Research on prediction and realization path of carbon peak of construction industry based on EGM-BP model

Abstract

In order to actively respond to the global climate and environmental challenges, and to help achieve the goal of carbon peaking and carbon neutrality, China aims to achieve carbon peaking by 2030. As the main contributor of energy consumption, construction industry transformation is imminent. This paper focuses on the development trend of carbon emissions in Anhui construction industry in the next 20 years, and how to help Anhui construction industry achieve the carbon peak target. The research process and conclusions are as follows: 1) Through literature review and gray correlation analysis, seven important factors affecting the construction industry are identified. 2) The gray prediction EGM(1,1) model was adopted to forecast the carbon emissions of the construction industry in Anhui Province from 2021 to 2040. The research results show that carbon emissions will increase year by year in the next 20 years. 3) The EGM-BP neural network model was used to further predict the carbon emissions from the construction industry in 2021–2040. It is expected that the carbon emissions from construction industry in Anhui Province will show an “inverted U″ trend in the next 20 years, and is expected to reach its peak in 2031, after which it will start to decline. Based on the results of the above study, we provide support paths for achieving the carbon peak in the construction industry from the perspective of improving and developing the construction system, and provide policy suggestions for relevant departments to accelerate the construction industry and carbon peak construction in Anhui Province.

The spatial effect of integrated economy on carbon emissions in the era of big data: a case study of China

“…Secondly, regarding the research on carbon peak prediction, most of the previous researchers used big data models and scenario analysis methods to predict the future growth of carbon emissions. And the results show that most of the provinces and cities in China can achieve the goal of a carbon peak by 2030, and only individual regions, such as Hubao, Eyu and Elm, have difficulties in achieving a carbon peak (Zhang et al, 2022b;Dai et al, 2022). Finally, according to existing research, public policy factors such as carbon emission trading pilot programs and low-carbon city pilot policies , industrial structure factors such as energy structure and industrial robots (Meng et al, 2018;Li and Zhou, 2021;Jiang et al, 2023), and macro technological factors such as outward direct investment, population aggregation, digital economy development (Zhao and Zhu, 2022;Liu et al, 2023), and technological innovation will all have an impact on carbon emissions, carbon intensity, or efficiency (Chen et al, 2023;Zha et al, 2023).…”

Predictive Modeling and Validation of Carbon Emissions from China’s Coastal Construction Industry: A BO-XGBoost Ensemble Approach

“…Population factors Total Population TP [33] Employment in the Construction Industry ECI [34][35][36] Economic factors Gross Domestic Product GDP [34] Urbanization Rate UR [34,36,37] Technological factors…”

Research on the Dynamic Coupling and Coordination of Science and Technology Innovation and Sustainable Development in Anhui Province

“…Because the GM (1, 1) model is based on exponential law forecasting, its predicted data can only show increasing or decreasing trends, which, to a certain extent, limits the reverse fluctuation trend due to special circumstances [52]. However, the ARIMA forecasting model can simulate the fluctuations of the data, which partially compensates for the inverse fluctuation trend ignored by the GM (1, 1) forecasting model [53].…”