IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1167-d729392.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1167/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/3/1167/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    2. Wanbei Jiang & Weidong Liu, 2020. "Provincial-Level CO 2 Emissions Intensity Inequality in China: Regional Source and Explanatory Factors of Interregional and Intraregional Inequalities," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    3. Jialin Liu & Yi Zhu & Qun Zhang & Fangyan Cheng & Xi Hu & Xinhong Cui & Lang Zhang & Zhenglin Sun, 2020. "Transportation Carbon Emissions from a Perspective of Sustainable Development in Major Cities of Yangtze River Delta, China," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    4. Wang, Wei-Zheng & Liu, Lan-Cui & Liao, Hua & Wei, Yi-Ming, 2021. "Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries," Energy Policy, Elsevier, vol. 151(C).
    5. Ji Han & Xing Meng & Yanqi Zhang & Jiabin Liu, 2017. "The Impact of Infrastructure Stock Density on CO 2 Emissions: Evidence from China Provinces," Sustainability, MDPI, vol. 9(12), pages 1-13, December.
    6. Rauf, Abdul & Zhang, Jin & Li, Jinkai & Amin, Waqas, 2018. "Structural changes, energy consumption and carbon emissions in China: Empirical evidence from ARDL bound testing model," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 194-206.
    7. Liu, Qianqian & Wang, Shaojian & Zhang, Wenzhong & Li, Jiaming & Kong, Yunlong, 2019. "Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives," Applied Energy, Elsevier, vol. 236(C), pages 163-171.
    8. Hui Wang & Jinzhuo Wu & Wenshu Lin & Zhaoping Luan, 2023. "Carbon Footprint Accounting and Influencing Factors Analysis for Forestry Enterprises in the Key State-Owned Forest Region of the Greater Khingan Range, Northeast China," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    9. Jiayu Liu & Feng Xu & Huan Wang & Xiao Zhang, 2023. "Investigating the Impacts of Built-Up Land Allocation on Carbon Emissions in 88 Cities of the Yangtze River Economic Belt Based on Panel Regressions," Land, MDPI, vol. 12(4), pages 1-15, April.
    10. Zhao, Yabo & Wang, Shaojian & Ge, Yuejing & Liu, Qianqian & Liu, Xiaofeng, 2017. "The spatial differentiation of the coupling relationship between urbanization and the eco-environment in countries globally: A comprehensive assessment," Ecological Modelling, Elsevier, vol. 360(C), pages 313-327.
    11. Xin Xu & Yuming Shen & Hanchu Liu, 2022. "What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    12. Weidong Chen & Ruoyu Yang, 2018. "Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    13. Elfasakhany, Ashraf, 2017. "Investigations on performance and pollutant emissions of spark-ignition engines fueled with n-butanol–, isobutanol–, ethanol–, methanol–, and acetone–gasoline blends: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 404-413.
    14. Chen Li & Heng Li & Xionghe Qin, 2022. "Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities," IJERPH, MDPI, vol. 19(3), pages 1-29, January.
    15. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    16. Feng Wang & Min Wu & Jiachen Hong, 2020. "Exploring the Effects of Industrial Structure, Technology, and Energy Efficiency on China’s Carbon Intensity and Their Contributions to Carbon Intensity Target," Sustainability, MDPI, vol. 12(19), pages 1-24, September.
    17. Wang, Shaojian & Li, Guangdong & Fang, Chuanglin, 2018. "Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2144-2159.
    18. Ziqi Meng & Min Liu & Qiannan She & Fang Yang & Lingbo Long & Xia Peng & Ji Han & Weining Xiang, 2018. "Spatiotemporal Characteristics of Ecological Conditions and Its Response to Natural Conditions and Human Activities during 1990–2010 in the Yangtze River Delta, China," IJERPH, MDPI, vol. 15(12), pages 1-16, December.
    19. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    20. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1167-:d:729392. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.