地理研究

地理研究 ›› 2019, Vol. 38 ›› Issue (9): 2188-2201.doi: 10.11821/dlyj020180774

• 论文 • 上一篇    下一篇

长三角地区城市土地与能源消费CO2排放的时空耦合分析

李建豹1,2,黄贤金3,4(),孙树臣5,揣小伟3   

  1. 1. 南京财经大学公共管理学院,南京 210023
    2. 南京财经大学政府管理研究中心,南京 210023
    3. 南京大学地理与海洋科学学院,南京 210023
    4. 国土资源部海岸带开发与保护重点实验室,南京 210023
    5. 聊城大学环境与规划学院,聊城 252059
  • 收稿日期:2018-07-26 修回日期:2018-10-30 出版日期:2019-09-20 发布日期:2019-09-11
  • 通讯作者: 黄贤金 E-mail:hxj369@nju.edu.cn
  • 作者简介:李建豹（1986-）,男,山东临清人,博士,讲师,主要研究方向为城镇化碳排放、土地利用与规划。E-mail： lijianbao888@126.com
  • 基金资助:
    国家自然科学基金项目(41901245);江苏省高等学校自然科学研究面上项目(19KJB170014);国家社会科学基金重大项目(17ZDA061)

Spatio-temporal coupling analysis of urban land and carbon dioxide emissions from energy consumption in the Yangtze River Delta region

LI Jianbao1,2,HUANG Xianjin3,4(),SUN Shuchen5,CHUAI Xiaowei3   

  1. 1. School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China
    2. Government Management Research Centre, Nanjing University of Finance & Economics, Nanjing 210023, China
    3. College of Geography and Oceanography Sciences, Nanjing University, Nanjing 210023, China
    4. The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210023, China
    5. School of Environment and Planning, Liaocheng University, Liaocheng 252059, Shandong, China
  • Received:2018-07-26 Revised:2018-10-30 Online:2019-09-20 Published:2019-09-11
  • Contact: Xianjin HUANG E-mail:hxj369@nju.edu.cn

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摘要：

探究城市土地与碳排放的时空耦合关系,是协调城市土地与生态环境亟待解决的重要问题。基于重心模型、总体耦合态势模型和空间耦合协调模型,使用建成区面积、能源消费和DMSP/OLS夜间灯光等数据,分析了1995—2013年长三角地区城市土地与能源消费碳排放的时空耦合特征,并考虑空间因素的影响,构建空间滞后面板Tobit模型分析其驱动因素。结果表明：① 1995—2013年,建成区面积与碳排放量总体上均呈增加趋势。建成区面积由1251 km 2,增加至4394 km 2,碳排放量由30389.49万t,增加至90405.22万t。城市土地与碳排放间呈显著的正相关;② 城市土地与碳排放空间差异明显,上海、南京、杭州、苏州和无锡的城市建成区面积相对较大,碳排放相对较高;③ 长三角地区城市土地与碳排放耦合关系总体上呈减弱-增强-波动的态势。协调关系处于不断演化过程中,低协调阶段的城市数量明显减小,高协调阶段的城市数量明显增加,且呈集聚分布特点。南京、无锡、苏州、杭州和宁波处于高协调阶段;④ 空间滞后面板Tobit模型结果表明：城镇化对城市土地与碳排放耦合协调度具有驱动和制动的双重作用。同时,人口密度、经济水平、产业结构和空间因素对其也具有重要影响。

关键词: 城市土地, 能源消费二氧化碳排放, 时空耦合, 驱动因素, 空间滞后面板Tobit模型, 长三角地区

Abstract:

Exploring the spatio-temporal coupling relationship between urban land and carbon emissions is an important issue to be solved urgently to coordinate urban land and ecological environment. Based on data such as built-up area, energy consumption, and DMSP/OLS night lights, the spatio-temporal coupling relationship between urban land and carbon emissions from energy consumption of the Yangtze River Delta region during 1995-2013 was analyzed by the gravity center model, overall coupled situation model, and spatial coupling coordination model. By taking into account the influence of spatial factors, spatial lag panel Tobit model was constructed to analyze its driving factors. The results are shown as follows: (1)During the period of 1995-2013, built-up area and carbon emissions from energy consumption generally increased. The built-up area increased from 1251 km 2 to 4394 km 2. Carbon emissions from energy consumption increased from 303.89 million tons to 904.05 million tons. There was significantly positive correlation between built-up area and carbon emissions. (2) There was obvious spatial disparity for urban land and carbon emissions from energy consumption. There were larger built-up areas and higher carbon emissions from energy consumption in Shanghai, Nanjing, Hangzhou, Suzhou and Wuxi. (3) The coupling relationship between urban land and carbon emissions of the Yangtze River Delta generally showed a pattern of first weakened, then strengthened and last fluctuated. The coordination relationship was in the process of continuous evolution. The number of cities in the low-coordination phase obviously decreased, while that in the high-coordination phase obviously increased. Nanjing, Wuxi, Suzhou, Hangzhou and Ningbo were in high coordination. (4) The results of spatial lag Tobit model show that urbanization played a dual role in driving and braking the coupling degree of carbon emissions between urban land and carbon emissions from energy consumption. Meanwhile, population density, economic level, industrial structure and spatial factors had an important impact on the coupling degree. Population density and industrial structure played a significantly positive role in improving the coupling degree. Economic level and spatial factors had a negative impact on the coupling degree.

Key words: urban land, carbon dioxide emissions from energy consumption, spatio-temporal coupling, driving factor, spatial lag panel Tobit model, Yangtze River Delta region