资源科学 ›› 2017, Vol. 39 ›› Issue (12): 2223-2232.doi: 10.18402/resci.2017.12.02

• • 上一篇    下一篇

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

唐志鹏1,2,3(), 刘卫东1,2,3(), 宋涛1,2   

  1. 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京100101
    2. 中国科学院地理科学与资源研究所,北京100101
    3. 中国科学院大学资源与环境学院,北京100049
  • 收稿日期:2017-09-07 修回日期:2017-11-20 出版日期:2017-12-31 发布日期:2017-12-31
  • 作者简介:

    作者简介:唐志鹏,男,四川成都人,博士,副研究员,研究方向为区域经济与区域可持续发展。E-mail:tangzp@igsnrr.ac.cn

  • 基金资助:
    中华人民共和国科学技术部国家重点研发计划项目(2016YFA0602804);国家自然科学基金项目(41430636;41571518)

Factors affecting China’s provincial carbon productivity based on mixed geographically weighted regression modeling

Zhipeng TANG1,2,3(), Weidong LIU1,2,3(), Tao SONG1,2   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling,Chinese Academy of Sciences,Beijing 100101,China
    2. Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
    3. College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2017-09-07 Revised:2017-11-20 Online:2017-12-31 Published:2017-12-31

摘要:

低碳经济成为应对全球气候变化的根本途径。低碳经济实质上就是要求单位碳排放产生更多的经济产出,即提高碳生产率。涉及地区低碳发展的影响因素很多,具有显著空间相关性的因素和无显著空间相关性的因素往往同时作用,在构建模型时需要综合考虑。本文在空间自相关方法的基础上首先确定了中国省域碳生产率影响因素的空间相关性,其中产业结构是全局变量,能源结构、技术进步和劳动生产率均是局域变量,再通过混合地理加权回归估计了“十一五”末和“十二五”末4个影响因素的回归参数值并作分析。研究结果显示:①能源结构(火电比重)对于碳生产率具有负向影响,而产业结构(服务业比重)、技术进步(年专利授权数量)和劳动生产率(单位从业人员的工业增加值)对于碳生产率具有正向影响;从回归参数估计值来看,产业结构的影响程度占据主导地位,其次是能源结构,再次是技术进步,最后为劳动生产率;②产业结构对碳生产率的正向影响程度在增大,能源结构对碳生产率的负向影响在空间分布上呈现出明显的自南向北递减特征,而技术进步和劳动生产率的正向影响则呈现出明显的自北向南递减特征;“十一五”末到“十二五”末,总体上能源结构和劳动生产率对碳生产率的影响程度在减小,而技术进步的影响在增大。最后,提出了相关的政策建议。

关键词: 碳生产率, 空间自相关, 混合地理加权回归, 影响因素, 中国

Abstract:

A low-carbon economy is an essential way to tackle global climate change. The essence of a low-carbon economy is to increase carbon productivity. Carbon productivity is often affected by impact factors of regional low-carbon development,including global variables and local variables. We need to consider these together in order to establish a precise model. Here we determined factors influencing carbon productivity in Chinese provinces based on spatial autocorrelation method. We found that the industrial structure is a global variable and that energy structure,technological progress and labor productivity are local variables. Four impact factors and regression parameter values at the end of 11th Five-Year Plan and 12th Five-Year Plan were estimated and analyzed based on mixed geographically weighted regression. We found that the structure of energy (power ratio)has a negative effect on carbon productivity,and industrial structure (service industry),technology (patent authorization number)and labor productivity (employees of the industrial added value)has a positive effect on carbon productivity. Judging from the estimated value of regression parameters,the influence degree of industrial structure occupies a leading position,followed by energy structure and technological progress,and finally for labor productivity. The impact of industrial structure on carbon productivity is increasing. The negative impact of energy structure on carbon productivity showed obvious decline from south to north spatially,while the positive impact of technical progress and labor productivity showed obvious declines from north to south. From the end of 11th Five-Year Plan to the end of the 12th Five-Year Plan,the impact of energy structure and labor productivity on carbon productivity decreased,and the impact of technological progress increased. Some policy suggestions are discussed.

Key words: carbon productivity, spatial autocorrelation, mixed geographically weighted regression, impact factor, China