产业结构高级化对碳生产率的影响研究——基于空间杜宾模型
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摘要
在经济高质量增长的新阶段,促进产业结构高级化与碳生产率提升的同向发展十分重要。基于2003—2016年中国30个省级行政区的面板数据,使用空间杜宾模型研究了产业结构高级化对碳生产率影响的空间效应。研究发现:产业结构高级化具有明显的区域差异特征,碳生产率增长的空间自相关特征显著;在考虑了空间相关性及相关控制变量后,产业结构高级化不仅能够促进本地区碳生产率提升,还会对邻接地区碳生产率形成显著的正向溢出效应。因此,从持续推进产业结构高级化、提升低碳技术效率、坚持引进外资和提升资本使用效率等方面提出提升碳生产率的对策建议。
In the new stage characterized by high-quality economic growth, it is important to promote the coordination between the sophistication of industrial structure and the carbon productivity. Based on panel data from 30 provinces and cities in China from 2003 to 2016, Spatial Durbin Model is used to study the spatial impact of industrial structure on carbon productivity. The results show that the sophistication of industrial structure has distinct regional differences, and the spatial autocorrelation of carbon productivity growth is significant. After considering the spatial correlation and related control variables, it is found that the sophistication of industrial structure can not only promote the improvement of carbon productivity in local region, but also have a significant positive spillover effect on carbon productivity in adjacent areas. Therefore, some measurements to improve carbon productivity are proposed from the aspects of continuing to promote the sophistication of industrial structure, improving the efficiency of low-carbon technologies, adhering to foreign investment, and improving capital efficiency.
In the new stage characterized by high-quality economic growth, it is important to promote the coordination between the sophistication of industrial structure and the carbon productivity. Based on panel data from 30 provinces and cities in China from 2003 to 2016, Spatial Durbin Model is used to study the spatial impact of industrial structure on carbon productivity. The results show that the sophistication of industrial structure has distinct regional differences, and the spatial autocorrelation of carbon productivity growth is significant. After considering the spatial correlation and related control variables, it is found that the sophistication of industrial structure can not only promote the improvement of carbon productivity in local region, but also have a significant positive spillover effect on carbon productivity in adjacent areas. Therefore, some measurements to improve carbon productivity are proposed from the aspects of continuing to promote the sophistication of industrial structure, improving the efficiency of low-carbon technologies, adhering to foreign investment, and improving capital efficiency.
引文
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[13] 王艳丽,王根济.环境规制、工业结构变动与碳生产率增长——基于1998—2013年省级工业行业动态面板数据的实证检验[J].经济与管理,2016,30(6):73-80.
[14] 高文静,柳亚琴,潘明清.工业化、城镇化对工业碳生产率的门槛效应分析[J].宏观经济研究,2017,12(4):112-121.
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[20] LESAGE J,PACE R K.Introduction to spatial econometrics[M].New York:CRC Press,Taylor & Francis Group, 2009.
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[22] 师博,沈坤荣.城市化、产业集聚与EBM能源效率[J].产业经济研究,2012,24(6):10-16.
[23] 俞会新, 林晓彤.基于EBM超效率模型的京津冀环境污染治理投资效率分析[J].河北工业大学学报(社会科学版), 2018,10(1):9-16.
[24] 韩清童.基于复杂网络与EBM模型的货运机场效率研究[D].大连:大连海事大学,2017.
[25] PERSYN D,WESTERLUND J.Error-correction-based cointegration tests for panel data[J].Stata Journal,2008,8(2):232-241.
[26] WESTERLUND J. Testing for error correction in panel data[J].Oxford Bulletin of Economics & Statistics, 2007, 69(6):709-748.
[27] 熊灵,魏伟,杨勇.贸易开放对中国区域增长的空间效应研究:1987—2009[J].经济学,2012,11(3): 1037-1058.
[28] 杨恺钧,褚天威.互联网发展、交通运输及进口贸易关系研究——基于中国省际面板数据空间计量分析[J].经济问题,2016,21(6):95-100.
[29] 丁志国,赵宜凯,赵晶.直接影响与空间溢出效应:中国城市化进程对城乡收入差距的影响路径识别[J].数量经济技术研究,2011,25(9):118-130.
(1)就一般意义而言,因变量的空间相关关系与自变量的空间相关关系可能相同也可能不同,此处认为两者的空间相关关系相同,统一采用邻接标准。
[2] KAYA Y,YOKOBORI K.Environment, energy and economy:strategies for sustainability[M].NewYork:United nations university press,1997.
[3] ENKVIST P,NAUCLéR T,OPPENHEIM JM. Business strategies for climate change[J].McKinsey quarterly,2008,24(2):24-37.
[4] BEINHOCKER E,OPPENHEIM J,IRONS B,et al.The carbon productivity challenge:Curbing climate change and sustaining economic growth[R].McKinsey climate change special initiative report series,2008.
[5] KIM C G, KIM TH.Analysis of carbon productivity in Korea agriculture[J].The Korean journal of agricultural economics,2014,24(2):21-34.
[6] HU X, LIU C. Carbon productivity: a case study in the Australian construction industry[J]. Journal of cleaner production, 2016, 112(9):2354-2362.
[7] NISHITANI K,KOKUBU K,KAJIWARA T.Does low-carbon supply chain management reduce greenhouse gas emissions more effectively than existing environmental initiatives?An empirical analysis of Japanese manufacturing firms[J].Journal of management control,2016,27(1):33-60.
[8] KIM S,JUNG Y.Carbon risk and the cost of debt[J].Accounting research,2017,42(2):169-213.
[9] 于雪霞.区域碳生产率变化差异成因分析[J].中国人口·资源与环境,2015,25(S1):344-349.
[10] 范晓莉.新型城镇化视角下中国碳生产率影响因素研究——基于VEC模型的动态关系分析[J].管理现代化,2015,35(1):70-72.
[11] 唐志鹏,刘卫东,宋涛.基于混合地理加权回归的中国省域碳生产率影响因素分析[J].资源科学,2017,32(12):2223-2232.
[12] 程琳琳,张俊飚,田云,等.中国省域农业碳生产率的空间分异特征及依赖效应[J].资源科学,2016, 38(2): 276-289.
[13] 王艳丽,王根济.环境规制、工业结构变动与碳生产率增长——基于1998—2013年省级工业行业动态面板数据的实证检验[J].经济与管理,2016,30(6):73-80.
[14] 高文静,柳亚琴,潘明清.工业化、城镇化对工业碳生产率的门槛效应分析[J].宏观经济研究,2017,12(4):112-121.
[15] 付凌晖.中国产业结构高级化与经济增长关系的实证研究[J].统计研究,2010,27(8):79-81.
[16] 叶阿忠,吴继贵,陈生明,等.空间计量经济学[M].厦门:厦门大学出版社,2015.
[17] ANSELIN L. The moran scatter plot as an ESDA tool to assess local instability inspatial association[M]. Morgantown:Research Institute of West Virginia University Press,1993.
[18] 沈能,王艳,王群伟.集聚外部性与碳生产率空间趋同研究[J].中国人口·资源与环境,2013, 23(12): 40-47.
[19] 林伯强,孙传旺.如何在保障中国经济增长前提下完成碳减排目标[J].中国社会科学,2011,23(1):64-76.
[20] LESAGE J,PACE R K.Introduction to spatial econometrics[M].New York:CRC Press,Taylor & Francis Group, 2009.
[21] TSUTSUI M, TONE K. An epsilon-based measure of efficiency in DEA[J].European Journal of Operational Research,2010,207(5):1554-1563.
[22] 师博,沈坤荣.城市化、产业集聚与EBM能源效率[J].产业经济研究,2012,24(6):10-16.
[23] 俞会新, 林晓彤.基于EBM超效率模型的京津冀环境污染治理投资效率分析[J].河北工业大学学报(社会科学版), 2018,10(1):9-16.
[24] 韩清童.基于复杂网络与EBM模型的货运机场效率研究[D].大连:大连海事大学,2017.
[25] PERSYN D,WESTERLUND J.Error-correction-based cointegration tests for panel data[J].Stata Journal,2008,8(2):232-241.
[26] WESTERLUND J. Testing for error correction in panel data[J].Oxford Bulletin of Economics & Statistics, 2007, 69(6):709-748.
[27] 熊灵,魏伟,杨勇.贸易开放对中国区域增长的空间效应研究:1987—2009[J].经济学,2012,11(3): 1037-1058.
[28] 杨恺钧,褚天威.互联网发展、交通运输及进口贸易关系研究——基于中国省际面板数据空间计量分析[J].经济问题,2016,21(6):95-100.
[29] 丁志国,赵宜凯,赵晶.直接影响与空间溢出效应:中国城市化进程对城乡收入差距的影响路径识别[J].数量经济技术研究,2011,25(9):118-130.
(1)就一般意义而言,因变量的空间相关关系与自变量的空间相关关系可能相同也可能不同,此处认为两者的空间相关关系相同,统一采用邻接标准。