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Analysis of Influencing Factors and Trend Forecast of CO 2 Emission in Chengdu-Chongqing Urban Agglomeration

Author

Listed:
  • Huibin Zeng

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Bilin Shao

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Genqing Bian

    (School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Hongbin Dai

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Fangyu Zhou

    (School of Applied English, Chengdu Institute Sichuan International Studies University, Chengdu 611844, China)

Abstract

Urban agglomeration is a primary source of global energy consumption and CO 2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO 2 emissions in urban agglomerations and prediction of the future trends of CO 2 emissions in urban agglomerations will help in the implementation of CO 2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO 2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO 2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO 2 emission scenarios to predict the energy CO 2 emission of the region and its cities. The results show that, first, the CO 2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO 2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO 2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO 2 emissions in the baseline scenario are low in the short term, but the CO 2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO 2 emissions.

Suggested Citation

  • Huibin Zeng & Bilin Shao & Genqing Bian & Hongbin Dai & Fangyu Zhou, 2022. "Analysis of Influencing Factors and Trend Forecast of CO 2 Emission in Chengdu-Chongqing Urban Agglomeration," Sustainability, MDPI, vol. 14(3), pages 1-30, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1167-:d:729392
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    References listed on IDEAS

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    1. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    2. Juchao Zhao & Shaohua Zhang & Kun Yang & Yanhui Zhu & Yuling Ma, 2020. "Spatio-Temporal Variations of CO 2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
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    Cited by:

    1. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
    2. Xiaodong Li & Ai Ren & Qi Li, 2022. "Exploring Patterns of Transportation-Related CO 2 Emissions Using Machine Learning Methods," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    3. Qing Wang & Yuhang Xiao, 2022. "Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China," Sustainability, MDPI, vol. 14(15), pages 1-29, August.
    4. Siyu Zhu & Ying Ding & Run Pan & Aifang Ding, 2023. "Analysis of Interprovincial Differences in CO 2 Emissions and Peak Prediction in the Yangtze River Delta," Sustainability, MDPI, vol. 15(8), pages 1-16, April.

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