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Alternative pathways to CO2 reduction in Gansu province, China

Author

Listed:
  • Guokui Wang
  • Xiaojia Guo
  • Jinxiu Fu
  • Qingyue Wei
  • Linlin Zhang

Abstract

Climate change has been considerable concerned because of the increasing greenhouse gas (GHG) emissions. Gansu province is a typical less-developed and heavy chemical industrial province, its CO 2 emission per unit of the gross domestic product (GDP) is 252.52 ton per million Chinese yuan (t/M-CNY) in 2019, which is 48.42% more than national average value. Gansu province faces the following dual pressures including maintaining economic growth and reducing carbon dioxide (CO 2 ) emissions. This paper establishes a low carbon development system dynamics model in order to investigate the effects of four carbon reduction measures (technical progress, industrial transformation, fuel substitution, and low carbon awareness) on reducing CO 2 emission over the period of 2020–2030. The simulation results indicate that, without direct intervention, the CO 2 emissions per unit of GDP is projected to be 171.34 t/M-CNY by 2030. While utilizing technical progress, implementing industrial transformation, fuel substitution, and low carbon awareness could potentially be 2.12%, 3.33%, 0.72% and 1.27%, respectively less than that. For the sake of achieving the goal of CO 2 reduction in the long run, the local government should address today’s industrial transformation and adopt reasonable combination of adjustment and control policies immediately.

Suggested Citation

  • Guokui Wang & Xiaojia Guo & Jinxiu Fu & Qingyue Wei & Linlin Zhang, 2022. "Alternative pathways to CO2 reduction in Gansu province, China," Energy & Environment, , vol. 33(4), pages 809-825, June.
    • Handle: RePEc:sae:engenv:v:33:y:2022:i:4:p:809-825
    DOI: 10.1177/0958305X211023182
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    1. He, Ling & Li, Xiaofan & Cui, Qi & Guan, Bing & Li, Meng & Chen, Hao, 2024. "Decarbonization pathways to subregional carbon neutrality in China based on the top-down multi-regional CGE model: A study of Guangxi," Energy, Elsevier, vol. 294(C).

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