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Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City

Author

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  • Zhicong Zhang

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Hao Xie

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
    Zhenjiang Institute for Innovation and Development, Nanjing Normal University, Zhenjiang 212016, China)

  • Jubing Zhang

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Xinye Wang

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China
    Zhenjiang Institute for Innovation and Development, Nanjing Normal University, Zhenjiang 212016, China)

  • Jiayu Wei

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Xibin Quan

    (School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210023, China)

Abstract

Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

Suggested Citation

  • Zhicong Zhang & Hao Xie & Jubing Zhang & Xinye Wang & Jiayu Wei & Xibin Quan, 2022. "Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City," IJERPH, MDPI, vol. 19(12), pages 1-23, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7165-:d:836408
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    as
    1. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    2. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.
    3. Jinyan Zhan & Haiming Yan & Bin Chen & Jiao Luo & Nana Shi, 2012. "Decomposition Analysis of the Mechanism Behind the Spatial and Temporal Patterns of Changes in Carbon Bio-Sequestration in China," Energies, MDPI, vol. 5(2), pages 1-13, February.
    4. Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
    5. Chong, Chin Hao & Tan, Wei Xin & Ting, Zhao Jia & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou, 2019. "The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    6. Klaus Hubacek & Kuishuang Feng & Bin Chen, 2011. "Changing Lifestyles Towards a Low Carbon Economy: An IPAT Analysis for China," Energies, MDPI, vol. 5(1), pages 1-10, December.
    7. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    8. Kai Liu & Xingping Wang & Yiran Yan, 2022. "Network Analysis of Industrial Symbiosis in Chemical Industrial Parks: A Case Study of Nanjing Jiangbei New Materials High-Tech Park," Sustainability, MDPI, vol. 14(3), pages 1-23, January.
    9. Ayoub Zeraibi & Daniel Balsalobre-Lorente & Khurram Shehzad, 2021. "Testing the Environmental Kuznets Curve Hypotheses in Chinese Provinces: A Nexus between Regional Government Expenditures and Environmental Quality," IJERPH, MDPI, vol. 18(18), pages 1-16, September.
    10. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
    11. Xiaodong Zhu & Chuhui Hua & Yingcui Sun, 2013. "Study of Jiangsu Manufacturing Energy Consumption Structure Under Low Carbon Economy," Springer Books, in: Feng Chen & Yisheng Liu & Guowei Hua (ed.), Ltlgb 2012, edition 127, chapter 0, pages 1001-1006, Springer.
    12. Li, Guangchen & Wei, Weixian, 2021. "Financial development, openness, innovation, carbon emissions, and economic growth in China," Energy Economics, Elsevier, vol. 97(C).
    13. Nocera, Silvio & Tonin, Stefania & Cavallaro, Federico, 2015. "The economic impact of greenhouse gas abatement through a meta-analysis: Valuation, consequences and implications in terms of transport policy," Transport Policy, Elsevier, vol. 37(C), pages 31-43.
    14. Jaehyeok Kim & Hyungwoo Lim & Ha-Hyun Jo, 2020. "Do Aging and Low Fertility Reduce Carbon Emissions in Korea? Evidence from IPAT Augmented EKC Analysis," IJERPH, MDPI, vol. 17(8), pages 1-15, April.
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    2. Ting Zhang & Longqian Chen & Ziqi Yu & Jinyu Zang & Long Li, 2022. "Spatiotemporal Evolution Characteristics of Carbon Emissions from Industrial Land in Anhui Province, China," Land, MDPI, vol. 11(11), pages 1-18, November.
    3. Yong Xiao & Cheng Yong & Wei Hu & Hanyun Wang, 2023. "Factors Influencing Carbon Emissions in High Carbon Industries in the Zhejiang Province and Decoupling Effect Analysis," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
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    5. Yuan Yuan & Ping Xu & Hui Zhang, 2023. "Spatial Zoning of Carbon Dioxide Emissions at the Intra-City Level: A Case Study of Nanjing, China," IJERPH, MDPI, vol. 20(5), pages 1-19, February.

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