Extended STIRPAT model-based driving factor analysis of energy-related CO2 emissions in Kazakhstan

被引:0
|
作者
Chuanhe Xiong
Shuang Chen
Rui Huang
机构
[1] Chinese Academy of Sciences,Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology
[2] Nanjing Normal University,Key Laboratory of Virtual Geographic Environment for the Ministry of Education
关键词
CO; emissions; Extended STIRPAT model; Ridge regression; Kazakhstan;
D O I
暂无
中图分类号
学科分类号
摘要
Extended stochastic impact by regression on population, affluence, and technology model incorporating ridge regression was used to analyze the driving mechanism of energy-related CO2 emissions in Kazakhstan during 1992–2014. The research period was divided into two stages based on GDP of Kazakhstan in 1991 (85.70 × 109 dollars), the first stage (1992–2002), GDP < 85.70 × 109 dollars, the stage of economic recovery; the second stage (2003–2014), GDP > 85.70 × 109 dollars, the stable economic development stage. The results demonstrated that (1) population scale and the technological improvement were the dominant contributors to promote the growth of the CO2 emissions during 1992–2014 in Kazakhstan. (2) Economic growth and industrialization played more positive effect on the increase of the CO2 emissions in the stable economic development stage (2003–2014) than those in the stage of economic recovery (1992–2002). The proportion of the tertiary industry, the trade openness, and foreign direct investment were transformed from negative factors into positive factors in the stable economic development stage (2003–2014). (3) Due to the over-urbanization of Kazakhstan before the independence, the level of urbanization continued to decline, urbanization was the first factor to curb CO2 emissions during 1992–2014. Finally, some policy recommendations are put forward to reduce energy-related carbon emissions.
引用
收藏
页码:15920 / 15930
页数:10
相关论文
共 50 条
  • [21] Analyzing extended STIRPAT model of urbanization and CO2 emissions in Asian countries
    Misbah Nosheen
    Muhammad Ali Abbasi
    Javed Iqbal
    Environmental Science and Pollution Research, 2020, 27 : 45911 - 45924
  • [22] Decarbonising energy-related CO2 emissions in the glass industry
    Zier, Michael
    Pflugradt, Noah
    Kotzur, Leander
    Stolten, Detlef
    Glass International, 2022, 45 (01): : 44 - 46
  • [23] Regional Characteristics of Impact Factors for Energy-Related CO2 Emissions in China, 1997–2010: Evidence from Tests for Threshold Effects Based on the STIRPAT Model
    Rong Yuan
    Tao Zhao
    Xianshuo Xu
    Jidong Kang
    Environmental Modeling & Assessment, 2015, 20 : 129 - 144
  • [24] Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang
    Wang, Changjian
    Wang, Fei
    Zhang, Xinlin
    Yang, Yu
    Su, Yongxian
    Ye, Yuyao
    Zhang, Hongou
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 67 : 51 - 61
  • [25] Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis
    Ma, Yan
    Song, Zhe
    Li, Shuangqi
    Jiang, Tangyang
    PLOS ONE, 2020, 15 (12):
  • [26] Forecasting the Energy-related CO2 Emissions of Turkey Using a Grey Prediction Model
    Hamzacebi, C.
    Karakurt, I.
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2015, 37 (09) : 1023 - 1031
  • [27] A small-sample hybrid model for forecasting energy-related CO2 emissions
    Meng, Ming
    Niu, Dongxiao
    Shang, Wei
    Energy, 2014, 64 : 673 - 677
  • [28] A small-sample hybrid model for forecasting energy-related CO2 emissions
    Meng, Ming
    Niu, Dongxiao
    Shang, Wei
    Energy, 2014, 64 : 673 - 677
  • [29] A small-sample hybrid model for forecasting energy-related CO2 emissions
    Meng, Ming
    Niu, Dongxiao
    Shang, Wei
    ENERGY, 2014, 64 : 673 - 677
  • [30] The driving forces and potential mitigation of energy-related CO2 emissions in China's metal industry
    Feng, Chao
    Huang, Jian-Bai
    Wang, Miao
    RESOURCES POLICY, 2018, 59 : 487 - 494