Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM

ZHANG Xinsheng; NIE Dawen; CHEN Zhangzheng

doi:10.13205/j.hjgc.202504005

Volume 43 Issue 4

Apr. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(4): 46-58

ZHANG X S，NIE D W，CHEN Z Z.Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM［J］.Environmental Engineering，2025，43（4）：46-58. doi: 10.13205/j.hjgc.202504005

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Research on a prediction model for carbon emissions of construction industry based on MIC feature extraction and ICEEMD-RIME-DHKELM

doi: 10.13205/j.hjgc.202504005

1. College of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China, China;
2. Shaanxi Institute of New Urbanization and Human Settlement Environment Research, Xi'an 710055, China

Received Date: 2024-04-29
Accepted Date: 2024-07-17
Rev Recd Date: 2024-07-02
Publish Date: 2025-04-01

Abstract

Abstract

As one of the important sources of global carbon emissions， the carbon emission prediction of the construction industry is crucial to promote the low-carbon transition and formulate effective carbon emission reduction policies. However， the existing carbon emission prediction models are limited in many aspects， especially in the accuracy of influencing factors selection， the integrity of data preprocessing， the complex dynamic changes of carbon emission data， the processing of nonlinear characteristics and so on. To solve these problems， this paper proposed a carbon emission prediction model for the construction industry based on maximum information coefficient （MIC） feature extraction， improved complementary ensemble empirical mode decomposition （ICEEMD）， RIME optimization algorithm， and deep hybrid kernel extreme learning machine （DHKELM）. First， according to the calculation method provided by the IPCC， this paper calculatesd the carbon emissions of China’s construction industry from 1992 to 2021， covering multiple factors of direct and indirect emissions. Combined with the STIRPAT model， 17 potential influencing factors were identified， including year-end total population， gross domestic product， construction area completed and energy mix. These factors fully reflected the driving mechanism of carbon emissions in the construction industry. Through grey correlation analysis and MIC method in two stages， this paper selected 12 key factors that had a significant impact on the carbon emissions of the construction industry. In the data preprocessing stage， the improved ICEEMD method was used to decompose the original carbon emission data， and several stationary sequences and one residual term were obtained. This decomposition process effectively reduced the noise in the data and enhanced the stationarity of the time series， thus providing more reliable data input for the subsequent modeling. Next， the RIME optimization algorithm was used to optimize the key parameters of the DHKELM model to further improve the model’s predictive power. DHKELM， as an effective deep learning algorithm， can adaptively capture the nonlinear characteristics of carbon emission data through the built-in hybrid kernel function. In the process of model construction and training， each decomposed sequence was input into the optimized DHKELM model for training and prediction. Finally， based on the forecast results of each decomposition sequence， the predicted value of carbon emissions of the construction industry as a whole was obtained. In order to evaluate the predictive performance of the model， the results of the model were compared with those of various benchmark models. The experimental results showed that the MIC-ICEEMD-RIME-DHKELM model was superior to other models in many performance indexes. Specifically， the model had a root mean square error of 0.2782 million tons， a mean absolute error of 0.2672 million tons， a mean absolute percentage error of 1.3783%， and an absolute correlation coefficient of 0.9576， showing its superior forecasting ability. Through the above analysis， the proposed MIC-ICEEMD-RIME-DHKELM model can effectively predict the carbon emission of the construction industry， and provide important theoretical support and practical reference for the carbon emission monitoring and policy formulation of the construction industry.

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