[Measurement of Efficiency， Total Volume Prediction， and Resource Allocation for China's Forest Carbon Sink].
摘要
In the context of the challenge posed by climate change and the pursuit of the "dual carbon" goals， accurately assessing the efficiency of forest carbon sinks in various regions， identifying the reasons behind efficiency differences， and accordingly proposing effective resource allocation pathways are of great significance for promoting coordinated regional development， improving resource utilization efficiency， and achieving carbon sequestration and emission reduction. Based on provincial data from China spanning from 2004 to 2021， a three-stage DEA model was constructed to measure the forest carbon sink efficiency of 30 provinces and municipalities， and a regional difference analysis was conducted. Simultaneously， a prediction model based on GAN and KOA-CNN-BiLSTM-Attention was established to forecast the total carbon sink targets that each region intends to achieve by 2030， and scenarios were set up to enhance the accuracy of the model. Based on the three-stage theory， further improvements were made to the inverse DEA model to discuss the resource allocation path planning schemes for various regions to achieve their predicted carbon sink targets. In particular， feedback was provided on the current input redundancies and deficiencies in each region. The results showed that： ① Although China's forest carbon sink efficiency showed a trend of growth， it was still at a moderately low level overall. Moreover， there were significant regional differences. Regions with higher efficiency levels were mainly distributed in the southwest and northeast， while those in North China had lower levels and require attention. The remaining regions fell in between and still had room for development. ② The core reasons for the differences in forest carbon sink efficiency among regions were natural resource endowments （including forest resources and natural conditions） and the degree of concern for forestry （including policy support and resource allocation preferences）. Scientific manag