Dynamic spatial allocation of future non-industrial CO₂ emissions using purpose-based travel demand forecasts
Abstract
This study proposes a dynamic spatial allocation framework for estimating future non-industrial CO 2 emissions at the municipal level. Previous studies have largely relied on static allocation approaches that apply present-day spatial distributions based on land-cover, population, or nighttime-light data to future periods without accounting for changes in regional activity patterns. To address this limitation, this study developed dynamic spatial allocation factors by linking purpose-based Origin–Destination (OD) trip forecasts with gross floor area (GFA) data. Building-use-specific trip indicators were developed and used to estimate future changes in GFA for residential, commercial, and public sectors. The estimated future GFA was then used to derive dynamic regional allocation factors for spatially distributing national-scale emission projections. The results showed that changes in GFA exhibited strong correlations with changes in building energy consumption (r = 0.577–0.772), substantially outperforming land-cover-based indicators (r = 0.201–0.279). The calibrated GFA estimation model reproduced the observed GFA with mean absolute percentage errors (MAPE) ranging from 8.31 to 13.47% and coefficients of determination (R 2 ) ranging from 0.978 to 0.996. Independent framework validation using municipal greenhouse gas inventories demonstrated that GFA-based allocation factors effectively represented the observed spatial distribution of emissions. The proposed framework dynamically updates regional allocation factors using future travel demand forecasts, enabling future changes in socioeconomic conditions and land-use patterns to be reflected in emission spatialization. The framework can be used to transform national-scale emission projections into high-resolution regional emission inventories and provides a practical approach for future climate policy assessment and regional carbon-neutrality planning.