Deep Learning-Based Study of Carbon Emissions Peak Pathways in Chinese Building Sector: Incorporating Legal and Policy Text Quantification

The decarbonization process of the carbon emissions in the Chinese building sector exerts a profound impact on the achievement of the national goals of carbon peak and carbon neutrality. Currently, there is limited literature quantifying the impact of laws and policies on the achievement of carbon peak in the Chinese building sector and further utilizing deep learning technology to characterize the carbon emissions peak path under uncertainty in the Chinese building sector. To address this issue, a quantitative framework of legal and policy incentive intensity is constructed to capture both the immediate effects and the long-term evolution of laws and policies, and the index of legal and policy incentive intensity for carbon emissions in the building sector in China from 2010 to 2022 is calculated. Based on this, a dynamic scenario forecasting model for carbon emissions in the Chinese building sector is developed by integrating a CNN-BiLSTM-AM model with the Monte Carlo simulation algorithm, embedded within the scenario analysis method. The model projects the dynamic trajectories of carbon emissions in the Chinese building sector under different scenarios from 2023 to 2050 and identifies effective schemes for controlling carbon emissions in the Chinese building sector. Results indicate that the growth in legal and policy incentive intensity was most significant during the 12th Five-Year Plan period in China. During the 13th Five-Year Plan in China, the legal and policy system became increasingly mature, leading to a diminishing marginal effect of newly issued policies. A negative growth in legal and policy incentive intensity was observed in 2020 due to the impact of the COVID-19 pandemic. From 2021 to 2022, the annual growth rate of policy intensity began to rebound. Under the current scenario, carbon emissions in the Chinese building sector are projected to reach its carbon peak in 2036 (±1), with a peak level of 28.617 (±1.047) × 108 t CO2. Energy consumption per unit floor space, population size, legal and policy incentive intensity, integrated carbon emission factor, and floor space per capita are identified as the most critical factors influencing the timing and value of carbon peaking. The research methodology employed in this study not only provides scientific insights for the emission reduction efforts in the building sector but is also applicable to related studies in other industries’ energy conservation and emission reduction. It holds universal value for environmental policymakers and strategic planners.