Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China

Yu Lin,Wenhui Chen,Junchang Liu

doi:10.3390/su13094687

Abstract

Forestry output efficiency is key to forestry development. China is now promoting the development of forestry, and thus the research on forestry output efficiency is of practical significance. Through the data envelopment analysis (DEA)-Malmquist index, spatial autocorrelation model, and fixed effect model of panel data, in this study, we analyzed the forestry output efficiency of China with indicators, such as the fixed asset input, employed personnel, total output value, and timber output, and drew the following conclusions. In the time series, the forestry total-factor productivity (TFP) in China saw a rapid increase, which is attributed to the technological progress change (TC), whereas the efficiency change (EC) imposed negative influences upon the forestry TFP. In the spatial distribution, there was a difference in the increase in the forestry output efficiency among the eastern, central, and western regions of China, with the eastern region having the fastest growth and the central region having the slowest growth. According to the spatial autocorrelation, there was spatial aggregation (high–high (HH) and low–low (LL)) with a significant positive correlation. Through the optimized fixed effect regression model, the fixed asset input, employed personnel, total output value, and timber output all had significant influences on the comprehensive technical efficiency of the forestry output, wherein the input indicators had negative influences, and the output indicators had positive influences.

Highlights

Forestry has always been a pillar for social and economic development, especially in the primary stage of modernization, and the complexity of forestry resources leads to the diversity of forestry output
Research has been conducted on the national forest output efficiency, with focuses on specific provinces, such as measurement of the forestry output efficiency, pure technical efficiency and scale efficiency of 21 prefecture-level cities in Guangdong Province [4], the assessment of the forestry output efficiency of Anhui Province from 1998 to 2013 [5], and the analysis of forestry output efficiency of Gansu Province [6] and Heilongjiang Province [7]
As for temporal analysis, the data envelopment analysis (DEA)-Malmquist index was used to analyze the temporal fluctuations of forestry input and output during the 20 years; the spatial autocorrelation model and fixed effects model were used to explore the spatial interrelation of the 31 provinces so as to unveil the national geographical aggregation of forestry output efficiency

Summary

Introduction

Forestry has always been a pillar for social and economic development, especially in the primary stage of modernization, and the complexity of forestry resources leads to the diversity of forestry output. The research on forestry input and output are important to sustainable forestry development and related to stable social and economic development. The research on forestry output efficiency, with a special focus on spatial and temporal distribution as well as influencing factors, is crucial to the enhancement of the economic benefit of forestry and the high-quality development of forestry in China. Concerning the forestry output efficiency of China, a number of studies have been conducted in the field of forest management and forest engineering [1], total factor productivity of forestry output efficiency [2], as well as sustainable forest development [3]. Research has been conducted on the national forest output efficiency, with focuses on specific provinces, such as measurement of the forestry output efficiency, pure technical efficiency and scale efficiency of 21 prefecture-level cities in Guangdong Province [4], the assessment of the forestry output efficiency of Anhui Province from 1998 to 2013 [5], and the analysis of forestry output efficiency of Gansu Province [6] and Heilongjiang Province [7]

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Methods

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Conclusion