Efficiency Evaluation of a Forestry Green Economy under a Multi-Dimensional Output Benefit in China—Based on Evidential Reasoning and the Cross Efficiency Model

Abstract

The efficiency evaluation of forestry green economy development is related to the direction of forestry development and plays an important role in balancing the economic and environmental issues within that forestry development. The existing research faces three challenges: first, the output indicator is singular; second, the perspective of a self-assessment is extremely limited; and third, the multi perspective fusion method is not in line with the mechanism of the cross efficiency evaluation model. To address these challenges and the characteristics of forestry development output, we constructed multi-level output indicators from four aspects: ecology, economy, society, and sustainability and used evidence reasoning to combine the output indicators. Based on the perspective of a cross evaluation among peers, four different cross efficiency values are defined from the evaluation relationship between the different decision-making units to obtain economic–aggressive, social–neutral, ecological–benevolent, sustainable–neutral, and comprehensive–neutral cross efficiencies. According to the relationship between self- and cross evaluation, an order conditional entropy cross efficiency aggregation model has been proposed and used to analyze the development efficiency of the forestry green economy in 31 Chinese provinces in 2019. Considering the uneven distribution of the forestry resources in China, the development in the 31 provinces and cities is divided into four types by discussing the relationship between the output indicators and efficiency, while the reasons for the unbalanced development and the poor comprehensive development are discussed according to five cross efficiencies.

Study on Spatial Spillover Effect of Forest Carbon Sink Efficiency in China considering Multiple Output Benefits

“…Considering that data-driven-oriented information fusion methods are more objective, scholars combine ER with DEA to synthesize indicators using mathematical rationalization, which can reduce indicators while preserving as much original information as possible. Subsequently, DEA models were used to evaluate the efficiency of the fused indicators [5,6,13].…”

“…Fewer studies in the existing literature construct a forest carbon sink indicator system through sub-dimensions from a comprehensive perspective, and the forest carbon sink indicator system constructed by Qiu et al [30] only takes forest carbon sink as an output indicator, while the other indicators are used as environmental variables aimed at regulating the forest carbon sink. In the indicator system constructed by Huang et al [13], the forest carbon sink indicator is categorised as an indicator under the ecological dimension. The indicator system constructed by the above study may not accurately reflect the full benefits brought by forest carbon sinks.…”

“…Third, the efficiency measurement before indicator fusion in this paper can maximise the retention of the efficiency information of the indicators. The traditional method of measuring the efficiency of a multi-dimensional indicator system is generally to integrate the indicators first and then measure the efficiency [5,6,13], which will lose part of the indicator efficiency information and can only get the final efficiency results, which is not conducive to the development of specific decision-making programmed. In this paper, we first calculated the cross-efficiency of the three-level indicators, and based on this cross-efficiency, we used the ER method to integrate the indicators level by level.…”

“…Moreover, most of the traditional methods for measuring the efficiency of forestry systems are based on the self-assessment perspective, which leads to the decision-making unit (DMU) exaggerating its own efficiency [11,12]. Moreover, most efficiency evaluation studies of multilevel systems perform indicator fusion followed by efficiency evaluation, which can lead to partial loss of efficiency information of the original indicators [13].…”

Forestry Resource Efficiency, Total Factor Productivity Change, and Regional Technological Heterogeneity in China

“…It shows a 12% increase in the economically viable harvested forest area if applied nationwide. Further, numerous studies have used DEA to evaluate the forestry efficiency in different regions and countries [23][24][25][26][27][28][29].…”