollutant will decline, gradually emissions and decline, the is emission–5.22%, and 5.13%,the reduction emission and e ff15.22%,ect reduction of the respectively. trading effect of mechanism the The trading above will mechanism data be continuously show will that, be weakened.withcontinuously the continuous Therefore, weakened. growth the countryTherefore, of the needs proportion the to country consider of frneedee the carbons emission to consider quota, reduction thethe emissionemission effect ofreduction reduction pollutants effect effect in the of of proportionpollutantspollutants will settingin the gradually proportion of the freedecline, carbonsetting and quota.of the the emission free carbon reduction quota. effect of the trading mechanism will be continuouslyIn exploring weakened. the effects Therefore, of the carbon the country trading mechanismneeds to consider of pollutant the emission discharge, reduction we also considereffect of thepollutants pollution in the rights proportion trading mechanism,setting of the whether free carbon the quota. impact on CO2 emissions will. Therefore, we conducted six sensitivity analysis experiments at C1–C3, and D1–D3 to explore the influence on carbon trading, emission trading transaction prices for different pollutants, and different pollutant free quota proportions, as shown in Figure8. To our surprise, we found that, through the simulation experiment, when the pollutant market price rises, carbon dioxide emissions will be reduced, but the reduction will be modest, as the effect is not extremely obvious. At the same time, the inhibition on corporate profits is not obvious (as shown in Figure9). Sustainability 2019, 11, x FOR PEER REVIEW 12 of 17

SustainabilitySustainability 20192019,, 1111,,Figure 3099x FOR PEER 7. The REVIEW impact of the free carbon quota ratio on pollutant emissions. 1212 of of 17

In exploring the effects of the carbon trading mechanism of pollutant discharge, we also consider the pollution rights trading mechanism, whether the impact on CO2 emissions will. Therefore, we conducted six sensitivity analysis experiments at C1–C3, and D1–D3 to explore the influence on carbon trading, emission trading transaction prices for different pollutants, and different pollutant free quota proportions, as shown in Figure 8. To our surprise, we found that, through the simulation experiment, when the pollutant market price rises, carbon dioxide emissions will be reduced, but the reduction will be modest, as the effect is not extremely obvious. At the same time, the inhibition on corporate profits is not obvious (as shown in Figure 9). We also conducted an experiment on the proportion of the free pollutant discharge quota and found that it had no significant effect. Given this situation, we believe that pollution rights trading has a small impact on carbon emissions trading because the total amount of pollutants is relatively small and the impact on the economy is small. Also, the government strictly controls SO2 and other pollution gases, and the market mechanism has a small effect, so it has no obvious effect on the carbon trading mechanism.Figure 7. The impact of the free carbon quota ratio on pollutant emissions. Figure 7. The impact of the free carbon quota ratio on pollutant emissions.

In exploring the effects of the carbon trading mechanism of pollutant discharge, we also consider the pollution rights trading mechanism, whether the impact on CO2 emissions will. Therefore, we conducted six sensitivity analysis experiments at C1–C3, and D1–D3 to explore the influence on carbon trading, emission trading transaction prices for different pollutants, and different pollutant free quota proportions, as shown in Figure 8. To our surprise, we found that, through the simulation experiment, when the pollutant market price rises, carbon dioxide emissions will be reduced, but the reduction will be modest, as the effect is not extremely obvious. At the same time, the inhibition on corporate profits is not obvious (as shown in Figure 9). We also conducted an experiment on the proportion of the free pollutant discharge quota and found that it had no significant effect. Given this situation, we believe that pollution rights trading hasSustainability a small impact2019, 11, xon FOR carbon PEER REVIEWemissions trading because the total amount of pollutants is relatively 13 of 17 small and the impact on the economy is small. Also, the government strictly controls SO2 and other Figure 8. The impact of pollutant trading prices on CO2 emissions. pollution gases, and Figurethe market 8. The impactmechanism of pollutant has a trading small priceseffect, on so CO it 2has emissions. no obvious effect on the carbon trading mechanism.

Figure 9. The impact of pollutant trading prices on Corporate profits. Figure 9. The impact of pollutant trading prices on Corporate profits. We also conducted an experiment on the proportion of the free pollutant discharge quota and found3.3.3. thatSimulation it had no Optimization significant effect. Given this situation, we believe that pollution rights trading has a smallWe already impact know on carbon that pollution emissions rights trading trading because and thecarbon total emissions amount of trading pollutants reduce is relatively emissions small and the impact on the economy is small. Also, the government strictly controls SO2 and other of CO2 and pollutantsFigure in the8. The Chongqing impact of pollutantregion, but trading at the prices same on time CO2 emissions.will have a negative impact to pollutionthe industrial gases, power and the economy. market mechanism Although China has a small is carrying effect, soout it carbon has no trading obvious and effect emission on the carbontrading tradingpilots, mechanism.there is no good policy to tell us how to set the carbon trading price and pollutant trading price, or how to set the quota proportion. Thus, this is still a question worth investigating. To better 3.3.3. Simulation Optimization use the carbon trading policy and emissions trading policy and reasonably level the economy, we conductedWe already a simulation know that optimization pollution rights experiment, trading and with carbon the emissionslowest economic trading reducecost to emissions achieve ofthe COlowest2 and pollutant pollutants discharge in the Chongqing amount. region,Therefore, but we at the set same the economic time will havelevel aof negative the Chongqing impact topower the industrialindustry powerat 32 economy.billion yuan Although to 33 Chinabillion is yuan carrying in 2025. out carbon By changing trading andthe emissionCO2 trading trading price, pilots, the proportion of free carbon quota, the pollutant trading price, and the proportion of free pollutant emission quota (E1–E3), we adjusted and optimized the results, and, finally, attained the optimal policy setting results. All of this can be done by creating an optimization experiment in Anylogic. In the experiment, we set the minimum sum of CO2 emissions and pollutant emissions as the objective function; the constraint condition is that the enterprise profit is more than 30 billion yuan, and the carbon trading price and pollutant trading price are independent variables for simulation. Through the chart (Figure 10), we clearly found that when the carbon market price is set at 73 yuan/ton, the proportion of free carbon quota is set at 0.7; the pollutant market price is set at 2470 yuan/tons; and the free pollutant quota ratio is set at 0.15 (Table 6), the emissions of carbon dioxide and pollutants are minimal, at least right now. If we simulate according to this data, we can attain a specific optimized situation, as shown in Figures 11 and 12.

Table 6. Initial parameters and optimal parameters.

P Carbon P pollutant Free P Quota Different Scenarios Free C Quota Ratio Trading (CNY) Trading (CNY) Ratio Initial parameters 30 0.5 1600 0.6 Optimal parameters 73 0.7 2470 0.15

Sustainability 2019, 11, x FOR PEER REVIEW 13 of 17

Figure 9. The impact of pollutant trading prices on Corporate profits.

3.3.3. Simulation Optimization We already know that pollution rights trading and carbon emissions trading reduce emissions of CO2 and pollutants in the Chongqing region, but at the same time will have a negative impact to the industrial power economy. Although China is carrying out carbon trading and emission trading Sustainabilitypilots, there2019 is, 11no, 3099good policy to tell us how to set the carbon trading price and pollutant trading13 of 17 price, or how to set the quota proportion. Thus, this is still a question worth investigating. To better use the carbon trading policy and emissions trading policy and reasonably level the economy, we thereconducted is no gooda simulation policy to optimization tell us how toexperiment, set the carbon with trading the lowest price andeconomic pollutant cost trading to achieve price, the or howlowest to pollutant set the quota discharge proportion. amount. Thus, Therefore, this is still we aset question the economic worth investigating.level of the Chongqing To better usepower the carbonindustry trading at 32 policybillion and yuan emissions to 33 billion trading yuan policy in and 2025. reasonably By changing level the the economy, CO2 trading we conducted price, the a simulationproportion optimizationof free carbon experiment, quota, the withpollutant the lowest trading economic price, and cost the to achieveproportion the of lowest free pollutant dischargeemission quota amount. (E1–E3), Therefore, we adju we setsted the and economic optimized level the of theresults, Chongqing and, finally, power attained industry the at 32 optimal billion yuanpolicy to setting 33 billion results. yuan All in 2025.of this By can changing be done the by COcreating2 trading an optimization price, the proportion experiment of free in carbonAnylogic. quota, the pollutantIn the experiment, trading price, we andset the the minimum proportion sum of free of CO pollutant2 emissions emission and quotapollutant (E1–E3), emissions we adjusted as the andobjective optimized function; the the results, constraint and, finally, condition attained is that the th optimale enterprise policy profit setting is more results. than All 30 ofbillion this canyuan, be doneand the by carbon creating trading an optimization price and experimentpollutant tradin in Anylogic.g price are independent variables for simulation. ThroughIn the the experiment, chart (Figure we set10), the we minimum clearly found sum ofthat CO when2 emissions the carbon and pollutantmarket price emissions is set asat the73 objectiveyuan/ton, function;the proportion the constraint of free carbon condition quota is that is se thet at enterprise 0.7; the pollutant profit is market more than price 30 is billion set atyuan, 2470 andyuan/tons; the carbon and the trading free pollutant price and quota pollutant ratio trading is set at price 0.15 (Table are independent 6), the emissions variables of carbon for simulation. dioxide Throughand pollutants the chart are (Figure minimal, 10), at we least clearly right found now. thatIf we when simulate the carbon according market to this price data, is set we at 73can yuan attain/ton, a thespecific proportion optimized of free situation, carbon quotaas shown is set in at Figures 0.7; the 11 pollutant and 12. market price is set at 2470 yuan/tons; and the free pollutant quota ratio is set at 0.15 (Table6), the emissions of carbon dioxide and pollutants are minimal, at least right now.Table If we 6. simulateInitial parameters according and to optimal this data, parameters. we can attain a specific optimized situation, as shown in Figures 11 and 12. P Carbon P pollutant Free P Quota DifferentWe can Scenarios find that although the economicFree aggregate C Quota hasRatio been reduced by 11.18% in 2025, the CO Trading (CNY) Trading (CNY) Ratio 2 emissionsInitial parameters had also been reduced 30 by 9.6%, and even 0.5 the pollutant emissions 1600 were reduced 0.6 by 80%, whichOptimal fully parameters proves the superiority 73 of the optimized 0.7 result. 2470 0.15

Figure 10. The optimal experiment.

Table 6. Initial parameters and optimal parameters.

P Carbon Trading Free C Quota P pollutant SustainabilityDiﬀerent 2019 Scenarios, 11, x FOR PEER REVIEW Free P Quota 14 Ratio of 17 (CNY) Ratio Trading (CNY)

Initial parameters 30Figure 10. The optimal 0.5 experiment. 1600 0.6 Optimal parameters 73 0.7 2470 0.15

Figure 11. Comparison of economic eﬀects. Figure 11. Comparison of economic effects.

Figure 12. Comparison of environmental effects.

We can find that although the economic aggregate has been reduced by 11.18% in 2025, the CO2 emissions had also been reduced by 9.6%, and even the pollutant emissions were reduced by 80%, which fully proves the superiority of the optimized result. In the experiment, we can find that the parameters before and after the simulation have changed significantly. In the price module, both carbon trading price and pollutant trading price increased significantly in 2025, which is consistent with the expectations of residents in the market survey. In the free quota ratio module, the free carbon quota ratio increased, while the free pollutant quota ratio decreased. We believe that, here, the role of carbon trading is the most significant, enterprises have been transformed, the proportion of coal and electricity has declined significantly, and the total emissions of CO2 have declined significantly. Therefore, the proportion of free carbon quota has increased at this time, which could reduce the pressure of enterprises and can be regarded as a disguised subsidy for enterprises. For the pollutant sector, because SO2 and other polluted gases are harmful, the government still strictly controls the discharge of pollutants, so the free quota ratio is low, and the discharge of pollutants is restricted by policy. On the whole, the price of carbon trading and the price of pollution rights trading will rise substantially in the future, the proportion of free quota of pollutants will continue to go down, the proportion of free carbon quota will change less, and the country will increase the proportion of the free quota to subsidize enterprises in a certain range. Therefore, for the carbon trading and the pollution rights trading market in Chongqing in the future, the government should guide the market towards prices according to the simulation results of the development or establish price setting systems to improve the trading mechanism, to Sustainability 2019, 11, x FOR PEER REVIEW 14 of 17

Figure 10. The optimal experiment.

Sustainability 2019, 11, 3099 Figure 11. Comparison of economic effects. 14 of 17

Figure 12. Comparison of environmental effects. Figure 12. Comparison of environmental effects. In the experiment, we can find that the parameters before and after the simulation have changed significantly.We can Infind the that price although module, the both economic carbon aggreg tradingate price has andbeen pollutant reduced tradingby 11.18% price in increased 2025, the significantlyCO2 emissions in 2025,had also which been is consistentreduced by with 9.6%, the and expectations even the pollutant of residents emissions in the market were reduced survey. Inby the80%, free which quota fully ratio proves module, the the superi freeority carbon of the quota optimized ratio increased, result. while the free pollutant quota ratio decreased.In the We experiment, believe that, we here, can the find role ofthat carbon the para tradingmeters is the before most significant,and after enterprisesthe simulation have beenhave transformed,changed significantly. the proportion In the of coalprice and module, electricity both has carbon declined trading significantly, price and and pollutant the total trading emissions price of increased significantly in 2025, which is consistent with the expectations of residents in the market CO2 have declined significantly. Therefore, the proportion of free carbon quota has increased at this time,survey. which In couldthe free reduce quota the ratio pressure module, of enterprises the free andcarbon can bequota regarded ratio asincreased, a disguised while subsidy the free for pollutant quota ratio decreased. We believe that, here, the role of carbon trading is the most enterprises. For the pollutant sector, because SO2 and other polluted gases are harmful, the government stillsignificant, strictly controlsenterprises the have discharge been oftransformed, pollutants, sothe the proportion free quota of ratiocoal isand low, electricity and the dischargehas declined of pollutantssignificantly, is restricted and the bytotal policy. emissions On the of whole, CO2 have the pricedeclined of carbon significantly. trading andTherefore, the price the of proportion pollution rightsof free trading carbon willquota rise has substantially increased at inthis the time, future, which the could proportion reduce of the free pressure quota ofof pollutantsenterprises willand continuecan be regarded to go down, as a the disguised proportion subsidy of free for carbon ente quotarprises. will For change the pollutant less, and thesector, country because will increaseSO2 and theother proportion polluted ofgases the freeare quotaharmful, to subsidizethe government enterprises still instrictly a certain controls range. the discharge of pollutants, so theTherefore, free quota for ratio the is carbon low, and trading the discharge and the pollutionof pollutants rights is restricted trading market by policy. in ChongqingOn the whole, in thethe future,price of the carbon government trading shouldand the guide price theof pollution market towards rights trading prices accordingwill rise substantially to the simulation in the resultsfuture, ofthe the proportion development of free or establishquota of pricepollutants setting will systems continue to improve to go down, the trading the proportion mechanism, of free to establishcarbon quota an enterprise will change without less, a and high the loss country of economy, will increase better reduce the proportion emissions, of and the take free care quota of the to regionalsubsidize environment. enterprises in a certain range. Therefore, for the carbon trading and the pollution rights trading market in Chongqing in the 4.future, Conclusions the government and Future should Research guide Needs the market towards prices according to the simulation results of the development or establish price setting systems to improve the trading mechanism, to This paper considers the relationship between the economy of Chongqing electric power industry and the regional environment under the combined effect of carbon emissions trading (NCET) and pollution rights trading (PRT). By analyzing the internal relations among the profits of enterprises involved in carbon emissions trading and pollution rights trading, and using the sensitivity analysis experiment, policy optimization simulation was carried out to provide theoretical support for the future development of power enterprises in Chongqing and even the whole region. The specific simulation results are as follows.

1. Under the influence of carbon emissions trading (NCET) and pollution rights trading (PRT), the pollutant discharge and carbon dioxide emissions of the power industry in Chongqing have been significantly reduced, and the environment has been improved noticeably, but at the same time, it will have a certain impact on the economy and reduce the profits of enterprises. 2. Under the effect of the carbon trading mechanism, reducing the amount of free quota or increasing the price of carbon trading will reduce the emission of pollutants; pollution rights trading (PRT) has a similar effect on carbon dioxide emissions, but not as strong. Sustainability 2019, 11, 3099 15 of 17

3. At a certain range of economic levels and corporate proﬁts, the simulation optimization can make the corporate proﬁt loss smaller under certain conditions and at the same time obtain a larger income from emission reduction.

Based on the above conclusions, we may put forward some useful policy suggestions for the country. We believe that, in the future, the state should continue to increase the intensity of carbon trading, and gradually increase the price of carbon trading, so that carbon emission rights as a commodity can exist for a long time. In terms of pollutant emissions, we believe that in China, the pollution subject base is still large, so we still need to reduce the proportion of the free pollutant quota, so as to make China’s environmental quality improve. In this study, the current situation of the power industry was firstly analyzed logically. Secondly, based on system dynamics theory, Anylogic software was used for model construction and model verification. Finally, different parameters were configured to conduct sensitivity analysis experiments, and corresponding conclusions were drawn through the analysis of charts and data, providing theoretical support for the improvement of the government’s emission reduction policy. However, this model also has some shortcomings. It only considers the influence of two trading mechanisms and fails to fully show the influence of power generation right transactions on Chongqing regional power enterprises. In addition, this paper only considers the situation of coal-fired power, without considering the impact of the trading mechanism under the circumstances of green power of enterprises and subsidies of the state to green power. Moreover, it does not carry out an in-depth analysis of the technology investment of enterprises themselves. We also did not analyze the carbon and polluting gases produced by the transport links of the supply chain for the coal needed for power generation. Therefore, based on the research in this paper, in the future, on the basis of carbon trading and pollution rights trading, we will consider the supply chain transportation link and enterprise investment as they relate to the model, so that the research can more fully reflect the reality, and provide strong theoretical support for corporate decision-making and national policy.

Author Contributions: Conceptualization, X.Y. and D.Z.; methodology, X.Y. and S.G.; software, S.G.; validation, S.G. and X.Y.; formal analysis, S.G.; investigation, S.G. and X.Y.; resources, S.G.; data curation, S.G. and X.S.; writing—original draft preparation, S.G. and X.Y.; writing—review and editing, S.G. and X.Y.; visualization, S.G.; supervision, X.Y.; project administration, D.Z. and X.Y.; funding acquisition, D.Z. and X.Y. Funding: This work is supported by the National Nature Science Foundation of China (71774080, 71501098, 71603135 and 71834003), China Postdoctoral Science Foundation Funded Project (2016M590453, 2018T110501), Science Foundation of Ministry of Education of China (17YJC630205) and the Fundamental Research Funds for the Central Universities (3082018NR2018011). Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. National Development and Reform Commission (NDRC). Notice on the Implementation of Pilot Carbon Emission Trading. Available online: http://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp?id=1349 (accessed on 29 October 2011). 2. National Development and Reform Commission (NDRC). The 13th Five-Year Plan for Electric Power Development. Available online: http://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp?id=398 (accessed on 22 December 2016). 3. National Development and Reform Commission (NDRC). Notice on the Implementation of Key Tasks in the National Carbon Emission Trading Market. Available online: http://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp? id=2944 (accessed on 18 December 2017). 4. Jiaxing City Government. Notice on the Issuance of Measures for Trading the Emission Rights of Major Pollutants in Jiaxing (for Trial Implementation). Available online: http://www.jiaxing.gov.cn/art/2007/9/27/ art_1590761_27355431.html (accessed on 27 September 2007). 5. Galinis, A.; Leeuwen, M. A cge model for lithuania: The future of nuclear energy. J. Policy Model. 2000, 22, 691–718. [CrossRef] Sustainability 2019, 11, 3099 16 of 17

6. Stern, N. The Economics of Climate Change, 1st ed.; TSO (The Stationery Oﬃce): Britain, UK, 2006; Volume 98, pp. 1–37.

7. Linares, P.; Javier, S.F.; Ventosa, M. Incorporating oligopoly, CO2 emissions trading and green certificates into a power generation expansion model. Automatica 2008, 44, 1608–1620. [CrossRef] 8. Keohane, N.O. Cap and Trade, Rehabilitated: Using Tradable Permits to Control U.S. Greenhouse Gases. Rev. Environ. Econ. Policy 2008, 3, 42–62. [CrossRef] 9. Zhanga, Y. Research on international carbon emissions trading and optimal exports scale of china carbon emissions. Procedia Environ. Sci. 2011, 10, 101–107. [CrossRef] 10. Cui, L.B.; Fan, Y.; Zhu, L.; Bi, Q.H. How will the emissions trading scheme save cost for achieving china’s 2020 carbon intensity reduction target? Appl. Energy 2014, 136, 1043–1052. [CrossRef] 11. Li, W.; Jia, Z.J. The impact of emission trading scheme and the ratio of free quota: A dynamic recursive cge model in china. Appl. Energy 2016, 174, 1–14. [CrossRef] 12. Tang, L.; Wu, J.Q.; Yu, L.A.; Bao, Q. Carbon allowance auction design of china s emissions trading scheme: \ A multi-agent-based approach. Energy Policy 2017, 102, 30–40. [CrossRef] 13. Zhang, Y.J.; Hao, J.F. Carbon emission quota allocation among china’s industrial sectors based on the equity and efficiency principles. Ann. Oper. Res. 2017, 255, 117–140. [CrossRef] 14. Zhao, X.G.; Wu, L.; Li, A. Research on the efficiency of carbon trading market in china. Renew. Sustain. Energy Rev. 2017, 79, 1–8. [CrossRef] 15. Li, L.; Liu, D.; Hou, J.; Xu, D.D.; Chao, W.B. The Study of the Impact of Carbon Finance Effect on Carbon Emissions in Beijing-Tianjin-Hebei Region—Based on Logarithmic Mean Divisia Index Decomposition Analysis. Sustainability 2019, 11, 1465. [CrossRef] 16. Zhou, J.G.; Li, Y.S.; Huo, X.J.; Xu, X.L. How to Allocate Carbon Emission Permits Among China’s Industrial Sectors Under the Constraint of Carbon Intensity? Sustainability 2019, 11, 914. [CrossRef] 17. Martin, R.; Muûls, M.; Wagner, U.J. The impact of the european union emissions trading scheme on regulated firms: What is the evidence after ten years? Rev. Environ. Econ. Policy 2016, 10, 129–148. [CrossRef] 18. Nugent, R.A. Teaching tools: A pollution rights trading game. Econ. Inq. 1997, 35, 679–685. [CrossRef] 19. Chen, B.W.; Xiao, Z.H. Study on the property right of emission trading in china. Adv. Mater. Res. 2011, 233, 2087–2090. [CrossRef] 20. Lu, Z. Emissions trading in china: Lessons from taiyuan so r, 2 r, emissions trading program. Sustain. \ \ Account. Manag. Policy J. 2011, 2, 27–44. [CrossRef]

21. Liao, X.L.; Li, X.L.; Zhang, B.X. System Dynamics Modeling and Simulation of China’s SO2 Emission Trading Policy. In Proceedings of the International Conference on Computer Distributed Control & Intelligent Environmental Monitoring, Changsha, China, 19–20 February 2011. 22. Li, X.Q.; Liu, M.L. A research on emission trading policy in electric power market based on network equilibrium theory. Adv. Mater. Res. 2013, 6, 869–870. [CrossRef]

23. Tu, Z.G.; Shen, R.J. Can china’s industrial SO2 emissions trading pilot scheme reduce pollution abatement costs? Sustainability 2014, 6, 7621–7645. [CrossRef] 24. Ma, X.L.; Wang, H.Q.; Wei, W.X. The role of emissions trading mechanisms and technological progress in achieving china’s regional clean air target: A cge analysis. Appl. Econ. 2019, 51, 155–169. [CrossRef] 25. Zhao, Q.R.; Chen, Q.H.; Xiao, Y.T.; Tian, G.Q.; Chu, X.L.; Liu, Q.M. Saving forests through development? Fuelwood consumption and the energy-ladder hypothesis in rural Southern China. Transform. Bus. Econ. 2017, 16, 199–219. 26. Tomas, B.; Dalia, S. Sustainability in the Electricity Sector through Advanced Technologies: Energy Mix Transition and Smart Grid Technology in China. Energies 2019, 12, 1142. 27. Chen, J.D.; Cheng, S.L.; Nikic, V.; Song, M.L. Quo Vadis? Major Players in Global Coal Consumption and Emissions Reduction. Transform. Bus. Econ. 2018, 17, 112–132. 28. Wu, G.; Baležentis, T.; Sun, C.W.; Xu, S.H. Source control or end-of-pipe control: Mitigating air pollution at the regional level from the perspective of the Total Factor Productivity change decomposition. Energy Policy 2019, 129, 1227–1239. [CrossRef]

29. Chappin, E.J.L.; Dijkema, G.P.J. On the impact of CO2 emission-trading on power generation emissions. Technol. Forecast. Soc. Change 2009, 76, 358–370. [CrossRef] Sustainability 2019, 11, 3099 17 of 17

30. Chu, J.C.; Zeng, M.; Yang, L.L. Carbon Emissions Trading and Sustainable Development of Power Industry. In Proceedings of the International Conference on Electrical & Control Engineering, Hubei, China, 25–27 June 2010. 31. Zhang, F.W.; Guo, Y.; Chen, X.P. Research on China’s Power Sector Carbon Emissions Trading Mechanism. Energy Procedia 2011, 12, 127–132. [CrossRef] 32. Huang, Y.; Liu, L.; Ma, X.M.; Pan, X. Abatement technology investment and emissions trading system: A case of coal-ﬁred power industry of shenzhen, china. Clean Technol. Environ. 2015, 17, 811–817. [CrossRef] 33. Oestreich, A.M.; Tsiakas, I. Carbon emissions and stock returns: Evidence from the EU Emissions Trading Scheme. J. Bank. Financ. 2015, 58, 294–308. [CrossRef] 34. Kahrl, F.; Williams, J.; Ding, J.H.; Hu, J.F. Challenges to China’s transition to a low carbon electricity system. Energy Policy 2011, 39, 4032–4041. [CrossRef] 35. China Environment Statistical Yearbook (CESY). Available online: http://www.mee.gov.cn/gzfw_13107/hjtj/ hjtjnb/201702/P020170223595802837498.pdf (accessed on 23 February 2017). 36. Zhang, J.R.; Wang, Z.D.; Tang, L.; Yu, L.A. Research on the impact of carbon emission trading policies in beijing-tianjin-hebei region based on system dynamics. Chin. J. Manag. Sci. 2016, 24, 1–8.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).