中国建筑业碳生产率的俱乐部收敛及成因
|
摘要
绿色发展和生态文明作为未来的发展战略已被写入中华人民共和国宪法。低碳经济是支撑和实现绿色发展和生态文明的经济形态,其实质是在完成CO_2减排目标的同时实现经济增长。碳生产率是连接CO_2减排与经济增长两个目标的桥梁,提高碳生产率是发展低碳经济的核心和关键。建筑业的能耗和CO_2排放分别占中国各产业总和的1/4和1/3,是绿色发展和生态文明建设需要重点关注的行业。本文提出三阶段方法框架,研究动态建筑业全要素碳生产率(DCP)的收敛俱乐部及初始成因:首先,基于数据包络分析求解的方向距离函数和Malmquist指数方法,测算2005—2016年中国30个省、自治区和直辖市的DCP;然后,用基于非线性时变因子模型的俱乐部检验方法,识别中国省际DCP的收敛俱乐部;最后,用ordered logit回归模型对可能影响"收敛俱乐部"形成的初始因素进行探寻。结果显示:(1)中国DCP的均值呈上升趋势、标准差呈扩大趋势,尤其是2010年以后的标准差急剧扩大;(2)中国省级DCP存在5个收敛俱乐部,但有13个省不收敛于任何俱乐部;(3)样本观测期初"建筑业市场化程度"越高的省份,归属于"DCP高的俱乐部"的概率越大。据此,提出如下提升DCP的管理启示:(1)促进低碳建造技术有效扩散,缩小各省份之间的DCP水平差距;(2)制定和实施建筑业低碳发展的相关政策举措时,不能简单地按地理区划,而需要考虑各省的异质性;(3)继续推进国有建筑业企业的市场化改革,进一步提高建筑业市场化水平,促进建筑业专业承包企业的发展,适当降低建筑业的产业集中度。该方法框架也可用于研究其他国家、地区或其他行业。
The green development and ecological civilization had been written into China's constitution as a development strategy. The low-carbon economy is an economic form that supports and realizes green development and ecological civilization. Its essence is to achieve economic growth while achieving the goal of carbon dioxide( CO_2) emission reduction. Carbon productivity is the bridge linking CO_2 emission reduction and economic growth. Increasing carbon productivity is the core and critical path to the development of the low-carbon economy. The energy consumption and CO_2 emissions from the construction industry account for one-fourth and one-third of the national total in China,respectively. The green development and ecological civilization need to focus on the construction industry. A three-phase procedural method framework was proposed to examine the dynamic construction industry total factor carbon productivity( DCP) convergence clubs and initial conditioning factors in this paper. Firstly,the directional distance function and Malmquist index method based on data envelopment analysis( DEA) was applied to estimate the DCP of 30 province,autonomous regions and municipalities in China from 2005 to 2016. Then,the club test method based on non-linear time-varying factor model was applied to identify the convergence clubs of China's provincial DCP. Finally,the ordered logit regression model was applied to explore the initial factors that may affect the formation of convergence clubs. The results showed that:(1)The arithmetic mean value of China's provincial DCP showed an upward trend and the standard deviation showed an expanding trend,and the standard deviation sharply expanded after 2010.(2)There are 5 convergence clubs in China's provincial DCP. But 13 provinces failed to converge to any club.(3)With the higher 'marketization of construction industry'degree at the beginning of the sample observation period,the greater the probability that the provinces belongs to club with higher DCP. Based on these findings,the following suggestions for improving the DCP are proposed:(1) The effective diffusion of low-carbon construction technologies should be promoted to narrow the gap between provinces DCP.(2) Instead of formulating and implementing relevant policies and measures for low-carbon development simply by geographical divisions,it is necessary to consider the heterogeneity among different provinces in construction industry.(3)In order for each province to become a member of a club with higher DCP,it is necessary to continue to promote the market-oriented reform of stateowned construction companies,support the development of professional companies,and appropriately lower the degree of industrial concentration. The three-phase procedural method framework can also be applied to study different industries in different countries or regions.
The green development and ecological civilization had been written into China's constitution as a development strategy. The low-carbon economy is an economic form that supports and realizes green development and ecological civilization. Its essence is to achieve economic growth while achieving the goal of carbon dioxide( CO_2) emission reduction. Carbon productivity is the bridge linking CO_2 emission reduction and economic growth. Increasing carbon productivity is the core and critical path to the development of the low-carbon economy. The energy consumption and CO_2 emissions from the construction industry account for one-fourth and one-third of the national total in China,respectively. The green development and ecological civilization need to focus on the construction industry. A three-phase procedural method framework was proposed to examine the dynamic construction industry total factor carbon productivity( DCP) convergence clubs and initial conditioning factors in this paper. Firstly,the directional distance function and Malmquist index method based on data envelopment analysis( DEA) was applied to estimate the DCP of 30 province,autonomous regions and municipalities in China from 2005 to 2016. Then,the club test method based on non-linear time-varying factor model was applied to identify the convergence clubs of China's provincial DCP. Finally,the ordered logit regression model was applied to explore the initial factors that may affect the formation of convergence clubs. The results showed that:(1)The arithmetic mean value of China's provincial DCP showed an upward trend and the standard deviation showed an expanding trend,and the standard deviation sharply expanded after 2010.(2)There are 5 convergence clubs in China's provincial DCP. But 13 provinces failed to converge to any club.(3)With the higher 'marketization of construction industry'degree at the beginning of the sample observation period,the greater the probability that the provinces belongs to club with higher DCP. Based on these findings,the following suggestions for improving the DCP are proposed:(1) The effective diffusion of low-carbon construction technologies should be promoted to narrow the gap between provinces DCP.(2) Instead of formulating and implementing relevant policies and measures for low-carbon development simply by geographical divisions,it is necessary to consider the heterogeneity among different provinces in construction industry.(3)In order for each province to become a member of a club with higher DCP,it is necessary to continue to promote the market-oriented reform of stateowned construction companies,support the development of professional companies,and appropriately lower the degree of industrial concentration. The three-phase procedural method framework can also be applied to study different industries in different countries or regions.
引文
[1]何建坤,滕飞,齐晔.新气候经济学的研究任务和方向探讨[J].中国人口·资源与环境,2014,24(8):1-8.
[2]LI W,WANG W,WANG Y,et al.Historical growth in total factor carbon productivity of the Chinese industry:a comprehensive analysis[J].Journal of cleaner production,2018,170:471-485.
[3]滕泽伟,胡宗彪,蒋西艳.中国服务业碳生产率变动的差异及收敛性研究[J].数量经济技术经济研究,2017(3):78-94.
[4]刘传江,赵晓梦.长江经济带全要素碳生产率的时空演化及提升潜力[J].长江流域资源与环境,2016,25(11):1635-1644.
[5]KAYA Y,YOKOBORI K.Environment,energy and economy:Strategies for sustainability[M].Tokyo:United Nations University Press,1998.
[6]张成,王建科,史文悦,等.中国区域碳生产率波动的因素分解[J].中国人口·资源与环境,2014(10):41-47.
[7]张丽峰.基于DEA模型的全要素碳生产率与影响因素研究[J].工业技术经济,2013(3):142-149.
[8]BEINHOCKER E,OPPENHEIM J,IRONS B,et al.The carbon productivity challenge:curbing climate change and sustaining economic growth[R/OL].[2018-08-12].https://www.mckinsey.com/~/media/Mc Kinsey/Business%20Functions/Sustainability%20and%20Resource%20Productivity/Our%20Insights/The%20carbon%20productivity%20challenge/MGI_carbon_productivity_challenge_report.ashx.
[9]李小平,王洋.“一带一路”沿线主要国家碳生产率收敛性及其影响因素分析[J].武汉大学学报(哲学社会科学版),2017,70(3):58-76.
[10]CHEN J,SHEN L,SONG X,et al.An empirical study on the CO2emissions in the Chinese construction industry[J].Journal of cleaner production,2017,168:645-654.
[11]PHILLIPS P C B,SUL D.Transition modeling and econometric convergence tests[J].Econometrica,2007,75(6):1771-1855.
[12]袁润松,丰超,王苗,等.中国区域绿色低碳生产率增长源泉分解研究[J].福建师范大学学报(哲学社会科学版),2016(5):9-16.
[13]杨翔,李小平,周大川.中国制造业碳生产率的差异与收敛性研究[J].数量经济技术经济研究,2015(12):3-20.
[14]YU Y,CHOI Y,WEI X,et al.Did China’s regional transport industry enjoy better carbon productivity under regulations?[J].Journal of cleaner production,2017,165:777-87.
[15]BARRO R J,SALA-I-MARTIN X.Convergence[J].Journal of political economy,1992,100(2):223-51.
[16]蔡海亚,徐盈之,孙文远.中国雾霾污染强度的地区差异与收敛性研究---基于省际面板数据的实证检验[J].山西财经大学学报,2017,39(3):1-14.
[17]张珩,罗剑朝,牛荣.产权改革与农信社效率变化及其收敛性:2008-2014年---来自陕西省107个县(区)的经验证据[J].管理世界,2017(5):92-106.
[18]景守武,张捷.我国省际能源环境效率收敛性分析[J].山西财经大学学报,2018(1):1-11.
[19]李健,盘宇章.中国城市生产率增长差异及收敛性分析[J].城市问题,2018(1):56-64.
[20]贺祥民,赖永剑.基于非线性时变因子模型的地区环境效率俱乐部收敛分析[J].软科学,2017,31(3):103-6.
[21]赖永剑,贺祥民.我国区域金融包容水平的俱乐部收敛研究---采用非线性时变因子模型的实证分析[J].西部论坛,2017,27(4):59-65.
[22]胡宗义,赵晟,唐李伟.中国农村金融发展的收敛性研究[J].统计与决策,2016(10):128-132.
[23]PARKER S,LIDDLE B.Analysing energy productivity dynamics in the OECD manufacturing sector[J].Energy economics,2017,67:91-97.
[24]APERGIS N,PAYNE J E.Per capita carbon dioxide emissions across US states by sector and fossil fuel source:evidence from club convergence tests[J].Energy economics,2017,63:365-372.
[25]BARTKOWSKA M,RIEDL A.Regional convergence clubs in Europe:identification and conditioning factors[J].Economic modelling,2012,29(1):22-31.
[26]ZHANG P,YOU J,JIA G,et al.Estimation of carbon efficiency decomposition in materials and potential material savings for China’s construction industry[J].Resources policy,2018(6):12.
[27]HORTA I M,CAMANHO A S.A nonparametric methodology for evaluating convergence in a multi-input multi-output setting[J].European journal of operational research,2015,246(2):554-561.
[28]FARRELL M J.The measurement of productive efficiency[J].Journal of the Royal Statistical Society series A(general),1957,120(3):253-290.
[29]CAVES D W,CHRISTENSEN L R,DIEWERT W E.The economic theory of index numbers and the measurement of input,output and productivity[J].Econometrica,1982,50(6):1393-1414.
[30]冯博,王雪青.中国建筑业能源经济效率与能源环境效率研究---基于SBM模型和面板Tobit模型的两阶段分析[J].北京理工大学学报(社会科学版),2015,17(1):14-22.
[31]GAO T,SHEN L,SHEN M,et al.Evolution and projection of CO2emissions for China’s cement industry from 1980 to 2020[J].Renewable&sustainable energy reviews,2017,74:522-537.
[32]JING R,CHENG J C P,GAN V J L,et al.Comparison of greenhouse gas emission accounting methods for steel production in China[J].Journal of cleaner production,2014,83:165-172.
[33]严玉廷,刘晶茹,丁宁,等.中国平板玻璃生产碳排放研究[J].环境科学学报,2017,37(8):3213-3219.
[34]HAO H,GENG Y,HANG W.GHG emissions from primary aluminum production in China:regional disparity and policy implications[J].Applied energy,2016,166:264-272.
[35]赵守清.试析基于行业标准的木材生产作业系统碳排放[J].花卉,2016(4):47-49.
[36]谭丹,王广斌,曹冬平.建筑业全要素生产率的增长特征及其影响因素[J].同济大学学报(自然科学版),2015,43(12):1901-1907.
[37]刘炳胜,陈晓红,王雪青,等.中国区域建筑产业TFP变化趋势与影响因素分析[J].系统工程理论与实践,2013,33(4):1041-1049.
[38]杨仕辉,余敏.碳配额不同分配机制下供应链碳减排优化策略[J].经济与管理评论,2016(6):35-42.
[39]王雪青,娄香珍,杨秋波.中国建筑业能源效率省际差异及其影响因素分析[J].中国人口·资源与环境,2012,22(2):56-61.
[40]胡颖,诸大建.中国建筑业CO2排放与产值、能耗的脱钩分析[J].中国人口·资源与环境,2015,25(8):50-57.
[2]LI W,WANG W,WANG Y,et al.Historical growth in total factor carbon productivity of the Chinese industry:a comprehensive analysis[J].Journal of cleaner production,2018,170:471-485.
[3]滕泽伟,胡宗彪,蒋西艳.中国服务业碳生产率变动的差异及收敛性研究[J].数量经济技术经济研究,2017(3):78-94.
[4]刘传江,赵晓梦.长江经济带全要素碳生产率的时空演化及提升潜力[J].长江流域资源与环境,2016,25(11):1635-1644.
[5]KAYA Y,YOKOBORI K.Environment,energy and economy:Strategies for sustainability[M].Tokyo:United Nations University Press,1998.
[6]张成,王建科,史文悦,等.中国区域碳生产率波动的因素分解[J].中国人口·资源与环境,2014(10):41-47.
[7]张丽峰.基于DEA模型的全要素碳生产率与影响因素研究[J].工业技术经济,2013(3):142-149.
[8]BEINHOCKER E,OPPENHEIM J,IRONS B,et al.The carbon productivity challenge:curbing climate change and sustaining economic growth[R/OL].[2018-08-12].https://www.mckinsey.com/~/media/Mc Kinsey/Business%20Functions/Sustainability%20and%20Resource%20Productivity/Our%20Insights/The%20carbon%20productivity%20challenge/MGI_carbon_productivity_challenge_report.ashx.
[9]李小平,王洋.“一带一路”沿线主要国家碳生产率收敛性及其影响因素分析[J].武汉大学学报(哲学社会科学版),2017,70(3):58-76.
[10]CHEN J,SHEN L,SONG X,et al.An empirical study on the CO2emissions in the Chinese construction industry[J].Journal of cleaner production,2017,168:645-654.
[11]PHILLIPS P C B,SUL D.Transition modeling and econometric convergence tests[J].Econometrica,2007,75(6):1771-1855.
[12]袁润松,丰超,王苗,等.中国区域绿色低碳生产率增长源泉分解研究[J].福建师范大学学报(哲学社会科学版),2016(5):9-16.
[13]杨翔,李小平,周大川.中国制造业碳生产率的差异与收敛性研究[J].数量经济技术经济研究,2015(12):3-20.
[14]YU Y,CHOI Y,WEI X,et al.Did China’s regional transport industry enjoy better carbon productivity under regulations?[J].Journal of cleaner production,2017,165:777-87.
[15]BARRO R J,SALA-I-MARTIN X.Convergence[J].Journal of political economy,1992,100(2):223-51.
[16]蔡海亚,徐盈之,孙文远.中国雾霾污染强度的地区差异与收敛性研究---基于省际面板数据的实证检验[J].山西财经大学学报,2017,39(3):1-14.
[17]张珩,罗剑朝,牛荣.产权改革与农信社效率变化及其收敛性:2008-2014年---来自陕西省107个县(区)的经验证据[J].管理世界,2017(5):92-106.
[18]景守武,张捷.我国省际能源环境效率收敛性分析[J].山西财经大学学报,2018(1):1-11.
[19]李健,盘宇章.中国城市生产率增长差异及收敛性分析[J].城市问题,2018(1):56-64.
[20]贺祥民,赖永剑.基于非线性时变因子模型的地区环境效率俱乐部收敛分析[J].软科学,2017,31(3):103-6.
[21]赖永剑,贺祥民.我国区域金融包容水平的俱乐部收敛研究---采用非线性时变因子模型的实证分析[J].西部论坛,2017,27(4):59-65.
[22]胡宗义,赵晟,唐李伟.中国农村金融发展的收敛性研究[J].统计与决策,2016(10):128-132.
[23]PARKER S,LIDDLE B.Analysing energy productivity dynamics in the OECD manufacturing sector[J].Energy economics,2017,67:91-97.
[24]APERGIS N,PAYNE J E.Per capita carbon dioxide emissions across US states by sector and fossil fuel source:evidence from club convergence tests[J].Energy economics,2017,63:365-372.
[25]BARTKOWSKA M,RIEDL A.Regional convergence clubs in Europe:identification and conditioning factors[J].Economic modelling,2012,29(1):22-31.
[26]ZHANG P,YOU J,JIA G,et al.Estimation of carbon efficiency decomposition in materials and potential material savings for China’s construction industry[J].Resources policy,2018(6):12.
[27]HORTA I M,CAMANHO A S.A nonparametric methodology for evaluating convergence in a multi-input multi-output setting[J].European journal of operational research,2015,246(2):554-561.
[28]FARRELL M J.The measurement of productive efficiency[J].Journal of the Royal Statistical Society series A(general),1957,120(3):253-290.
[29]CAVES D W,CHRISTENSEN L R,DIEWERT W E.The economic theory of index numbers and the measurement of input,output and productivity[J].Econometrica,1982,50(6):1393-1414.
[30]冯博,王雪青.中国建筑业能源经济效率与能源环境效率研究---基于SBM模型和面板Tobit模型的两阶段分析[J].北京理工大学学报(社会科学版),2015,17(1):14-22.
[31]GAO T,SHEN L,SHEN M,et al.Evolution and projection of CO2emissions for China’s cement industry from 1980 to 2020[J].Renewable&sustainable energy reviews,2017,74:522-537.
[32]JING R,CHENG J C P,GAN V J L,et al.Comparison of greenhouse gas emission accounting methods for steel production in China[J].Journal of cleaner production,2014,83:165-172.
[33]严玉廷,刘晶茹,丁宁,等.中国平板玻璃生产碳排放研究[J].环境科学学报,2017,37(8):3213-3219.
[34]HAO H,GENG Y,HANG W.GHG emissions from primary aluminum production in China:regional disparity and policy implications[J].Applied energy,2016,166:264-272.
[35]赵守清.试析基于行业标准的木材生产作业系统碳排放[J].花卉,2016(4):47-49.
[36]谭丹,王广斌,曹冬平.建筑业全要素生产率的增长特征及其影响因素[J].同济大学学报(自然科学版),2015,43(12):1901-1907.
[37]刘炳胜,陈晓红,王雪青,等.中国区域建筑产业TFP变化趋势与影响因素分析[J].系统工程理论与实践,2013,33(4):1041-1049.
[38]杨仕辉,余敏.碳配额不同分配机制下供应链碳减排优化策略[J].经济与管理评论,2016(6):35-42.
[39]王雪青,娄香珍,杨秋波.中国建筑业能源效率省际差异及其影响因素分析[J].中国人口·资源与环境,2012,22(2):56-61.
[40]胡颖,诸大建.中国建筑业CO2排放与产值、能耗的脱钩分析[J].中国人口·资源与环境,2015,25(8):50-57.