Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE

Original Publication: Basel : MDPI, c2004-

Carbon Dioxide*/analysis ; Economic Development*; China ; Forecasting ; Industry

Accurate predictions of CO 2 emissions have important practical significance for determining the best measures for reducing CO 2 emissions and accomplishing the target of reaching a carbon peak. Although some existing models have good modeling accuracy, the improvement of model specifications can provide a more accurate grasp of a system's future and thus help relevant departments develop more effective targeting measures. Therefore, considering the shortcomings of the existing grey Bernoulli model, in this paper, the traditional model is optimized from the perspectives of the accumulation mode and background value optimization, and the novel grey Bernoulli model NFOGBM(1,1,α,β) is constructed. The effectiveness of the model is verified by using CO 2 emissions data from seven major industries in Shaanxi Province, China, and future trends are predicted. The conclusions are as follows. First, the new fractional opposite-directional accumulation and optimization methods for background value determination are effective and reasonable, and the prediction performance can be enhanced. Second, the prediction accuracy of the NFOGBM(1,1,α,β) is higher than that of the NGBM(1,1) and FANGBM(1,1). Third, the forecasting results show that under the current conditions, the CO 2 emissions generated by the production and supply of electricity and heat are expected to increase by 23.8% by 2030, and the CO 2 emissions of the other six examined industries will decline.

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Keywords: CO2 emissions; NFOGBM(1,1,α,β); background value; forecasting; fractional opposite-direction accumulation

142M471B3J (Carbon Dioxide)

Date Created: 20220514 Date Completed: 20220519 Latest Revision: 20220716

20221213

PMC9105360

10.3390/ijerph19094953

35564347