Study On Index System Design And Efficiency Evaluation Of Forest Carbon Sink Driven By Data

Since the term "carbon sink" was introduced,it has become a hot issue of concern in the world.As the largest carbon reservoir in the terrestrial ecosystem,forests have a significant effect on reducing the concentration of greenhouse gases in the atmosphere and slowing down global warming.Achieving "peak carbon and carbon neutrality" is China’s solemn commitment to actively respond to global climate change and a major strategy to integrate economic and social development with ecological civilization construction.Therefore,it is of theoretical and practical significance to reasonably measure the efficiency and total factor productivity of China’s forest carbon sinks,to explore the internal causes of inefficiency and total factor productivity regression,and to optimize resource allocation to promote the green and sustainable development of China’s forests.The data envelopment analysis(DEA)method is the most widely used non-parametric efficiency analysis method by experts and scholars in various fields,so this paper adopts the DEA-based method and its extensions to measure the efficiency of forest carbon sinks.This paper first constructs an evaluation index system with human,capital and land as input indicators and forest carbon sink as output indicators to measure the efficiency of forest carbon sink and total factor productivity in China during 2013-2019 for evaluation and analysis.On this basis,the future changes of input indicators are measured by a gray prediction model,theorems are given to illustrate the necessary conditions for the increase of carbon sink output,and the inverse DEA model is used to predict the value of carbon sink increase.Finally,considering the comprehensiveness of evaluation indicators,we read widely the literature and collect corresponding indicators to construct a multi-level complex system evaluation indicator system.Meanwhile,to avoid distortion of efficiency evaluation caused by too many indicators,the Evidence-based Reasoning(ER)model is used to simplify the indicators;considering that the green development of forest carbon sinks needs to be evaluated from multiple perspectives,five types of cross-efficiency matrices are defined for sustainability economic,social,ecological benefits and comprehensive evaluation,and the ordered conditional entropy theory is introduced to construct a new cross-efficiency matrix that considers both self-evaluation order information and cross-evaluation order information The ordered conditional entropy cross-efficiency aggregation method is constructed by considering both self-evaluation sequential information and cross-evaluation sequential information,proving its effectiveness by comparison and giving an empirical study.The research results are obtained as follows:(1)The results of DEA model and Malmquist index show that the efficiency of forest carbon sink in China is gradually improving,with obvious differences in efficiency among provinces,especially in areas with obvious differences in forest resources.The improvement of technical efficiency drives up the total factor productivity.The factor allocation structure of each province needs to be optimized.(2)The results of the inverse DEA model show that there is still more room to improve the efficiency of forest carbon sinks in China,and the development of each region is uneven and uncoordinated,and the waste of resources is more serious,so better allocation of resources is needed.(3)The results of multi-perspective and multi-level cross-efficiency evaluation show that the development of green regions in China’s forestry is unbalanced and varies greatly in different dimensions,so it is necessary to clarify the development of weak rings and determine the development path in a targeted manner according to the situation of each province.The main innovation points of this paper are as follows:(1)To construct a perfect multi-input and multi-output forest carbon sink efficiency evaluation index system to ensure the comprehensiveness of the evaluation perspective.At the same time,in order to avoid the complicated calculation caused by too many indicators and the situation that the results of each subject tend to be too close to 1,the ER method is used to simplify the indicators and form a comprehensive output indicator.(2)The optimization of efficiency and the rationality of resource allocation are the guarantee for the long-term sustainability of forest carbon sink development.Therefore,from the perspective of efficiency,the theorem is given to judge the necessary conditions for the increase of carbon sink under the premise of ensuring the priority of efficiency.Considering the rationality of resource input allocation,the input and output of resources are reverse optimized to guide the optimal allocation of carbon sink resources among provinces.(3)From multiple perspectives such as incentive,neutrality,and benevolence,reflecting evaluation differences and decision makers’ preferences,the self-evaluation and mutual evaluation of multiple efficiency subjects are cross-considered.Considering also the optimal weight information used for self-evaluation in the crossover model,a new ordered conditional entropy(OCE)crossover efficiency aggregation method is proposed and its effectiveness is verified in defining the crossover efficiency conditional entropy with the self-evaluation results as the conditional attribute set and the crossover evaluation results as the decision attributes.