IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipas0360544221020892.html
   My bibliography  Save this article

Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road

Author

Listed:
  • Zhong, Liang
  • Liu, Xiaosheng
  • Ao, Jianfeng

Abstract

The ambiguous relationship between the international economy, energy, and carbon emissions has become a significant factor restricting sustainable development. This study attempts to reveal the spatiotemporal dynamics and interrelationships of gross domestic product (GDP), electric power consumption (EPC), and carbon emissions (CE) in the Belt and Road (B&R) regions using Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing data. Annual nighttime light (ANL) images were synthesized, and the accuracy of the ANL data was improved using the multiperiod mask denoising method. Then, the GDP, EPC, and CE at 0.5 km resolution were modelled. Finally, the spatiotemporal dynamics of regional development were comprehensively analysed at multiple scales. The results reveal that the development in the eastern and western parts of the B&R regions has significant differences. China, India, and some countries in Southeast Asia have developed rapidly and in a relatively balanced manner, whereas the development of Central and Eastern Europe and western Russia has been relatively slow and uncoordinated. The geographical centre of the overall development of the B&R continued to migrate to the southeast. This study provides more detailed and comprehensive insights into GDP, EPC, and CE in the B&R regions.

Suggested Citation

  • Zhong, Liang & Liu, Xiaosheng & Ao, Jianfeng, 2022. "Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221020892
    DOI: 10.1016/j.energy.2021.121841
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221020892
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121841?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    3. Alicia Garcia Herrero & Jianwei Xu, 2017. "China's Belt and Road Initiative: Can Europe Expect Trade Gains?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 25(6), pages 84-99, November.
    4. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    5. 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.
    6. Lean, Hooi Hooi & Smyth, Russell, 2010. "CO2 emissions, electricity consumption and output in ASEAN," Applied Energy, Elsevier, vol. 87(6), pages 1858-1864, June.
    7. 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.
    8. 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.
    9. Huang, Yiping, 2016. "Understanding China's Belt & Road Initiative: Motivation, framework and assessment," China Economic Review, Elsevier, vol. 40(C), pages 314-321.
    10. 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.
    11. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    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. 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).
    2. Bega, François & Lin, Boqiang, 2023. "China's belt & road initiative energy cooperation: International assessment of the power projects," Energy, Elsevier, vol. 270(C).

    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. 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.
    2. 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).
    3. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    4. 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.
    5. Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    6. 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).
    7. 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.
    8. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    9. 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).
    10. Yanjun Wang & Fei Teng & Mengjie Wang & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(13), pages 1-29, June.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Shi, Kaifang & Yang, Qingyuan & Fang, Guangliang & Yu, Bailang & Chen, Zuoqi & Yang, Chengshu & Wu, Jianping, 2019. "Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China," Energy, Elsevier, vol. 167(C), pages 641-653.
    16. Salmanzadeh-Meydani, N. & Fatemi Ghomi, S.M.T., 2019. "The causal relationship among electricity consumption, economic growth and capital stock in Iran," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1230-1256.
    17. Yuan, Jiahai & Li, Xinying & Xu, Chuanbo & Zhao, Changhong & Liu, Yuanxin, 2019. "Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method," Energy, Elsevier, vol. 176(C), pages 623-640.
    18. Damoah, Kaku Attah & Giovannetti, Giorgia & Marvasi, Enrico, 2022. "Do country centrality and similarity to China matter in the allocation of belt and road projects?," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 660-674.
    19. Yizhen Wu & Mingyue Jiang & Zhijian Chang & Yuanqing Li & Kaifang Shi, 2020. "Does China’s Urban Development Satisfy Zipf’s Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data," IJERPH, MDPI, vol. 17(4), pages 1-26, February.
    20. 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).

    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:eee:energy:v:239:y:2022:i:pa:s0360544221020892. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.