Prediction of carbon emissions in China's construction industry using an improved grey prediction model

As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucial for energy conservation and emission reduction. In this paper, we utilize the carbon emission data from China's national and provincial construction sectors from 2012 to 2021, employ the grey prediction model optimized by the particle swarm optimization algorithm, coupled with a metabolic algorithm, to forecast the carbon emissions of the construction industry across China and its provinces. The results demonstrate that: (1) The dynamic grey prediction model combined with the metabolism algorithm has a better prediction effect than the classical model, and the relative error is reduced from 5.103 % to 0.874 %. (2) The carbon emissions of China's construction industry will continue to rise in the next decade, but the growth rate will decrease, and the proportion of indirect carbon emissions continues to increase. (3) There is a marked regional disparity in carbon emissions, with the eastern region exhibiting higher emission levels yet slower growth. In contrast, the western region has lower emission levels but experiences faster growth. These studies provide valuable insights for both the existing approaches to energy conservation and emission reduction, as well as for future policy improvements.