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
发布时间：2018-10-08 07:12
【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。
[Abstract]:Low-carbon economy is the fundamental way to deal with global climate change. Low carbon economy essentially requires more economic output per unit of carbon emissions, that is, increasing carbon productivity. There are many factors affecting the development of low carbon in the region. The factors with significant spatial correlation and those without significant spatial correlation often act simultaneously, so it is necessary to consider comprehensively when building the model. Based on the spatial autocorrelation method, this paper first determines the spatial correlation of the influencing factors of China's provincial carbon productivity, in which the industrial structure is a global variable, and the energy structure, technological progress and labor productivity are local variables. The regression parameters of the four influencing factors in the end of the Eleventh Five-Year Plan and the end of the 12th Five-Year Plan were estimated and analyzed by mixed geographical weighted regression. The results show that: (1) the energy structure (specific gravity of thermal power) has a negative effect on carbon productivity. Industrial structure (proportion of service industry), technological progress (number of annual patent license) and labor productivity (industrial added value of unit employee) have positive effects on carbon productivity. The influence of industrial structure is dominant, followed by energy structure, technological progress, and finally labor productivity. (2) the positive impact of industrial structure on carbon productivity is increasing. The negative effect of energy structure on carbon productivity shows an obvious downward trend from south to north in spatial distribution. The positive effects of technological progress and labor productivity are obviously decreasing from north to south, and from the end of the 11th Five-Year Plan to the end of the 12th Five-Year Plan, the overall impact of energy structure and labor productivity on carbon productivity is decreasing. And the impact of technological progress is increasing. Finally, the relevant policy recommendations are put forward.
【作者单位】： 中国科学院区域可持续发展分析与模拟重点实验室;中国科学院地理科学与资源研究所;中国科学院大学资源与环境学院;
【基金】：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)
【分类号】：F832.5;X196
本文编号：2255888
[Abstract]:Low-carbon economy is the fundamental way to deal with global climate change. Low carbon economy essentially requires more economic output per unit of carbon emissions, that is, increasing carbon productivity. There are many factors affecting the development of low carbon in the region. The factors with significant spatial correlation and those without significant spatial correlation often act simultaneously, so it is necessary to consider comprehensively when building the model. Based on the spatial autocorrelation method, this paper first determines the spatial correlation of the influencing factors of China's provincial carbon productivity, in which the industrial structure is a global variable, and the energy structure, technological progress and labor productivity are local variables. The regression parameters of the four influencing factors in the end of the Eleventh Five-Year Plan and the end of the 12th Five-Year Plan were estimated and analyzed by mixed geographical weighted regression. The results show that: (1) the energy structure (specific gravity of thermal power) has a negative effect on carbon productivity. Industrial structure (proportion of service industry), technological progress (number of annual patent license) and labor productivity (industrial added value of unit employee) have positive effects on carbon productivity. The influence of industrial structure is dominant, followed by energy structure, technological progress, and finally labor productivity. (2) the positive impact of industrial structure on carbon productivity is increasing. The negative effect of energy structure on carbon productivity shows an obvious downward trend from south to north in spatial distribution. The positive effects of technological progress and labor productivity are obviously decreasing from north to south, and from the end of the 11th Five-Year Plan to the end of the 12th Five-Year Plan, the overall impact of energy structure and labor productivity on carbon productivity is decreasing. And the impact of technological progress is increasing. Finally, the relevant policy recommendations are put forward.
【作者单位】： 中国科学院区域可持续发展分析与模拟重点实验室;中国科学院地理科学与资源研究所;中国科学院大学资源与环境学院;
【基金】：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)
【分类号】：F832.5;X196
【相似文献】
相关期刊论文 前3条
1 刘玲;丁浩;;提高我国碳生产率面临的挑战及对策研究[J];价值工程;2010年14期
2 张丽峰;;碳生产率的经济学背景及其内涵分析[J];经济问题探索;2013年05期
3 瞿明凯;李卫东;张传荣;黄标;;地理加权回归及其在土壤和环境科学上的应用前景[J];土壤;2014年01期
相关重要报纸文章 前1条
1 本报首席记者 任荃 本报实习生 赵方宇;碳生产率 未来经济“紧箍咒”[N];文汇报;2009年
相关硕士学位论文 前1条
1 王殿武;中国省际工业绿色全要素生产率的测度及空间效应[D];吉林大学;2014年
，本文编号：2255888
本文链接：https://www.wllwen.com/jingjilunwen/jingjililun/2255888.html
