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Research Article

Regional industrial development trend under the carbon goals in China

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Pages 8029-8046 | Received 17 Feb 2023, Accepted 08 Jun 2023, Published online: 18 Jun 2023

References

  • Akinsola, F. A., M. M. Ologundudu, M. O. Akinsola, and N. M. Odhiambo. 2022. Industrial development, urbanization and pollution nexus in Africa. Heliyon 8 (11):e11299. doi:10.1016/j.heliyon.2022.e11299.
  • Aras, S., and V. M. Hanifi. 2022. An interpretable forecasting framework for energy consumption and CO2 emissions. Applied Energy 328:120163. doi:10.1016/j.apenergy.2022.120163.
  • Avenyo, E. K., and F. Tregenna. 2022. Greening manufacturing: Technology intensity and carbon dioxide emissions in developing countries. Applied Energy 324:119726. doi:10.1016/j.apenergy.2022.119726.
  • Chu, X. L., and R. J. Zhao. 2021. A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation. Journal of Intelligent & Fuzzy Systems 41 (6):7473–84. doi:10.3233/JIFS-211435.
  • Gao, S., M. X. Cao, and L. Dan. 2023. Analysis of carbon emission characteristics and peak prediction in Jiangsu Province. Resources and Industry 25: 1–9. doi:10.13776/j.cnki.resourcesindustries.20230509.001.
  • Gao, M. Y., H. L. Yang, Q. Z. Xiao, and M. Goh. 2020. A novel fractional grey Riccati model for carbon emission prediction. Journal of Cleaner Production 282:124471. doi:10.1016/j.jclepro.2020.124471.
  • Han, N., and X. Y. Luo. 2022. Carbon emission peak prediction and emission reduction potential in the Beijing Tianjin Hebei region from a multi scenario perspective. Journal of Natural Resources 37 (5):1277–88. doi:10.31497/zrzyxb.20220512.
  • Huan, Y., M. S. Hassan, M. N. Tahir, H. Mahmood, H. Al-Darwesh, and R. I. 2022. The role of energy use in testing N – Shaped relation between industrial development and environmental quality for Chinese economy. Energy Strategy Reviews 43:100905. doi:10.1016/j.esr.2022.100905.
  • Jiang, H. T., J. Yin, Y. H. Qiu, B. Zhang, Y. Ding, and R. Xia. 2022. Industrial carbon emission efficiency of cities in the pearl river basin: Spatiotemporal dynamics and driving forces. Land 11 (8):1129. doi:10.3390/land11081129.
  • Li, Y. C. 2022. Path-breaking industrial development reduces carbon emissions: Evidence from Chinese Provinces, 1999–2011. Energy Policy 167:113046. doi:10.1016/j.enpol.2022.113046.
  • Li, J. C., S. Q. Lu, and Z. Q. Guo. 2023. Research on prediction and peak scenario simulation of interprovincial carbon emissions in China. Technology, Economics and Management Research 3:21–25.
  • Ning, L. Q., L. J. Pei, and F. Li. 2021. Forecast of China’s carbon emissions based on ARIMA method. Discrete Dynamics in Nature & Society 2021:1–12. doi:10.1155/2021/1441942.
  • Pan, S. Y., and M. L. Zhang. 2023. Prediction and influencing factors of carbon dioxide emissions in Gansu Province based on BP neural network. Environmental Engineering 42:1–12. http://kns.cnki.net/kcms/detail/11.2097.X.20230426.1326.002.html.
  • Shahzad, U., N. Schneider, and J. M. Ben. 2021. How coal and geothermal energies interact with industrial development and carbon emissions? An autoregressive distributed lags approach to the Philippines. Resources Policy 74:102342. doi:10.1016/j.resourpol.2021.102342.
  • Tzu-Li, T. 2005. The indirect measurement of tensile strength of material by the grey prediction model GMC (1, n). Measurement Science & Technology 16 (6):1322–28. doi:10.1088/0957-0233/16/6/013.
  • Wang, M., W. Wang, and L. F. Wu. 2022. Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China. Energy 243:123024. doi:10.1016/j.energy.2021.123024.
  • Wang, Y., H. X. Yang, and R. X. Sun. 2020. Effectiveness of China’s provincial industrial carbon emission reduction and optimization of carbon emission reduction paths in “lagging regions”: Efficiency-cost analysis. Journal of Environmental Management 275:111221. doi:10.1016/j.jenvman.2020.111221.
  • Wang, S. H., Y. C. Zhao, W. Zhang, and Y. Liu. 2022. Analysis of influencing factors and peak scenario prediction of carbon emissions in the Beijing Tianjin Hebei region - based on the perspective of supply side reform. Journal of Beijing University of Technology (Social Sciences Edition) 24 (6):54–66.
  • Wu, L., and B. Fu. Fractional order reverse accumulation GM (1,1) model and its properties. 2017. Statistics and Decision Making 18:33–36. doi:10.13546/j.cnki.tjyjc.2017.18.007.
  • Xie, N. Y., Y. Zhang, and C. Huang. 2022. The impact of digital economy on industrial carbon emission efficiency: Evidence from Chinese Provincial data. Mathematical Problems in Engineering 2022:1–12. doi:10.1155/2022/6583809.
  • Xiong, P. P., L. S. Xiao, Y. C. Liu, Z. Yang, Y. F. Zhou, and S. Cao. 2021. Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters. Journal of Intelligent & Fuzzy Systems 41 (6):6137–48. doi:10.3233/JIFS-202711.
  • Yang, F., G. C. Zhang, J. Sun, F. J. Xie, X. W. Chuai, and R. L. Sun. 2023. Carbon peak scenario in a city in the Yangtze River Delta based on the LEAP model. Environmental Science 44:1–19. doi:10.13227/j.hjkx.202301129.
  • Yan, G. H., L. M. Jiang, and C. Q. Xu. 2022. How environmental regulation affects industrial green total factor productivity in China: The role of internal and external channels. Sustainability 14 (20):13500. doi:10.3390/su142013500.
  • Yao, M. X., M. W. Wang, and Y. D. Lei. 2023. Prediction of carbon peak in Shanghai based on the STIRPAT model. Journal of Fudan University (Natural Science Edition) 62 (2):226–37.
  • Yiqiong, L., and L. Miao. 2020. Industrial carbon emission efficiency in the Yangtze River economic belt and its influencing factors. International Journal of Design & Nature and Ecodynamics 15 (1):25–32. doi:10.18280/ijdne.150104.
  • Zhang, X. Y., M. F. Shen, Y. P. Luan, W. J. Cui, and X. Q. Lin. 2022. Spatial evolutionary characteristics and influencing factors of urban industrial carbon emission in China. International Journal of Environmental Research and Public Health 19 (18):11227. doi:10.3390/ijerph191811227.
  • Zhang, L., Y. Yan, W. Xu, J. Sun, Y. Y. Zhang, and D. Gong. 2022. Carbon emission calculation and influencing factor analysis based on industrial big data in the “Double Carbon” Era. Computational Intelligence and Neuroscience 2022:1–12. doi:10.1155/2022/2815940.
  • Zhao, C., X. C. Song, X. Y. Liu, P. Shen, C. Chen, and L. Liu. 2022. Prediction and analysis of carbon emission peak in Zhejiang Province based on the STIRPAT model. Ecological Economy 38 (6):29–34.
  • Zhu, Y. K., H. G. Gao, and T. Xiao. 2021. Green technology innovation, industrial structure optimization, and high-quality economic development in industrial enterprises. Statistics & Decision 37 (19):111–15.

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