Abstract
China ranks first globally in carbon emissions. Accurate carbon emissions forecasting is crucial for devising effective mitigation strategies and has thus become a focal point of academic research. This study explores carbon emission drivers across the economy, demography, industry, and energy domains. We empirically analyze China’s 1990–2021 emissions data to forecast levels for 2022–2025. Introducing a novel stacking integrated learning model refined with a whale optimization algorithm (WOA), this research employs World Bank data, China statistics, and BP data to verify the model’s validity. Findings reveal that the proposed WOA-Stacking integrated model significantly outperforms in forecasting carbon emissions. At current rates, China’s carbon emission intensity in 2025 is predicted to be 3.88% lower than 2020, likely missing its 14th Five-Year Plan target. Additionally, the key factors affecting carbon emission intensity are total electricity consumption, per capita energy consumption, trade openness, urbanization rate, and the previous period’s carbon emission intensity data. The GDP, fixed asset investment, total energy consumption, urban population, and total population drive total carbon emissions. Therefore, China must improve policies targeting these factors to accelerate future emission mitigation.
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References
Acheampong, A. O., & Boateng, E. B. (2019). Modelling carbon emission intensity: Application of artificial neural network. Journal of Cleaner Production, 225, 833–856. https://doi.org/10.1016/j.jclepro.2019.03.352
Ahn, J. M., Kim, J., Kim, H., & Kim, K. (2023). Watershed environmental impact assessment for extreme climates based on shared socioeconomic pathway climate change scenarios. Ecological Indicators, 154, 110685. https://doi.org/10.1016/j.ecolind.2023.110685
Amiriebrahimabadi, M., & Mansouri, N. (2023). A comprehensive survey of feature selection techniques based on whale optimization algorithm. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-17329-y
Behera, B., Haldar, A., & Sethi, N. (2023c). Investigating the direct and indirect effects of information and communication technology on economic growth in the emerging economies: Role of financial development, foreign direct investment, innovation, and institutional quality. Information Technology for Development. https://doi.org/10.1080/02681102.2023.2233463
Behera, B., Haldar, A., & Sethi, N. (2023b). Agriculture, food security, and climate change in South Asia: A new perspective on sustainable development. Environment Development and Sustainability. https://doi.org/10.1007/s10668-023-03552-y
Behera, B., Behera, P., & Sethi, N. (2023a). Decoupling the role of renewable energy, green finance and political stability in achieving the sustainable development goal 13: Empirical insight from emerging economies. Sustainable Development, 32(1), 119–137. https://doi.org/10.1002/sd.2657
Birdsall, N. (1992). Another look at population and global warming. World Bank Publications.
BP. (2022). The 2022 BP statistical review of world energy. https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.
Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A machine learning approach to predict air quality in California. Complexity, 2020. https://doi.org/10.1155/2020/8049504
Chang, N. (2015). Changing industrial structure to reduce carbon dioxide emissions: A Chinese application. Journal of Cleaner Production, 103, 40–48. https://doi.org/10.1016/j.jclepro.2014.03.003
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
Chen, X., & Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics, 99(6), 323–329. https://doi.org/10.1016/j.ygeno.2012.04.003
Council, S. (2021). Action plan for carbon dioxide peaking before 2030. https://en.ndrc.gov.cn/policies/202110/t20211027_1301020.html.
Cui, H., Wu, R., & Zhao, T. (2018). Decomposition and forecasting of CO2 emissions in China’s power sector based on STIRPAT model with selected PLS model and a novel hybrid PLS-Grey-Markov model. Energies, 11(11), 2985. https://doi.org/10.3390/en11112985
de Alegría, I. M., Basañez, A., de Basurto, P. D., & Fernández-Sainz, A. (2016). Spain’s fulfillment of its Kyoto commitments and its fundamental greenhouse gas (GHG) emission reduction drivers. Renewable and Sustainable Energy Reviews, 59, 858–867. https://doi.org/10.1016/j.rser.2015.12.208
Dong, B., Xu, Y., & Fan, X. (2020). How to achieve a win–win situation between economic growth and carbon emission reduction: Empirical evidence from the perspective of industrial structure upgrading. Environmental Science and Pollution Research, 27, 43829–43844. https://doi.org/10.1007/s11356-020-09883-x
Ertugrul, H. M., Cetin, M., Seker, F., & Dogan, E. (2016). The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries. Ecological Indicators, 67, 543–555. https://doi.org/10.1016/j.ecolind.2016.03.027
Farhani, S., & Ozturk, I. (2015). Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22, 15663–15676. https://doi.org/10.1007/s11356-015-4767-1
Feng, R., Zheng, H., Gao, H., Zhang, A., Huang, C., Zhang, J., et al., (2019). Recurrent neural network and random forest for analysis and accurate forecast of atmospheric pollutants: A case study in Hangzhou, China. Journal of Cleaner Production, 231, 1005–1015. https://doi.org/10.1016/j.jclepro.2019.05.319
Feng, Y., Wu, H., Jin, Y., Wang, L., & Zeng, B. (2023). How does population aging affect carbon emissions?—Analysis based on the multiple mediation effect model. Environmental Science and Pollution Research, 30(14), 41419–41434. https://doi.org/10.1007/s11356-023-25186-3
Gu, R., Li, C., Li, D., Yang, Y., & Gu, S. (2022). The impact of rationalization and upgrading of industrial structure on carbon emissions in the Beijing-Tianjin-Hebei urban agglomeration. International Journal of Environmental Research and Public Health, 19(13), 7997. https://doi.org/10.3390/ijerph19137997
Guo, D., Chen, H., & Long, R. (2018). Can China fulfill its commitment to reducing carbon dioxide emissions in the Paris agreement? Analysis based on a back-propagation neural network. Environmental Science and Pollution Research, 25, 27451–27462. https://doi.org/10.1007/s11356-018-2762-z
Hao, J., Gao, F., Fang, X., Nong, X., Zhang, Y., & Hong, F. (2022). Multi-factor decomposition and multi-scenario prediction decoupling analysis of China’s carbon emission under dual carbon goal. Science of the Total Environment, 841, 156788. https://doi.org/10.1016/j.scitotenv.2022.156788
IEA. (2022). Global energy review: CO2 emissions in 2021. https://www.iea.org/reports/global-energy-review-co2-emissions-in-2021-2.
IPCC. (2018). Global warming of 1.5 °C. https://www.ipcc.ch/sr15/download/#full.
Kallio, J., Tervonen, J., Räsänen, P., Mäkynen, R., Koivusaari, J., & Peltola, J. (2021). Forecasting office indoor CO2 concentration using machine learning with a 1 year dataset. Building and Environment, 187, 107409. https://doi.org/10.1016/j.buildenv.2020.107409
Khan, M. K., Teng, J. Z., Khan, M. I., & Khan, M. O. (2019). Impact of globalization, economic factors and energy consumption on CO2 emissions in Pakistan. Science of the Total Environment, 688, 424–436. https://doi.org/10.1016/j.scitotenv.2019.06.065
Li, G., Zeng, S., Li, T., Peng, Q., & Irfan, M. (2023a). Analysing the effect of energy intensity on carbon emission reduction in Beijing. International Journal of Environmental Research and Public Health, 20(2), 1379. https://doi.org/10.3390/ijerph20021379
Li, M., Wang, W., De, G., Ji, X., & Tan, Z. (2018). Forecasting carbon emissions related to energy consumption in Beijing-Tianjin-Hebei region based on grey prediction theory and extreme learning machine optimized by support vector machine algorithm. Energies, 11(9), 2475. https://doi.org/10.3390/en11092475
Li, X., Ren, A., & Li, Q. (2022). Exploring patterns of transportation-related CO2 emissions using machine learning methods. Sustainability, 14(8), 4588. https://doi.org/10.3390/su14084588
Li, Y. (2020). Forecasting Chinese carbon emissions based on a novel time series prediction method. Energy Science & Engineering, 8(7), 2274–2285. https://doi.org/10.1002/ese3.662
Li, Y., Huang, S., Miao, L., & Wu, Z. (2023b). Simulation analysis of carbon peak path in China from a multi-scenario perspective: Evidence from random forest and back propagation neural network models. Environmental Science and Pollution Research, 30(16), 46711–46726. https://doi.org/10.1007/s11356-023-25544-1
Li, Y., Wei, Y., & Dong, Z. (2020). Will China achieve its ambitious goal?—Forecasting the CO2 emission intensity of China towards 2030. Energies, 13(11), 2924. https://doi.org/10.3390/en13112924
Li, Y., Yang, X., Ran, Q., Wu, H., Irfan, M., & Ahmad, M. (2021). Energy structure, digital economy, and carbon emissions: Evidence from China. Environmental Science and Pollution Research, 28, 64606–64629. https://doi.org/10.1007/s11356-021-15304-4
Li, Z., Li, Y., & Shao, S. (2019). Analysis of influencing factors and trend forecast of carbon emission from energy consumption in China based on expanded STIRPAT model. Energies, 12(16), 3054. https://doi.org/10.3390/en12163054
Lin, S., Wang, S., Marinova, D., Zhao, D., & Hong, J. (2017). Impacts of urbanization and real economic development on CO2 emissions in non-high income countries: Empirical research based on the extended STIRPAT model. Journal of Cleaner Production, 166, 952–966. https://doi.org/10.1016/j.jclepro.2017.08.107
Liu, Y., Sun, H., Meng, B., Jin, S., & Chen, B. (2023). How to purchase carbon emission right optimally for energy-consuming enterprises? Analysis based on optimal stopping model. Energy Economics, 124, 106758. https://doi.org/10.1016/j.eneco.2023.106758
Menon, B. G., Sahadev, S., Mahanty, A., Praveensal, C. J., & Asha, G. (2023). Trivariate causality between economic growth, energy consumption, and carbon emissions: Empirical evidence from India. Energy Efficiency, 16(5). https://doi.org/10.1007/s12053-023-10118-4
Miao, S., Zhang, X., Han, Y., Sun, W., Liu, C., & Yin, S. (2018). Random forest algorithm for the relationship between negative air ions and environmental factors in an urban park. Atmosphere, 9(12), 463. https://doi.org/10.3390/atmos9120463
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mumuni, S., & Joseph Aleer, M. (2023). Zero Hunger by 2030—Are we on track? Climate variability and change impacts on food security in Africa. Cogent Food & Agriculture, 9(1), 2171830. https://doi.org/10.1080/23311932.2023.2171830
Nakhli, M. S., Shahbaz, M., Ben Jebli, M., & Wang, S. (2022). Nexus between economic policy uncertainty, renewable & non-renewable energy and carbon emissions: Contextual evidence in carbon neutrality dream of USA. Renewable Energy, 185, 75–85. https://doi.org/10.1016/j.renene.2021.12.046
Narayan, P. K., & Narayan, S. (2010). Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy, 38(1), 661–666. https://doi.org/10.1016/j.enpol.2009.09.005
Nguyen, A. T., Lu, S. H., & Nguyen, P. T. T. (2021). Validating and forecasting carbon emissions in the framework of the environmental Kuznets curve: The case of Vietnam. Energies, 14(11), 3144. https://doi.org/10.3390/en14113144
Nieto, P. G., Combarro, E. F., del Coz Díaz, J., & Montañés, E. (2013). A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Applied Mathematics and Computation, 219(17), 8923–8937. https://doi.org/10.1016/j.amc.2013.03.018
Niu, D., Wang, K., Wu, J., Sun, L., Liang, Y., Xu, X., et al., (2020). Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. Journal of Cleaner Production, 243, 118558. https://doi.org/10.1016/j.jclepro.2019.118558
Obiedat, R., Harfoushi, O., Qaddoura, R., Al-Qaisi, L., & Al-Zoubi, A. M. (2021). An evolutionary-based sentiment analysis approach for enhancing government decisions during COVID-19 pandemic: The case of Jordan. Applied Sciences, 11(19), 9080. https://doi.org/10.3390/app11199080
Pan, C., Wang, H., Guo, H., & Pan, H. (2021). How do the population structure changes of China affect carbon emissions? An empirical study based on ridge regression analysis. Sustainability, 13(6), 3319. https://doi.org/10.3390/su13063319
Pao, H. T., Fu, H. C., & Tseng, C. L. (2012). Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400–409. https://doi.org/10.1016/j.energy.2012.01.037
Pao, H. T., & Tsai, C. M. (2011). Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy, 36(5), 2450–2458. https://doi.org/10.1016/j.energy.2011.01.032
Queiros, Q., McKenzie, D. J., Dutto, G., Killen, S., Saraux, C., & Schull, Q. (2024). Fish shrinking, energy balance and climate change. Science of the Total Environment, 906, 167310. https://doi.org/10.1016/j.scitotenv.2023.167310
Ren, F., & Long, D. (2021). Carbon emission forecasting and scenario analysis in Guangdong province based on optimized fast learning network. Journal of Cleaner Production, 317, 128408. https://doi.org/10.1016/j.jclepro.2021.128408
Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & dos Santos Coelho, L. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals, 135, 109853. https://doi.org/10.1016/j.chaos.2020.109853
Sethi, L., Behera, B., & Sethi, N. (2023). Do green finance, green technology innovation, and institutional quality help achieve environmental sustainability? Evidence from the developing economies. Sustainable Development. https://doi.org/10.1002/sd.2811
Shen, B., Yang, X., Xu, Y., Ge, W., Liu, G., Su, X., et al., (2023). Can carbon emission trading pilot policy drive industrial structure low-carbon restructuring: New evidence from China. Environmental Science and Pollution Research, 30(14), 41553–41569. https://doi.org/10.1007/s11356-023-25169-4
Shuai, C., Chen, X., Wu, Y., Tan, Y., Zhang, Y., & Shen, L. (2018). Identifying the key impact factors of carbon emission in China: Results from a largely expanded pool of potential impact factors. Journal of Cleaner Production, 175, 612–623. https://doi.org/10.1016/j.jclepro.2017.12.097
Shuai, C., Shen, L., Jiao, L., Wu, Y., & Tan, Y. (2017). Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011. Applied Energy, 187, 310–325. https://doi.org/10.1016/j.apenergy.2016.11.029
Sun, H., Chen, T., & Wang, C. N. (2023). Spatial impact of digital finance on carbon productivity. Geoscience Frontiers, 101674. https://doi.org/10.1016/j.gsf.2023.101674
Sun, W., Wang, C., & Zhang, C. (2017). Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization. Journal of Cleaner Production, 162, 1095–1101. https://doi.org/10.1016/j.jclepro.2017.06.016
Tian, X., Chang, M., Shi, F., & Tanikawa, H. (2014). How does industrial structure change impact carbon dioxide emissions? A comparative analysis focusing on nine provincial regions in China. Environmental Science & Policy, 37, 243–254. https://doi.org/10.1016/j.envsci.2013.10.001
Tong, M., Duan, H., & He, L. (2021). A novel grey verhulst model and its application in forecasting CO2 emissions. Environmental Science and Pollution Research, 28, 31370–31379. https://doi.org/10.1007/s11356-020-12137-5
Wang, P., Wu, W., Zhu, B., & Wei, Y. (2013). Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong province, China. Applied Energy, 106, 65–71. https://doi.org/10.1016/j.apenergy.2013.01.036
Wang, Q., & Li, L. (2021). The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions. Sustainable Production and Consumption, 28, 760–774. https://doi.org/10.1016/j.spc.2021.06.029
Wang, S., Fang, C., & Wang, Y. (2016a). Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data. Renewable and Sustainable Energy Reviews, 55, 505–515. https://doi.org/10.1016/j.rser.2015.10.140
Wang, Z., Zhang, B., & Liu, T. (2016b). Empirical analysis on the factors influencing national and regional carbon intensity in China. Renewable and Sustainable Energy Reviews, 55, 34–42. https://doi.org/10.1016/j.rser.2015.10.077
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1
Wu, W., Ma, X., Zhang, Y., Li, W., & Wang, Y. (2020). A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. Science of the Total Environment, 707, 135447. https://doi.org/10.1016/j.scitotenv.2019.135447
Wu, Y., Xiong, Y., Tian, X., Liu, Y., & Shi, F. (2018). Decoding the carbonization mode of the south coastal economic zone in China from the perspective of a dynamic industrial structure. Journal of Cleaner Production, 199, 518–528. https://doi.org/10.1016/j.jclepro.2018.07.139
Xu, L., Chen, N., & Chen, Z. (2017). Will China make a difference in its carbon intensity reduction targets by 2020 and 2030? Applied Energy, 203, 874–882. https://doi.org/10.1016/j.apenergy.2017.06.087
Xu, S. C., He, Z. X., & Long, R. Y. (2014). Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI. Applied Energy, 127, 182–193. https://doi.org/10.1016/j.apenergy.2014.03.093
Yang, H., Li, X., Ma, L., & Li, Z. (2021). Using system dynamics to analyse key factors influencing China’s energy-related CO2 emissions and emission reduction scenarios. Journal of Cleaner Production, 320, 128811. https://doi.org/10.1016/j.jclepro.2021.128811
Yang, J., Cai, W., Ma, M., Li, L., Liu, C., Ma, X., et al., (2020). Driving forces of China’s CO2 emissions from energy consumption based on Kaya-LMDI methods. Science of the Total Environment, 711, 134569. https://doi.org/10.1016/j.scitotenv.2019.134569
Yaw Naminse, E., & Zhuang, J. (2018). Economic growth, energy intensity, and carbon dioxide emissions in China. Polish Journal of Environmental Studies, 27(5), 2193–2201. https://doi.org/10.15244/pjoes/78619
Yuan, J., Qin, Z., Huang, H., Gan, X., Li, S., & Li, B. (2023). State of health estimation and remaining useful life prediction for a lithium-Ion battery with a two-layer stacking regressor. Energies, 16(5), 2313. https://doi.org/10.3390/en16052313
Zeng, H., Shao, B., Bian, G., Dai, H., & Zhou, F. (2022). Analysis of influencing factors and trend forecast of CO2 emission in Chengdu–Chongqing urban agglomeration. Sustainability, 14(3), 1167. https://doi.org/10.3390/su14031167
Zhang, C., Su, B., Zhou, K., & Yang, S. (2019). Decomposition analysis of China’s CO2 emissions (2000–2016) and scenario analysis of its carbon intensity targets in 2020 and 2030. Science of the Total Environment, 668, 432–442. https://doi.org/10.1016/j.scitotenv.2019.02.406
Zhang, C., & Tan, Z. (2016). The relationships between population factors and China’s carbon emissions: Does population aging matter? Renewable and Sustainable Energy Reviews, 65, 1018–1025. https://doi.org/10.1016/j.rser.2016.06.083
Zhang, Y. J., & Da, Y. B. (2015). The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renewable and Sustainable Energy Reviews, 41, 1255–1266. https://doi.org/10.1016/j.rser.2014.09.021
Zhao, H., Guo, S., & Zhao, H. (2017). Energy-related CO2 emissions forecasting using an improved LSSVM model optimized by whale optimization algorithm. Energies, 10(7), 874. https://doi.org/10.3390/en10070874
Zhu, B., & Zhang, T. (2021). The impact of cross-region industrial structure optimization on economy, carbon emissions and energy consumption: A case of the Yangtze river Delta. Science of the Total Environment, 778, 146089. https://doi.org/10.1016/j.scitotenv.2021.146089
Zhu, Q., & Peng, X. (2012). The impacts of population change on carbon emissions in China during 1978–2008. Environmental Impact Assessment Review, 36, 1–8. https://doi.org/10.1016/j.eiar.2012.03.003
Zou, C., Xiong, B., Huaqing, X., Zheng, D., Zhixin, G., Ying, W., et al., (2021). The role of new energy in carbon neutral. Petroleum Exploration and Development, 48(2), 480–491. https://doi.org/10.1016/S1876-3804(21)60039-3
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This research was funded by the National Natural Science Foundation of China (Grant No. 81973791).
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Guo, Y., Ma, L., Duan, Y. et al. Forecasting China’s carbon emission intensity and total carbon emissions based on the WOA-Stacking integrated model. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04752-w
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DOI: https://doi.org/10.1007/s10668-024-04752-w