Simulating Seoul's greenbelt policy with a machine learning-based land-use change model

Abstract

This study builds a machine-learning-based land-use change (ML-LUC) model to analyze the effect of green belt (GB) regulation in the Seoul metropolitan area (SMA) and predict the spatially explicit development potential of the land within the GB under the assumption of a no-GB policy scenario. It stands out for its ML-LUC application to simulate counterfactual planning for urban land use regulation. After comparing the predictive power of extreme gradient boosting (XGB), random forest (RF), and artificial neural network (ANN), the ML-LUC model utilizes the XGB algorithm due to its outperformance. Three scenarios based on SMA's land market demand were simulated to estimate the potential population and employment within the GB under the no-GB policy: high, moderate, and low land market demand. The results suggest 0.6 to 1.5 million residents, 0.2 to 0.5 million manufacturing jobs, and 0.4 to 1.0 million service sector jobs could have been located within the GB, accounting for 3 % to 6 % of total population and 5 % to 13 % of all employment in SMA. The findings imply the GB regulation prevents population and employment from locating within the GB, pushing them to central Seoul or suburbs beyond the GB under a closed-city assumption.