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Urban public lighting classification method and analysis of energy and environmental effects based on SDGSAT-1 glimmer imager data

Author

Listed:
  • Lv, Zhuoran
  • Guo, Huadong
  • Zhang, Lu
  • Liang, Dong
  • Zhu, Qi
  • Liu, Xuting
  • Zhou, Heng
  • Liu, Yiming
  • Gou, Yiting
  • Dou, Xinyu
  • Chen, Guoqiang

Abstract

Urban lighting system is an important part of urban space service functions. With the continuous advancement of sustainable development goals, governments of various countries are gradually paying attention to the energy, social, economic and environmental benefits of urban lighting systems, and promote the use of LED lighting to replace HPS lamp lighting to save urban energy consumption. In this study, we used the glimmer imager data of SDGSAT-1 launched in 2021, combined with the streetlight dataset of the District of Columbia, to establish a classification model for distinguishing LED lights from HPS lamps, with an overall recognition accuracy of 80.3% and the kappa coefficient is 0.603. Based on this model, we identify light types in the central cities of China’s three urban agglomerations: Beijing, Shanghai, Guangzhou, and Shenzhen, and describe their lighting composition and spatial distribution patterns. Additionally, we make a preliminary estimate of the money and electricity consumption saved by lighting renovations, and analyzed the impact of urban lighting changes on public perception. This study combines SDGSAT-1 data to propose a training model in urban lighting classification and applies it to a wide range of urban, the results of which help to provide data to support the implementation of SDG7.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923017191
    DOI: 10.1016/j.apenergy.2023.122355
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    1. 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.
    2. Brandon C. Welsh & David P. Farrington, 2008. "Effects of Improved Street Lighting on Crime," Campbell Systematic Reviews, John Wiley & Sons, vol. 4(1), pages 1-51.
    3. 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).
    4. Yang, Wangming & Luan, Yibo & Liu, Xiaolei & Yu, Xiaoyong & Miao, Lijuan & Cui, Xuefeng, 2017. "A new global anthropogenic heat estimation based on high-resolution nighttime light data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 4, pages 1-11.
    5. Tilottama Ghosh & Sharolyn J. Anderson & Christopher D. Elvidge & Paul C. Sutton, 2013. "Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being," Sustainability, MDPI, vol. 5(12), pages 1-32, November.
    6. 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.
    7. Djuretic, Andrej & Kostic, Miomir, 2018. "Actual energy savings when replacing high-pressure sodium with LED luminaires in street lighting," Energy, Elsevier, vol. 157(C), pages 367-378.
    8. 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.
    9. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    10. 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.
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