Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region

Abstract

It is still necessary to regularly investigate the breakdown of socio-economic elements as a starting point for analyzing the effects of diverse human production activities on PM2.5 intensity from industrial and regional viewpoints. In this paper, the emission factor model was adopted to measure PM2.5 emissions in the Beijing–Tianjin–Hebei (BTH) region at the regional and industrial levels. The logarithmic mean Divisia index (LMDI) decomposition model was employed to analyze the factors affecting PM2.5 emissions related to energy consumption in the BTH region at the regional and sectoral levels. Building on this foundation, a system dynamics (SD) model was established to make a prediction regarding PM2.5 pollution in the BTH region in 2025. This study found that secondary industry was a major source of PM2.5 emissions in the BTH region. Coal remained the main form of energy consumption in the BTH region. Secondly, the effect size of the factors affecting PM2.5 intensity ranked in the order of energy intensity, energy structure, and industrial structure. Thirdly, in 2025, PM2.5 emissions in the BTH region will decline appreciably, but there is still a certain gap in terms of meeting the targets of “the 14th Five-Year Plan” between the three provinces and cities. These results indicate that the BTH region should achieve the effective management of PM2.5 pollution at the source through the following initiatives: it is necessary to carry out the continuous adjustment of energy structures to gradually increase the proportion of clean energy; we must steadily promote the decline in energy intensity reduction, and gradually strengthen scientific and technological innovation; and we must continue to promote the optimization of the industrial structure and increase the proportion of tertiary industry every year.

Modeling CO2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model

“…The framework outperforms traditional machine learning algorithms in terms of explainability and accuracy. Li, Shi, Sun, Xing, Zhang, and Xue [44] conduct a simulation and forecasting study on the factors affecting PM2.5 emissions related to energy consumption in the Beijing-Tianjin-Hebei region. Their findings underscore the importance of effective management strategies to mitigate PM2.5 pollution.…”