地理学报 ›› 2018, Vol. 73 ›› Issue (3): 414-428.doi: 10.11821/dlxb201803003

• 地表过程与生态环境 • 上一篇    下一篇

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素

王少剑1(),苏泳娴2,3,赵亚博4   

  1. 1. 中山大学地理科学与规划学院 广东省城市化与地理环境空间模拟重点实验室,广州 510275
    2. 广州地理研究所,广州 510070
    3. 广东省地理空间信息技术与应用公共实验室,广州 510070
    4. 广州大学地理科学学院,广州 510006
  • 收稿日期:2017-04-21 出版日期:2018-03-20 发布日期:2018-03-23
  • 基金资助:
    国家自然科学基金项目(41601151);广东省自然科学基金项目(2016A030310149);广州市珠江科技新星

Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions

WANG Shaojian1(),SU Yongxian2,3,ZHAO Yabo4   

  1. 1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, SunYat-sen University, Guangzhou 510275, China
    2. Guangzhou Institute of Geography, Guangzhou 510070, China
    3. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China
    4. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • Received:2017-04-21 Online:2018-03-20 Published:2018-03-23
  • Supported by:
    [Foundation: National Natural Science Foundation of China, No.41601151; National Natural Science Foundation of Guangdong Province, No.2016A030310149;;Pearl River S&T Nova Program of Guangzhou]

摘要:

基于1992-2013年中国城市遥感模拟反演碳排放数据,采用空间自相关、空间马尔科夫矩阵和动态空间面板数据模型,在同时考虑碳排放的时空滞后效应和不同地理经济空间权重矩阵的条件下,对城市碳排放的演化路径和关键影响因素进行了定量识别和减排政策探讨。研究表明,中国城市能源消费碳排放的区域差异正逐步缩小,空间上呈现出明显的高排放俱乐部集聚特征,同时碳排放类型演化具有明显的路径依赖特征;面板数据模型估计结果表明经济增长与人均碳排放呈现显著的倒“U”型曲线关系,而绝大多数城市的人均碳排放处于随经济发展而增加的阶段,二产偏重的经济结构和投资的粗放增长共同正向作用于城市碳排放,而人口的集聚效应、技术水平的提升、对外开放度和公路运输强度的增加则共同抑制城市碳排放水平的提高。因此未来要抑制促增因素和发挥促降因素的作用才能有效降低城市碳排放;优化产业结构、精简粗放投资、增加研发强度以及提升公路通达性是未来实现中国城市节能减排的有效途径。

关键词: 城市碳排放, 空间溢出效应, 动态空间面板模型, 减排政策, EKC曲线, 中国

Abstract:

Carbon emissions are increasing due to human activities related with the energy consumptions for economic development. Thus, attention has been paid to the reduction of the growth of carbon emissions and formulation of policies for addressing climate change. Although most studies have explored the driving forces behind carbon emissions in China, literature lacks studies at the city-level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we applied the spatial autocorrelation, spatial Markov-chain transitional matrices, dynamic spatial panel model and Sys-GMM to empirically estimate the key determinants of carbon emissions at the city-level and discuss its spatial spillover effects in consideration of spatiotemporal lag effects and different geographical and economic weighting matrices. Results indicated that the regional inequalities of city-level carbon emissions decreased over time and presented an obvious spatial spillover effect and high-emission "club" agglomeration. In addition, the evolution of the emission pattern has the characteristic of obvious path dependence. Panel data analysis results indicated that there was a significant U-shaped curve that can reflect the relationship between carbon emissions and GDP per capita. In addition, carbon emissions per capita are increasing with economic growth for most cities. High-proportion of secondary industry and extensive growth of investment exerted significantly positive effects on China's city-level carbon emissions. Conversely, rapid population agglomeration, the improvement of technology level, the increase of trade openness and road density play an inhibiting role in carbon emissions. Therefore, in order to reduce carbon emissions, the Chinese government should inhibit the effects of promotion factors and enhance the effects of mitigation factors. Combining with the analysis of results, we argued that optimizing the industrial structure, streamlining the extensive investment, increasing the level of technology and improving the road accessibility are the effective ways to increase energy savings and reduce carbon emissions in China.

Key words: carbon emissions, spatial spillover effect, dynamic spatial panel data model, emission policies, EKC curve, China