Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Abstract

Forests play a very important role in carbon dioxide emissions and climate change, and the development of China’s forestry is of great significance to our citizens. However, it is an arduous task for us to improve forestry output at a high and good level while taking environmental factors into account. In this paper, the non-expected super-efficiency SBM (slacks-based measure) model was used to measure the forestry ecological efficiency (FEE) of 31 provinces in China from 2004 to 2018, and the spatial and temporal evolution of FEE in different regions of China was analysed by using spatial econometric method. Tobit regression and random forest algorithm were selected to analyze the influence on FEE. The results showed that, firstly, the average annual increase of the national total factor productivity change of China’s forestry was 1.2%, and that the average annual increase of the national total factor productivity change in the eastern region was lower than that in the central and western regions. Secondly, the distribution of China’s FEE of the northeast and the south was higher, and FEE of China’s middle regions was relatively lower in 2004, but then the FEE in Northeast China has decreased, and the FEE has increased gradually from north to south in 2018. The agglomeration of high-tech industries in most regions of China had obvious positive spatial correlation characteristics in 2018. Thirdly, there was a negative correlation between forestry fixed assets investment and FEE, environmental regulation was an important factor affecting the ecological efficiency of forestry in China, and the level of economic development and industrial structure also had a certain impact on FEE.

Economic Efficiency of Forest Enterprises—Empirical Study Based on Data Envelopment Analysis

“…In many cases, researchers focused mainly on operational efficiency of forests [7][8][9]. When it comes to forestry efficiency many authors involved ecological [10], socio-eco-efficiency [11,12], climate change [13][14][15], ecosystem services [16,17], bio-based economy [18,19], forest certification [20][21][22][23], and forest offices organizational structure [24]. The papers on the current topic appear to broadly cover the problem of forestry efficiency.…”

Efficiency Evaluation and the Impact Factors of Sustainable Forestry Development in China: Adoption of Super-Efficiency Data Envelopment Analysis and Malmquist Index Methods

“…Additionally, studies have applied parametric and non-parametric approaches to quantify productivity growth, efficiency, and outsourcing in manufacturing and service industries in the context of static, dynamic, and firm-specific modeling [5,33,81,82]. Their study revealed efficient methodologies for measuring productivity [83].…”

“…The super-efficiency DEA model can be divided into the CCR model with a CRS and the BCC model with a VRS. The CCR model is a recognized assumption for measuring the overall efficiency of a DMU [28,39,82,97]. Thus, changes in scale and technical efficiency marginally affect the actual output.…”

Assessing the Technical Efficiency of Timber Production during the Transition from a Production-Oriented Management Model to a Multifunctional One: A Case from Poland 1990–2019

“…The usefulness of DEA for evaluating management efficiency was analyzed by Shiba [17] using the example of Japanese forests. DEA is widely used for measuring eco-efficiency by analyzing both the desirable and undesirable outputs of forest enterprises [18][19][20][21]. Within the forest industry, Shasi and Dia [22] used DEA to measure sawmill efficiency in Ontario; it was also employed by Diaz-Balterio et al [23] to study Spain's wood-based industry.…”