Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model

The construction industry in China has long been confronted with significant concerns related to fossil fuel dependence and low energy efficiency. However, under the policy guidance of China’s “dual carbon” goals, it has emerged as the leading sector in achieving a reduction in carbon emissions through technological innovation in recent years. To accurately assess the carbon emission reduction potential of the construction industry and support the attainment of the dual carbon goals, this study constructs a generalized induced ordered weighted averaging (GIOWA) combination forecasting model, integrating support vector regression (SVR) and a long short-term memory neural network (LSTM). A case study is conducted based on historical data (1997–2021) from the construction industry, and the research findings indicate that: (1) the GIOWA combination forecasting model effectively integrates the algorithmic strengths of SVR and LSTM, achieving an average prediction accuracy of 98.16%, which signifies a remarkable improvement over both individual models; (2) the carbon emissions in China’s construction industry will maintain a downward trend during the period 2022–2030, although the decline rate is expected to decrease gradually; (3) by 2030, a reduction of nearly 35% in carbon emissions is anticipated relative to the historical peak. This study provides evidence-based decision support for relevant policy formulation.