Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications

Abstract

Simulations of sustainable land use and management are required to achieve targets to reduce pollution and carbon emissions. Limited research has been conducted on synergistic pollution and carbon reduction (SPCR) in land-use simulations. This study proposed a framework for land-use simulation focused on SPCR. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the entropy weight-based technique for order of preference by similarity to an ideal solution (TOPSIS) were used to optimize the land-use structure according to minimum net carbon, nitrogen, and phosphorus emissions. The cellular automata (CA) Markov model was then utilized to simulate the land-use spatial pattern according to the optimal conditions. The proposed framework was applied to the Dongjiang River Basin, South China, and three other scenarios (natural development (ND), carbon minimization (CM), and pollution minimization (PM)) were designed to validate the effectiveness of pollution and carbon emissions reduction under the SPCR scenario. The land-use structure and the pollution and carbon emissions in the scenarios were compared. The results showed the following. (1) The proportions of cultivated land, woodland, grassland, water, and construction land In the SPCR scenario accounted for 14%, 72%, 4%, 3%, and 7% of the total area, respectively. The carbon, nitrogen, and phosphorus emissions were 42.4%, 6.6%, and 7.8% lower, respectively, in the SPCR scenario than in the ND scenario, demonstrating the advantages of simultaneous pollution and carbon reduction. (2) The kappa coefficient of the CA-Markov model was 0.8729, indicating high simulation accuracy. (3) The simulated land-use spatial patterns exhibited low spatial heterogeneity under the CM, PM, and SPCR scenarios. However, there were significant disparities between the ND and SPCR scenarios. The cultivated and construction land areas were significantly smaller in the SPCR scenario than in the ND scenario. In contrast, the woodland and grassland areas were larger, with most differences in the central and southwestern regions of the Dongjiang River Basin. The results of the current study can be used to formulate effective land use policies and strategies in the Dongjiang Basin and similar areas to achieve the Coupling coordination between pollution reduction and carbon reduction. Policy recommendations include increasing the proportion of woodland and grassland, implementing reasonable constraints on expanding cultivated and construction lands, and establishing farmland red lines to promote synergistic pollution and carbon reduction.

Keywords: CA Markov model; Entropy weight-based TOPSIS; Land-use optimization; NSGA-Ⅱ.