IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2269-d751260.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2269/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2269/
    Download Restriction: no
    ---><---

    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. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    2. 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.

    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. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    2. Qiao, Renlu & Liu, Xiaochang & Gao, Shuo & Liang, Diling & GesangYangji, Gesang & Xia, Li & Zhou, Shiqi & Ao, Xiang & Jiang, Qingrui & Wu, Zhiqiang, 2024. "Industrialization, urbanization, and innovation: Nonlinear drivers of carbon emissions in Chinese cities," Applied Energy, Elsevier, vol. 358(C).
    3. Qingwei Shi & Jingxin Gao & Xia Wang & Hong Ren & Weiguang Cai & Haifeng Wei, 2020. "Temporal and Spatial Variability of Carbon Emission Intensity of Urban Residential Buildings: Testing the Effect of Economics and Geographic Location in China," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    4. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    5. 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.
    6. Yajing Liu & Shuai Zhou & Ge Zhang, 2023. "Spatio-Temporal Dynamics and Driving Forces of Multi-Scale Emissions Based on Nighttime Light Data: A Case Study of the Pearl River Delta Urban Agglomeration," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    7. Hui Wang & Guifen Liu & Kaifang Shi, 2019. "What Are the Driving Forces of Urban CO 2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    8. Yongguang Zhu & Deyi Xu & Saleem H. Ali & Ruiyang Ma & Jinhua Cheng, 2019. "Can Nighttime Light Data Be Used to Estimate Electric Power Consumption? New Evidence from Causal-Effect Inference," Energies, MDPI, vol. 12(16), pages 1-14, August.
    9. 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).
    10. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    11. Yongxing Li & Wei Guo & Peixian Li & Xuesheng Zhao & Jinke Liu, 2023. "Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    12. Maowen Sun & Boyi Liang & Xuebin Meng & Yunfei Zhang & Zong Wang & Jia Wang, 2024. "Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China," Land, MDPI, vol. 13(6), pages 1-24, June.
    13. Jincai Zhao & Qianqian Liu, 2021. "Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    14. Yuyang Chang & Geli Zhang & Tianzhu Zhang & Zhen Xie & Jingxia Wang, 2020. "Vegetation Dynamics and Their Response to the Urbanization of the Beijing–Tianjin–Hebei Region, China," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    15. 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.
    16. 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).
    17. Ran Guo & Hong Leng & Qing Yuan & Shiyi Song, 2022. "Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China," Land, MDPI, vol. 11(5), pages 1-20, May.
    18. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    19. Bin Liu & Jiehua Lv, 2024. "Spatiotemporal Evolution and Tapio Decoupling Analysis of Energy-Related Carbon Emissions Using Nighttime Light Data: A Quantitative Case Study at the City Scale in Northeast China," Energies, MDPI, vol. 17(19), pages 1-26, September.
    20. 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.

    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:4:p:2269-:d:751260. 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.