IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p5227-d1189026.html
   My bibliography  Save this article

Carbon Peak Scenario Simulation of Manufacturing Carbon Emissions in Northeast China: Perspective of Structure Optimization

Author

Listed:
  • Caifen Xu

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

  • Yu Zhang

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

  • Yangmeina Yang

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

  • Huiying Gao

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

Abstract

The manufacturing industry is the pillar industry of China’s economy and a major carbon emitter, and its carbon emission reduction efforts directly determine whether the country’s carbon emission reduction target can be successfully met. In the context of the goals of the carbon peak and carbon neutrality policy, we examine the impact of manufacturing structure optimization on carbon emissions from 2003 to 2020 through a spatial econometric model, taking the old industrial centers in Northeast China as an example. We then apply a machine learning model to simulate manufacturing carbon emissions during the carbon peak stage and identify the optimal path for carbon emission reduction, which is important for promoting manufacturing carbon emission reduction in Northeast China. Since the goal of low-carbon economic development has gradually replaced the goal of maximizing economic efficiency in recent years, manufacturing structure optimization has come to focus on energy saving and emission reduction. Therefore, we define manufacturing structure optimization from the dual perspective of technology and energy consumption to broaden the existing research perspective. The results show the following: (1) The overall trend in manufacturing structure optimization in Northeast China is steadily improving, and the level of manufacturing structure optimization from the technology perspective is higher than that from the energy consumption perspective. (2) Manufacturing structure optimization and manufacturing carbon emissions in Northeast China both show a positive spatial correlation. Manufacturing structure optimization in Northeast China can effectively promote carbon emission reduction, and it also has a spatial spillover effect. (3) The carbon emission reduction effect of manufacturing structure optimization from the energy consumption perspective is better than that from the technology perspective, and the carbon emission reduction effect under the institutional innovation scenario is better than that under the baseline scenario and the technological innovation scenario. Focusing on manufacturing structure optimization from both technology and energy consumption perspectives, as well as continuously improving technological innovation and institutional innovation, can help to achieve manufacturing carbon emission reduction in Northeast China.

Suggested Citation

  • Caifen Xu & Yu Zhang & Yangmeina Yang & Huiying Gao, 2023. "Carbon Peak Scenario Simulation of Manufacturing Carbon Emissions in Northeast China: Perspective of Structure Optimization," Energies, MDPI, vol. 16(13), pages 1-31, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5227-:d:1189026
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/5227/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/5227/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jan Oosterhaven & Lourens Broersma, 2007. "Sector Structure and Cluster Economies: A Decomposition of Regional Labour Productivity," Regional Studies, Taylor & Francis Journals, vol. 41(5), pages 639-659.
    2. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    3. Rahman, Mohammad Mafizur & Kashem, Mohammad Abul, 2017. "Carbon emissions, energy consumption and industrial growth in Bangladesh: Empirical evidence from ARDL cointegration and Granger causality analysis," Energy Policy, Elsevier, vol. 110(C), pages 600-608.
    4. Jian Liu & Qingshan Yang & Yu Zhang & Wen Sun & Yiming Xu, 2019. "Analysis of CO 2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
    5. Yang Yang & Fan He & Junping Ji & Xin Liu, 2022. "Peaking Carbon Emissions in a Megacity through Economic Restructuring: A Case Study of Shenzhen, China," Energies, MDPI, vol. 15(19), pages 1-24, September.
    6. Wenwen Tang & Lihan Cui & Sheng Zheng & Wei Hu, 2022. "Multi-Scenario Simulation of Land Use Carbon Emissions from Energy Consumption in Shenzhen, China," Land, MDPI, vol. 11(10), pages 1-16, September.
    7. Changyou Zhang & Wenyu Zhang & Weina Luo & Xue Gao & Bingchen Zhang, 2021. "Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition," Energies, MDPI, vol. 14(18), pages 1-23, September.
    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. Dawei Feng & Wenchao Xu & Xinyu Gao & Yun Yang & Shirui Feng & Xiaohu Yang & Hailong Li, 2023. "Carbon Emission Prediction and the Reduction Pathway in Industrial Parks: A Scenario Analysis Based on the Integration of the LEAP Model with LMDI Decomposition," Energies, MDPI, vol. 16(21), pages 1-15, October.

    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. Villanthenkodath, Muhammed Ashiq & Mahalik, Mantu Kumar, 2021. "Does economic growth respond to electricity consumption asymmetrically in Bangladesh? The implication for environmental sustainability," Energy, Elsevier, vol. 233(C).
    2. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    3. Jikun Jiang & Shenglai Zhu & Weihao Wang, 2022. "Carbon Emissions, Economic Growth, Urbanization, and Foreign Trade in China: Empirical Evidence from ARDL Models," Sustainability, MDPI, vol. 14(15), pages 1-15, August.
    4. Abdul Rehman & Hengyun Ma & Magdalena Radulescu & Crenguta Ileana Sinisi & Zahid Yousaf, 2021. "Energy Crisis in Pakistan and Economic Progress: Decoupling the Impact of Coal Energy Consumption in Power and Brick Kilns," Mathematics, MDPI, vol. 9(17), pages 1-15, August.
    5. Zhang, Yu & Zhang, Sufang, 2018. "The impacts of GDP, trade structure, exchange rate and FDI inflows on China's carbon emissions," Energy Policy, Elsevier, vol. 120(C), pages 347-353.
    6. Mustansar, Talreja, 2023. "Financial innovation, technological improvement and bank’ profitability," OSF Preprints 8wy95, Center for Open Science.
    7. Jiansuo Pei & Erik Dietzenbacher & Jan Oosterhaven & Cuihong Yang, 2011. "Accounting for China's Import Growth: A Structural Decomposition for 1997–2005," Environment and Planning A, , vol. 43(12), pages 2971-2991, December.
    8. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    9. Ioannis Charalampopoulos & Fotoula Droulia & Jeffrey Evans, 2023. "The Bioclimatic Change of the Agricultural and Natural Areas of the Adriatic Coastal Countries," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    10. Dong Jichang & He Jing & Li Xiuting & Mou Xindi & Dong Zhi, 2020. "The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis," Journal of Systems Science and Information, De Gruyter, vol. 8(1), pages 1-16, February.
    11. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    12. Khan, Zeeshan & Hussain, Muzzammil & Shahbaz, Muhammad & Yang, Siqun & Jiao, Zhilun, 2020. "Natural resource abundance, technological innovation, and human capital nexus with financial development: A case study of China," Resources Policy, Elsevier, vol. 65(C).
    13. Guangyue Xu & Peter Schwarz & Xiaojing Shi & Nathan Duma, 2023. "Scenario Paths of Developing Forest Carbon Sinks for China to Achieve Carbon Neutrality," Land, MDPI, vol. 12(7), pages 1-19, June.
    14. Xu, Guangyue & Dong, Haoyun & Xu, Zhenci & Bhattarai, Nishan, 2022. "China can reach carbon neutrality before 2050 by improving economic development quality," Energy, Elsevier, vol. 243(C).
    15. Hongzhong Fan & Md Ismail Hossain & Mollah Aminul Islam & Yassin Elshain Yahia, 2019. "The Impact of Trade, Technology and Growth on Environmental Deterioration of China and India," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(1), pages 1-29, January.
    16. Marian Rizov & Patrick Paul Walsh, 2011. "Is There a Rural-Urban Divide? Location and Productivity of UK Manufacturing," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 641-656.
    17. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    18. Rizov, Marian & Oskam, Arie & Walsh, Paul, 2012. "Is there a limit to agglomeration? Evidence from productivity of Dutch firms," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 595-606.
    19. Angeliki N. Menegaki, 2019. "The ARDL Method in the Energy-Growth Nexus Field; Best Implementation Strategies," Economies, MDPI, vol. 7(4), pages 1-16, October.
    20. Rauf, Abdul & Zhang, Jin & Li, Jinkai & Amin, Waqas, 2018. "Structural changes, energy consumption and carbon emissions in China: Empirical evidence from ARDL bound testing model," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 194-206.

    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:jeners:v:16:y:2023:i:13:p:5227-:d:1189026. 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.