Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Abstract

As a pillar industry of national economy, China’s construction industry is still facing the status of substantial energy consumption and high CO2 emissions, which is a key field of energy conservation and emission reduction. In CO2 emissions research, it is essential to focus on analyzing the present and future trends of CO2 emissions in China’s construction industry. This article introduces a novel prediction model, in which the weighted algorithm is combined with Elman neural network (ENN) optimized by Adaptive Boosting algorithm (Adaboost) for evaluating future CO2 emissions in China’s construction industry. Firstly, logarithmic mean Divisia index (LMDI) is used to decompose CO2 emissions into economy, structural, intensity, and population indicators, posing as inputs to the weighted Adaboost-ENN model. Then, through comparison with other three models based on the data of total CO2 emissions in China’s construction industry during 2004-2016, there is evidence that the proposed model makes a favorable prediction performance. On this basis, we employ scenario analysis to predict future trend of CO2 emissions in China’s construction industry. It can be found that the peak of CO2 emissions in China’s construction industry will be achieved before 2030 in high carbon scenario (HS) and baseline carbon scenario (BS), whereas it will not be realized in low carbon scenario (LS). Finally, the specific policy recommendations related to energy conservation and emission reduction in China’s construction industry are proposed.

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