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Prediction of Shanghai Electric Power Carbon Emissions Based on Improved STIRPAT Model

Author

Listed:
  • Haibing Wang

    (Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China)

  • Bowen Li

    (Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China)

  • Muhammad Qasim Khan

    (Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, 800 Dongchuan Road, Minhang District, Shanghai 200240, China)

Abstract

Energy is the bridge connecting the economy and the environment and electric energy is an important guarantee for social production. In order to respond to the national dual-carbon goals, a new power system is being constructed. Effective carbon emission forecasts of power energy are essential to achieve a significant guarantee for low carbon and clean production of electric power energy. We analyzed the influencing factors of carbon emissions, such as population, per capita gross domestic product (GDP), urbanization rate, industrial structure, energy consumption, energy structure, regional electrification rate, and degree of opening to the outside world. The original Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was improved, and the above influencing factors were incorporated into the model for modeling analysis. The ridge regression algorithm was adopted to analyze the biased estimation of historical data. The carbon emission prediction model of Shanghai electric power and energy based on elastic relationship was established. According to the “14th Five-Year” development plan for the Shanghai area, we set up the impact factor forecast under different scenarios to substitute into the forecast models. The new model can effectively assess the carbon emissions of the power sector in Shanghai in the future.

Suggested Citation

  • Haibing Wang & Bowen Li & Muhammad Qasim Khan, 2022. "Prediction of Shanghai Electric Power Carbon Emissions Based on Improved STIRPAT Model," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13068-:d:940132
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    References listed on IDEAS

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    Cited by:

    1. Wei Hu & Tingting Zheng & Yi Zhang, 2024. "Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-25, July.
    2. Rao, Yanchun & Wang, Xiuli & Li, Hengkai, 2024. "Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light," Energy, Elsevier, vol. 294(C).
    3. Congguang Xu & Wei Xiong & Simin Zhang & Hailiang Shi & Shichao Wu & Shanju Bao & Tieqiao Xiao, 2025. "Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model," Land, MDPI, vol. 14(3), pages 1-21, February.

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