生态与农村环境学报 ›› 2022, Vol. 38 ›› Issue (10): 1273-1281.doi: 10.19741/j.issn.1673-4831.2022.0125

• 生态城市建设与碳中和专题 • 上一篇    下一篇

长三角城市群减污降碳驱动因素研究

马伟波, 赵立君, 王楠, 张龙江, 李海东   

  1. 生态环境部南京环境科学研究所, 江苏 南京 210042
  • 收稿日期:2022-02-21 出版日期:2022-10-25 发布日期:2022-10-22
  • 通讯作者: 李海东,E-mail:lihd2020@163.com E-mail:lihd2020@163.com
  • 作者简介:马伟波(1991-),男,陕西宝鸡人,助理研究员,硕士,主要研究方向为城市生态与矿山生态保护修复。E-mail:maweibo@nies.org
  • 基金资助:
    中央级公益性科研院所基本科研业务专项(GYZX220308,GYZX210101)

Study on Driving Factors of Pollution and Carbon Reduction in the Yangtze River Delta Urban Agglomerations

MA Wei-bo, ZHAO Li-jun, WANG Nan, ZHANG Long-jiang, LI Hai-dong   

  1. Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
  • Received:2022-02-21 Online:2022-10-25 Published:2022-10-22

摘要: 城市发展的同时也面临着应对气候变化、生态环境保护和经济发展的多重压力,探究城市群尺度减污降碳的时空驱动特征及其演变特征,有助于更好地理解城市化与环境的互动关系。以长三角城市群为研究对象,基于碳排放水平和污染物排放数据构建了减污降碳强度指标,并通过时空地理加权回归(GTWR)方法和随机森林(RF)方法分析经济发展、产业结构、土地利用结构、人口以及气候变化对减污降碳强度指标的时空驱动特征及驱动因素重要性变迁特征。结果表明:2003-2017年,长三角城市群整体减污降碳强度呈下降趋势,27个城市平均减污降碳强度由2003年的0.23下降到2017年的0.05;其中,滁州2003-2017年的平均减污降碳强度仍处于较高水平的梯队,且其减污降碳强度变幅为18.62%。在考虑时间效应后,GTWR模型R2与调整R2均高于0.96,与GWR模型相比,拟合精度和优度提升较大;上海、苏州和浙江9市受园林绿地面积的负向驱动,导致减污降碳强度下降。能源消费总量在2003-2007、2008-2012和2013-2017年3个时期的重要性排名均为第1,同时园林绿地面积的重要性排名从2003-2007年的第5逐渐上升到2013-2017年的第2,人口密度和人口总量的重要性排名虽然靠后,但总体上重要性呈上升趋势。总体而言,GTWR模型对减污降碳强度具有较好的时空拟合能力,减污降碳强度指标时空分异特征显著。建议长三角城市群提升能源利用效率,大力发展第三产业,优化土地利用结构。

关键词: 减污降碳, 时空效应, 驱动机制, 长三角城市群

Abstract: Urban development is simultaneously facing multiple pressures of climate change, ecological protection, and economic development. Therefore, exploring the characteristics of spatial and temporal driving factors for pollution and carbon reduction at the urban agglomeration scale and their evolution characteristics will help us develop a better understanding of the interaction between urbanization and the environment. Taking the Yangtze River Delta Urban Agglomeration (YRDUA) as the research object, the Index of Pollution and Carbon Reduction (IPCR) is constructed on the basis of carbon emission levels and pollutant emission data. Moreover, the spatial and temporal driving characteristics of economic development, industrial structure, land use structure, population, and climate change on the IPCR and the changes in the importance of the driving factors are analyzed by the Geographically and Temporally Weighted Regression (GTWR) and Random Forest (RF) methods. The results show that: from 2003 to 2017, the overall IPCR of the YRDUA showed a downward trend, with the average IPCR of the 27 cities decreasing from 0.23 in 2003 to 0.05 in 2017; during this period, Chuzhou was still in the higher echelon of the average IPCR, with the amplitude of variation 18.62%. Considering the time effect, the adjusted R2 and R2 of the GTWR model are higher than 0.96, which improves the fitting accuracy and superiority compared with the GWR model. Shanghai, Suzhou, and nine cities in Zhejiang province are negatively driven by the size of landscaped green areas, which leads to the IPCR decrease. Total energy consumption ranks 1st in importance in three periods, 2003-2007, 2008-2012, and 2013-2017, while landscaped green area gradually increased in importance from the 5th in 2003-2007 to the 2nd in 2013-2017. Although population density and total population ranked lower, the overall importance registered an upward trend. Generally speaking, the GTWR model has an excellent spatial and temporal fitting ability for the IPCR, and the spatial and temporal effects of the IPCR are significant. We suggest that the YRDUA to improve energy utilization efficiency, increase efforts on developing tertiary industries and optimize land use structure.

Key words: pollution and carbon reduction, spatial and temporal effect, driving mechanism, Yangtze River Delta Urban Agglomeration

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