资源与产业 ›› 2019, Vol. 21 ›› Issue (5): 70-77.DOI: 10.13776/j.cnki.resourcesindustries.20190916.001

• 非主题来稿选登 • 上一篇    下一篇

江苏省工业CO2排放绩效与减排潜力研究——基于空间面板数据的实证分析

吴凤平尹天娇   

  1. (河海大学 商学院(水资源高效利用与工程安全国家工程研究中心),江苏 南京 210098)
  • 收稿日期:2019-02-18 修回日期:2019-09-10 出版日期:2019-10-20 发布日期:2020-01-04
  • 通讯作者: 尹天娇 yintianjiao930@126.com
  • 基金资助:
    国家自然科学基金项目(41471457,71774048)

CARBON DIOXIDE EMISSION PERFORMANCE AND EMISSION REDUCTION POTENTIAL IN JIANGSU'S INDUSTRY BASED ON SPATIAL PANEL DATA ANALYSIS

WU Fengping, YIN Tianjiao   

  1. (Business School, Hehai University, State Engineer Research Center for Water Resource Use and Engineering Safety, Nanjing 210098, China)
  • Received:2019-02-18 Revised:2019-09-10 Online:2019-10-20 Published:2020-01-04

摘要: 碳排放与经济发展息息相关,科学合理的测算碳排放效率是实现低碳经济的必要环节。本文以全要素的框架为基础,采用随机前沿模型和核密度估计法,对2005—2015年江苏省各地市的工业总产值、资本投入、劳动力投入、能源投入和工业CO2排放量构造的面板数据做了实证分析,研究了江苏省工业CO2排放绩效。结果表明,江苏省各市的工业CO2排放绩效存在着时空差异,差值最大达48.2%,绩效呈现出“苏中>苏南>苏北”的格局,连云港位居减排潜力第一位。提高江苏省工业CO2排放绩效水平,可从调整能源结构、增加科技支出、推进区域合作入手。

关键词: 工业, CO2排放绩效, 随机前沿模型, 面板数据, 核密度估计

Abstract: Carbon dioxide emission is closely related to economy. A rational measurement of carbon emission efficiency is a key step to low carbon economy. This paper uses random frontier model and core density estimation to analyze the panel data of industrial GDP, capital input, laboring input, energy input and industrial carbon dioxide emission in Jiangsu's cities during 2005 to 2015, and carbon dioxide emission performance based on the entire elements framework. Results show that the carbon dioxide emission performance varies as time and space with up to 48.2% in difference, central Jiangsu sitting at the top, and then southern and northern. Lianyungang city ranks the first in emission reduction. This paper presents suggestions on raising Jiangsu's industrial carbon dioxide emission performance from adjusting energy structure, increasing input on science and technology and boosting regional cooperation.

Key words: industry, carbon dioxide emission performance, random frontier model, panel data, core density estimation

中图分类号: