The influence and forecast of three industries and energy structure on regional carbon emission

References

• Amjadi, G., T. Lundgren, and W. Zhou. 2022. A dynamic analysis of industrial energy efficiency and the rebound effect: Implications for carbon emissions and sustainability. Energy Efficiency 15 (54). doi:10.1007/s12053-022-10059-4.
   Google Scholar
• Chen, Z., and J. Wu. 2022. Evolution of logistics industry carbon emissions in Heilongjiang Province, China. Sustainability 14 (15):9758. doi:10.3390/su14159758.
   Web of Science ®Google Scholar
• Ghosh, S., S. Dinda, C. Das, S. Dutta, and D. Bera. 2022. Spatial-explicit carbon emission-sequestration balance estimation and evaluation of emission susceptible zones in an Eastern Himalayan city using pressure-sensitivity-resilience framework: An approach towards achieving low carbon cities. Journal of Cleaner Production 336:130417. doi:10.1016/j.jclepro.2022.130417.
   Web of Science ®Google Scholar
• Guo, S. 2020. Carbon emission trading price prediction based on BP neural network. Zhongnan University of Economics and Law. doi:10.27660/d.cnki.gzczu.2020.000775.
   Google Scholar
• Guo, L., L. Bai, and C. Wang. 2022. Growth mechanism and trend prediction of carbon emissions in Chongqing based on “three-life space”. Environmental Pollution & Prevention 44 (6):816–23. doi:10.15985/j.cnki.1001-3865.2022.06.021.
   Google Scholar
• Han, N., and X. Luo. 2022. Carbon emission peak prediction and emission reduction potential in Beijing-Tianjin-Hebei region from multi-scenario perspective. Journal of Natural Resources 37 (5):1277–88. doi:10.31497/zrzyxb.20220512.
   Google Scholar
• Huang, J., and C. Wen. 2022. The impact of private sector energy investment, innovation and energy consumption on China’s carbon emissions. Renewable Energy 195. doi:10.1016/J.RENENE.2022.06.131.
   Web of Science ®Google Scholar
• Hu, J., and Z. Luo. 2022. Research on energy consumption structure and carbon peak-to-peak prediction of China’s manufacturing industry under multi-scenario simulation. Jiangxi Social Sciences 42 (4):50–60+207.
   Google Scholar
• Hu, J., Z. Luo, and F. Li. 2022. Forecast of China’s carbon emission intensity under “carbon peak” target: Analysis based on LSTM and ARIMA-BP model. Science of Finance and Economics 2:89–101.
   Google Scholar
• Jing, G. W. 2022. Carbon emission trading pilot policy and high-quality regional economic development. Contemporary Economic Management 44 (6):50–59. doi:10.13253/j.cnki.ddjjgl.2022.06.006.
   Google Scholar
• Khanna, N., N. Zhou, D. Fridley, and J. Ke. 2016. Quantifying the potential impacts of China’s power-sector policies on coal input and CO2 emissions through 2: A bottom-up perspective. Utilities Policy 41:128–38. doi:10.1016/j.jup.2016.07.001.
   Web of Science ®Google Scholar
• Krzysztof, P., and P. Maciej. 2019. Neural-network-based iterative learning control of nonlinear systems. ISA Transactions. https://www.sciencedirect.com/science/article/pii/S0019057819303908.
   Google Scholar
• Kwakwa, P., K. Adjei-Mantey, and F. Adusah-Poku. 2022. The effect of transport services and ICTs on carbon dioxide emissions in South Africa. Environmental Science and Pollution Research 30 (4):10457–68. doi:10.1007/s1135602222863-7.
   PubMed Web of Science ®Google Scholar
• Li, Z., S. Yin, Y. Jiang, and Y.-L. LYU. 2022. Allometric relationship and formation mechanism between economic growth and carbon emissions in Yangtze River Delta. Journal of Natural Resources 37 (6):1507–23. doi:10.31497/zrzyxb.20220610.
   Google Scholar
• Pan, Y., C., Wang, B., Yu, Y. Wang, Z., Yang, and K. Ren. 2022. Prediction model of total agricultural carbon emissions in Henan Province based on neural network and non-inear surface fitting. Research Square. doi:10.21203/rs.3.rs-1726918/v1.
   PubMedGoogle Scholar
• Ren, F., and D. Long. 2021. Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized fast learning network. Journal of Cleaner Production 317:317. doi:10.1016/J.JCLEPRO.2021.128408.
   Web of Science ®Google Scholar
• Shah, M., M. Usman, H. Obekpa, and S. Abbas. 2022. Nexus between environmental vulnerability and agricultural productivity in BRICS: What are the roles of renewable energy, environmental policy stringency, and technology? Environmental Science and Pollution Research 30 (6):15756–74. doi:10.1007/s11356-022-23179-2.
   PubMed Web of Science ®Google Scholar
• Usman, M., and M. Radulescu. 2022. Examining the role of nuclear and renewable energy in reducing carbon footprint: Does the role of technological innovation really create some difference? Science of the Total Environment 841:156662. doi:10.1016/j.scitotenv.2022.156662.
   PubMed Web of Science ®Google Scholar
• Vogele, S., K. Govorukha, P. Mayer, I. Rhoden, D. Rübbelke, and W. Kuckshinrichs. 2023. Effects of a coal phase-out in Europe on reaching the UN sustainable development goals. Environment Development and Sustainability 25 (1):879–916. doi:10.1007/s10668-021-02083-8.
   Web of Science ®Google Scholar
• Wang, B. 2021. Traditional Chinese medicine pulse detection system based on multi-peak fitting algorithm. Northern University for Nationalities. doi:10.27754/d.cnki.gbfmz.2021.000148.
   Google Scholar
• Wei, G., Y. Kang, H. Fan, Yu, X., and Ma, M. 2022. Study on carbon emission accounting and prediction of construction industry in heavy industrial cities. Ecological Economy 1–12. http://kns.cnki.net/kcms/detail/53.1193.F.20220715.0959.002.html.
   Google Scholar
• Woo, T. H. 2020. Analysis for the resilience modeling in implications of climate mitigation incorporated with the nuclear energy. Energy Sources Part A: Recovery, Utilization, and Environmental Effects 1–9. doi:10.1080/15567036.2020.1849460.
   Web of Science ®Google Scholar
• Wu, Y., Y. Zhang, C. Xia, A. Chinnathambi, O. Nasif, B. Gavurová, M. Sekar, A. Anderson, N. T. L. Chi, and A. Pugazhendhi. 2023. Assessing the effects of ammonia (NH3) as the secondary fuel on the combustion and emission characteristics with nano-additives. Fuel 336:126831. doi:10.1016/j.fuel.2022.126831.
   Web of Science ®Google Scholar
• Xiao, P., Y. Zhang, P. Qian, M. Lu, Z. Yu, J. Xu, C. Zhao, and H. Qian. 2022. Spatiotemporal characteristics, decoupling effect and driving factors of carbon emission from cultivated land utilization in Hubei Province. International Journal of Environmental Research and Public Health 19 (15):15. doi:10.3390/IJERPH19159326.
   Web of Science ®Google Scholar
• Xiong, P., S. Cao, and Z. Yang. 2021. Grey correlation analysis of carbon emissions in East China. Journal of Dalian University of Technology (Social Sciences) 42 (1):36–44. doi:10.19525/j.issn1008-407x.2021.01.005.
   Google Scholar
• Yang, J., H. Chen, Z. Liu, and Zhao, R. 2022. Analysis and prediction of carbon emission based on information entropy and multi-factor grey system model. Journal of South-Central University for Nationalities (Natural Science Edition) 41 (1):123–28.
   Google Scholar
• Yan, C., and L. Hou. 2021. Research on land use change and carbon emissions in Shaanxi Province based on grey theory. Journal of Xi’an Polytechnic University 37 (1):25–31. doi:10.19322/j.cnki.issn.1006-4710.2021.01.004.
   Google Scholar
• Yan, Z., W. Li, T. Yan, Wang, J., Chen, L., Lu, Y., Liu, H., et al 2018. Application and effectiveness of BP neural network algorithm in carbon emission assessment of maize production in the oasis of Hebei. Chinese Journal of Eco-Agriculture 26 (8):1100–06. doi:10.13930/j.cnki.cjea.180084.
   Google Scholar
• Yan, J., Z. Zhang, M. Chen, T. Lin, and K. Yuan. 2022. How will Chinese cities reduce their carbon emissions? Evidence from spatial differences. Environmental Science and Pollution Research 29 (48):72461–79. doi:10.1007/s11356-022-20605-3.
   PubMed Web of Science ®Google Scholar
• You, J., G. Ding, and L. Zhang. 2022. Heterogeneous dynamic correlation research among industrial structure distortion, two-way FDI and carbon emission intensity in China. Sustainability 15. doi:10.3390/SU14158988.
   Google Scholar
• Zhang, D., T. Wang, and J. Zhi. 2022. Carbon emission prediction and eco-economic analysis of Shandong Province based on IPSO-BP neural network model. Ecological Science 41 (1):149–58. doi:10.14108/j.cnki.1008-8873.2022.01.017.
   Google Scholar
• Zhao, F., J. L. Guo, and L. F. Wu. 2022. Grey uncertain prediction of carbon emissions peak from thirty-one provinces and municipalities in China. Energy Sources, Part A: Recovery, Utilization and Environmental 3 (44):6111–28. doi:10.1080/15567036.2022.2095061.
   Google Scholar
• Zhao, B., L. Sun, and L. Qin. 2022. Optimization of China’s provincial carbon emission transfer structure under the dual constraints of economic development and emission reduction goals. Environmental Science and Pollution Research 29 (33):50335–51. doi:10.1007/s11356-022-19288-7.
   PubMedGoogle Scholar
• Zhao, C., H. Zhu, W. Liu, and Dong, M. 2011. A phase correlation extension method based on multi-peak fitting. Computer Engineering 37 (11):228–30. doi:10.3969/j.issn.1000-3428.2011.11.079.
   Google Scholar
• Zheng, Y., Y. Long, and H. Fan. 2022. The analysis of spatial–temporal effects of relevant factors on carbon intensity in China. Stochastic Environmental Research and Risk Assessment 36 (11):3785–802. doi:10.1007/s00477-022-02226-x.
   PubMed Web of Science ®Google Scholar
• Zhong, C., F. Dong, Y. Geng, and Q. Dong. 2022. Toward carbon neutrality: The transition of the coal industrial chain in China. Frontiers in Environmental Science 10. doi:10.3389/FENVS.2022.962257.
   Web of Science ®Google Scholar
• Zhou, Y., Y. Chen, and J. Zhang. 2013. Measurement of acid dissociation constants of acid-base indicators by Gaussian multipeak fitting technique. Experimental Technology & Management 30 (12):58–61. doi:10.16791/j.cnki.sjg.2013.12.017.
   Google Scholar