Efficiency Evaluation of Sustainable Forestry Development: Comparison
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Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Fredrick Oteng Agyeman.

Forestry is the underpinning of economic and environmental civilization for sustainable economic development. Forestry benefits ecosystems and local dwellings; thus, transforming and advancing forest products in a civilized society is critical to building a progressive community.

 

  • total factor productivity
  • forestry listed company

1. Introduction

Forestry is the foundation of ecological civilization construction for the sustainable development of economies [1,2,3,4,5][1][2][3][4][5]. Forestry enhances the environment and human habitation [2,6][2][6]. Thus, for economic growth and the realization of balanced forest development, the environment, and the global economy, it is imperative to comprehend the current state of the forestry industry and its accompanying resource utilization in China and accurately assess the forestry industry’s performance [7,8,9][7][8][9]. Forestry’s eco-friendly mechanism is the foundation of sustainable socioeconomic development [5,10][5][10]. As an essential industry, forestry significantly contributes to ecological construction, climate maintenance, and forest product supply for a sustainable environment [11,12,13][11][12][13]. Therefore, promoting a healthy forest is crucial for building a well-off society in an all-encompassing way [14,15][14][15]. The transformation of ecological advantages into economic advantages to achieve greening and ecological productivity has been critical for achieving economic growth [16,17][16][17]. 

2. Efficiency Evaluation of Sustainable Forestry Development

In assessing the improvement and management of enterprises and forestry industries, many researchers have attached great importance and focused on evaluating and identifying the feasible factors in improving forestry management and related enterprises by adopting several indicators and methodologies for analysis. The summary of gaps in the literature conducted on forestry industries and firms’ efficiency performance through countrywide analysis based on methods, variables used for analysis, and key findings from previous studies, is demonstrated in Table 1. It is apparent that studies have been conducted on forestry product consumption and management at the international and domestic levels, which furnishes an excellent foundation for further studies to enhance the efficiency measurement of forestry industries in China. 
Table 1.
Comparative literature review and study gap summary analysis.
Literature Region/Country Methodology Used Variables Analyzed Key Related Findings
[1] Congo Contribution analysis approach Timber organizations, government agencies, and NGOs Contribution analysis is critical for tracing several causal pathways through a web of interactions for marginal and indirect contributions.
[3] Central Italy Conservation methods Multi-span greenhouse The conservation approach is suitable for sustainable building systems, energy efficiency, and reduction in energy consumption.
[4] China The extended economic model and space econometrics Forestry products,

Labor and Capital		 The study revealed a statistically significant spatial correlation in China’s forestry products.
[6] China Terrestrial laser scanning and hemispherical photography Forest canopy,

woods		 The findings revealed the ability of terrestrial-based algorithms to capture the leaf area index of forest stands at varying densities.
[7] China Entity value input-output models, Forest resource input-output model Paper products, furniture, and other timber products The study showed that the demand for forest products differs significantly across industries in China.
[12] Pakistan Interview, questionnaire survey, textbooks, and Internet materials Forest products: timber and fuelwood The study showed that forest is beneficial in controlling erosion, improving aesthetic beautification, and regulating temperature.
[27][18] China Grey relation analysis and DEA approach Forest products The findings revealed the average comprehensive efficiency of fourteen companies represented by 93% and 7% of waste resources.
[28][19] Portugal DEA and Malmquist’s index models Technology firms of Madan Parque DEA and Malmquist index models are relevant for measuring firms’ efficiency and productivity change.
[29][20] China Three-stage DEA and stochastic frontier analysis Cultural and creative industries Three-stage DEA and stochastic frontier methods are critical for analyzing enterprises’ efficiencies.
[57][21] United Kingdom Case study Forest products, ecosystem, and services The study revealed that improving our comprehension of human-modified tropical forest ecology with microclimate knowledge supports ecosystem conservation and restoration.
[58][22] Sweden Survey and triangulation approach Forest products Rehabilitation in forest management improves the mental and physical health of individuals.
[59][23] China Case study Forest products, landscape, and ecosystem dimensions Urban forestry is an approach to adapting natural resource management.
[60][24] Tennessee Harvesting and transportation survey Forest products The research findings reveal the variations in the input used for costing harvesting operations and the difficulty of harvesting systems.
[61][25] United States of America (USA), Canada, Taiwan Data envelopment analysis and stochastic frontier analysis Forestry product data from Web of Science The study revealed that forest industries are interested in competitive benchmarking, while forest management organizations focused on applied benchmarking for internal analysis.
[62][26] United States of America Network data envelopment analysis and slacked-based network model of super efficiency Data from real commercial banks in the USA Network DEA has the propensity to rank efficient DMUs than the conventional DEA.
[63][27] China Super efficiency slacked-based measure model Forestry ecological efficiency, forestry fixed assets The findings demonstrated China’s forestry ecological efficiency is low in the northeast but increased in the south of China.
Furthermore, the embodiment of forest ecology is vital for the growth and functioning of the forest ecosystem, controlling the microclimate and water balance, and providing habitats for organisms [57][21]. Within a forest ecosystem, forest ecology helps establish the science of how organisms interact with the environment. Forest ecology and diversity play a key role in enhancing human activities, such as teaching and recreation in a forest environment [58][22]. Forestry resource management techniques have incorporated interdisciplinary, multifaceted, and tremendous technological advancements in controlling hazardous activities that endanger the environment [59,64][23][28]. Thus, the long-term supply of forest products and their measurement process, including timber and pulpwood, are critical components in determining the long-term profitability of forest operations and performance [60,65,66][24][29][30]. To reconcile environmental protection with economic development goals, policymakers have prioritized the ecological modernization concept of developing green infrastructure in strategic spatial plans as a potential for the growth of forestry enterprises while employing diverse methodologies for analysis [61,67,68,69,70][25][31][32][33][34]. Hence, extant studies have indicated the broad application of using different methodologies to measure the operating performance of listed companies at the industrial level: gray correlation, factor analysis, and DEA [62,71,72,73,74,75][26][35][36][37][38][39]. For instance, in determining the operational performance of forestry companies in China and the five critical factors affecting profitability, asset operation capacity, growth capacity, debt repayment capacity, and equity expansion capacity, the factor analysis and DEA methods were employed for analysis [76][40]. Additionally, factor analysis has been used to evaluate the comprehensive performance of 22 listed companies in China’s small and medium enterprises by ranking the companies’ performance based on average scores [62][26]. Furthermore, the super-efficiency DEA method has been utilized to evaluate management performance in listed logistics companies to counter the inherent limitations of the traditional DEA method [63][27]. Some studies have also applied the value-added economic approach to assess the performance of indigenous industries. The improved evaluation model supports their findings in determining the operating performance of listed companies in Shanghai and Shenzhen [77,78][41][42]. Recent tourism and forest assessment investigations have applied a new set of DEA approaches, including dynamic network data envelopment analysis and microdata [79,80][43][44]. 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][5][45][46][47]. Their study revealed efficient methodologies for measuring productivity [83][48]. Again, research has demonstrated that integrating the SFA and DEA methodologies to measure enterprises’ total factor productivity (TFP) helps countercheck whether the findings obtained can be verified [84,85][49][50]. Nevertheless, these studies indicated unfavorable and uncoordinated industry development compared with development capacity and operational level, profitability and solvency, and insufficient debt financing capacity. It has also been established that employing the super efficiency DEA model with the Malmquist index methodology to analyze the overall operating performance of listed forestry companies from a dynamic and static perspective furnishes credible and accurate findings [26][51]. Again, the gray correlation and the DEA method were used to measure listed forestry companies’ input and output indicators to determine their performance [27][18]. The study’s findings reveal that forestry companies must improve their efficiency to remain competitive in a sluggish market environment and to reduce ineffective resource utilization [65,86][29][52]. Research has indicated that establishing relationships between research variables requires the application of regression models that provide accurate information about the connection linking single or multiple independent variables and a target variable [5,8,9,87,88][5][8][9][53][54]. Furthermore, research has demonstrated that applying truncated regression methodologies, such as the Tobit regression and DEA model, to investigate the influencing elements of enterprises’ performance yields accurate and precise findings [5]. Thus, advanced regression models help analyze multiple samples, achieve consistency in estimations, and identify the disparities between variables compared to conventional regression approaches [5]. The DEA-Tobit model was used to evaluate the pharmaceutical, sports, machinery and equipment, agriculture, food, and beverage industries in China, and it revealed dynamic results [89,90,91][55][56][57]. Moreover, factor analysis was employed to measure listed forestry companies’ profitability, debt servicing, operation, and development [92][58]. From the aforementioned literary works, there is evidence that some scholars have used many methodologies to evaluate the operating performance of listed forestry companies [62,89,93,94,95][26][55][59][60][61]. Thus, factor analysis and DEA are the most frequent performance evaluation methods applied to test listed forestry companies. In addition, most results were inconsistent. The factor analysis and traditional DEA methods have limitations to a certain extent. The factor analysis method uses financial data for evaluation based on economic indicators, primarily to compress most information from multiple indicators into fewer indicators to measure the enterprise’s comprehensive performance. The conventional DEA approach can select a representative sample of input-output indicators based on specific research purposes [28,87,96][19][53][62]. It can only estimate the potential of particular aspects of enterprises. Furthermore, the traditional DEA evaluation method can only be analyzed from a static perspective and cannot reflect the dynamic development trends of the entire industry. Thus, studies have shown that multiple decision-making units would be relatively effective simultaneously when the super-efficient DEA model is applied [26][51]. Research also suggests that the SFA model is adequate to obtain the robustness of the conventional and super-efficient DEA methods [29][20].

 

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