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Spatiotemporal Heterogeneity of the Characteristics and Influencing Factors of Energy-Consumption-Related Carbon Emissions in Jiangsu Province Based on DMSP-OLS and NPP-VIIRS

Author

Listed:
  • Hongzhi Meng

    (School of Public Administration, Hohai University, Nanjing 211100, China)

  • Xiaoke Zhang

    (School of Public Administration, Hohai University, Nanjing 211100, China
    Center for Environmental and Social Studies, Hohai University, Nanjing 211100, China)

  • Xindong Du

    (School of Public Administration, Hohai University, Nanjing 211100, China)

  • Kaiyuan Du

    (The Second Institute of Geological and Mineral Resources Survey of Henan, Zhengzhou 450001, China
    Henan Shanshui Geological Tourism Resources Development Co., Ltd., Zhengzhou 450001, China)

Abstract

Scientific estimations and the dynamic monitoring of the development trend of carbon emissions from energy consumption with a long time series can provide the scientific basis for formulating and implementing regional carbon-reduction strategies. Based on DMSP-OLS and NPP-VIIRS night-time light data, a pixel-scale estimation model of energy-consumption carbon emissions in Jiangsu Province from 2000 to 2019 was constructed. The spatiotemporal evolution characteristics and influencing factors were analyzed using the GIS method and a GTWR (geographically and temporally weighted regression) model. The results showed that: (1) The goodness of fit of the image-fusion correction of the two night-time light data sources from 2012 to 2013 was 0.894; the goodness of fit of the carbon-emission estimation model by stages was above 0.99; and the average relative error was 7.71%, which met the requirement for the estimation accuracy. (2) During the study period, the total carbon emissions from energy consumption in Jiangsu Province continued to increase, rising from 94.7618 million tons to 313.3576 million tons, with an annual growth rate of 6.50%; and the growth rate presented an upward trend of “slow-accelerate-decelerate”. Spatially, it showed an unbalanced distribution pattern of “low north and high south”. (3) Per-capita GDP and energy intensity were the core driving factors affecting carbon emissions in Jiangsu Province over the past 20 years. Energy intensity had the greatest driving effect on carbon emissions in southern Jiangsu, while per-capita GDP had the greatest influence in central and northern Jiangsu. Coordinating the relationship between central, north, and south Jiangsu is of great significance for the realization of the sustainable economic and social development of the double carbon goal.

Suggested Citation

  • Hongzhi Meng & Xiaoke Zhang & Xindong Du & Kaiyuan Du, 2023. "Spatiotemporal Heterogeneity of the Characteristics and Influencing Factors of Energy-Consumption-Related Carbon Emissions in Jiangsu Province Based on DMSP-OLS and NPP-VIIRS," Land, MDPI, vol. 12(7), pages 1-17, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1369-:d:1189679
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    References listed on IDEAS

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    1. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    2. Nepal, Rabindra & Paija, Nirash, 2019. "A multivariate time series analysis of energy consumption, real output and pollutant emissions in a developing economy: New evidence from Nepal," Economic Modelling, Elsevier, vol. 77(C), pages 164-173.
    3. Pitchaikani Mala & Muthusamy Palanivel & Siluvayan Priyan & Anuwat Jirawattanapanit & Grienggrai Rajchakit & Pramet Kaewmesri, 2022. "Sustainable Supply Chain System for Defective Products with Different Carbon Emission Strategies," Sustainability, MDPI, vol. 14(23), pages 1-22, December.
    4. Henriques, Sofia Teives & Borowiecki, Karol J., 2017. "The drivers of long-run CO2 emissions in Europe, North America and Japan since 1800," Energy Policy, Elsevier, vol. 101(C), pages 537-549.
    5. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    6. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
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