Carbon Emissions and Innovation Cities: A SHAP-Model-Based Study on Decoupling Trends and Policy Implications in Coastal China
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Fanbo Li & Hongfeng Zhang, 2022. "How the “Absorption Processes” of Urban Innovation Contribute to Sustainable Development—A Fussy Set Qualitative Comparative Analysis Based on Seventy-Two Cities in China," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
- Mukund Sundararajan & Amir Najmi, 2019. "The many Shapley values for model explanation," Papers 1908.08474, arXiv.org, revised Feb 2020.
- Lu Liu & Shenshen Si & Jing Li, 2023. "Research on the Effect of Regional Talent Allocation on High-Quality Economic Development—Based on the Perspective of Innovation-Driven Growth," Sustainability, MDPI, vol. 15(7), pages 1-21, April.
- Lu, Qinli & Yang, Hong & Huang, Xianjin & Chuai, Xiaowei & Wu, Changyan, 2015. "Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China," Energy, Elsevier, vol. 82(C), pages 414-425.
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.- Razzaq, Asif & Sharif, Arshian & Ozturk, Ilhan & Skare, Marinko, 2022. "Inclusive infrastructure development, green innovation, and sustainable resource management: Evidence from China’s trade-adjusted material footprints," Resources Policy, Elsevier, vol. 79(C).
- Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
- Hu'e Sullivan & Hurlin Christophe & P'erignon Christophe & Saurin S'ebastien, 2022. "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring," Papers 2212.05866, arXiv.org, revised Jan 2025.
- Hongli Zhang & Lei Shen & Shuai Zhong & Ayman Elshkaki, 2020. "Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
- Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
- Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2021. "Cohort Shapley value for algorithmic fairness," Papers 2105.07168, arXiv.org.
- Jie Zhang & Zhencheng Xing & Jigan Wang, 2016. "Analysis of CO 2 Emission Performance and Abatement Potential for Municipal Industrial Sectors in Jiangsu, China," Sustainability, MDPI, vol. 8(7), pages 1-15, July.
- Wenguang Zhang & Ting Lei & Yu Gong & Jun Zhang & Yirong Wu, 2022. "Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
- Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.
- Xin Li & Chunlei Huang & Shaoguo Zhan & Yunxi Wu, 2022. "The Carbon Emission Reduction Effect of City Cluster—Evidence from the Yangtze River Economic Belt in China," Energies, MDPI, vol. 15(17), pages 1-14, August.
- Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
- Liang, Wei & Gan, Ting & Zhang, Wei, 2019. "Dynamic evolution of characteristics and decomposition of factors influencing industrial carbon dioxide emissions in China: 1991–2015," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 93-106.
- Zhang, Xi & Geng, Yong & Shao, Shuai & Dong, Huijuan & Wu, Rui & Yao, Tianli & Song, Jiekun, 2020. "How to achieve China’s CO2 emission reduction targets by provincial efforts? – An analysis based on generalized Divisia index and dynamic scenario simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
- Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
- Jiancheng Qin & Hui Tao & Chinhsien Cheng & Karthikeyan Brindha & Minjin Zhan & Jianli Ding & Guijin Mu, 2020. "Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China," Sustainability, MDPI, vol. 12(3), pages 1-15, February.
- Gongmin Zhao & Yining Zhang & Yongjie Wu, 2024. "Implementation Effect, Long-Term Mechanisms, and Industrial Upgrading of the Low-Carbon City Pilot Policy: An Empirical Study Based on City-Level Panel Data from China," Sustainability, MDPI, vol. 16(19), pages 1-18, September.
- Oluwatoyin J. Gbadeyan & Joseph Muthivhi & Linda Z. Linganiso & Nirmala Deenadayalu, 2024. "Decoupling Economic Growth from Carbon Emissions: A Transition toward Low-Carbon Energy Systems—A Critical Review," Clean Technol., MDPI, vol. 6(3), pages 1-38, August.
- Ronald Richman & Mario V. Wuthrich, 2021. "LocalGLMnet: interpretable deep learning for tabular data," Papers 2107.11059, arXiv.org.
- Kumar, Rishabh & Koshiyama, Adriano & da Costa, Kleyton & Kingsman, Nigel & Tewarrie, Marvin & Kazim, Emre & Roy, Arunita & Treleaven, Philip & Lovell, Zac, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
- Jing-Li Fan & Jian-Da Wang & Ling-Si Kong & Xian Zhang, 2018. "The carbon footprints of secondary industry in China: an input–output subsystem analysis," 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. 91(2), pages 635-657, March.
More about this item
Keywords
Tapio decoupling model; spatiotemporal evolution; carbon emissions; innovation cities; interpretable machine learning;All these keywords.
Statistics
Access and download statisticsCorrections
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:17:y:2025:i:8:p:3344-:d:1631028. 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.