Urban carbon emission scenario prediction and multi-objective land use optimization strategy under carbon emission constraints

Abstract

With the serious challenge of persistently high carbon emissions in China, the construction of low-carbon cities is poised to become a trend in future development. However, there has been limited research quantitatively exploring carbon emission spatial targets and efficient algorithmic optimization in a systematic manner. To investigate regional carbon emission compliance and systematic optimization methods, this study takes Shanghai Pudong New Area in China as a case study. It integrates an improved Kaya identity model for forecasting, carbon emission intensity analysis, and an enhanced multi-objective genetic algorithm for land-use optimization. The study aims to predict and account for carbon emissions, compare them against carbon emission targets, analyze spatial influencing factors, and consider relevant policy constraints to simulate low-carbon land-use layouts. The findings reveal the following: (1) Through scenario forecasting and carbon emission accounting analysis, it is evident that Pudong New Area's carbon emissions do not meet the target requirements. There is potential for optimization in terms of the balance between residential and workplace spaces, land use mix, and ecological areas. (2) After applying the genetic algorithm optimization, the carbon emissions in the land-use layout plan are reduced by 11.2%–27.88 million tC compared to the original planning scheme. (3) In simulated scenarios, there are significant changes compared to planned land-use layouts, including a 49% increase in the balance between residential and workplace areas, a 20% increase in land use mix, and a 33.9% increase in per capita park green space.This systematic optimization approach effectively predicts carbon emissions, adjusts land-use layout, and achieves a reduction in carbon emissions. The conclusions provide valuable technical support for low-carbon land-use planning and offer insights for the low-carbon development of other cities."