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Li, M.; Wang, X.; Agyeman, F.O.; Gao, Y.; Sarfraz, M. Efficiency Evaluation of Sustainable Forestry Development. Encyclopedia. Available online: (accessed on 13 April 2024).
Li M, Wang X, Agyeman FO, Gao Y, Sarfraz M. Efficiency Evaluation of Sustainable Forestry Development. Encyclopedia. Available at: Accessed April 13, 2024.
Li, Mingxing, Xinxing Wang, Fredrick Oteng Agyeman, Ya Gao, Muddassar Sarfraz. "Efficiency Evaluation of Sustainable Forestry Development" Encyclopedia, (accessed April 13, 2024).
Li, M., Wang, X., Agyeman, F.O., Gao, Y., & Sarfraz, M. (2023, May 18). Efficiency Evaluation of Sustainable Forestry Development. In Encyclopedia.
Li, Mingxing, et al. "Efficiency Evaluation of Sustainable Forestry Development." Encyclopedia. Web. 18 May, 2023.
Efficiency Evaluation of Sustainable Forestry Development

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]. Forestry enhances the environment and human habitation [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]. Forestry’s eco-friendly mechanism is the foundation of sustainable socioeconomic development [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]. Therefore, promoting a healthy forest is crucial for building a well-off society in an all-encompassing way [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]

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.
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 [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 [22]. Forestry resource management techniques have incorporated interdisciplinary, multifaceted, and tremendous technological advancements in controlling hazardous activities that endanger the environment [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 [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 [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 [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 [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 [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 [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 [41][42]. Recent tourism and forest assessment investigations have applied a new set of DEA approaches, including dynamic network data envelopment analysis and microdata [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][45][46][47]. Their study revealed efficient methodologies for measuring productivity [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 [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 [51]. Again, the gray correlation and the DEA method were used to measure listed forestry companies’ input and output indicators to determine their performance [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 [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][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 [55][56][57]. Moreover, factor analysis was employed to measure listed forestry companies’ profitability, debt servicing, operation, and development [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 [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 [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 [51]. Research also suggests that the SFA model is adequate to obtain the robustness of the conventional and super-efficient DEA methods [20].


  1. Delahais, T.; Toulemonde, J. Making rigorous causal claims in a real-life context: Has research contributed to sustainable forest management? Evaluation 2017, 23, 370–388.
  2. Duan, Q.; Kan, L.; Tsai, S.-B. Analysis on Forestry Economic Growth Index Based on Internet Big Data. Math. Probl. Eng. 2021, 2021, 2286629.
  3. Marucci, A.; Carlini, M.; Castellucci, S.; Cappuccini, A. Energy Efficiency of a Greenhouse for the Conservation of Forestry Biodiversity. Math. Probl. Eng. 2013, 2013, 768658.
  4. Wang, G.; Chen, J.; Deng, X. Modelling Analysis of Forestry Input-Output Elasticity in China. Int. J. For. Res. 2016, 2016, 4970801.
  5. Zhu, C.; Zhu, N.; Shan, W.U.H. Eco-Efficiency of Industrial Investment and Its Influencing Factors in China Based on a New SeUo-SBM-DEA Model and Tobit Regression. Math. Probl. Eng. 2021, 2021, 5329714.
  6. Yun, T.; Li, W.; Sun, Y.; Xue, L. Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography. Math. Probl. Eng. 2015, 2015, 206108.
  7. Chen, W.; Xu, D.; Liu, J. The forest resources input-output model: An application in China. Ecol. Indic. 2015, 51, 87–97.
  8. Oliveira, G.M.V.; de Mello, J.M.; de Mello, C.R.; Scolforo, J.R.S.; Miguel, E.P.; Monteiro, T.C. Behavior of wood basic density according to environmental variables. J. For. Res. 2021, 33, 497–505.
  9. Li, M. Carbon stock and sink economic values of forest ecosystem in the forest industry region of Heilongjiang Province, China. J. For. Res. 2021, 33, 875–882.
  10. Pan, X.-C. Research on Ecological Civilization Construction and Environmental Sustainable Development in the New Era. IOP Conf. Ser. Earth Environ. Sci. 2018, 153, 062080.
  11. Danso Marfo, T.; Datta, R.; Vranová, V.; Ekielski, A. Ecotone Dynamics and Stability from Soil Perspective: Forest-Agriculture Land Transition. Agriculture 2019, 9, 228.
  12. Ullah, S.; Noor, R.S.; Abid, A.; Mendako, R.K.; Waqas, M.M.; Shah, A.N.; Tian, G. Socio-Economic Impacts of Livelihood from Fuelwood and Timber Consumption on the Sustainability of Forest Environment: Evidence from Basho Valley, Baltistan, Pakistan. Agriculture 2021, 11, 596.
  13. Brack, D. Sustainable Consumption and Production of Forest Products; United Nations Forum on Forests: New York, NY, USA, 2018; pp. 1–74.
  14. Selecky, T.; Bellingrath-Kimura, S.D.; Kobata, Y.; Yamada, M.; Guerrini, I.A.; Umemura, H.M.; Dos Santos, D.A. Changes in Carbon Cycling during Development of Successional Agroforestry. Agriculture 2017, 7, 25.
  15. Shao, Q.; Janus, T.; Punt, M.J.; Wesseler, J. The Conservation Effects of Trade with Imperfect Competition and Biased Policymakers. Agriculture 2018, 8, 108.
  16. Agyeman, F.O.; Zhiqiang, M.; Li, M.; Sampene, A.K.; Dapaah, M.F.; Kedjanyi, E.A.; Buabeng, P.; Li, Y.; Hakro, S.; Heydari, M. Probing the Effect of Governance of Tourism Development, Economic Growth, and Foreign Direct Investment on Carbon Dioxide Emissions in Africa: The African Experience. Energies 2022, 15, 4530.
  17. Niedermaier, K.M.; Atkins, J.W.; Grigri, M.S.; Bond-lamberty, B.; Gough, C.M. Structural complexity and primary production resistance are coupled in a temperate forest. Front. For. Glob. Chang. 2022, 5, 941851.
  18. Li, Y.X.; Zhang, Z.G. Performance Evaluation and Optimization of Listed Forestry Companies in China—Based on GRA Index Screening and Data Envelopment Analysis. For. Econ. 2019, 9, 60–66.
  19. Grilo, A.; Santos, J. Measuring efficiency and productivity growth of new technology-based firms in business incubators: The portuguese case study of madan parque. Sci. World J. 2015, 2015, 936252.
  20. Li, M.; Sun, H.; Agyeman, F.O.; Su, J.; Hu, W. Efficiency Measurement and Heterogeneity Analysis of Chinese Cultural and Creative Industries: Based on Three-Stage Data Envelopment Analysis Modified by Stochastic Frontier Analysis. Front. Psychol. 2022, 12.
  21. Jucker, T.; Jackson, T.D.; Zellweger, F.; Swinfield, T.; Gregory, N.; Williamson, J.; Slade, E.M.; Phillips, J.W.; Bittencourt, P.R.L.; Blonder, B.; et al. A Research Agenda for Microclimate Ecology in Human-Modified Tropical Forests. Front. For. Glob. Chang. 2020, 2, 92.
  22. Nordh, H.; Grahn, P.; Währborg, P. Meaningful activities in the forest, a way back from exhaustion and long-term sick leave. Urban For. Urban Green. 2009, 8, 207–219.
  23. Pei, N.; Wang, C.; Sun, R.; Xu, X.; He, Q.; Shi, X.; Gu, L.; Jin, J.; Liao, J.; Li, J.; et al. Towards an integrated research approach for urban forestry: The case of China. Urban For. Urban Green. 2019, 46, 126472.
  24. Abbas, D.; Hodges, D.; Heard, J. Costing the forest operations and the supply of hardwood in Tennessee. Croat. J. For. Eng. 2019, 40, 49–54.
  25. Strange, N.; Bogetoft, P.; Aalmo, G.O.; Talbot, B.; Holt, A.H.; Astrup, R. Applications of DEA and SFA in benchmarking studies in forestry: State-of-the-art and future directions. Int. J. For. Eng. 2021, 32, 87–96.
  26. Lun, R.; Jin-lin, L. The Application of Factor Analysis in the Evaluation of Comprehensive Performance of Listed Companies on the Small and Medium-sized Enterprises Board. Appl. Stat. Manag. 2005, 1, 75–80.
  27. Huang, Z.; Zhang, B. Performance evaluation of listed logistics companies in China. In Proceedings of the Statistics and Decision, Piscataway, NJ, USA, 6–12 May 2007; IEEE: Piscataway, NJ, USA, 2007; pp. 83–85.
  28. Zhang, M.; Li, M.; Sun, H.; Agyeman, F.O. Investigation of Nexus between Knowledge Learning and Enterprise Green Innovation Based on Meta-Analysis with a Focus on China. Energies 2022, 15, 159.
  29. Rudinskaya, T.; Boskova, I. Asymmetric price transmission and farmers’ response in the Czech dairy chain. Agric. Econ.-Czech 2021, 2021, 163–172.
  30. Lemm, R.; Blattert, C.; Holm, S.; Bont, L.; Thees, O. Improving economic management decisions in forestry with the sorsim assortment model. Croat. J. For. Eng. 2020, 41, 71–83.
  31. Grădinaru, S.R.; Hersperger, A.M. Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban For. Urban Green. 2019, 40, 17–28.
  32. Dong, P.; Zhuang, S.; Lin, X.; Zhang, X. Economic evaluation of forestry industry based on ecosystem coupling. Math. Comput. Model. 2013, 58, 1010–1017.
  33. Li, L.; Hao, T.; Chi, T. Evaluation on China’s forestry resources efficiency based on big data. J. Clean. Prod. 2017, 142, 513–523.
  34. Viitala, E.-J.; Hänninen, H. Measuring the efficiency of public forestry organizations. For. Sci. 1998, 44, 298–307.
  35. Liu, Y.; Zhang, L.; Xue, Y. Performance evaluation of Chinese listing corporation based on DEA—Agricultural listing corporation as an example. In Proceedings of the 2014 International Conference on Management Science & Engineering 21st Annual Conference Proceedings, Helsinki, Finland, 17–19 August 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1462–1468.
  36. Sheng Bao, J.; Xiaoke, Z.; Haibin, Z. Performance evaluation and influencing factors of China’s machinery and equipment industry—Based on the super-efficient DEA-Tobit model. J. Shanxi Univ. Financ. Econ. 2011, 33, 64–71.
  37. Liu, L.; Zhan, X. Analysis of financing efficiency of Chinese agricultural listed companies based on machine learning. Complexity 2019, 2019, 9190273.
  38. Tian, S.Y.; Xu, W.L. Evaluation of China’s forestry input-output efficiency based on DEA modeling. Resour. Sci. 2012, 34, 1944–1950.
  39. Golshani, H.; Khoveyni, M.; Valami, H.B.; Eslami, R. A slack-based super efficiency in a two-stage network structure with intermediate measures. Alex. Eng. J. 2019, 58, 393–400.
  40. Biao, X.; Fengjun, L.; Zetian, F. Evaluation on Business Performance and Efficiency of Agriculture Industry. Agric. Technol. Econ. 2000, 4, 36–39.
  41. Wang, P.X.; Lin, C.; Li, B.X. Study on the Integrated EVA Performance Measurement of Listed Companies. Appl. Stat. Manag. 2006, 25, 186–194.
  42. Zheng, R. Analysis of Performance Evaluation of China’s Agricultural Listed Companies: Based on the Perspective of EVA model. Agric. Technol. Econ. 2011, 194, 95–102.
  43. Huang, X.-J.; An, R.; Yu, M.-M.; He, F.-F. Tourism efficiency decomposition and assessment of forest parks in China using dynamic network data envelopment analysis. J. Clean. Prod. 2022, 363, 132405.
  44. An, R.; Huang, X. Forest park Efficiency and Influencing Factors in Fujian Province-Based on Dynamic Network DEA and Micro Data. For. Chem. Rev. 2021, 42, 1510–1524.
  45. Alqahtani, F.; Mayes, D.G.; Brown, K. Islamic bank efficiency compared to conventional banks during the global crisis in the GCC region. J. Int. Financ. Mark. Inst. Money 2017, 51, 58–74.
  46. Toloo, M.; Nalchigar, S. A new integrated DEA model for finding most BCC-efficient DMU. Appl. Math. Model. 2009, 33, 597–604.
  47. Chen, S.; Yao, S. Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces. Forest 2021, 12, 142.
  48. Heshmati, A. Productivity Growth, Efficiency and Outsourcing in. J. Econ. Surv. 2000, 17, 79–112.
  49. Hossain, M.K.; Kamil, A.A.; Baten, M.A.; Mustafa, A. Stochastic Frontier Approach and Data Envelopment Analysis to Total Factor Productivity and Efficiency Measurement of Bangladeshi Rice. PLoS ONE 2012, 7, e46081.
  50. Parmeter, C.F.; Zelenyuk, V. Combining the virtues of stochastic frontier and data envelopment analysis. Oper. Res. 2019, 67, 1628–1658.
  51. Peng, Y.; Tao, K.; Zhang, K. Performance Evaluation Research on Forest Listed Companies—Based on DEA and Malmquist Model. For. Econ. 2017, 94–98.
  52. Lee, J.-Y. Comparing SFA and DEA methods on measuring production efficiency for forest and paper companies. For. Prod. J. 2005, 55, 51–56.
  53. Ali, M.; Debela, M.; Bamud, T. Technical efficiency of selected hospitals in Eastern Ethiopia. Health Econ. Rev. 2017, 7, 24.
  54. Zhong, K.; Li, C.; Wang, Q. Evaluation of Bank Innovation Efficiency with Data Envelopment Analysis: From the Perspective of Uncovering the Black Box between Input and Output. Mathematics 2021, 9, 3318.
  55. Bing, C.; Sheng Bao, J. Performance evaluation and influencing factors of listed companies in China’s pharmaceutical industry: DEA-Tobit empirical study based on panel data. J. Cent. Univ. Financ. Econ. 2013, 312, 62–68.
  56. Bing, C.; Sheng Bao, J. Performance Evaluation of Chinese Agricultural Listed Companies: Based on the SORM-BCC Super Efficiency Model and Malmquist’s DEA-Tobit Analysis. Agric. Technol. Econ. 2012, 4, 114–127.
  57. Ying, Z.; Po, C. Performance Evaluation and Influencing Factors of Chinese Sports Industry Listed Companies: An Empirical Study of DEA-Tobit Based on Panel Data. J. Wuhan Inst. Phys. Educ. 2016, 50, 34–41.
  58. Wenhe, L. Evaluation on the Business Performance of Forestry Listing Corporation in China. Issues For. Econ. 2015, 35, 543–547.
  59. Yuan, S. Research on Financial Performance Evaluation of Listed Agricultural Companies in China—Based on VRS-DEA and Malmquist Index. Proc. Bus. Econ. Stud. 2020, 3, 10–16.
  60. Abbas, D.; Di Fulvio, F.; Marchi, E.; Spinelli, R.; Schmidt, M.; Bilek, T.; Han, H.S. A proposal for an integrated methodological and scientific approach to cost used forestry machines. Croat. J. For. Eng. 2021, 42, 63–75.
  61. Li, Y.; Gao, L. Corporate social responsibility of forestry companies in China: An analysis of contents, levels, strategies, and determinants. Sustainability 2019, 11, 4379.
  62. Jialu, S.; Zhiqiang, M.; Mingxing, L.; Agyeman, F.O.; Yue, Z. Efficiency Evaluation and Influencing Factors of Government Financial Expenditure on Environmental Protection: An SBM Super-efficiency Model Based on Undesired Outputs. Probl. Ekorozw. 2022, 17, 140–150.
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