自然资源学报 ›› 2020, Vol. 35 ›› Issue (2): 329-342.doi: 10.31497/zrzyxb.20200207

• 研究论文 • 上一篇    下一篇

基于城市化的浙江省湾区经济带碳排放时空分布特征及影响因素分析

沈杨1, 汪聪聪2, 高超2, 丁镭1   

  1. 1. 宁波职业技术学院商贸外语学院,宁波 315800;
    2. 宁波大学地理与空间信息技术系,宁波 315211
  • 收稿日期:2019-05-24 修回日期:2019-08-27 出版日期:2020-02-28 发布日期:2020-02-28
  • 通讯作者: 高超(1978- ),男,安徽全椒人,博士,教授,主要从事洪涝灾害风险评估分析、气候变化与水文水资源研究。E-mail: gaoqinchao1@163.com
  • 作者简介:沈杨(1969- ),女,安徽宣城人,硕士,教授,主要从事环境经济、生态旅游研究。E-mail: 0502009@nbpt.edu.cn
  • 基金资助:
    浙江省哲学社会科学规划课题(19NDJC011Z); 浙江省软科学研究计划项目(2019C35108)

Spatio-temporal distribution and its influencing factors of carbon emissions in economic zone of Zhejiang Bay Area based on urbanization

SHEN Yang1, WANG Cong-cong2, GAO Chao2, DING Lei1   

  1. 1. Institute of Business Foreign Languages, Ningbo Polytechnic, Ningbo 315800, Zhejiang, China;
    2. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, Zhejiang, China
  • Received:2019-05-24 Revised:2019-08-27 Online:2020-02-28 Published:2020-02-28

摘要: 基于STIRPAT扩展模型与2003-2017年浙江省湾区经济带面板数据,分析碳排放时空分布特征并应用时空地理加权回归模型(GTWR)实证考察城市化视角下碳排放的驱动机制及其时空异质性。结果表明:(1)浙江省湾区经济带各地市碳排放规模逐年增加但增幅不大,增速放缓,碳排放量差异明显,碳排放强度逐年下降,空间上呈现自西南向东北逐渐加大的趋势;(2)经济发展水平和对外开放程度是碳排放的主导因素,其他依次为能源消费结构、技术进步和城市化;(3)各影响因素呈现较强的时空异质性,不同时间、地区各驱动要素的波动方向和强度并不相同。在此基础上具体分析各地区碳排放驱动因素影响情况,为实现区域差异化碳减排策略提供指导。

关键词: 碳排放, GTWR模型, 城市化, 湾区经济带

Abstract: Urbanization becomes the primary source of carbon emissions and energy demand growth in China with industrialization entering the middle-later stages. With the current high-consumption and high-emission urban development model, the resulting resource consumption and carbon emissions become a major environmental issue and a sustainability problem. Based on the panel data of the bay area economic zone of Zhejiang province from 2003 to 2017, this paper analyzed the overall spatial and temporal distribution characteristics of carbon emissions of the economic zone. The main sources of carbon emissions will be studied in detail by incorporating the urbanization level, energy consumption structure, land use, economic development level, industrial structure, population agglomeration, openness, and technological progress into the STIRPAT (the Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model. We further investigated the driving mechanism of carbon emissions and spatial and temporal heterogeneity of carbon emissions from the perspective of urbanization using the geographically and temporally weighted regression (GTWR). The results showed that: (1) The scale of carbon emissions has increased with time, but the growth rate has not been large with a decreasing growth rate. Carbon emissions in different regions are significantly different. The carbon emission is less intense through the years with a spatial distribution pattern of increasing from the southwest to the northeast. (2) The level of economic development and the degree of openness to the outside world are the dominant factors that affect the carbon emission level, followed by energy consumption structure, technological progress, and urbanization. (3) These influencing factors show substantial spatiotemporal heterogeneity that the direction and intensity of the fluctuations of various driving factors vary from time to time and region to region. This paper analyzed the impact of the driving factors of carbon emissions in the economic zone of Zhejiang Bay Area explicitly, providing scientific support for the implementation of regionally differentiated carbon emission reduction strategies. It is necessary to fully examine the actual development of each region to formulate a differentiated carbon emission reduction regulation strategy. In particular, these measures should be taken into consideration for reducing carbon emissions in the area: enhancing the efficiency of comprehensive utilization of resources by accelerating the construction of science and technology innovation corridors, and promoting the transformation and upgrading of traditional industries by fully introducing modern service industries and high-end manufacturing based on information technology.

Key words: urbanization, GTWR model, bay area economic zone, carbon emissions