Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model
Abstract
1. Introduction
2. Methodology
2.1. GIOWA Combination Forecasting Model
2.2. Individual Forecasting Models
2.2.1. Support Vector Regression Model
2.2.2. Long Short-Term Memory Neural Network
2.3. Evaluation Metrics
3. Case Study
3.1. In-Sample Forecasting Results and Analysis
3.1.1. SVR-Based Predictions
3.1.2. LSTM-Based Predictions
3.1.3. GIOWA Combination Forecasting Results
3.2. Out-of-Sample Forecasting Results and Analysis
3.2.1. Prediction Results of Individual Models
3.2.2. Prediction Results of the GIOWA Model
4. Conclusions
- (1)
- Both SVR and LSTM models prove to be effective for forecasting carbon emissions in China’s construction industry, with their accuracy surpassing 95%. The prediction results of the SVR and LSTM models indicate that the carbon emissions in China’s construction industry will continue to decrease from 2022 to 2030. Therefore, the Chinese government should implement targeted strategies, including moderating the labor demands of the construction industry [61], advancing new-type urbanization [62], and optimizing the energy consumption structure [63,64], so as to sustain the downward trend in construction carbon emissions. Given that the residuals of the SVR and LSTM models exhibit opposite signs relative to the zero-error baseline, the prediction accuracy can be further enhanced with forecast combination techniques.
- (2)
- The GIOWA combination forecasting model demonstrates superior predictive performance across varying averaging exponent λ, with all prediction accuracy exceeding 98%, outperforming the individual models on four key error metrics including RMSRE and MAPE. Therefore, the GIOWA model is more effective and accurate in capturing nonlinear variation characteristics in construction carbon emissions and generating reliable projections. Based on the forecast results, the carbon emissions of the construction industry will reduce to 29.99 million tons of CO2 by 2030, representing a reduction of nearly 35% compared to the peak of 46.52 million tons in 2017. This indicates that the construction industry in China is transitioning from extensive development patterns to green intensive practices, attributable to China’s nationwide promotion of prefabricated buildings [65] and energy-efficient construction technologies [66,67]. However, given that a deceleration in the carbon emission reduction rate from 4.81% to 1.66% is projected during the forecast period, the Chinese government, the construction industry, and relevant sectors must collaborate closely to guarantee the timely realization of the dual carbon goals.
- (3)
- There are certain limitations in this study. Current analysis is established on national-level annual emission data due to availability constraints. Subsequent studies can employ provincial or municipal datasets with a quarterly or monthly frequency to enhance spatial resolution and temporal granularity, thereby facilitating real-time emission monitoring and the implementation of dynamic early-warning mechanisms. Additionally, the current research remains deficient in exploring the impact of uncertainty on the combination forecasting results. This gap will be addressed and investigated more thoroughly in future studies.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
GIOWA | Generalized induced ordered weighted averaging |
SVR | Support vector regression |
LSTM | Long short-term memory |
GM | Grey model |
OWA | Ordered weighted averaging |
IOWA | Induced ordered weighted averaging |
IOWGA | Induced ordered weighted geometric averaging |
IOWHA | Induced ordered weighted harmonic averaging |
SSE | Sum of squared errors |
TECCI | Total energy consumption of the construction industry |
COPC | Construction output per capita |
SA | Simple averaging |
IVW | Inverse Variance Weighting |
BMA | Bayesian Model Averaging |
BGOC | Bates Granger optimal combination |
MSE | Mean squared error |
MAE | Mean absolute error |
RMSRE | Root mean squared relative error |
MAPE | Mean absolute percentage error |
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Variables | Mean | Max | Min | Std. Dev |
---|---|---|---|---|
Construction CO2 emissions (million tons) | 33.37 | 46.75 | 15.27 | 10.63 |
Construction workforce (million people) | 36.66 | 55.30 | 19.94 | 13.10 |
Urban resident population (million people) | 656.03 | 914.25 | 394.49 | 163.94 |
Urbanization rate | 0.49 | 0.65 | 0.32 | 0.10 |
TECCI (Mtce) | 51.96 | 96.08 | 11.79 | 27.57 |
COPC (kCNY/capita) | 240.40 | 532.41 | 43.43 | 155.54 |
Errors | SVR | LSTM | λ = 1 | λ = −1 | λ → 0 | λ = 1/2 | λ = 1/4 |
---|---|---|---|---|---|---|---|
MSE | 0.9602 | 2.7804 | 0.5946 | 0.5946 | 0.5910 | 0.5926 | 0.5915 |
MAE | 0.7362 | 1.3480 | 0.5970 | 0.5964 | 0.6027 | 0.5946 | 0.5944 |
RMSRE | 3.26% | 5.14% | 2.37% | 2.37% | 2.35% | 2.36% | 2.36% |
MAPE | 2.40% | 4.04% | 1.87% | 1.87% | 1.88% | 1.86% | 1.86% |
Average accuracy | 97.60% | 95.96% | 98.13% | 98.13% | 98.12% | 98.14% | 98.14% |
Errors | SA | IVW | BMA | BGOC | λ = 1 | λ = 1/2 | λ = 1/4 |
---|---|---|---|---|---|---|---|
MSE | 1.0636 | 0.8078 | 1.0140 | 0.8069 | 0.5946 | 0.5926 | 0.5915 |
MAE | 0.8258 | 0.7365 | 0.7391 | 0.7391 | 0.5970 | 0.5946 | 0.5944 |
RMSRE | 3.22% | 2.93% | 2.92% | 2.92% | 2.37% | 2.36% | 2.36% |
MAPE | 2.55% | 2.36% | 2.53% | 2.37% | 1.87% | 1.86% | 1.86% |
Average accuracy | 97.45% | 97.64% | 97.47% | 97.63% | 98.13% | 98.14% | 98.14% |
Variables | MAPE | RMSRE | Accuracy |
---|---|---|---|
Construction workforce | 3.35% | 4.44% | 96.65% |
Urban resident population | 0.49% | 0.57% | 99.51% |
Urbanization rate | 0.34% | 0.39% | 99.66% |
TECCI | 3.30% | 4.09% | 96.70% |
Total construction output value | 1.25% | 1.39% | 98.75% |
Year | Forecast Value | ||
---|---|---|---|
2022 | 0.8920 | 0.1080 | 38.77 |
2023 | 0.8310 | 0.1690 | 37.00 |
2024 | 0.7730 | 0.2270 | 35.41 |
2025 | 0.7340 | 0.2660 | 33.94 |
2026 | 0.6990 | 0.3010 | 32.76 |
2027 | 0.6610 | 0.3390 | 31.83 |
2028 | 0.6480 | 0.3520 | 31.03 |
2029 | 0.6360 | 0.3640 | 30.42 |
2030 | 0.6290 | 0.3710 | 29.99 |
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Hu, T.; Bao, Z.; Zhang, B.; Gao, X. Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model. Mathematics 2025, 13, 1955. https://doi.org/10.3390/math13121955
Hu T, Bao Z, Zhang B, Gao X. Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model. Mathematics. 2025; 13(12):1955. https://doi.org/10.3390/math13121955
Chicago/Turabian StyleHu, Tianyue, Zhiheng Bao, Baiyang Zhang, and Xinnan Gao. 2025. "Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model" Mathematics 13, no. 12: 1955. https://doi.org/10.3390/math13121955
APA StyleHu, T., Bao, Z., Zhang, B., & Gao, X. (2025). Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model. Mathematics, 13(12), 1955. https://doi.org/10.3390/math13121955