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应用生态学报 ›› 2020, Vol. 31 ›› Issue (10): 3529-3538.doi: 10.13287/j.1001-9332.202010.016

• 研究论文 • 上一篇    下一篇

福建省行业碳排放驱动因素分解及其与经济增长脱钩关系

赖文亭1,2, 王远1,2,3*, 黄琳琳1,2, 黄逸敏1,2, 罗进1,2, 陈华阳1,2   

  1. 1福建师范大学福建省亚热带资源与环境重点实验室, 福州 350007;
    2福建师范大学地理科学学院, 福州 350007;
    3南京大学环境学院, 污染控制与资源化研究国家重点实验室, 南京 210023
  • 收稿日期:2020-06-21 接受日期:2020-08-07 出版日期:2020-10-15 发布日期:2021-04-15
  • 通讯作者: * E-mail: y.wang@fjnu.edu.cn
  • 作者简介:赖文亭, 女, 1993年生, 硕士。主要从事能源与环境、经济地理与区域发展等方面的研究。E-mail: 784704345@qq.com
  • 基金资助:
    福建省自然科学基金项目(2018J01736)资助

Decomposition of driving factors of industry-related CO2 emissions and its decoupling with economic growth in Fujian Province, China

LAI Wen-ting1,2, WANG Yuan1,2,3*, HUANG Lin-lin1,2, HUANG Yi-min1,2, LUO Jin1,2, CHEN Hua-yang1,2   

  1. 1Fujian Provincial Key Laboratory for Subtropical Resources and Environment, Fujian Normal University, Fuzhou 350007, China;
    2School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China;
    3State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
  • Received:2020-06-21 Accepted:2020-08-07 Online:2020-10-15 Published:2021-04-15
  • Contact: * E-mail: y.wang@fjnu.edu.cn
  • Supported by:
    Natural Science Foundation of Fujian Province (2018J01736).

摘要: 主要部门和重点行业的二氧化碳排放是区域碳排放的主要来源。厘清行业碳排放影响因素,分析其与区域经济增长相互关系及程度,从而推动部门和行业碳减排对于落实区域碳排放总量控制具有重要意义。本研究运用对数平均迪式指数分解法(LMDI)和Tapio脱钩模型对福建省1997—2017年13个主要碳排放行业进行碳排放驱动因素分解和脱钩分析。结果表明: 电力、热力的生产和供应业是福建省主要的碳排放行业,1997—2017年,其碳排放总量由18.89 Mt上升到120.63 Mt,增长量为101.74 Mt。有色金属冶炼及压延加工业、纺织业、黑色金属冶炼及压延加工业的碳排放增长速度最快,其年均增长率分别为18.1%、12.1%、12.1%。13个主要行业碳排放变动的驱动因素中,经济增长效应和人口规模效应是主要的正向驱动因素,能源结构效应、能源强度效应和产业结构效应的抑制作用在不断增强。从脱钩关系来看,13个主要碳排放行业的脱钩指数整体呈下降趋势。从“十一五”时期开始,部分行业开始出现不同程度的强脱钩。而在“十三五”时期,农、林、牧、渔、水利业表现为扩张负脱钩,电力、热力的生产和供应业表现为弱负脱钩。能源结构效应和能源强度效应对各行业实现脱钩的影响较大,产业结构效应的脱钩努力较小,人口规模效应未做出脱钩努力。

关键词: 行业碳排放, 对数平均迪式指数分解(LMDI)模型, Tapio模型

Abstract: The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industry (12.1%), and ferrous metal smelting and rolling processing industry (12.1%). Among the influence factors for the changes in carbon emissions in 13 major industries, economic growth effect and population scale effect were the main positive driving factors, while the restraining effects of energy structure, energy intensity, and industrial structure were continuously increasing. In terms of decoupling relationship, the decoupling index between economic growth and industry-related CO2 emissions showed a downward trend on the whole. Since the 11th Five-Year-Plan period, some industries had begun to show strong decoupling to some extent. The farming, forestry, animal husbandry, fishery and water conservancy industry exhibited expansive negative decoupling, whereas the electricity and heat production and supply industry exhibited weak negative decoupling during 13th Five-Year Plan period. The effects of energy structure and energy intensity had substantial impacts on the decoupling with economic growth for various industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.

Key words: industry-related CO2 emission, LMDI model, Tapio model