IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v233-234y2019ip170-181.html
   My bibliography  Save this article

Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels

Author

Listed:
  • Shi, Kaifang
  • Yu, Bailang
  • Zhou, Yuyu
  • Chen, Yun
  • Yang, Chengshu
  • Chen, Zuoqi
  • Wu, Jianping

Abstract

Due to the continuing industrialization and urbanization, China’s CO2 emissions have experienced a rapid increase in recent 30 years. The increase of CO2 emissions will not only effect country’s own sustainable development, but also potentially pose a negative impact on the global climate stability. Since the socioeconomic development is sensitive to geographic scales and regional heterogeneity, a systematic investigation of spatiotemporal variations (SV) of CO2 emissions and their impact factors (IF) across different levels will help to develop more effective and reasonable policies and measures for CO2 emissions mitigation. However, multi-scale analysis of those issues is still lacking. Hence, using two administrative levels (e.g., prefectures or provinces) in China as experimental objects, this study attempted to quantify and compare SV and IF of CO2 emissions from nighttime light images and socioeconomic data at different levels using the variation coefficient (VC), spatial autocorrelation spatial model, and spatial econometric model. Our results show that the VC of CO2 emissions is uninterruptedly increases from 0.66 in 1997 to 0.73 in 2006, and then gradually decreases to 0.69 in 2012 at the provincial level, and it consistently decreases from 1.29 in 1997 to 1.03 in 2012 at the prefectural level. The Global Moran’s I of CO2 emissions increases from 1997 to 2012 at the provincial and prefectural levels. Specifically, the Global Moran’s I gradually increases from 0.23 in 1997 to 0.27 in 2012 at the provincial level, while it shows a rapid growth trend, from 0.23 in 1997 to 0.34 in 2012 at the prefectural level. The proportion of second industry has been demonstrated as a major factor influencing CO2 emissions at different levels, while gross domestic product, urbanization rate, and population play more important roles in CO2 emissions at the prefectural level. This study illustrates that China’s CO2 emissions are sensitive to the spatial-temporal hierarchy of multi-mechanisms, and suggests that “proceed in the light of local conditions” strategies can help Chinese government for CO2 emissions mitigation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:170-181
    DOI: 10.1016/j.apenergy.2018.10.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191831609X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.10.050?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. Shaojian Wang & Chuanglin Fang & Guangdong Li, 2015. "Spatiotemporal Characteristics, Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    2. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2010. "CO2 emissions, energy consumption and economic growth in BRIC countries," Energy Policy, Elsevier, vol. 38(12), pages 7850-7860, December.
    3. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    4. 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.
    5. Al-mulali, Usama, 2012. "Factors affecting CO2 emission in the Middle East: A panel data analysis," Energy, Elsevier, vol. 44(1), pages 564-569.
    6. Grunewald, Nicole & Jakob, Michael & Mouratiadou, Ioanna, 2014. "Decomposing inequality in CO2 emissions: The role of primary energy carriers and economic sectors," Ecological Economics, Elsevier, vol. 100(C), pages 183-194.
    7. 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.
    8. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    9. Al-mulali, Usama & Lee, Janice YM & Hakim Mohammed, Abdul & Sheau-Ting, Low, 2013. "Examining the link between energy consumption, carbon dioxide emission, and economic growth in Latin America and the Caribbean," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 42-48.
    10. Sweety Pandey & Mrutyunjaya Mishra, 2015. "CO2 Emissions and Economic Growth of SAARC Countries: Evidence from a Panel VAR Analysis," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 1(2), pages 23-33, December.
    11. Burnett, J. Wesley & Bergstrom, John C. & Dorfman, Jeffrey H., 2013. "A spatial panel data approach to estimating U.S. state-level energy emissions," Energy Economics, Elsevier, vol. 40(C), pages 396-404.
    12. Ang, B.W & Zhang, F.Q & Choi, Ki-Hong, 1998. "Factorizing changes in energy and environmental indicators through decomposition," Energy, Elsevier, vol. 23(6), pages 489-495.
    13. 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.
    14. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    15. Li, Huanan & Mu, Hailin & Zhang, Ming & Li, Nan, 2011. "Analysis on influence factors of China's CO2 emissions based on Path–STIRPAT model," Energy Policy, Elsevier, vol. 39(11), pages 6906-6911.
    16. 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.
    17. 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.
    18. Yunjing Wang & Yoshitsugu Hayashi & Jin Chen & Qiang Li, 2014. "Changing Urban Form and Transport CO 2 Emissions: An Empirical Analysis of Beijing, China," Sustainability, MDPI, vol. 6(7), pages 1-22, July.
    19. 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.
    20. Malte Meinshausen & Nicolai Meinshausen & William Hare & Sarah C. B. Raper & Katja Frieler & Reto Knutti & David J. Frame & Myles R. Allen, 2009. "Greenhouse-gas emission targets for limiting global warming to 2 °C," Nature, Nature, vol. 458(7242), pages 1158-1162, April.
    21. 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.
    22. Andreoni, Valeria & Galmarini, Stefano, 2016. "Drivers in CO2 emissions variation: A decomposition analysis for 33 world countries," Energy, Elsevier, vol. 103(C), pages 27-37.
    23. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    24. Ana Petrović & Maarten van Ham & David Manley, 2018. "Multiscale Measures of Population: Within- and between-City Variation in Exposure to the Sociospatial Context," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(4), pages 1057-1074, July.
    25. Zhou, Yuyu & Clarke, Leon & Eom, Jiyong & Kyle, Page & Patel, Pralit & Kim, Son H. & Dirks, James & Jensen, Erik & Liu, Ying & Rice, Jennie & Schmidt, Laurel & Seiple, Timothy, 2014. "Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework," Applied Energy, Elsevier, vol. 113(C), pages 1077-1088.
    26. 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.
    27. Jayanthakumaran, Kankesu & Verma, Reetu & Liu, Ying, 2012. "CO2 emissions, energy consumption, trade and income: A comparative analysis of China and India," Energy Policy, Elsevier, vol. 42(C), pages 450-460.
    28. 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.
    29. Long, Ruyin & Shao, Tianxiang & Chen, Hong, 2016. "Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors," Applied Energy, Elsevier, vol. 166(C), pages 210-219.
    30. Wang, Shaojian & Li, Guangdong & Fang, Chuanglin, 2018. "Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2144-2159.
    31. Mussini, Mauro & Grossi, Luigi, 2015. "Decomposing changes in CO2 emission inequality over time: The roles of re-ranking and changes in per capita CO2 emission disparities," Energy Economics, Elsevier, vol. 49(C), pages 274-281.
    32. Zhang, Yanxia & Wang, Haikun & Liang, Sai & Xu, Ming & Liu, Weidong & Li, Shalang & Zhang, Rongrong & Nielsen, Chris P. & Bi, Jun, 2014. "Temporal and spatial variations in consumption-based carbon dioxide emissions in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 60-68.
    33. Clarke-Sather, Afton & Qu, Jiansheng & Wang, Qin & Zeng, Jingjing & Li, Yan, 2011. "Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO2 emissions in China 1997-2007," Energy Policy, Elsevier, vol. 39(9), pages 5420-5428, September.
    34. Wang, Changjian & Wang, Fei & Zhang, Xinlin & Yang, Yu & Su, Yongxian & Ye, Yuyao & Zhang, Hongou, 2017. "Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 51-61.
    35. Pappas, Dimitrios & Chalvatzis, Konstantinos J. & Guan, Dabo & Ioannidis, Alexis, 2018. "Energy and carbon intensity: A study on the cross-country industrial shift from China to India and SE Asia," Applied Energy, Elsevier, vol. 225(C), pages 183-194.
    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. 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.
    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. 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).
    4. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    5. 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.
    6. 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.
    7. 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.
    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, Lei & Xu, Linyu & Yang, Zhifeng, 2019. "Inequality of industrial carbon emissions of the urban agglomeration and its peripheral cities: A case in the Pearl River Delta, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 438-447.
    10. Wanbei Jiang & Weidong Liu, 2020. "Provincial-Level CO 2 Emissions Intensity Inequality in China: Regional Source and Explanatory Factors of Interregional and Intraregional Inequalities," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    11. 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.
    12. 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.
    13. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    14. Chen, Jiandong & Cheng, Shulei & Song, Malin, 2017. "Decomposing inequality in energy-related CO2 emissions by source and source increment: The roles of production and residential consumption," Energy Policy, Elsevier, vol. 107(C), pages 698-710.
    15. Chen, Jing & Zhou, Chunshan & Wang, Shaojian & Li, Shijie, 2018. "Impacts of energy consumption structure, energy intensity, economic growth, urbanization on PM2.5 concentrations in countries globally," Applied Energy, Elsevier, vol. 230(C), pages 94-105.
    16. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. 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.
    18. 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.
    19. 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).
    20. Ling Xiong & Shaozhou Qi, 2018. "Financial Development And Carbon Emissions In Chinese Provinces: A Spatial Panel Data Analysis," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(02), pages 447-464, March.

    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:233-234:y:2019:i::p:170-181. 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.