Spatial-Temporal Evolution And Influencing Factors Of Green Total Factor Productivity In Forestry In China

As an important part of the national economy,forestry has made outstanding contributions to national economic growth and ecological construction.China’s forestry economic output value is increasing,forest resources are increasingly rich,however,forestry development also has the phenomenon of insufficient technological innovation,low production efficiency,and lack of professional quality and skills of employees.At the same time,natural disasters such as forest fires and forest diseases and pests have caused the loss of forestry value,and the development of manufacturing industries such as forest products industry has brought about problems such as large-scale forest felling and production waste discharge,which will also cause damage to the ecological environment balance.At present,forestry development should not only overcome its own production efficiency and other problems,but also achieve the goal of harmonious coexistence with natural ecology in the era when "green" continues to become the mainstream of development.In order to understand the current situation of forestry development in China and the development problems encountered,this paper uses the ultraefficient SBM model to measure the green total factor productivity of forestry,and comprehensively evaluates the economic and ecological benefits of forestry development.The temporal and spatial evolution law of green total factor productivity of forestry was excavated by using the Thiel index,kernel density estimation,Moran index and Markov transfer,and a panel regression model and spatial econometric model were constructed to explore the influence of various factors on the green total factor productivity of forestry,and finally put forward policy suggestions conducive to improving the green total factor productivity of forestry.In order to deeply explore the green total factor productivity of forestry in China,this paper sorted out and summarized the relevant research literature at home and abroad,and confirmed the necessity of the research topic in this paper.This paper was based on qualitative methods to summarize the relevant conceptual theories and analyze the input and output of forestry green total factor productivity,and set the basic ideas of this study.The ultra-efficient SBM model was used to calculate the green total factor productivity of forestry in 30 provinces(municipalities and autonomous regions)in China from 2004 to 2020,and it was found that the green production efficiency of forestry in China had improved.On this basis,the evolution of forestry green total factor productivity differences between regions was analyzed by using the Thiel index and kernel density estimation,and it was concluded that the differences in green total factor productivity of forestry between regions tended to be stable,but polarization continued to emerge.The spatial autocorrelation analysis of green total factor productivity of forestry found that there was a spatial positive autocorrelation.At the same time,the probability of short-term and long-term Markov transfer of forestry green total factor productivity is measured,and it is found that its state transition has certain sustainability and spatial spillover benefits in both the long and short term,and the state transfer mobility was weak.For exploring the relevant influencing factors of forestry green total factor productivity deeply,the panel regression model and the spatial econometric model were constructed for analysis,and it was found that the economic development level,technological innovation,market value of forestry practitioners and forest pest control rate had a positive impact on forestry green total factor productivity,and the influence effect of forestry industry structure showed a nonlinear structure,and there was a certain spatial spillover effect in the spatial range.At the same time,panel regression models and spatial econometric regression models were constructed for different economic regions,and the regional heterogeneity of the influencing mechanism of each factor was analyzed.Finally,targeted policy recommendations were put forward based on the research results.