地理学报 ›› 2019, Vol. 74 ›› Issue (6): 1131-1148.doi: 10.11821/dlxb201906005

• 城市与区域发展 • 上一篇    下一篇

中国城市碳排放强度的空间溢出效应及驱动因素

王少剑,黄永源   

  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
  • 收稿日期:2018-04-04 修回日期:2019-03-11 出版日期:2019-06-25 发布日期:2019-06-20
  • 作者简介:王少剑(1986-), 男, 河南驻马店人, 博士, 副教授, 中国地理学会会员(S110011019M), 研究方向为城市地理、城市与区域规划。E-mail: 1987wangshaojian@163.com
  • 基金资助:
    国家自然科学基金项目(41601151);广东省特支计划;广州市珠江科技新星

Spatial spillover effect and driving forces of carbon emission intensity at city level in China

WANG Shaojian,HUANG Yongyuan   

  1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2018-04-04 Revised:2019-03-11 Online:2019-06-25 Published:2019-06-20
  • Supported by:
    National Natural Science Foundation of China(41601151);Guangdong Special Support Program;Pearl River S&T Nova Program of Guangzhou

摘要:

采用核密度估计、空间自相关、空间马尔科夫链和面板分位数回归等方法对1992-2013年全国283个城市碳排放强度的空间溢出效应和驱动因素进行了分析。① 核密度估计结果表明,中国城市碳排放强度总体均值下降,差异在逐步缩小。② 空间自相关Moran's I指数表明城市碳排放强度存在显著的空间集聚性且空间集聚性在逐渐增强,但空间集聚水平的变化逐年缩小。③ 空间马尔科夫链分析结果表明:第一,中国城市碳排放强度存在马太效应,低强度与高强度的城市在相邻年份转移过程中呈现维持初始状态的特征。第二,城市碳排放“空间溢出”效应明显,且不同区域背景下溢出效应存在异质性,即若与碳排放强度低的城市为邻,该城市的碳强度能够增加向上转移的概率,反之亦然。④ 面板分位数结果显示:在碳排放强度低的城市,经济增长、技术进步、适当的人口密度起到减排作用;外商投资强度与交通排放是使碳强度增大的主要因素。在碳排放强度高的城市,人口密度是重要的减排因素,技术进步暂时没起减排作用;工业排放、粗放式的资本投资以及城市土地蔓延则是碳强度上升的主要因素。

关键词: 中国城市尺度, 碳排放强度, 空间溢出, 空间马尔科夫链, 面板分位数回归

Abstract:

Since the Paris Climate Change Conference in 2015, reducing carbon emission and lowering carbon intensity has become a global consensus to deal with climate change. Due to different economic development stages, carbon intensity is regarded as a better index to measure regional energy-related carbon emissions. Although previous scholars have made great efforts to explore the spatiotemporal patterns and key driving factors of carbon intensity in China, the results lack the perspective from city level because of limited availability of statistical data of city-level carbon emission. In this study, based on carbon intensity of 283 cities in China from 1992-2013, we used the kernel density estimation, spatial autocorrelation, spatial Markov-chain and quantile regression panel model to empirically reveal its spatial spillover effects and explore the critical impact factors of carbon intensity at the city level. Our result indicates that although the total carbon emission increased during the study period, carbon intensity saw a gradual decline and regional differences were shrinking. Secondly, the city-level carbon intensity presented a strong spatial spillover effect and diverse regional backgrounds exerted heterogeneous effects on regions. Thirdly, quantile panel data analysis result showed that for low-intensity cities, on the one hand, FDI and transport sector were main contributing factors, and economic growth, technical progress and high population density negatively affected carbon intensity. On the other hand, industrial activity, extensive growth of investment and urban sprawl were key promoting factors for high-intensity cities, and population density was beneficial to emission reduction task. Furthermore, technological advance has not exerted negative influence on carbon intensity in high-intensity cities. At last, we suggested that Chinese government should take different carbon intensity levels into full consideration before policy making.

Key words: city level, carbon emission intensity, spatial spillover effect, spatial Markov-chain, quantile regression panel model