Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China

Abstract

Against the backdrop of the intensifying global climate crisis, urban construction land (UCL), as a major source of carbon emissions, faces the severe challenge of balancing emissions reduction and development in its low-carbon transformation. This study is dedicated to filling the theoretical and methodological gap in the refined assessment of urban construction land carbon effects (UCLCE) spatial heterogeneity among regions, and proposes and validates an innovative block-scale prediction framework. To achieve this goal, this study takes the central urban area of Changxing, Zhejiang Province, as the study area and establishes a BP neural network model for predicting UCLCE based on multi-source data such as building energy consumption and built environment elements (BEF). The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, p < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. Optimization trials on representative blocks achieve up to 25.45% carbon reduction. This study provides theoretical foundations for understanding UCLCE spatial heterogeneity while delivering scientifically grounded tools for diagnosing built environment issues and advancing low-carbon optimization in urban renewal contexts. These contributions carry significant theoretical and practical implications.