Abstract
With the continuous emission of greenhouse gases, climate issues such as global warming have attracted widespread attention. As the largest CO2 emitter, China proposes the target of reaching the CO2 emissions peak by 2030 at the 75th United Nations General Assembly. To determine whether China can realize the goal, we construct an assessment system consisting of a new discrete grey prediction model on the basis of a rolling mechanism and an improved IPCC method. First, the new grey prediction model is used to predict the CO2 emissions and GDP from 2021 to 2030, and then, the enhanced IPCC method is used to obtain the carbon intensity from 2021 to 2030. In line with the direct judgment based on CO2 emissions and the indirect judgment based on the comparison between the AADR of carbon intensity and the AAIR of GDP, we find that China faces great challenges and difficulties in achieving its carbon peaking target by 2030. Finally, based on the forecast data and China’s current situation, some policy recommendations are put forward to accelerate China’s CO2 peak goal.
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Data availability
The datasets used or analyzed in the current study can be obtained from China Statistical Yearbook and IEA database.
References
Chen W, Wang FJ, Zheng B, Cai W (2017) Non-Euclidean distance fundamental solution of Hausdorff derivative partial differential equations. Eng Anal Bound Elem 84:213–219. https://doi.org/10.1016/j.enganabound.2017.09.003
Chou JM, Li YM, Xu Y, Zhao WX, Li JN, Hao YD (2022) Carbon dioxide emission characteristics and peak trend analysis of countries along the Belt and Road. Environ Sci Pollut Res 30(34):81881–81895. https://doi.org/10.1007/s11356-022-22699-1
Cong JH, Qin LM, Wang XP, Kang WM, Zhang YX, Liu QY (2017) Research on Shanxi’s CO2 emissions peak based on STIRPAT model. In: In 2nd International Conference on Judicial, Administrative and Humanitarian Problems of State Structures and Economic Subjects (JAHP 2017). Atlantis Press, pp 283–289. https://doi.org/10.2991/jahp-17.2017.59
Cui J, Shan DM, Liu SF (2015) Novel grey model for predicting casualties of strong earthquakes erupting in high population density areas. In: 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, pp 277–280. https://doi.org/10.1109/GSIS.2015.7301868
Dai DW, Li KX, Zhao SH, Zhou B (2022) Research on prediction and realization path of carbon peak of construction industry based on EGM-BP model. Front Energy Res 10:981097. https://doi.org/10.3389/fenrg.2022.981097
Ding ST, Zhang M, Song Y (2019) Exploring China’s carbon emissions peak for different carbon tax scenarios. Energy Policy 129:1245–1252. https://doi.org/10.1016/j.enpol.2019.03.037
Du Q, Wang N, Che L (2015) Forecasting China’s per capita carbon emissions under a new three-step economic development strategy. J Resour Ecol 6(5):318–323. https://doi.org/10.5814/j.issn.1674-764x.2015.05.005
Hao Y, Wei YM (2015) When does the turning point in China’s CO2 emissions occur? Results based on the Green Solow model. Environ Dev Econ 20(6):723–745. https://doi.org/10.1017/S1355770X15000017
Li FF, Xu Z, Ma H (2018) Can China achieve its CO2 emissions peak by 2030? Ecol Indic 84:337–344. https://doi.org/10.1016/j.ecolind.2017.08.048
Li WX, Bao L, Li Y, Si HY, Li YM (2022) Assessing the transition to low-carbon urban transport: a global comparisons. Resour Conserv Recycl 180:106179. https://doi.org/10.1016/j.resconrec.2022.106179
Liu WD, Jiang WB, Tang ZP, Han MY (2022) Pathways to peak carbon emissions in China by 2030: an analysis in relation to the economic growth rate. Sci China Earth Sci 65(6):1057–1072. https://doi.org/10.1007/s11430-021-9901-y
Luo D, Wei BL, Li YW (2015) The optimization grey incidence analysis models. In: 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, pp 167–172. https://doi.org/10.1109/GSIS.2015.7301849
Ma X, Wu WQ, Zeng B, Wang Y, Wu XX (2020) The conformable fractional grey system model. ISA Trans 96:255–271. https://doi.org/10.1016/j.isatra.2019.07.009
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Perissi I, Jones A (2022) Investigating European Union decarbonization strategies: evaluating the pathway to carbon neutrality by 2050. Sustainability 14(8):4728. https://doi.org/10.3390/su14084728
Qian WY, Wang J (2020) An improved seasonal GM (1, 1) model based on the HP filter for forecasting wind power generation in China. Energy 209:118499. https://doi.org/10.1016/j.energy.2020.118499
Schleussner CF, Ganti G, Rogelj J, Gidden MJ (2022) An emission pathway classification reflecting the Paris Agreement climate objectives. Commun Earth Environ 3(1):135. https://doi.org/10.1038/s43247-022-00467-w
Wang CN, Chou MT, Hsu HP, Wang JW (2017) Sridhar S (2017) The efficiency improvement by combining HHO gas, coal and oil in boiler for electricity generation. Energies 10(2):251. https://doi.org/10.3390/en10020251
Wang HL, He JK (2019) China’s pre-2020 CO2 emission reduction potential and its influence. Front Energy 13:571–578. https://doi.org/10.1007/s11708-019-0640-0
Wang WJ, Wang JX (2021) Determinants investigation and peak prediction of CO2 emissions in China’s transport sector utilizing bio-inspired extreme learning machine. Environ Sci Pollut Res 28(39):55535–55553. https://doi.org/10.1007/s11356-021-14852-z
Wang ZX, Li Q (2019) Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model. J Clean Prod 207:214–224. https://doi.org/10.1016/j.jclepro.2018.10.010
Wu LF, Liu SF, Yao LG, Xu RT, Lei XP (2015) Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model. Soft Computing 19:483–488. https://doi.org/10.1007/s00500-014-1268-y
Wu WQ, Ma X, Zhang YY, Li WP, Wang Y (2020) A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. Sci Total Environ 707:135447. https://doi.org/10.1016/j.scitotenv.2019.135447
Xie HJ, Zuo XR, Chen YM, Yan HX, Ni JJ (2022) Numerical model for static chamber measurement of multi-component landfill gas emissions and its application. Environ Sci Pollut Res 29(49):74225–74241. https://doi.org/10.1007/s11356-022-20951-2
Xie NM, Liu SF (2009) Discrete grey forecasting model and its optimization. App Math Model 33(2):1173–1186. https://doi.org/10.1016/j.apm.2008.01.011
Zhou WH, Zeng B, Wang JZ, Luo XS, Liu XZ (2021) Forecasting Chinese carbon emissions using a novel grey rolling prediction model. Chaos Solit Fractals 147:110968. https://doi.org/10.1016/j.chaos.2021.110968
Funding
This work was financially supported by the Natural Science Foundation of China (No. 22162007), the Science and Technology Supporting Project of Guizhou Province ( [2021]480), the Science and Technology Supporting Project of Guizhou Province ([2023]379), and the Wengfu (Group) Co., Ltd. Technology Development Project (WH-220787(YF)). Project from Guizhou Institute of Innovation and development of dual-carbon and new energy technologies (DCRE-2023-05)
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HL: model establishment, conceptualization, investigation, and writing—original draft; YX: data curation and formal analysis; WW: writing—review and editing; JZ: conceptualization; GL: investigation; and MT: supervised the project, funding acquisition, methodology, manuscript design, and writing—review and editing. All authors read and approved the final manuscript.
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Lu, ., Xu, Y., Wang, W. et al. Can China reach the CO2 peak by 2030? A forecast perspective. Environ Sci Pollut Res 30, 123497–123506 (2023). https://doi.org/10.1007/s11356-023-30812-1
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DOI: https://doi.org/10.1007/s11356-023-30812-1