Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model

被引:0
|
作者
Zhengqi Wei
Keke Wei
Jincheng Liu
机构
[1] Chifeng University,
[2] Huazhong University of Science and Technology Tongji Medical College,undefined
关键词
Tapio decoupling model; STIRPAT model; Ridge regression; Carbon emission forecast; Scenario analysis; Henan Province;
D O I
暂无
中图分类号
学科分类号
摘要
In order to cope with global warming, China has put forward the “30 · 60” plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the “carbon peak” goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of “carbon peaking” and “carbon neutralization” as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.
引用
收藏
页码:52679 / 52691
页数:12
相关论文
共 50 条
  • [31] Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China
    Xiong, Chuanhe
    Chen, Shuang
    Xu, Liting
    GROWTH AND CHANGE, 2020, 51 (03) : 1401 - 1416
  • [32] Study on the Decoupling Relationship and Rebound Effect between Economic Growth and Carbon Emissions in Central China
    Liu, Ke
    Zhao, Mingxue
    Xie, Xinyue
    Zhou, Qian
    SUSTAINABILITY, 2022, 14 (16)
  • [33] The impact of regional policy implementation on the decoupling of carbon emissions and economic development
    Ma, Xiaoyue
    Zhao, Congyu
    Song, Chenchen
    Meng, Danni
    Xu, Mei
    Liu, Ran
    Yan, Yamin
    Liu, Zhengguang
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 355
  • [34] Decoupling between Economic Growth and Carbon Emissions: Based on Four Major Regions in China
    Shen, Tao
    Hu, Runpu
    Hu, Peilin
    Tao, Zhang
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2023, 20 (02)
  • [35] The decoupling effect between net agricultural carbon emissions and economic growth based on LCA
    Wu, Yimin
    Chen, Ding
    Luo, Muchen
    Gao, Fengwei
    Li, Zhuangzhuang
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2025, 27 (04) : 8357 - 8381
  • [36] Driving factors and decoupling trend analysis between agricultural CO2 emissions and economic development in China based on LMDI and Tapio decoupling
    Yang, Jieqiong
    Luo, Panzhu
    Li, Langping
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2022, 19 (12) : 13093 - 13113
  • [37] Relationship between carbon emissions and economic development: Case study of six countries
    Cutlip L.
    Fath B.D.
    Environment, Development and Sustainability, 2012, 14 (3) : 433 - 453
  • [38] An investigation into the relationship between China's economic development and carbon dioxide emissions
    Yuan, Chaoqing
    Yang, Yingjie
    Liu, Sifeng
    Fang, Zhigeng
    CLIMATE AND DEVELOPMENT, 2017, 9 (01) : 66 - 79
  • [39] Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network
    Chai Ziyuan
    Yan Yibo
    Zibibula Simayi
    Yang Shengtian
    Maliyamuguli Abulimiti
    Wang Yuqing
    Environmental Science and Pollution Research, 2022, 29 : 31781 - 31796
  • [40] Is electricity consumption of Chinese counties decoupled from carbon emissions? A study based on Tapio decoupling index
    Liu, Fengqi
    Kang, Yuxin
    Guo, Kun
    Energy, 2022, 251