Scenario simulation of carbon balance in carbon peak pilot cities under the background of the "dual carbon" goals

Abstract

Under the "dual carbon" goals, targeting issues such as the difficulty in changing the high-carbon economic development model in pilot cities and the inability of previous prediction models to meet current needs, this paper provides an in-depth analysis of carbon stocks and emissions in a peak pilot City spanning from 2000 to 2020. Utilizing the PLUS model, this study forecasts land use/cover data under diverse future scenarios, encompassing natural development (ND) as well as ecological protection (EP). Moreover, the Bi-LSTM deep learning model is developed using six influencing factors to simulate carbon emissions. The research also examined the spatiotemporal changes in carbon budget and balance. The findings of the study reveal several significant conclusions:(1) The PLUS model demonstrated high predictive accuracy in forecasting future land-use types, achieving an average overall accuracy exceeding 0.89 and a Kappa value of 0.8568; The Bi-LSTM model achieved the highest accuracy among all competing models, with an $R^2$ score reaching 0.864. (2) Under the EP scenario from 2020 to 2030, the rate of decline in carbon storage has slowed down ($6.44\times {10}^{6}t$ of carbon storage have been avoided from disappearing), and land use efficiency has significantly improved. Due to the protection of ecological land, a certain carbon sink effect has been generated, resulting in lower regional carbon emissions compared to the ND scenario, emphasizing the importance and necessity of setting ecological red lines for carbon stock optimization. (3) Carbon payment areas are primarily concentrated in urban centers, and over time, these areas and carbon compensation zones each account half of the total area. (4) Under different scenarios, the carbon balance of built land has been partially mitigated, and the overall trend is developing favorably.