Western Collaborative Innovation Research Center for Energy Economy and Regional Development, Xi'an University of Finance and Economics, Xi'an 710100, China.
Int J Environ Res Public Health. 2022 Apr 19;19(9):4953. doi: 10.3390/ijerph19094953.
Accurate predictions of CO emissions have important practical significance for determining the best measures for reducing CO 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 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 emissions generated by the production and supply of electricity and heat are expected to increase by 23.8% by 2030, and the CO emissions of the other six examined industries will decline.
准确预测 CO 排放量对确定减少 CO 排放量的最佳措施和实现碳峰值目标具有重要的实际意义。虽然一些现有模型具有良好的建模精度，但改进模型规格可以更准确地把握系统的未来，从而帮助相关部门制定更有效的针对性措施。因此，考虑到现有灰色伯努利模型的缺点，本文从积累模式和背景值优化的角度对传统模型进行了优化，构建了新的灰色伯努利模型 NFOGBM(1,1,α,β)。通过使用中国陕西省七个主要行业的 CO 排放量数据验证了模型的有效性，并对未来趋势进行了预测。结论如下。首先，新的分数反向积累和背景值确定优化方法是有效和合理的，可以提高预测性能。其次，NFOGBM(1,1,α,β)的预测精度高于 NGBM(1,1)和 FANGBM(1,1)。第三，预测结果表明，在当前情况下，到 2030 年，电力和热力生产供应产生的 CO 排放量预计将增长 23.8%，其他六个受检行业的 CO 排放量将下降。
