Optimizing land use patterns to improve the contribution of land use planning to carbon neutrality target

Abstract

Land use planning has the potential to diminish carbon storages and exacerbate carbon emissions, and therefore improving its contribution to achieve carbon neutrality should be a priority. In this study, we proposed an integrated framework to reveal the interrelation between land use and carbon neutrality. We employed the Linear Programming Model (LPM), Markov, Future Land Use Simulation (FLUS), emission coefficients and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) to predict land use patterns in Hainan Island, China and assessed the potential for reducing carbon emissions and increasing carbon storages under four scenarios: natural development (ND), spatial planning (SP), low-carbon emission (LE), and high-carbon storage (HS) by 2035. The results demonstrate that the new Territory Spatial Planning in 2035 can effectively reduce carbon emissions and increase carbon sinks. Specifically, compared to the ND scenario, carbon emissions will decrease by 5.37 % and carbon storages increase by 0.11 % in the SP scenario. Furthermore, the optimized land use patterns in the low-carbon scenarios will result in a greater reduction in carbon emissions and a larger increase in carbon sinks than the SP scenario. Specifically, compared to the SP scenario, carbon emissions will decrease by 11.83 % in the LE scenario, and carbon storages increase by 4.81 % in the HS scenario. Through the integration of planning and carbon neutrality via land use optimization, this study broadened the theoretical analysis framework and deepened our comprehension of the relationship between land use and carbon neutrality. Moreover, the insights derived from our findings offer valuable information to policymakers on carbon neutrality policy-making and land use planning in Hainan and other regions facing the similar challenges.