基于空间计量的中国省域火电行业碳排放效率分析
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摘要
火力发电是电力行业碳排放的主要来源,火电行业降低碳排放强度对中国实现节能减排,低碳发展具有重要意义。采用2003—2016年30个省份面板数据,利用超效率SBM模型测算中国30个省市火电行业碳排放效率,并结合Moran指数对火电行业碳排放效率空间相关性进行诊断,再利用空间面板模型,分析影响火电行业碳排放效率的因素。结果表明:中国省域火电行业碳排放效率存在一定区域差异,表现为东西中部依次递减,且区域间火电行业碳排放效率具有显著空间相关性,并随着时间的推移加强。影响因素中,人口规模、人均收入水平、政府投资、城镇化水平对中国电力系统碳排放效率的影响为正,产业结构、能源结构的影响为负,同时人均收入水平、城镇化水平存在正向空间溢出应,产业结构、政府投资存在负向空间溢出效应,企业研发水平对电力系统碳排放效率影响不显著。
Thermal power generation is the main source of carbon emissions in power industry, and reducing the intensity of carbon emissions in thermal power industry is of great significance for China to achieve energy conservation, emission reduction and low-carbon development. Based on the panel data of 30 provinces from 2003 to 2016, the carbon emission efficiency of thermal power industry in 30 provinces and cities in China is estimated by using Super-SBM model, and the spatial correlation of carbon emission efficiency of thermal power industry is diagnosed by combining Moran index. And then, the factors affecting carbon emission efficiency of thermal power industry are analyzed by using spatial panel model. The results show that there exist some regional differences in the carbon emission efficiency of thermal power industry in China, presenting a tendency that the carbon emission efficiency of thermal power industry decreases successively from east to west, and there exists a significant spatial correlation among the regions which is strengthened with the passage of time. Among the influencing factors, population size, per capita income level, government investment and urbanization level have positive effects on carbon emission efficiency of China power system while industrial structure and government investment have negative spatial spillover effect, and the level of enterprises' R&D has no significant impact on carbon emission efficiency of power system.
Thermal power generation is the main source of carbon emissions in power industry, and reducing the intensity of carbon emissions in thermal power industry is of great significance for China to achieve energy conservation, emission reduction and low-carbon development. Based on the panel data of 30 provinces from 2003 to 2016, the carbon emission efficiency of thermal power industry in 30 provinces and cities in China is estimated by using Super-SBM model, and the spatial correlation of carbon emission efficiency of thermal power industry is diagnosed by combining Moran index. And then, the factors affecting carbon emission efficiency of thermal power industry are analyzed by using spatial panel model. The results show that there exist some regional differences in the carbon emission efficiency of thermal power industry in China, presenting a tendency that the carbon emission efficiency of thermal power industry decreases successively from east to west, and there exists a significant spatial correlation among the regions which is strengthened with the passage of time. Among the influencing factors, population size, per capita income level, government investment and urbanization level have positive effects on carbon emission efficiency of China power system while industrial structure and government investment have negative spatial spillover effect, and the level of enterprises' R&D has no significant impact on carbon emission efficiency of power system.
引文
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[2]刘红琴,王高天,陈品文,等.地区电力行业碳排放水平测算及其特点分析[J].生态经济,2018(4):34-39.
[3]ANG B W. Decomposition Analysis For Policymaking in Energy:Which is the prefered method?[J].Energy Policy,2004,32(9):1131-1139.
[4]KAYA Y,YOKOBORI K.Environment,energy and economy:Strategies for sustainability[M].United Nations University Press,1997.
[5]潘家华,庄贵阳,郑艳,等.低碳经济的概念辨识及核心要素分析[J].国际经济评论,2010(4):88-101.
[6]LIN B,FEI R.Regional differences of CO2 emissions performance in China’s chemical industry[J].Journal of Cleaner Production,2015,87:235-244.
[7]FARE R.GROSSKOPF S, PASURKA C A.Environmental production functions and environmental directional distance functions[J].Energy,2007,32(7):1055-1066.
[8]CHEN S, GOLLY J.Green pruductivity growth in China’s industrial economy[J].Energy Economics,2014,44:89-98.
[9]林善浪,张作雄,刘国平.技术创新、空间集聚和区域碳生产率[J].中国人口·资源与环境,2013(23):36-45.
[10]龙如银,邵天翔.中国三大经济圈碳生产率差异及影响因素[J].资源科学,2015(6):1249-1257.
[11]张诗青,王建伟.中国交通运输碳排放及影响因素时空差异分析[J].环境科学学报,2017(12):4787-4797.
[12]BURNETT J W,BERGSTORM J C,DORFMAN J H.A spatial data approach to estimating U.S. state-level energy emissions[J].Energy Economics,2013,40(2):396-404.
[13]ZHANG Q,YANG J,SUN Z, et al. Analyzing the impact factors of energy-related CO2 emissions in China:What can spatial panel regressions tell us?[J].Journal of Cleaner Production,2017,32:213-223.
[14]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017(4):687-697.
[15]李若影.基于空间计量模型的中国交通运输业碳排放影响因素分析[D].西安:长安大学,2017.
[16]马大来.中国农业能源碳排放效率的空间异质性及其影响因素——基于空间面板数据模型的实证研究[J].资源开发与市场,2018(12):1693-1705.
[17]任玉珑,黄守军,张谦. 计及碳排放权交易的电力市场纵向合作减排策略研究[J].产业经济研究,2010(5):51-57.
[18]ZHAO X,MA Q,YANG R.Factor Influencing CO2 emissions in China’s power industry:Co-intergration analysis[J].Energy Policy,2013,57(6):89-98.
[19]YUAN J, ZHAO C,YU S,et al.Electricity consumpition and economic growth in China:Cointegration and co-feature Analysis[J].Energy Economics,2007,29(6):1179-1191.
[20]林伯强.电力消费与中国经济增长:基于生产函数的研究[J].管理世界,2003(11):18-27.
[21]付坤,齐绍洲.中国省级电力碳排放责任核算方法及应用[J].中国人口·资源与环境,2014(4):27-34.
[22]王常凯,谢宏佐.中国电力碳排放动态特征及影响因素研究[J].中国人口·资源与环境,2015(4):21-27.
[23]齐绍州,林灿.电力系统碳排放效率的影响因素——基于长江经济带空间动态面板实证研究[J].环境经济研究,2016(1):91-105.
[24]王喜平,王玥玥.基于LMDI的电力行业CO2排放影响因素分析[J].黑龙江电力,2016(4):343-366.
[25]TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research,2001,130(3):498-509.
[26]马大来,陈仲常.中国省级碳排放效率的空间计量[J].中国人口·资源与环境,2015(1):67-77.
[27]马晓明,张灿,熊思琴,等.中国区域工业环境效率及其影响因素:基于SUPER-SBM的实证分析[J].生态经济,2018(11):96-102.
[28]吴玉鸣,李建霞.省域经济增长与电力消费的局域空间计量经济分析[J].地理科学,2013(1):30-35.
[29]ELHORST J P.MATLAB software for spatial pane[J].Internal Regional Science Review,2012(3):389-405.
[30]郝宇,廖华,魏一鸣.中国能源消费和电力消费的环境库涅尼兹曲线:基于面板数据空间计量模型的分析[J].中国软科学,2014(11):134-146.