Analysis of Spatio-Temporal Characteristics and Trend Forecast of Building Industry VOCs Emissions in China

Abstract

Emissions of volatile organic compounds (VOCs) from the building industry in China are increasing. Predicting future trends in China’s building industry VOCs will help the implementation of China’s construction VOCs emission reduction policy. The aim of this study is to combine Granger causality analysis, Ridge regression, GM(1,N), and categorical boosting (CatBoost) methods for the analysis of factors influencing and trend prediction of VOCs emissions from building industry in China. Firstly, the spatial and temporal characteristics of building industry VOCs emissions in China were calculated, based on building industry VOCs emissions data from 2006 to 2020 for each province and city in China. Secondly, Granger causality tests and STIRPAT models were used to examine the influencing factors of construction VOCs, combined with ridge regression to estimate the elasticity coefficients of the influencing factors and feature screening, which were used as input features for prediction. Finally, a combination of the improved GM(1,N) model and the CatBoost model was used to predict and compare the results with those of the GM(1,N) model, the support vector regression model (SVR), the random forest (RF), and the CatBoost model. The results show that the combined model with improved GM(1,N) and CatBoost has better prediction accuracy than the other models. China’s building industry VOCs emissions are increasing year on year and are not expected to reach their peak by 2030. The size of the population, the number of people employed in the building industry, and the area of housing floor space under construction are important influencing factors that affect VOCs emissions from building industry in China. Based on the predicted results for the different scenario settings, building industry VOCs emissions are lower in the short term for the baseline scenario and in the long term for the high-speed scenario.