IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4795-d1485521.html
   My bibliography  Save this article

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

Author

Listed:
  • Bin Liu

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jiehua Lv

    (College of Economics and Management, Northeast Forestry University, Harbin 150040, China)

Abstract

As the world’s second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran’s I, while significant local Moran’s I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4795-:d:1485521
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4795/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4795/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang, Chuanglin & Wang, Shaojian & Li, Guangdong, 2015. "Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities," Applied Energy, Elsevier, vol. 158(C), pages 519-531.
    2. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    3. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    4. Menggen Chen & Songyangyang Zhao & Jiawen Wang, 2023. "The Impact of the Digital Economy on Regional Carbon Emissions: Evidence from China," Sustainability, MDPI, vol. 15(20), pages 1-34, October.
    5. Qi Wang & Jiejun Huang & Han Zhou & Jiaqi Sun & Mingkun Yao, 2022. "Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    6. 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.
    7. Ruixi Dong & Fengying Yan, 2021. "Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China," Land, MDPI, vol. 10(11), pages 1-20, October.
    8. Myles R. Allen & David J. Frame & Chris Huntingford & Chris D. Jones & Jason A. Lowe & Malte Meinshausen & Nicolai Meinshausen, 2009. "Warming caused by cumulative carbon emissions towards the trillionth tonne," Nature, Nature, vol. 458(7242), pages 1163-1166, April.
    9. 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.
    10. Xiaochun Zhao & Mei Jiang & Wei Zhang, 2022. "Decoupling between Economic Development and Carbon Emissions and Its Driving Factors: Evidence from China," IJERPH, MDPI, vol. 19(5), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    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. 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.
    4. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    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. 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.
    7. Sun, Lu & Liu, Wenjing & Li, Zhaoling & Cai, Bofeng & Fujii, Minoru & Luo, Xiao & Chen, Wei & Geng, Yong & Fujita, Tsuyoshi & Le, Yiping, 2021. "Spatial and structural characteristics of CO2 emissions in East Asian megacities and its indication for low-carbon city development," Applied Energy, Elsevier, vol. 284(C).
    8. 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.
    9. Sun, Xudong & Li, Jiashuo & Qiao, Han & Zhang, Bo, 2017. "Energy implications of China's regional development: New insights from multi-regional input-output analysis," Applied Energy, Elsevier, vol. 196(C), pages 118-131.
    10. Chuanlong Li & Yuanqing Li & Kaifang Shi & Qingyuan Yang, 2020. "A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China," IJERPH, MDPI, vol. 17(10), pages 1-13, May.
    11. Xiaolei Huang & Jinpei Ou & Yingjian Huang & Shun Gao, 2023. "Exploring the Effects of Socioeconomic Factors and Urban Forms on CO 2 Emissions in Shrinking and Growing Cities," Sustainability, MDPI, vol. 16(1), pages 1-20, December.
    12. Lv, Zhuoran & Guo, Huadong & Zhang, Lu & Liang, Dong & Zhu, Qi & Liu, Xuting & Zhou, Heng & Liu, Yiming & Gou, Yiting & Dou, Xinyu & Chen, Guoqiang, 2024. "Urban public lighting classification method and analysis of energy and environmental effects based on SDGSAT-1 glimmer imager data," Applied Energy, Elsevier, vol. 355(C).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    18. Chunshan Zhou & Shijie Li & Shaojian Wang, 2018. "Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China’s Megacities," IJERPH, MDPI, vol. 15(8), pages 1-18, July.
    19. 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.
    20. Ke Luo & Shuo Chen & Shixi Cui & Yuantao Liao & Yu He & Chunshan Zhou & Shaojian Wang, 2023. "Examining the Overall and Heterogeneous Impacts of Urban Spatial Structure on Carbon Emissions: A Case Study of Guangdong Province, China," Land, MDPI, vol. 12(9), pages 1-19, September.

    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:jeners:v:17:y:2024:i:19:p:4795-:d:1485521. 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.