Iftikhar Hasnain, Khan Murad, Żywiołek Justyna, Khan Mehak, Linkolk López-Gonzales Javier
Department of Statistics, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
Escuela de Posgrado, Universidad Peruana Unión, Lima, 15468, Peru.
Heliyon. 2024 Jun 19;10(13):e33148. doi: 10.1016/j.heliyon.2024.e33148. eCollection 2024 Jul 15.
Carbon dioxide (CO) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO emissions. Thus, this study comprehensively analyzes forecasting CO emissions by comparing various hybrid combinations of regression and time series methods to explore the CO emissions in Pakistan. First, divide the yearly time series of CO emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.
尽管全球在努力应对气候变化，但二氧化碳（CO）排放量仍在持续上升。能源行业的排放是一个紧迫的全球问题，造成了毁灭性影响。因此，准确且高效地预测CO排放量至关重要。为此，本研究通过比较回归和时间序列方法的各种混合组合，全面分析了巴基斯坦的CO排放预测情况。首先，将CO排放的年度时间序列划分为长期曲线趋势序列和残差子序列。长期曲线趋势子序列采用参数和非参数回归方法建模，而各种标准时间序列模型用于预测残差子序列。然后，将每个子序列的预测结果进行合并，以获得CO排放的最终预测值。这项工作使用了四种不同的平均误差精度、一项统计检验和图形分析作为性能指标，来评估所提出的混合预测技术。研究结果证实，所提出的混合组合预测技术在预测CO排放方面具有高度准确性和效率。同样，根据所提出的最终最优混合组合预测模型，到2030年巴基斯坦的人均CO排放量将达到1.130215公吨。巴基斯坦不断上升的排放趋势表明，必须实施创新性解决方案来遏制这一趋势。因此，政府必须对碳足迹进行定价，规范来自零碳源的电力，减少人口，鼓励在人口密集地区植树造林，采用清洁技术，并为研究提供资金。
