环境规制、能源供需结构与工业污染
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摘要
山西省作为煤炭生产和消费大省,其环境问题也日益凸显。本文基于主成分分析,采用完全修正一般最小二乘法及协整分析方法对山西省的时间序列数据进行回归分析。结果表明,山西省煤炭供需结构比重越高,工业部门的污染排放强度随之增加,且煤炭生产结构对工业部门造成的环境负外部性影响要远超煤炭消费结构所带来的影响。山西省资源型经济体制改革相较于环境影响评价法的执行,对降低工业污染排放强度有更明显的刺激推动作用。具体来看,转变煤炭能源消费结构行为比转变其生产结构行为更有减少工业污染排放强度的潜力。为加快生态文明建设,实现"美丽山西"的目标,一方面要进一步扩大环境治理投资并坚定不移地执行中央政府出台的环境保护法律来直接降低工业污染排放强度;另一方面,要不遗余力地贯彻落实并深化山西省资源型经济体制改革,进一步调整产业结构,加大力度降低工业经济发展对煤炭的依赖。不断引进具有创新能力和先进生产技术水平的外商在当地投资设厂,激发市场的活力并刺激企业间竞争力,优化产品生产效率与质量,从根源解决山西省生态环境的负外部性影响。
As a big province of coal production and consumption,Shanxi's environmental problems are becoming increasingly prominent. By constructing the industrial pollution index using principal component analysis( PCA) and the data of Shanxi province,we utilize several time-series estimators including Fully-Modified OLS and Canonical Cointegration Regression to deal with the problems of serial correlation and endogeneity. The results show that the higher the share of coal production and consumption both increase the industrial pollution intensity. And the share of coal production causes more serious negative externality to the environment. The resource-based economy reform significantly stimulates the decrease of industrial pollution intensity via adjusting the structure of coal energy production and consumption. Specifically,adjusting the share of coal consumption has higher potential to reduce industrial pollution intensity. Therefore,to accelerate the establishment of ecological civilization and achieve the goal of beautiful Shanxi,it is imperative to expand the investment of environmental governance and firmly implement the stringent environmental law to decline the industrial pollution directly. On the other hand,we should spare no effort to enforce and deepen the resource-oriented economic reform in Shanxi province. In the meantime,it is necessary to adjust the industrial structure and reduce the dependence of industrial economic development on the coal production and consumption. Another policy handle is to introduce the foreign investors with innovative ability and advanced production technology to set up factories. This measure can trigger the vitality of market entities,stimulate competitiveness of enterprises as well as optimize the production efficiency and quality of products. Only in this way can Shanxi province completely tackle the negative externalities in ecological environment.
As a big province of coal production and consumption,Shanxi's environmental problems are becoming increasingly prominent. By constructing the industrial pollution index using principal component analysis( PCA) and the data of Shanxi province,we utilize several time-series estimators including Fully-Modified OLS and Canonical Cointegration Regression to deal with the problems of serial correlation and endogeneity. The results show that the higher the share of coal production and consumption both increase the industrial pollution intensity. And the share of coal production causes more serious negative externality to the environment. The resource-based economy reform significantly stimulates the decrease of industrial pollution intensity via adjusting the structure of coal energy production and consumption. Specifically,adjusting the share of coal consumption has higher potential to reduce industrial pollution intensity. Therefore,to accelerate the establishment of ecological civilization and achieve the goal of beautiful Shanxi,it is imperative to expand the investment of environmental governance and firmly implement the stringent environmental law to decline the industrial pollution directly. On the other hand,we should spare no effort to enforce and deepen the resource-oriented economic reform in Shanxi province. In the meantime,it is necessary to adjust the industrial structure and reduce the dependence of industrial economic development on the coal production and consumption. Another policy handle is to introduce the foreign investors with innovative ability and advanced production technology to set up factories. This measure can trigger the vitality of market entities,stimulate competitiveness of enterprises as well as optimize the production efficiency and quality of products. Only in this way can Shanxi province completely tackle the negative externalities in ecological environment.
引文
[1]柴莎莎,延军平,杨谨菲.山西经济增长与环境污染水平耦合协调度[J].干旱区资源与环境,2011,25(1):130-134.
[2]崔峰,韩晓敏.山西省经济增长与环境污染耦合关系研究[J].河南科学,2014,32(10):2190-2195.
[3]崔满红,李海生.资源依赖型区域转型发展的财政金融对策研究[M].北京:中国财政经济出版社,2012.
[4]国家统计局.《中国统计年鉴》[M].北京:中国统计出版社,2013.
[5]国家统计局.《中国统计年鉴》[M].北京:中国统计出版社,2010.
[6]国家统计局能源统计司.《中国能源统计年鉴》[M].北京:中国统计出版社,2013.
[7]国家发展和改革委员会.《国家应对气候变化规划(2014-2020年)》[R]. 2014.
[8]李锴,齐绍洲.贸易开放,经济增长与中国二氧化碳排放[J].经济研究,2011,(11):60-72.
[9]刘淑清.民生视角下山西省居民收入问题研究[M].北京:中国财政经济出版社,2012.
[10]陆旸.环境规制影响了污染密集型商品的贸易比较优势吗?[J].经济研究,2009,(4):28-40.
[11]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济,2014,(4):19-31.
[12]彭皓玥.自然资源约束下的中国资源型区域可持续发展研究[M].北京:中国财政经济出版社,2009.
[13]齐晓燕,郭丕斌.煤炭生产与经济增长和环境污染的关系研究:以山西省为例[J].中国矿业,2013,22(11):41-45.
[14]山西省统计局.《山西能源经济六十年》[M].北京:中国统计出版社,2009.
[15]陶学荣.公共行政管理学导论[M].北京:清华大学出版社有限公司,2005.
[16]王芳.中国工业污染现状及其影响因素研究-基于省际面板数据的实证分析[J].齐鲁师范学院学报,2014,29(4):128-134.
[17]王书斌,徐盈之.环境规制与雾霾脱钩效应——基于企业投资偏好的视角[J].中国工业经济,2015,(4):18-30.
[18]闫蕊.高经济增长及能源消耗趋势下的上海市工业污染排放量预测研究[D].无锡:江南大学,2013.
[19]闫新华,赵国浩.经济增长与环境污染的VAR模型分析-基于山西的实证研究[J].经济问题,2009,6:59-62.
[20]叶金珍,安虎森.开征环保税能有效治理空气污染吗?[J].中国工业经济,2017,(5):54-74.
[21]张复明,景普秋.资源型经济及其转型研究述评[J].中国社会科学,2006,(6):78-87.
[22]Ang,J. B. CO2Emissions,Research and Technology Transfer in China[J]. Ecological Economics,2009,68(10):2658-2665.
[23]Belsley,D. A.,E. Kuh,and R. E. Welsch. Regression Diagnostics:Identifying Influential Data and Sources of Collinearity[M]. Germany:John Wiley&Sons,Interscience,2005.
[24]Cole,M. A.,R. J. R. Elliot,and S. Wu. Industrial Activity and the Environment in China:An Industrylevel Analysis[J]. China Economic Review,2008,19(3):393-408.
[25]Copeland,B. R. International Trade and the Environment:Policy Reform in a Polluted Small Open Economy[J]. Journal of Environmental Economics and Management,1994,26(1):44-65.
[26]Dean,J. M. Trade and the Environment[R]. 1992.
[27]Dean,J. M. Testing the Impact of Trade Liberalization on the Environment:Theory and Evidence[J].Trade,Global Policy,and the Environment,2002,(402):55-63.
[28]Grossman,G. M. and A. B. Krueger. Environmental Impacts of a North American Free Trade Agreement[R]. 1991.
[29]Guan,D.,G. P. Peters,C. L. Weber,and K. Hubacek. Journey to World Top Emitter:An Analysis of the Driving Forces of China's Recent CO2Emissions Surge[J]. Geophysical Research Letters,2009,36(4).
[30]Han,H. L. Small Sample Properties of Canonical Cointegrating Regressions[J]. Empirical Economics,1996,21(2):235-253.
[31]Hansen,B. E. and P. C. B. Phillips. Estimation and Inference in Models of Cointegration:A Simulation Study[J]. Advances in Econometrics,1990,8(1989):225-248.
[32]Hong Kong Trade Development Council[HKTDC]. China’s Environmental Market[EB/OL].(2017-12-09)[2018-06-02]. www.hktdc.com/Research.
[33]Huang,J. and J. Xia. Regional Competition,Heterogeneous Factors and Pollution Intensity in China:A Spatial Econometric Analysis[J]. Sustainability,2016,8(2):171.
[34]National Development and Reform Commission of China. Intended Nationally Determined Contribution(INDC)[R]. 2015.
[35]Konuki,M. T. The Effects of Monetary and Fiscal Policy on Aggregate Demand in a Small Open Economy:An Application of the Structural Error Correction Model[R]. 2000.
[36]Kirkulak,B.,B. Qiu,and W. Yin. The Impact of FDI on Air Quality:Evidence from China[J]. Journal of Chinese Economic and Foreign Trade Studies,2011,4(2):81-98.
[37]Ouyang,X. and B. Lin. An Analysis of the Driving Forces of Energy-related Carbon Dioxide Emissions in China’s Industrial Sector[J]. Renewable and Sustainable Energy Reviews,2015,45:838-849.
[38]Montalvo,J. G. Comparing Cointegrating Regression Estimators:Some Additional Monte Carlo Results[J]. Economics Letters,1995,48(3-4):229-234.
[39]Park,J. Y. Canonical Cointegrating Regressions[J]. Econometrica:Journal of the Econometric Society,1992:119-143.
[40]Pearson,C. S. Multinational Corporations,Environment,and the Third World:Business Matters[R]. 1987.
[41]Phillips,P. C. B and B. E. Hansen. Statistical Inference in Instrumental Variables Regression with I(1)Processes[J]. The Review of Economic Studies,1990,57(1):99-125.
[42]Phillips,P. C. B and M. Loretan. Estimating Long-run Economic Equilibria[J]. The Review of Economic Studies,1991,58(3):407-436.
[43]Ren,S.,B. Yuan,X. Ma. and X. Chen. International Trade,FDI(Foreign Direct Investment)and Embodied CO2Emissions:A Case Study of China’s Industrial Sectors[J]. China Economic Review,2014a,28:123-134.
[44]Ren,S.,B. Yuan,X. Ma and X. Chen. The Impact of International Trade on China’s Industrial Carbon Emissions Since its Entry into WTO[J]. Energy Policy,2014b,69:624-634.
[45]Shao,S.,L. L. Yang,,M. B. Yu,and M. L. Yu. Estimation,Characteristics,and Determinants of Energy-related Industrial CO2Emissions in Shanghai(China),1994–2009[J]. Energy Policy,2011,39(10):6476-6494.
[46]Xu,B. and B. Lin. Regional Differences of Pollution Emissions in China:Contributing Factors and Mitigation Strategies[J]. Journal of Cleaner Production,2016,112:1454-1463.
[47]Zhang,J.,M. Fu,Y. Geng,and J. Tao. Energy Saving and Emission Reduction:A Project of Coal-resource Integration in Shanxi Province,China[J]. Energy Policy,2011,39(6):3029-3032.
[48]Zhang,L.,P. K. Adom,and Y. An. Regulation-induced Structural Break and the Long-run Drivers of Industrial Pollution Intensity in China[J]. Journal of Cleaner Production,2018,198:121-132.
[49]Zhang,L. and Y. An. The Government Capacity on Industrial Pollution Management in Shanxi Province:A Response Impulse Analysis[J]. Journal of Environmental Management,2018,(223):1037-1046.
[50]Zhang,Y. J. The Impact of Financial Development on Carbon Emissions:An Empirical Analysis in China[J]. Energy Policy,2011,39:2197–2203.
[51]Zhou,X. Y.,J. Zhang,and J. P. Li. Industrial Structural Transformation and Carbon Dioxide Emissions in China[J]. Energy Policy,2013,57:43–51.
(1)本文选取的六类污染物中的五类的计量单位是一致的,故根据六类污染物提取PCA指数是合理的。
(1)Phillips和Hansen(1990)、Phillips和Loretan(1991)针对时间序列小样本,从理论角度阐述并运用Monte Carlo方法论证了FMOLS估计量相较于其他估计量得到的较小估计偏误。其中,Phillips和Hansen(1990)还指出当样本容量仅有50个时,模拟得到的结果精确性是相当好的。Hansen和Phillips(1990)不仅从理论上得出在样本容量为100时,FMOLS表现出较好的偏误纠正性,还用消费函数的实例加以说明。Park(1992),Montalvo(1995)和Han(1996)同样从理论上一致得出针对有限样本容量尤其是小样本,CCR得到的估计值的均方根误差和偏差要小于最小二乘估计量的估计结果,即表明CCR对模型的估计有较高的精准度。Konuki(2000)从实证角度分析了瑞士从1975Q4-1996Q4这20年间的货币与财政政策对于该国整体需求的影响,依旧得到了稳健可信的实证研究结果。
(1)本文的时间序列分析中,对煤炭生产量在总能源生产中的比重以及煤炭消费量在总能源消费中的比重分别进行了回归分析,并以此为依据进行对比。该方法可能并不是最合理,但目前尚无更合适的方法来研究比较。
(1)为使得计算结果尽可能准确、一致、合理、可比,所有估算均选用MP3、MC3长期估计结果。
(1)在采矿业与经济学中,贱金属指不包括贵金属的黑色金属及有色金属。主要有铁、钢、铜、镍、铝、铅、锌、锡、钨、钼、镁等。
[2]崔峰,韩晓敏.山西省经济增长与环境污染耦合关系研究[J].河南科学,2014,32(10):2190-2195.
[3]崔满红,李海生.资源依赖型区域转型发展的财政金融对策研究[M].北京:中国财政经济出版社,2012.
[4]国家统计局.《中国统计年鉴》[M].北京:中国统计出版社,2013.
[5]国家统计局.《中国统计年鉴》[M].北京:中国统计出版社,2010.
[6]国家统计局能源统计司.《中国能源统计年鉴》[M].北京:中国统计出版社,2013.
[7]国家发展和改革委员会.《国家应对气候变化规划(2014-2020年)》[R]. 2014.
[8]李锴,齐绍洲.贸易开放,经济增长与中国二氧化碳排放[J].经济研究,2011,(11):60-72.
[9]刘淑清.民生视角下山西省居民收入问题研究[M].北京:中国财政经济出版社,2012.
[10]陆旸.环境规制影响了污染密集型商品的贸易比较优势吗?[J].经济研究,2009,(4):28-40.
[11]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济,2014,(4):19-31.
[12]彭皓玥.自然资源约束下的中国资源型区域可持续发展研究[M].北京:中国财政经济出版社,2009.
[13]齐晓燕,郭丕斌.煤炭生产与经济增长和环境污染的关系研究:以山西省为例[J].中国矿业,2013,22(11):41-45.
[14]山西省统计局.《山西能源经济六十年》[M].北京:中国统计出版社,2009.
[15]陶学荣.公共行政管理学导论[M].北京:清华大学出版社有限公司,2005.
[16]王芳.中国工业污染现状及其影响因素研究-基于省际面板数据的实证分析[J].齐鲁师范学院学报,2014,29(4):128-134.
[17]王书斌,徐盈之.环境规制与雾霾脱钩效应——基于企业投资偏好的视角[J].中国工业经济,2015,(4):18-30.
[18]闫蕊.高经济增长及能源消耗趋势下的上海市工业污染排放量预测研究[D].无锡:江南大学,2013.
[19]闫新华,赵国浩.经济增长与环境污染的VAR模型分析-基于山西的实证研究[J].经济问题,2009,6:59-62.
[20]叶金珍,安虎森.开征环保税能有效治理空气污染吗?[J].中国工业经济,2017,(5):54-74.
[21]张复明,景普秋.资源型经济及其转型研究述评[J].中国社会科学,2006,(6):78-87.
[22]Ang,J. B. CO2Emissions,Research and Technology Transfer in China[J]. Ecological Economics,2009,68(10):2658-2665.
[23]Belsley,D. A.,E. Kuh,and R. E. Welsch. Regression Diagnostics:Identifying Influential Data and Sources of Collinearity[M]. Germany:John Wiley&Sons,Interscience,2005.
[24]Cole,M. A.,R. J. R. Elliot,and S. Wu. Industrial Activity and the Environment in China:An Industrylevel Analysis[J]. China Economic Review,2008,19(3):393-408.
[25]Copeland,B. R. International Trade and the Environment:Policy Reform in a Polluted Small Open Economy[J]. Journal of Environmental Economics and Management,1994,26(1):44-65.
[26]Dean,J. M. Trade and the Environment[R]. 1992.
[27]Dean,J. M. Testing the Impact of Trade Liberalization on the Environment:Theory and Evidence[J].Trade,Global Policy,and the Environment,2002,(402):55-63.
[28]Grossman,G. M. and A. B. Krueger. Environmental Impacts of a North American Free Trade Agreement[R]. 1991.
[29]Guan,D.,G. P. Peters,C. L. Weber,and K. Hubacek. Journey to World Top Emitter:An Analysis of the Driving Forces of China's Recent CO2Emissions Surge[J]. Geophysical Research Letters,2009,36(4).
[30]Han,H. L. Small Sample Properties of Canonical Cointegrating Regressions[J]. Empirical Economics,1996,21(2):235-253.
[31]Hansen,B. E. and P. C. B. Phillips. Estimation and Inference in Models of Cointegration:A Simulation Study[J]. Advances in Econometrics,1990,8(1989):225-248.
[32]Hong Kong Trade Development Council[HKTDC]. China’s Environmental Market[EB/OL].(2017-12-09)[2018-06-02]. www.hktdc.com/Research.
[33]Huang,J. and J. Xia. Regional Competition,Heterogeneous Factors and Pollution Intensity in China:A Spatial Econometric Analysis[J]. Sustainability,2016,8(2):171.
[34]National Development and Reform Commission of China. Intended Nationally Determined Contribution(INDC)[R]. 2015.
[35]Konuki,M. T. The Effects of Monetary and Fiscal Policy on Aggregate Demand in a Small Open Economy:An Application of the Structural Error Correction Model[R]. 2000.
[36]Kirkulak,B.,B. Qiu,and W. Yin. The Impact of FDI on Air Quality:Evidence from China[J]. Journal of Chinese Economic and Foreign Trade Studies,2011,4(2):81-98.
[37]Ouyang,X. and B. Lin. An Analysis of the Driving Forces of Energy-related Carbon Dioxide Emissions in China’s Industrial Sector[J]. Renewable and Sustainable Energy Reviews,2015,45:838-849.
[38]Montalvo,J. G. Comparing Cointegrating Regression Estimators:Some Additional Monte Carlo Results[J]. Economics Letters,1995,48(3-4):229-234.
[39]Park,J. Y. Canonical Cointegrating Regressions[J]. Econometrica:Journal of the Econometric Society,1992:119-143.
[40]Pearson,C. S. Multinational Corporations,Environment,and the Third World:Business Matters[R]. 1987.
[41]Phillips,P. C. B and B. E. Hansen. Statistical Inference in Instrumental Variables Regression with I(1)Processes[J]. The Review of Economic Studies,1990,57(1):99-125.
[42]Phillips,P. C. B and M. Loretan. Estimating Long-run Economic Equilibria[J]. The Review of Economic Studies,1991,58(3):407-436.
[43]Ren,S.,B. Yuan,X. Ma. and X. Chen. International Trade,FDI(Foreign Direct Investment)and Embodied CO2Emissions:A Case Study of China’s Industrial Sectors[J]. China Economic Review,2014a,28:123-134.
[44]Ren,S.,B. Yuan,X. Ma and X. Chen. The Impact of International Trade on China’s Industrial Carbon Emissions Since its Entry into WTO[J]. Energy Policy,2014b,69:624-634.
[45]Shao,S.,L. L. Yang,,M. B. Yu,and M. L. Yu. Estimation,Characteristics,and Determinants of Energy-related Industrial CO2Emissions in Shanghai(China),1994–2009[J]. Energy Policy,2011,39(10):6476-6494.
[46]Xu,B. and B. Lin. Regional Differences of Pollution Emissions in China:Contributing Factors and Mitigation Strategies[J]. Journal of Cleaner Production,2016,112:1454-1463.
[47]Zhang,J.,M. Fu,Y. Geng,and J. Tao. Energy Saving and Emission Reduction:A Project of Coal-resource Integration in Shanxi Province,China[J]. Energy Policy,2011,39(6):3029-3032.
[48]Zhang,L.,P. K. Adom,and Y. An. Regulation-induced Structural Break and the Long-run Drivers of Industrial Pollution Intensity in China[J]. Journal of Cleaner Production,2018,198:121-132.
[49]Zhang,L. and Y. An. The Government Capacity on Industrial Pollution Management in Shanxi Province:A Response Impulse Analysis[J]. Journal of Environmental Management,2018,(223):1037-1046.
[50]Zhang,Y. J. The Impact of Financial Development on Carbon Emissions:An Empirical Analysis in China[J]. Energy Policy,2011,39:2197–2203.
[51]Zhou,X. Y.,J. Zhang,and J. P. Li. Industrial Structural Transformation and Carbon Dioxide Emissions in China[J]. Energy Policy,2013,57:43–51.
(1)本文选取的六类污染物中的五类的计量单位是一致的,故根据六类污染物提取PCA指数是合理的。
(1)Phillips和Hansen(1990)、Phillips和Loretan(1991)针对时间序列小样本,从理论角度阐述并运用Monte Carlo方法论证了FMOLS估计量相较于其他估计量得到的较小估计偏误。其中,Phillips和Hansen(1990)还指出当样本容量仅有50个时,模拟得到的结果精确性是相当好的。Hansen和Phillips(1990)不仅从理论上得出在样本容量为100时,FMOLS表现出较好的偏误纠正性,还用消费函数的实例加以说明。Park(1992),Montalvo(1995)和Han(1996)同样从理论上一致得出针对有限样本容量尤其是小样本,CCR得到的估计值的均方根误差和偏差要小于最小二乘估计量的估计结果,即表明CCR对模型的估计有较高的精准度。Konuki(2000)从实证角度分析了瑞士从1975Q4-1996Q4这20年间的货币与财政政策对于该国整体需求的影响,依旧得到了稳健可信的实证研究结果。
(1)本文的时间序列分析中,对煤炭生产量在总能源生产中的比重以及煤炭消费量在总能源消费中的比重分别进行了回归分析,并以此为依据进行对比。该方法可能并不是最合理,但目前尚无更合适的方法来研究比较。
(1)为使得计算结果尽可能准确、一致、合理、可比,所有估算均选用MP3、MC3长期估计结果。
(1)在采矿业与经济学中,贱金属指不包括贵金属的黑色金属及有色金属。主要有铁、钢、铜、镍、铝、铅、锌、锡、钨、钼、镁等。