煤炭产业是我国国民经济的基础能源和支柱产业，同时也是高污染、高能耗产业。在习近平总书记向世界做出中国“2030年前碳达峰、2060年前碳中和”目标承诺及全球绿色低碳发展背景下，我国煤炭产业面临绿色低碳转型的新挑战和革命性要求。政界、业界和学界针对煤炭产业的绿色低碳发展做了大量的前期工作和研究，但未从根本与实际上解决煤炭产业“三高”瓶颈问题，究其原因主要是政府激励性措施或限制性手段等政策落实不到位。基于此，本文立足碳达峰背景，采用定性与定量、理论与实践相结合的研究范式，应用系统动力学方法对煤炭产业绿色低碳发展政策进行了系统研究。

首先，系统梳理煤炭产业绿色低碳发展政策相关研究，基于我国煤炭产业绿色低碳发展现状，对2020年“碳达峰”提出前、后煤炭产业绿色低碳发展政策及其趋势进行了深入分析。其次，通过系统边界及构成内容分析，确定出煤炭产业绿色低碳系统包括经济、社会、能源、环境和政策五大子系统，根据各子系统变量及关系构建出煤炭产业绿色低碳系统动力学模型。再次，在对初始状态模拟的基础上，结合碳达峰目标要求，设置政策调控因子，分别对单一政策、双政策组合和多政策组合下煤炭产业绿色低碳发展进行情景模拟，得出如下研究结论：2002-2019年间，煤炭消费量虽得到有效的控制，但煤炭消费在第二产业中的比重仍很高，煤炭产量增加也在不断促进煤炭供需形式的明显好转，同时在控制碳排放和污染物总量上取得了一定的成果，碳达峰对煤炭产业绿色低碳发展提出新的挑战和要求；初始状态下，未来以煤为主的能源消费结构和生态环境未得到有效改善，除废气排放总量外，废水排放总量、固废排放总量和碳排放总量保持增长的趋势，当前亟需有效的政策推动煤炭产业的绿色低碳发展；单一政策下，各政策的实施效果排序为：税收政策>技术政策>产业政策>资源政策>初始状态；双政策组合下，产业-税收政策组合在煤炭产消方面约束作用较为显著，技术-税收政策组合在环境污染和碳排放方面作用显著；多政策组合下，产业-技术-税收政策组合在三政策组合中效果最优，四政策组合比所有政策及组合模拟效果都好。最后，结合政策情景模拟结果，从调整产业结构、优化能源结构、鼓励技术创新和完善税费体系四大方面，提出促进煤炭产业绿色低碳发展的一些政策建议。

通过以上研究，将助推我国煤炭产业绿色低碳发展，也可为政府部门制定、完善煤炭产业相关政策提供理论、方法支持和参考依据。

Coal industry is the basic energy and pillar industry of my country's national economy, and it is also a high pollution and high energy consumption industry. Under the background of General Secretary Xi Jinping's commitment to the world's "carbon peak by 2030 and carbon neutrality by 2060" and the global trend of green and low-carbon development. China's coal industry is facing new challenges and revolutionary changes in green and low-carbon transformation. The political, industrial and academic circles have done a lot of preliminary work and research on the green and low-carbon development of the coal industry, but they have not fundamentally and practically solved the bottleneck problem of the “three highs” of the coal industry. The main reason for this is the government’s incentive measures or restrictions means and other policies are not implemented properly. Therefore, based on the background of carbon peaking, this paper adopts a research paradigm combining qualitative and quantitative, theory and practice, and applies the system dynamics method to systematically study the green and low-carbon development policy of the coal industry.

First, systematically sort out the relevant research on the green and low-carbon development policies of the coal industry. Based on the current situation of the green and low-carbon development of the coal industry in China, it also made an in-depth analysis of the green and low-carbon development policies and trends of the coal industry before and after the "carbon peak" in 2020. Secondly, through the analysis of the system boundary and composition content, it is determined that the green and low-carbon system of the coal industry includes five subsystems of economy, society, energy, environment and policy. According to the variables and relationships of various subsystems, a green and low-carbon system dynamics model of the coal industry is constructed. Thirdly, on the basis of simulating the initial state, set policy control factors, and conduct scenario simulations for the green and low-carbon development of the coal industry under single policy, dual policy combination and multi-policy combination. The following research conclusions are drawn: From 2002 to 2019, although coal consumption has been effectively controlled, the proportion of coal consumption in the secondary industry is still very high, and the increase in coal production is also promoting a significant improvement in coal supply and demand, while controlling carbon emissions and pollutants. Some achievements have been made in the total amount, and Carbon Peak has put forward new challenges and requirements for the green and low-carbon development of the coal industry; In the initial state, the coal-dominated energy consumption structure and ecological environment have not been effectively improved in the future. In addition to the total amount of exhaust gas emissions, the total amount of wastewater discharge, total solid waste discharge and total carbon emissions will maintain an increasing trend. Effective policies are needed to promote the green and low-carbon development of the coal industry; Under a single policy, the ranking of the implementation effects of each policy is that tax policy > technical policy > industrial policy > resource policy > initial state; Under the combination of dual policies, the industry-tax policy combination plays a more significant role in restricting coal production and consumption, and the combination of technology-tax policies has a significant effect on environmental pollution and carbon emissions. Under the multi-policy combination, the industry-technology-tax policy combination has the best effect in the three-policy combination, and the four-policy combination is better than all the policies and the simulation effect of the combination. Finally, combined with the results of the policy scenario simulation, some policy suggestions for promoting the green and low-carbon development of the coal industry are put forward from the four aspects of adjusting the industrial structure, optimizing the energy structure, encouraging technological innovation and improving the tax and fee system.

Through the above research, it will promote the green and low-carbon development of China's coal industry, and also provide theoretical and methodological support for government departments to formulate and improve coal industry-related policies.

[1] Chen L, Yang Z, Chen B. Scenario analysis and path selection of low-carbon transformation in China based on a modified IPAT model[J]. PloS one, 2013, 8(10): e77699.

[2] Xiumei S, Min Z. Research on development model of low-carbon economy in resource-based cities[J]. Energy Procedia, 2011 (11): 3191-3197.

[3] Babi K, Asselin H, Benzaazoua M. Stakeholders’ perceptions of sustainable mining in Morocco: a case study of the abandoned Kettara mine[J]. The Extractive Industries and Society, 2016, 3(1): 185-192.

[4] Requate T. Dynamic incentives by environmental policy instruments—a survey[J]. Ecological Economics, 2005, 54(2-3): 175-195.

[5] Campiglio E. Beyond carbon pricing: The role of banking and monetary policy in financing the transition to a low-carbon economy[J]. Ecological Economics, 2016, 121: 220-230.

[6] Morrison G. China, United States, Korea take lead in clean energy and low-Carbon initiatives[J]. Research Technology Management, 2009, 52(6): 2.

[7] Galinato G I, Yoder J K. An integrated tax-subsidy policy for carbon emission reduction[J]. Resource and Energy Economics, 2010, 32(3): 310-326.

[8] Omer A M. Focus on low carbon technologies: The positive solution[J]. Renewable and Sustainable Energy Reviews, 2008, 12(9): 2331-2357.

[9] Fuller M C, Portis S C, Kammen D M. Toward a low-carbon economy: municipal financing for energy efficiency and solar power[J]. Environment: Science and Policy for Sustainable Development, 2009, 51(1): 22-33.

[10] Althouse J, Guarini G, Porcile J G. Ecological macroeconomics in the open economy: Sustainability, unequal exchange and policy coordination in a center-periphery model[J]. Ecological Economics, 2020, 172: 106628.

[11] Bloom N. The impact of uncertainty shocks[J]. econometrica, 2009, 77(3): 623-685.

[12] Anser M K, Yousaf Z, Usman B, et al. Management of water, energy, and food resources: go for green policies[J]. Journal of Cleaner Production, 2020, 251: 119662.

[13] Kolln K, Prakash A. EMS-based environmental regimes as club goods: Examining variations in firm-level adoption of ISO 14001 and EMAS in UK, US and Germany[J]. Policy Sciences, 2002, 35(1): 43-67.

[14] Jones C. Exploring new ways of assessing the effect of regulation on environmental management[J]. Journal of cleaner production, 2010, 18(13): 1229-1250.

[15] Papathanasiou D, Anderson D. Uncertainties in responding to climate change: on the economic value of technology policies for reducing costs and creating options[J]. The Energy Journal, 2001, 22(3).

[16] Swanson J. Business dynamics—systems thinking and modeling for a complex world[J]. Journal of the Operational Research Society, 2002, 53(4): 472-473.

[17] Barney G O. The Global 2000 Report to the President and the Threshold 21 model: influences of Dana Meadows and system dynamics[J]. System Dynamics Review: The Journal of the System Dynamics Society, 2002, 18(2): 123-136.

[18] Sušnik J, Masia S, Indriksone D, et al. System dynamics modelling to explore the impacts of policies on the water-energy-food-land-climate nexus in Latvia[J]. Science of The Total Environment, 2021, 775: 145827.

[19] Chica-Morales P, Muñoz V F, Domenech A J. System Dynamics as Ex Ante Impact Assessment Tool in International Development Cooperation: Study Case of Urban Sustainability Policies in Darkhan, Mongolia[J]. Sustainability, 2021, 13(8): 4595.

[20] Gozini H, Zahraie B, Ravar Z. System dynamics modeling of water–energy nexus for resource-saving policy assessment[J]. International Journal of Environmental Research, 2021, 15(2): 349-367.

[21] Liao Z, Li Y, Xiong W, et al. An In-Depth Assessment of Water Resource Responses to Regional Development Policies Using Hydrological Variation Analysis and System Dynamics Modeling[J]. Sustainability, 2020, 12(14): 5814.

[22] Pan X, Li M, Xu H, et al. Simulation on the effectiveness of carbon emission trading policy: A system dynamics approach[J]. Journal of the operational research society, 2021, 72(7): 1447-1460.

[23] Guo Y M, Shi Y R. Impact of the VAT reduction policy on local fiscal pressure in China in light of the COVID-19 pandemic: A measurement based on a computable general equilibrium model[J]. Economic Analysis and Policy, 2021, 69: 253-264.

[24] Mahadevan R, Amir H, Nugroho A. How pro-poor and income equitable are tourism taxation policies in a developing country? Evidence from a computable general equilibrium model[J]. Journal of Travel Research, 2017, 56(3): 334-346.

[25] Carbone J C, Rivers N. The Impacts of Unilateral Climate Policy on Competitiveness: Evidence From Computable General Equilibrium Models[J]. Review of Environmental Economics and Policy, 2017, 11(1): 24-42.

[26] Stenberg L C, Siriwardana M. A computable general equilibrium model for environmental policy analysis: The case of deforestation in the Philippines[M]. Nova Science Publishers, 2009.

[27] Ji X, Rana P, Chia W M, et al. Post-TPP Trade Policy Options for ASEAN and Its Dialogue Partners:'Preference Ordering'Using CGE Analysis[J]. East Asian Economic Review, 2018, 22(2): 177-215.

[28] 焦嶕,赵国浩.煤炭产业绿色低碳发展研究评述[J].生产力研究,2018(12):154-161.

[29] 孙毅,景普秋.资源型区域绿色转型模式及其路径研究[J].中国软科学,2012(12):152-161.

[30] 孙秀梅. 资源型城市低碳转型机理与调控对策研究[D].中国矿业大学,2011.

[31] 李媛媛.财税政策促进自然资源合理利用的若干思考[J].税务与经济,2018(05):96-102.

[32] 房红.财税政策支持资源型城市绿色转型的文献综述[J/OL].会计之友,2020(07):145-148.

[33] 周娜,吴巧生,汪金伟.中国页岩气产业发展的财税政策仿真[J].中国地质大学学报(社会科学版),2019,19(03):75-89.

[34] 孟凡生,韩冰.政府环境规制对企业低碳技术创新行为的影响机制研究[J].预测,2017,36(01):74-80.

[35] 吴锦明.新能源产业发展需要哪些“政策红利”[J].人民论坛,2019(08):66-67.

[36] 汪忠杰,刘一恒.低碳经济视角下的我国清洁能源产业发展路径设计[J].改革与战略,2015,31(12):138-140+145.

[37] 云小鹏.基于CGE模型的能源与环境财税政策协同影响效应研究[J].经济问题,2019(07):37-44.

[38] 李云燕,张彪.改革环境资源价格政策推动循环经济发展[J].环境保护,2013,41(01):45-46.

[39] 江苏省物价局课题组.完善资源环境价格政策体系促进经济结构调整和转型升级研究[J].价格理论与实践,2015(10):39-43.

[40] 李敏,刘春学,李春雪,徐爽.云南省水资源政策效应模拟及预测研究[J].地域研究与开发,2020,39(01):133-138.

[41] 苏轶娜,李雪梅.中国优势矿产资源管理政策及其评价[J].中国人口·资源与环境,2017,27(S1):164-167.

[42] 黄光球,徐聪.低碳视角下新能源产业发展影响因素及其动态仿真分析[J].重庆理工大学学报(自然科学),2020,34(12):206-217.

[43] 陈劲,阳镇.新发展格局下的产业技术政策：理论逻辑、突出问题与优化[J].经济学家,2021(02):33-42.

[44] 田利军,王皓羽,王丹阳.绿色发展政策与技术创新关系研究——基于民航业的实验研究证据[J].价格理论与实践,2020(09):144-147.

[45] 陈强远,林思彤,张醒.中国技术创新激励政策：激励了数量还是质量[J].中国工业经济,2020(04):79-96.

[46] 莫神星.应对气候变化下发展低碳能源科技的路径[J].中国能源,2018,40(03):32-36.

[47] 张济建,于连超,毕茜,潘俊.媒体监督、环境规制与企业绿色投资[J].上海财经大学学报,2016,18(05):91-103.

[48] 凤亚红,张慕原.环境规制对煤炭行业绿色技术创新的影响分析[J].煤炭工程,2017,49(10):166-168+172.

[49] 曾军,姚庆国,李跃,于向宇.新旧动能转换背景下煤炭产业碳减排路径仿真研究——以山东省为例[J].数学的实践与认识,2019,49(23):312-321.

[50] 周雄勇,许志端,郗永勤.中国节能减排系统动力学模型及政策优化仿真[J].系统工程理论与实践,2018,38(06):1422-1444.

[51] 黄光球,徐聪.低碳经济视角下能源产业可持续发展与政策仿真研究[J].煤炭工程,2020,52(05):187-193.

[52] 顾明瑞,王帆,王舒鸿.基于系统动力学的中国绿色发展政策仿真研究[J].中国环境管理,2021,13(03):126-135.

[53] 徐颖,李潘婷,李瑞平.垃圾分类和资源化利用协同治理动力机制和政策仿真研究[J].建筑经济,2021,42(S1):403-407.

[54] 郝维宝,雷颜溪,李盛国,董纪昌.基于系统动力学的我国节能环保产业政策仿真研究[J].数学的实践与认识,2019,49(24):21-34.

[55] 张樨樨,刘鹏.中国海洋牧场生态系统优化的政策仿真与模拟[J].中国人口·资源与环境,2019,29(12):168-176.

[56] 程云鹤,王宛昊,周强.安徽绿色经济发展系统动力学模型及政策仿真[J].华东经济管理,2019,33(06):14-23.

[57] 李毅,石威正,胡宗义.基于CGE模型的碳税政策双重红利效应研究[J].财经理论与实践,2021,42(04): 82-89.

[58] 周迪,蔡晓婷,张达泉,张雨帆.基于CGE模型的碳税政策模拟——以广东省为例[J].气候变化研究进展,2020,16(04):516-525.

[59] 吴正,田贵良,胡雨灿.基于开放式水资源嵌入型CGE模型的税改政策经济影响与节水效应[J].资源科学,2021,43(11):2264-2276.

[60] 柴芳芳.基于CGE模型的企业财税激励政策研究[J].科技经济市场,2020(10):108-109.

[61] 李侨敏,王晓岭.中美贸易摩擦背景下我国稳外资政策有效性评估：基于异质性CGE模型的分析[J].国际经贸探索,2021,37(09):51-67.

[62] 李娜,石敏俊,张卓颖,陈志钢.基于多区域CGE模型的长江经济带一体化政策效果分析[J].中国管理科学,2020,28(12):67-76.

[63] 吴妍,徐维祥.基于区域动态CGE模型的雾霾治理政策模拟分析——以京津冀地区为例[J].浙江树人大学学报(人文社会科学),2020,20(04):71-79.

[64] 贺玲,崔琦,陈浩,宋涛.基于CGE模型的中国煤炭产能政策优化[J].资源科学,2019,41(06):1024-1034.

[65] 安祺.中国环境决策费用效益分析的工具选择及应用[J].环境与可持续发展,2017,42(02):22-27.

[66] 柳君波,徐向阳,霍志佳,龙影.中国煤炭格局变化对煤矿甲烷排放的影响及原因[J].生态经济,2021,37(07):176-182.

[67] 马翠梅,戴尔阜,刘乙辰,王亚慧,王芳.中国煤炭开采和矿后活动甲烷逃逸排放研究[J].资源科学,2020,42(02):311-322.

[68] Miller S M, Michalak A M, Detmers R G, et al. China’s coal mine methane regulations have not curbed growing emissions[J]. Nature Communications, 2019, 10(1): 1-8.

[69] 李健,郭姣,苑清敏.京津冀协同发展背景下能源需求预测与政策影响研究[J].干旱区资源与环境,2018,32(05):5-11.

[70] 李健,李宁宁.京津冀绿色发展政策模拟及优化研究[J].大连理工大学学报(社会科学版),2021,42(04):14-25.

[71] 张玉林.煤炭资源税改革对安徽省煤炭行业价格和生产效率的影响[D].湖南大学,2017.

[72] 蔡栋梁,闫懿,程树磊.碳排放补贴、碳税对环境质量的影响研究[J].中国人口·资源与环境,2019,29(11):59-70.

[73] 陈锡康,杨翠红,祝坤福,王会娟,李鑫茹,姜青言.2021年中国经济增长速度的预测分析与政策建议[J].中国科学院院刊,2021,36(01):37-46.

[74] 丁凡,王艳,李思一,吴叶君,喻顺祥,黄振中.中国可持续发展系统动力学仿真模型──环境部分[J].计算机仿真,1998(01):8-10.