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An Estimating Method for Carbon Emissions of China Based on Nighttime Lights Remote Sensing Satellite Images

Author

Listed:
  • Tianjiao Yang

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Jing Liu

    (Department of Construction Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Haibo Mi

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Zhicheng Cao

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Yiting Wang

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Huichao Han

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Jiahui Luan

    (China Astronautics Standards Institute, Beijing 100071, China)

  • Zhaoxuan Wang

    (China Astronautics Standards Institute, Beijing 100071, China)

Abstract

In September 2020, China proposed the achievement of the emission reduction targets of “carbon peak” and “carbon neutral” by 2030 and 2060, respectively. As an important area of energy consumption in addition to industry and transportation, the construction industry has great energy-saving potential and is gradually becoming the key to achieving China’s energy-saving and emission-reduction goals. Energy data is an important basic support for measuring carbon emissions, analyzing energy-saving potential, and formulating energy-saving targets. In order to solve the he lack of data on China’s carbon emissions, this paper uses lamplight remote sensing image data in the study. Combined with China’s eastern, central, and western regions of building carbon emissions data and the establishment of a partition of China building carbon emissions calculation model, panel data found building carbon emissions and smooth lamp brightness values between the balanced relations. After that, using the building carbon emissions models of the three regions, the building carbon emissions of 30 provinces, 360 cities, and 2778 counties in China were measured, and the changing trends and temporal and spatial directions of building carbon emissions at three spatial scales were analyzed. The results showed that although the total carbon emissions of civil buildings in China has been increasing year by year, its average annual growth rate is gradually slowing down. In addition, the temporal and spatial development directions of carbon emissions from buildings of different spatial scales are basically the same, and they all show a trend of shifting to the east.

Suggested Citation

  • Tianjiao Yang & Jing Liu & Haibo Mi & Zhicheng Cao & Yiting Wang & Huichao Han & Jiahui Luan & Zhaoxuan Wang, 2022. "An Estimating Method for Carbon Emissions of China Based on Nighttime Lights Remote Sensing Satellite Images," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2269-:d:751260
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    References listed on IDEAS

    as
    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. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    3. Lu, Heli & Liu, Guifang, 2014. "Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting," Applied Energy, Elsevier, vol. 131(C), pages 297-306.
    4. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    5. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    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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    Cited by:

    1. Yaohui Liu & Wenyi Liu & Peiyuan Qiu & Jie Zhou & Linke Pang, 2023. "Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data," Land, MDPI, vol. 12(7), pages 1-19, July.
    2. Man Li & Yanfang Zhang & Huancai Liu, 2022. "Carbon Neutrality in Shanxi Province: Scenario Simulation Based on LEAP and CA-Markov Models," Sustainability, MDPI, vol. 14(21), pages 1-17, October.

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