IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v268y2020ics0306261920302087.html
   My bibliography  Save this article

Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery

Author

Listed:
  • Yang, Di
  • Luan, Weixin
  • Qiao, Lu
  • Pratama, Mahardhika

Abstract

Climate warming due to carbon emissions has been recognized as a threat to food security, human health and natural ecosystem, and carbon emission reduction is a challenging job for each country in the world. In this study, an ensemble structure-based neural network model (NNEnsemble) is proposed to analyze the nonlinear relationship between the Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) data, and province-scale statistical data on carbon emissions. Given the challenge of obtaining urban-scale carbon emission data, a weighted coefficient strategy by using the NSL data were employed to analyze the carbon emissions at the urban scale. The performance of the proposed method was found to be superior to that of comparable methods with respect to various evaluation indices. Under these circumstances, a hot spot and Standard deviational ellipse analysis of three northeast provinces in China was conducted from 1998 to 2013. The results can promote a better understanding of the spatio-temporal characteristics of carbon emissions in three northeastern Chinese provinces. Moreover, the developed application software based on NNEnsemble can serve as a basis for the development of carbon emission mitigation policies for other provinces.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920302087
    DOI: 10.1016/j.apenergy.2020.114696
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920302087
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114696?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. 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. Wang, Hongtao & Yang, Yi & Keller, Arturo A. & Li, Xiang & Feng, Shijin & Dong, Ya-nan & Li, Fengting, 2016. "Comparative analysis of energy intensity and carbon emissions in wastewater treatment in USA, Germany, China and South Africa," Applied Energy, Elsevier, vol. 184(C), pages 873-881.
    3. Duan, Cuncun & Chen, Bin & Feng, Kuishuang & Liu, Zhu & Hayat, Tasawar & Alsaedi, Ahmed & Ahmad, Bashir, 2018. "Interregional carbon flows of China," Applied Energy, Elsevier, vol. 227(C), pages 342-352.
    4. Wang, Miao & Feng, Chao, 2017. "Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors," Applied Energy, Elsevier, vol. 190(C), pages 772-787.
    5. Zhang, Chuanguo & Zhao, Wei, 2014. "Panel estimation for income inequality and CO2 emissions: A regional analysis in China," Applied Energy, Elsevier, vol. 136(C), pages 382-392.
    6. Xie, Xuan & Shao, Shuai & Lin, Boqiang, 2016. "Exploring the driving forces and mitigation pathways of CO2 emissions in China’s petroleum refining and coking industry: 1995–2031," Applied Energy, Elsevier, vol. 184(C), pages 1004-1015.
    7. Zhang, Junjie & Yu, Biying & Wei, Yi-Ming, 2018. "Heterogeneous impacts of households on carbon dioxide emissions in Chinese provinces," Applied Energy, Elsevier, vol. 229(C), pages 236-252.
    8. Long, Yin & Yoshida, Yoshikuni, 2018. "Quantifying city-scale emission responsibility based on input-output analysis – Insight from Tokyo, Japan," Applied Energy, Elsevier, vol. 218(C), pages 349-360.
    9. Guokui Wang & Xingpeng Chen & Zilong Zhang & Chaolan Niu, 2015. "Influencing Factors of Energy-Related CO 2 Emissions in China: A Decomposition Analysis," Sustainability, MDPI, vol. 7(10), pages 1-19, October.
    10. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    11. Jing Tian & Hua Liao & Ce Wang, 2015. "Spatial–temporal variations of embodied carbon emission in global trade flows: 41 economies and 35 sectors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 1125-1144, September.
    12. Feng, Jing-Chun & Yan, Jinyue & Yu, Zhi & Zeng, Xuelan & Xu, Weijia, 2018. "Case study of an industrial park toward zero carbon emission," Applied Energy, Elsevier, vol. 209(C), pages 65-78.
    13. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
    14. Ye, Bin & Jiang, JingJing & Li, Changsheng & Miao, Lixin & Tang, Jie, 2017. "Quantification and driving force analysis of provincial-level carbon emissions in China," Applied Energy, Elsevier, vol. 198(C), pages 223-238.
    15. 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.
    16. Meng, Lina & Graus, Wina & Worrell, Ernst & Huang, Bo, 2014. "Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a ," Energy, Elsevier, vol. 71(C), pages 468-478.
    17. 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.
    18. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    19. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    20. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    21. Zhu Liu & Dabo Guan & Scott Moore & Henry Lee & Jun Su & Qiang Zhang, 2015. "Climate policy: Steps to China's carbon peak," Nature, Nature, vol. 522(7556), pages 279-281, June.
    22. 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.
    23. 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.
    24. Wang, Yutao & Yang, Xuechun & Sun, Mingxing & Ma, Lei & Li, Xiao & Shi, Lei, 2016. "Estimating carbon emissions from the pulp and paper industry: A case study," Applied Energy, Elsevier, vol. 184(C), pages 779-789.
    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. 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.
    2. Feng Han & Min Huang, 2022. "Land Misallocation and Carbon Emissions: Evidence from China," Land, MDPI, vol. 11(8), pages 1-30, July.
    3. 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).
    4. Chao Wu & Miaomiao Chen & Lei Zhou & Xiaojin Liang & Wei Wang, 2020. "Identifying the Spatiotemporal Patterns of Traditional Villages in China: A Multiscale Perspective," Land, MDPI, vol. 9(11), pages 1-21, November.
    5. Jiang Zhu & Xiang Li & Huiming Huang & Xiangdong Yin & Jiangchun Yao & Tao Liu & Jiexuan Wu & Zhangcheng Chen, 2023. "Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    6. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    7. Du, Mengbing & Zhang, Xiaoling & Xia, Lang & Cao, Libin & Zhang, Zhe & Zhang, Li & Zheng, Heran & Cai, Bofeng, 2022. "The China Carbon Watch (CCW) system: A rapid accounting of household carbon emissions in China at the provincial level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    8. Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
    9. 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).
    10. Feng Dong & Guoqing Li & Yajie Liu & Qing Xu & Caixia Li, 2023. "Spatial-Temporal Evolution and Cross-Industry Synergy of Carbon Emissions: Evidence from Key Industries in the City in Jiangsu Province, China," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    11. Wang, Ailun & Hu, Shuo & Li, Jianglong, 2021. "Does economic development help achieve the goals of environmental regulation? Evidence from partially linear functional-coefficient model," Energy Economics, Elsevier, vol. 103(C).
    12. Wu, Si & Hu, Shougeng & Frazier, Amy E., 2021. "Spatiotemporal variation and driving factors of carbon emissions in three industrial land spaces in China from 1997 to 2016," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    13. Huo, Weidong & Qi, Jie & Yang, Tong & Liu, Jialu & Liu, Miaomiao & Zhou, Ziqi, 2022. "Effects of China's pilot low-carbon city policy on carbon emission reduction: A quasi-natural experiment based on satellite data," Technological Forecasting and Social Change, Elsevier, vol. 175(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. 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. 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.
    3. 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.
    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. 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.
    6. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 813-833.
    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 & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    9. 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.
    10. 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.
    11. 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.
    12. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    13. 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.
    14. 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.
    15. Xianzhao Liu & Xu Yang & Ruoxin Guo, 2020. "Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method," Sustainability, MDPI, vol. 12(7), pages 1-24, March.
    16. 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.
    17. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    18. Xiao, Hao & Sun, Ke-Juan & Bi, Hui-Min & Xue, Jin-Jun, 2019. "Changes in carbon intensity globally and in countries: Attribution and decomposition analysis," Applied Energy, Elsevier, vol. 235(C), pages 1492-1504.
    19. Liu, Qianqian & Wang, Shaojian & Zhang, Wenzhong & Li, Jiaming & Kong, Yunlong, 2019. "Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives," Applied Energy, Elsevier, vol. 236(C), pages 163-171.
    20. Yannan Zhou & Jixia Huang & Mingxiang Huang & Yicheng Lin, 2019. "The Driving Forces of Carbon Dioxide Equivalent Emissions Have Spatial Spillover Effects in Inner Mongolia," IJERPH, MDPI, vol. 16(10), pages 1-14, May.

    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:appene:v:268:y:2020:i:c:s0306261920302087. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.