Enhancing natural carbon sinks: Simulation of land use change under different carbon market scenarios by developing an ANN-ABM model

The carbon market mechanism presents an innovative approach to achieving carbon neutrality, however, its impact on regional carbon sink enhancement remains uncertain. This paper proposes an analysis framework based on an Artificial Neural Network-Agent-Based Model (ANN-ABM) to simulate the potential contribution of carbon market mechanism in enhancing carbon sinks. Taking the case of Chongming, China, the results demonstrate significant economic potential with forest land expansion from 2010 to 2020, equivalent to 30.69 % of eco-compensation. Under scenarios introducing a carbon market, there is an increased probability of converting land to high carbon sink types. Compared to the baseline scenario, carbon prices at 1.34 US$/t and 6.27 US$/t result in additional funds of 8.94 % and 41.46 %, respectively, and increase the carbon sink by 12.52 % and 30.28 %. The findings contribute to an understanding of how the value of carbon sinks can be realized through market mechanism and offer guidance for innovative eco-compensation approaches.