Carbon Emission Influencing Factors and Scenario Prediction for Construction Industry in Beijing-Tianjin-Hebei

In this paper, the factors causing the change in carbon emissions from direct energy consumption in the construction industry in Beijing-Tianjin-Hebei are decomposed using the logarithmic mean divisia index (LMDI) method to analyze the effect values and contribution rates of each macrofactor. Based on the decomposition results and given relevant national policies, five scenarios are set up for each influencing factor, and a regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis is applied to each scenario combination for scenario prediction, forming a scientific and reasonable theoretical system to predict the future time of carbon peaking and carbon neutrality in the construction industry of Beijing-Tianjin-Hebei. The results show that (1) energy intensity and energy structure have a suppressive effect on direct energy consumption carbon emissions in the construction industry in Beijing-Tianjin-Hebei, and the industrial structure, economy, and population will promote an increase in carbon emissions. Energy intensity and the economy have a more significant effect on carbon emissions in the construction industry. (2) The peak year of carbon emissions varies with different scenarios, and the energy efficiency scenario achieves peak carbon in 2028, the earliest peak time, and the lowest peak, as it is the optimal emission reduction projection scenario.

Automated summary

This paper decomposes the factors causing changes in carbon emissions from direct energy consumption in the construction industry in Beijing-Tianjin-Hebei using the logarithmic mean divisia index (LMDI) method. It analyzes the effect values and contribution rates of each macrofactor. Based on the decomposition results and relevant national policies, five scenarios are set up for each influencing factor, and scenario prediction is conducted using regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis. The results provide insights into the future time of carbon peaking and carbon neutrality in the construction industry of Beijing-Tianjin-Hebei.