Prediction of carbon emissions with historical data

Abstract

Reducing carbon emissions is fundamental to achieving carbon neutrality. Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios; however, uncertainties in future development often lead to deviations from these assumptions. To address this limitation, this study proposes a data-driven approach for evaluating national carbon emissions using historical data. Countries with similar energy consumption patterns were selected as reference samples, and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak. Key indicators, including peak levels, timing, plateau duration, and post-peak decline rates, were identified. The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures. Applying this framework to China suggests a carbon peak between 2027 and 2030, in the range of 14.207 to 16.234 Gt, followed by a gradual decline from 2031 to 2036. Compared with the average results of the existing studies, the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%, respectively. This study proposes a top-down methodology that provides a transparent, reproducible, and empirical framework for forecasting carbon emission pathways, thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.

Keywords

carbon emissions / historical data / bootstrap / assessment / sustainable development

References

[1] WANG F, HARINDINTWALI J D, WEI K, et al. Climate change: Strategies for mitigation and adaptation[J]. The Innovation Geoscience, 2023, 1(1): 100015.

[2] DEN ELZEN M, FEKETE H, HÖHNE N, et al. Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?[J]. Energy Policy, 2016, 89: 224-236.

[3] LI S J, WU L H. Effects of regional integration on industrial green transformation[J]. Journal of Southeast University (English Edition), 2023, 39(2): 204-212.

[4] DU Y Y, LIU H B, HUANG H. Bibliometric analysis of research progress and trends on carbon emission responsibility accounting[J]. Sustainability, 2024, 16(9): 3721.

[5] XU P Y, ZHAO L D. Analysis of health insurance reform strategies from a risk-sharing perspective based on the Markov model[J]. Journal of Southeast University (English Edition), 2025, 41(1): 118-126.

[6] WINTER J, DOLTER B, FELLOWS G K. Carbon pricing costs for households and the progressivity of revenue recycling options in Canada[J]. Canadian Public Policy/Analyse de Politiques, 2023, 49(1): 13-45.

[7] YANG J, CAI W, MA M D, et al. Driving forces of China’s CO₂ emissions from energy consumption based on Kaya-LMDI methods[J]. Science of the Total Environment, 2020, 711: 134569.

[8] LI W W, WANG W P, WANG Y, et al. Industrial structure, technological progress and CO₂ emissions in China: Analysis based on the STIRPAT framework[J]. Natural Hazards, 2017, 88(3): 1545-1564.

[9] YU B Y, ZHAO Z H, WEI Y M, et al. Approaching national climate targets in China considering the challenge of regional inequality[J]. Nature Communications, 2023, 14: 8342.

[10] ZHAO Z, XUAN X, ZHANG F, et al. Scenario analysis of renewable energy development and carbon emission in the Beijing-Tianjin-Hebei Region[J]. Land, 2022, 11(10): 1659.

[11] MARANGONI G, TAVONI M, BOSETTI V, et al. Sensitivity of projected long-term CO₂ emissions across the shared socioeconomic pathways[J]. Nature Climate Change, 2017, 7(2): 113-117.

[12] KLUSAK P, AGARWALA M, BURKE M, et al. Rising temperatures, falling ratings: The effect of climate change on sovereign creditworthiness[J]. Management Science, 2023, 69(12): 7468-7491.

[13] LIU W. EKC test study on the relationship between carbon dioxide emission and regional economic growth[J]. Carbon Management, 2020, 11(4): 415-425.

[14] MAHADEVAN R, SUARDI S. Globalisation, tourism and environmental Kuznets curve[J]. Tourism Economics, 2025, 31(5): 911-932.

[15] YUAN Q, CAI H H, JIANG Y, et al. The asymmetric effect of global value chain on environmental quality: Implications for environmental management[J]. Journal of Environmental Management, 2024, 365: 121470.

[16] LEMPERT R, SCHEFFRAN J, SPRINZ D F. Methods for long-term environmental policy challenges[J]. Global Environmental Politics, 2009, 9(3): 106-133.

[17] YU W, HAN R Z. Effect of contract choice on upstream carbon emission reduction considering carbon taxation[J]. Journal of Southeast University (English Edition), 2019, 35(1): 135-141.

[18] SLAMERŠAK A, KALLIS G, O’NEILL D W. Energy requirements and carbon emissions for a low-carbon energy transition[J]. Nature Communications, 2022, 13: 6932.

[19] BP. World energy statistics yearbook 2023[M/OL]. [2025-03-27]. http://www.bp.com/statisticalreview.

[20] CRIPPA M, GUIZZARDI D, BANJA M, et al. CO₂ emissions of all world countries—2022 Report[R]. Luxembourg: European Commission, 2022.

[21] EFRON B. Bootstrap methods: Another look at the jackknife[J]. The Annals of Statistics, 1979, 7(1): 1-26.

[22] PIMENTEL H, BRAY N L, PUENTE S, et al. Differential analysis of RNA-seq incorporating quantification uncertainty[J]. Nature Methods, 2017, 14(7): 687-690.

[23] WAN C, XU Z, PINSON P, et al. Probabilistic forecasting of wind power generation using extreme learning machine[J]. IEEE Transactions on Power Systems, 2014, 29(3): 1033-1044.

[24] GONÇALVES S, HOUNYO U, PATTON A J, et al. Bootstrapping two-stage quasi-maximum likelihood estimators of time series models[J]. Journal of Business & Economic Statistics, 2023, 41(3): 683-694.

[25] LEVIN K, RICH D. Turning points: Trends in countries’ reaching peak greenhouse gas emissions over time[M]. Washington, DC, USA: World Resources Institute, 2017.

[26] ZHU B Z, WANG K F, WANG P. A study on the phased drivers of carbon emissions growth in China[J]. Economic Perspectives, 2015, 11: 79-89.

[27] YANG S S. Analysis of China’s carbon emissions trajectory, driving factors and reduction measures[J]. Environmental Science & Technology, 2020, 43(1): 98-104.

[28] DING Z L. Study on the framework roadmap for China’s carbon neutrality[J]. China Industry and Information Technology, 2021(8): 54-61.

[29] HAN Y L, LIU Y P, LIU X. Decoupling re-analysis of CO₂ emissions and economic growth from two dimensions[J]. Frontiers in Energy Research, 2022, 10: 896529.

[30] LIU R P, FANG Y R, PENG S, et al. Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces[J]. Journal of Environmental Management, 2024, 365: 121667.

[31] ZHENG X Q, WANG J Y, CHEN Y, et al. Potential pathways to reach energy-related CO₂ emission peak in China: Analysis of different scenarios[J]. Environmental Science and Pollution Research, 2023, 30(24): 66328-66345.

[32] KONG H J, SHI L F, DA D, et al. Simulation of China’s carbon emission based on influencing factors[J]. Energies, 2022, 15(9): 3272.

[33] CHARLIER D, FODHA M, KIRAT D. Residential CO₂ emissions in Europe and carbon taxation: A country-level assessment[J]. The Energy Journal, 2023, 44(5): 187-206.

[34] BOTHNER F, SCHRADER S M, BANDAU F, et al. Never let a serious crisis go to waste: The introduction of supplemental carbon taxes in Europe[J]. Journal of Public Policy, 2022, 42(2): 343-363.

[35] FANKHAUSER S. A practitioner’s guide to a low-carbon economy: Lessons from the UK[J]. Climate Policy, 2013, 13(3): 345-362.

[36] YUE X H, PENG M Y, ANSER M K, et al. The role of carbon taxes, clean fuels, and renewable energy in promoting sustainable development: How green is nuclear energy?[J]. Renewable Energy, 2022, 193: 167-178.

[37] PAN J Y, DU L Z, WU H T, et al. Does environmental law enforcement supervision improve corporate carbon reduction performance? Evidence from environmental protection interview[J]. Energy Economics, 2024, 132: 107441.

[38] YE C M, WU L H. Dynamic evaluation of digital and green development policies based on text mining of the PMC framework[J]. Journal of Southeast University (English Edition), 2024, 40(3): 319-326.

Funding

National Natural Science Foundation of China(52470211)

Special Foundation of Jiangsu Province Science and Technology Plan(BZ2024017)

RECLAIM Network Plus Project(EP/W034034/1)