IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6813-d830496.html
   My bibliography  Save this article

Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT

Author

Listed:
  • Qi Wang

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China)

  • Jiejun Huang

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China)

  • Han Zhou

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China)

  • Jiaqi Sun

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China)

  • Mingkun Yao

    (School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430062, China)

Abstract

Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6813-:d:830496
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6813/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/11/6813/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feng Dong & Ruyin Long & Hong Chen & Xiaohui Li & Qingliang Yang, 2013. "Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.
    2. Liu, Yu & Tan, Xiu-Jie & Yu, Yang & Qi, Shao-Zhou, 2017. "Assessment of impacts of Hubei Pilot emission trading schemes in China – A CGE-analysis using TermCO2 model," Applied Energy, Elsevier, vol. 189(C), pages 762-769.
    3. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    4. Castells-Quintana, David & Dienesch, Elisa & Krause, Melanie, 2021. "Air pollution in an urban world: A global view on density, cities and emissions," Ecological Economics, Elsevier, vol. 189(C).
    5. Liu, Nan & Ma, Zujun & Kang, Jidong, 2015. "Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis," Energy Policy, Elsevier, vol. 87(C), pages 28-38.
    6. David Font Vivanco & René Kemp & Ester Voet & Reinout Heijungs, 2014. "Using LCA-based Decomposition Analysis to Study the Multidimensional Contribution of Technological Innovation to Environmental Pressures," Journal of Industrial Ecology, Yale University, vol. 18(3), pages 380-392, May.
    7. Pengyan Zhang & Jianjian He & Xin Hong & Wei Zhang & Chengzhe Qin & Bo Pang & Yanyan Li & Yu Liu, 2017. "Regional-Level Carbon Emissions Modelling and Scenario Analysis: A STIRPAT Case Study in Henan Province, China," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    8. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    9. Shigeto, Sawako & Yamagata, Yoshiki & Ii, Ryota & Hidaka, Masato & Horio, Masayuki, 2012. "An easily traceable scenario for 80% CO2 emission reduction in Japan through the final consumption-based CO2 emission approach: A case study of Kyoto-city," Applied Energy, Elsevier, vol. 90(1), pages 201-205.
    10. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    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. Zheng Jiang & Shuohua Zhang & Wei Li, 2022. "Exploration of Urban Emission Mitigation Pathway under the Carbon Neutrality Target: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    2. Gulmira Abbas & Alimujiang Kasimu, 2023. "Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan," Sustainability, MDPI, vol. 15(15), pages 1-27, July.
    3. Man Li & Yanfang Zhang & Huancai Liu, 2022. "Carbon Neutrality in Shanxi Province: Scenario Simulation Based on LEAP and CA-Markov Models," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    4. Weijia Li & Yuejiao Wang, 2023. "Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy," Sustainability, MDPI, vol. 15(17), pages 1-17, August.

    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. Xinlin Zhang & Yuan Zhao & Qi Sun & Changjian Wang, 2017. "Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    2. Mi, Zhifu & Zheng, Jiali & Meng, Jing & Zheng, Heran & Li, Xian & Coffman, D'Maris & Woltjer, Johan & Wang, Shouyang & Guan, Dabo, 2019. "Carbon emissions of cities from a consumption-based perspective," Applied Energy, Elsevier, vol. 235(C), pages 509-518.
    3. Xin Li & Xiandan Cui & Minxi Wang, 2017. "Analysis of China’s Carbon Emissions Base on Carbon Flow in Four Main Sectors: 2000–2013," Sustainability, MDPI, vol. 9(4), pages 1-13, April.
    4. Liangen Zeng & Haiyan Lu & Yenping Liu & Yang Zhou & Haoyu Hu, 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015," Energies, MDPI, vol. 12(16), pages 1-21, August.
    5. Junbo Wang & Liu Chen & Lu Chen & Xiaohui Zhao & Minxi Wang & Yiyi Ju & Li Xin, 2019. "City-Level Features of Energy Footprints and Carbon Dioxide Emissions in Sichuan Province of China," Energies, MDPI, vol. 12(10), pages 1-14, May.
    6. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    7. Yu, Yantuan & Zhang, Ning & Kim, Jong Dae, 2020. "Impact of urbanization on energy demand: An empirical study of the Yangtze River Economic Belt in China," Energy Policy, Elsevier, vol. 139(C).
    8. 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.
    9. Ghazala Aziz & Zouheir Mighri, 2022. "Carbon Dioxide Emissions and Forestry in China: A Spatial Panel Data Approach," Sustainability, MDPI, vol. 14(19), pages 1-40, October.
    10. Li, Ding & Gao, Ming & Hou, Wenxuan & Song, Malin & Chen, Jiandong, 2020. "A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach," Energy Policy, Elsevier, vol. 147(C).
    11. Doğan, Buhari & Ghosh, Sudeshna & Hoang, Dung Phuong & Chu, Lan Khanh, 2022. "Are economic complexity and eco-innovation mutually exclusive to control energy demand and environmental quality in E7 and G7 countries?," Technology in Society, Elsevier, vol. 68(C).
    12. Sheng, Pengfei & Guo, Xiaohui, 2016. "The Long-run and Short-run Impacts of Urbanization on Carbon Dioxide Emissions," Economic Modelling, Elsevier, vol. 53(C), pages 208-215.
    13. Cai, Bofeng & Lu, Jun & Wang, Jinnan & Dong, Huijuan & Liu, Xiaoman & Chen, Yang & Chen, Zhanming & Cong, Jianhui & Cui, Zhipeng & Dai, Chunyan & Fang, Kai & Feng, Tong & Guo, Jie & Li, Fen & Meng, Fa, 2019. "A benchmark city-level carbon dioxide emission inventory for China in 2005," Applied Energy, Elsevier, vol. 233, pages 659-673.
    14. 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.
    15. Cai, Bofeng & Cui, Can & Zhang, Da & Cao, Libin & Wu, Pengcheng & Pang, Lingyun & Zhang, Jihong & Dai, Chunyan, 2019. "China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. 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.
    17. Wen, Hong-xing & Chen, Zhe & Yang, Qian & Liu, Jin-yi & Nie, Pu-yan, 2022. "Driving forces and mitigating strategies of CO2 emissions in China: A decomposition analysis based on 38 industrial sub-sectors," Energy, Elsevier, vol. 245(C).
    18. Kong-Qing Li & Ran Lu & Rui-Wen Chu & Dou-Dou Ma & Li-Qun Zhu, 2018. "Trends and Driving Forces of Carbon Emissions from Energy Consumption: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 10(12), pages 1-13, November.
    19. Xuecheng Wang & Xu Tang & Zhenhua Feng & Yi Zhang, 2019. "Characterizing the Embodied Carbon Emissions Flows and Ecological Relationships among Four Chinese Megacities and Other Provinces," Sustainability, MDPI, vol. 11(9), pages 1-19, May.
    20. Yan Wang & Weihua Xiao & Yicheng Wang & Baodeng Hou & Heng Yang & Xuelei Zhang & Mingzhi Yang & Lishan Zhu, 2018. "Exploring City Development Modes under the Dual Control of Water Resources and Energy-Related CO 2 Emissions: The Case of Beijing, China," Sustainability, MDPI, vol. 10(9), pages 1-16, 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:jsusta:v:14:y:2022:i:11:p:6813-:d:830496. 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.