The nonlinear influence of land conveyance on urban carbon emissions: An interpretable ensemble learning-based approach

Abstract

Land allocation and pricing substantially impact carbon emissions, yet their nonlinear effects remain understudied. This research employs ensemble machine learning models to examine the complex relationships between land conveyance and per capita carbon emissions across 104 major Chinese cities from 2009 to 2017. The results reveal that keeping industrial land allocations below 35% helps reduce emissions, whereas higher ratios increase emissions. Allocating over 8% and 33% to business and public land respectively also lowers emissions. Land prices demonstrate heterogeneity – a higher residential land price promotes efficiency only when its relative price level to the comprehensive land price is below 1.1. The findings highlight customised policies balancing development and emissions reduction, based on local conditions and development stages, can forge sustainable pathways. Overall, the nonlinear modelling quantifies nuanced emissions responses to land allocation thresholds and strategic pricing incentives. By considering these complex mechanisms, urban planners can devise tailored strategies that simultaneously nurture growth and curb emissions. The novel method and evidence-based insights contribute to planning support systems and sustainable policy-making.