College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Sci Total Environ. 2022 Feb 10;807(Pt 2):150859. doi: 10.1016/j.scitotenv.2021.150859. Epub 2021 Oct 9.
Air pollution and other environmental problems caused by excessive emissions of greenhouse gases have become a comprehensive problem requiring joint global treatment. To consider the characteristics of different regions and different countries in terms of greenhouse gas emissions for accurate prediction, a new information priority generalized accumulative grey model (NIPGAGM(1,1,k)) is proposed. The new model maintains the structure of the traditional grey model and the basic result characteristics of its features. This research further deduces the calculation formulas of the model's time response sequence and parameter estimation. Furthermore, an optimization model is established to search the parameters using a detailed optimization algorithm. The optimization value of the new model is determined by the intelligent optimization algorithm. Then, the new model is applied to the greenhouse gas emission prediction of the Shanghai Cooperation Organization (SCO) member states. The numerical results are compared with those of existing models. Finally, according to the forecast results of greenhouse gas emissions in these regions, reasonable suggestions for clean energy production are proposed.
空气污染和其他由温室气体过度排放引起的环境问题已经成为一个需要全球共同处理的综合性问题。为了考虑到不同地区和不同国家在温室气体排放方面的特点，进行准确预测，提出了一种新的信息优先广义累加灰色模型（NIPGAGM(1,1,k)）。新模型保持了传统灰色模型的结构和其特征的基本结果特性。本研究进一步推导出模型时间响应序列和参数估计的计算公式。此外，还建立了一个优化模型，使用详细的优化算法来搜索参数。新模型的优化值由智能优化算法确定。然后，将新模型应用于上海合作组织（SCO）成员国的温室气体排放预测。将数值结果与现有模型进行比较。最后，根据这些地区温室气体排放的预测结果，提出了合理的清洁能源生产建议。
