Skip to main content
Log in

Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach

  • S.I.: Agriculture Analytics, BigData and Sustainable Development
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Agresti, A. (2015). Foundations of linear and generalized linear models. Hoboken, NJ: Wiley.

    Google Scholar 

  • Alzamora, R. M., & Apiolaza, L. A. (2013). A DEA approach to assess the efficiency of radiata pine logs to produce New Zealand structural grades. Journal of Forest Economics, 19, 221–233.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  • Banker, R. D., & Morey, R. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34, 513–521.

    Article  Google Scholar 

  • Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: A robust approach. Journal of Econometrics, 106, 1–25.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Handbook on data envelopment analysis. New York: Springer.

    Book  Google Scholar 

  • Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis, 24, 93–121.

    Article  Google Scholar 

  • Daraio, C., & Simar, L. (2007). Nonparametric efficiency analysis: A multivariate conditional quantile approach. Journal of Econometrics, 140, 375–400.

    Article  Google Scholar 

  • De Witte, K., & Kortelainen, M. (2008). Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete environmental variables, CES discussion paper series PS 08.33, MRA Paper 14034.

  • De Witte, K., & Kortelainen, M. (2013). What explains performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics, 45, 2401–2412.

    Article  Google Scholar 

  • Díaz-Balteiro, L., Herruzo, A. C., Martinez, M., & González-Pachón, J. (2006). An analysis of productive efficiency and innovation using DEA: An application to Spain’s wood-based industry. Forest Policy and Economics, 8, 762–773.

    Article  Google Scholar 

  • Dos Santos, R. B. N. (2011). Efficiency in Brazilian forest industry base via milestones. Reviste Arvores, 35, 1319–1326.

    Article  Google Scholar 

  • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42, 151–157.

    Article  Google Scholar 

  • Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8.

    Article  Google Scholar 

  • European Commission. (2013). A new EU forest strategy: For forests and the forest-based sector, COM(2013) 659 final. Retrieved 27 June, 2019, from http://eur-lex.europa.eu/resource.html?uri=cellar:21b27c38-21fb-11e3-8d1c-01aa75ed71a1.0022.01/DOC_1&format=PDF.

  • Eurostat. (2019). Agriculture, forestry and fishery statistics—2016 edition. Retrieved 30 June, 2019, from https://doi.org/10.2785/917017.

  • Food and Agriculture Organization. (FAO). (2015). FAO yearbook of forest products. Retrieved 30 June, 2019, from http://www.fao.org/3/a-i7304m.pdf.

  • Food and Agriculture Organization. (FAO). (2019). FAOSTAT-forestry database. Retrieved 30 June, 2019, from http://www.fao.org/forestry/statistics/80938@180724/en/.

  • Gutiérrez, E., & Lozano, S. (2013). Avoidable damage assessment of forest fires in European countries: An efficient frontier approach. European Journal of Forest Research, 132(1), 9–21.

    Article  Google Scholar 

  • Hailu, A., & Veeman, T. S. (2003). Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries. Canadian Journal Forests Research, 33, 1653–1660.

    Article  Google Scholar 

  • Hemmasi, A., Talaeipour, M., Khademi-Eslam, H., Farzipoor, S. R., & Pourmousa, S. H. (2011). Using DEA window analysis for performance evaluation of Iranian Wood panels industry. African Journal of Agricultural Research, 6, 1802–1806.

    Google Scholar 

  • Hoff, A. (2007). Second stage DEA: Comparison of approaches for modelling the DEA score. European Journal of Operational Research, 181, 425–435.

    Article  Google Scholar 

  • Hseu, J. S., & Shang, J. K. (2005). Productivity changes of pulp and paper industry in OECD countries, 1991–2000: A non-parametric Malmquist approach. Forest Policy and Economics, 7, 411–422.

    Article  Google Scholar 

  • Kao, C. (2010). Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization. Omega, 38, 484–491.

    Article  Google Scholar 

  • Kao, C., Chang, P. L., & Hwang, S. N. (1993). Data envelopment analysis in measuring the efficiency of forest management. Journal of Environmental Management, 38, 78–83.

    Article  Google Scholar 

  • Kao, C., & Yang, C. Y. (1992). Reorganization of forest districts via efficiency measurement. European Journal of Operational Research, 58, 356–362.

    Article  Google Scholar 

  • Korkmaz, M. (2011). Measuring the productive efficiency of forest enterprises in Mediterranean Region of Turkey using data envelopment analysis. African Journal of Agricultural Research, 6, 4522–4532.

    Google Scholar 

  • LeBel, L. G., & Stuart, W. B. (1998). Technical efficiency evaluation of logging contractors using a nonparametric model. Journal of Forest Engineering, 9, 15–24.

    Google Scholar 

  • Li, Y., Chan, H. K., & Zhang, T. (2018). Environmental production and productivity growth: evidence from European paper and pulp manufacturing. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3126-2.

    Article  Google Scholar 

  • Li, L., Hao, T., & Chi, T. (2017). Evaluation on China’s forestry resources efficiency based on big data. Journal of Cleaner Production, 142, 513–523.

    Article  Google Scholar 

  • Limaei, S. M. (2013). Efficiency of Iranian forest industry based on DEA models. Journal of Forestry Research, 24, 759–765.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41, 3–15.

    Article  Google Scholar 

  • Lozano, S., & Adenso-Díaz, B. (2018). Network DEA-based biobjective optimization of product flows in a supply chain. Annals of Operations Research, 264, 307–323.

    Article  Google Scholar 

  • Lozano, S., & Villa, G. (2017). Data envelopment analysis of systems with multiple modes of functioning. Annals of Operations Research, 278, 17–41.

    Article  Google Scholar 

  • McDonald, J. (2009). Using least squares and tobit in second stage DEA efficiency analyses. European Journal of the Operational Research, 197, 792–798.

    Article  Google Scholar 

  • Nyrud, A. Q., & Baardsen, S. (2003). Production efficiency and productivity growth in Norwegian sawmilling. Forest Science, 49, 89–97.

    Google Scholar 

  • Nyrud, A. Q., & Bergseng, E. R. (2002). Production efficiency and size in Norwegian sawmilling. Scandinavian Journal of Forest Research, 17, 566–575.

    Article  Google Scholar 

  • Obi, O. F., & Visser, R. (2017). Operational efficiency analysis of New Zealand timber harvesting contractors using data envelopment analysis. International Journal of Forest Engineering, 28, 85–93.

    Article  Google Scholar 

  • Otsuki, T., Hardie, I. W., & Reis, E. J. (2002). The implication of property rights for joint agriculture–timber productivity in the Brazilian Amazon. Environment and Development Economics, 7, 299–323.

    Article  Google Scholar 

  • Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11, 619–632.

    Article  Google Scholar 

  • Pascual, U. (2005). Land use intensification potential in slash-and-burn farming through improvements in technical efficiency. Ecological Economics, 52, 497–511.

    Article  Google Scholar 

  • Pena, E. A., & Slate, E. H. (2006). Global validation of linear model assumptions. Journal of American Statistical Association, 101, 341–354.

    Article  Google Scholar 

  • R Development Core Team. (2019). R: A language and environment for statistical computing. Retrieved May 15, 2019, from https://www.r-project.org/.

  • Ramalho, E. A., Ramalho, J. J. S., & Henriques, P. D. (2010). Fractional regression models for second stage DEA efficiency analyses. Journal of Productivity Analysis, 34, 239–255.

    Article  Google Scholar 

  • Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys, 25, 19–68.

    Article  Google Scholar 

  • Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2012). A supremum-type RESET test for binary choice models. Economics Bulletin, 32, 905–912.

    Google Scholar 

  • Ramalho, J. J. S. (2015). frm: Regression analysis of fractional responses. R package version 1.2.2. Retrieved 27 June, 2019, from https://CRAN.R-project.org/package=frm.

  • Rhodes, E. (1986). An exploratory of variations in performance among US national parks. In R. Silkman (Ed.), Measuring efficiency: An assessment of data envelopment analysis (pp. 47–71). San Francisco: Jossey-Bass.

    Google Scholar 

  • Ripoll-Zarraga, A. E., & Lozano, S. (2018). A centralised DEA approach to resource reallocation in Spanish airports. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03271-6.

    Article  Google Scholar 

  • Salehirad, N., & Sowlati, T. (2007). Dynamic efficiency analysis of primary wood producers in British Columbia. Mathematical and Computer Modelling, 45, 1179–1188.

    Article  Google Scholar 

  • Sauer, J., & Abdallah, J. M. (2007). Forest diversity, tobacco production and resource management in Tanzania. Forest Policy and Economics, 9, 421–439.

    Article  Google Scholar 

  • Shiba, M. (1997). Measuring the efficiency of managerial and technical performances in forestry activities by means of DEA. International Journal of Forest Engineering, 8, 7–19.

    Google Scholar 

  • Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.

    Article  Google Scholar 

  • Sowlati, T. (2005). Efficiency studies in forestry using data envelopment analysis. Forest Products Journal, 55, 49–57.

    Google Scholar 

  • Šporčić, M., & Landekić, M. (2014). Nonparametric model for business performance evaluation in forestry. In J. Awrejcewicz (Ed.), Computational and numerical simulations. London: IntechOpen. https://doi.org/10.5772/57042.

    Chapter  Google Scholar 

  • Sun, J., Sun, D., & Guo, S. (2014). Evaluation on the efficiency of biomass power generation industry in China. The Scientific World Journal, Article ID 831372.

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.

    Article  Google Scholar 

  • UN. (2011). The European forest sector outlook study II: 20102030. United Nations. UNECE/FAO. Retrieved 27 June, 2019, from http://www.unece.org/fileadmin/DAM/timber/publications/sp-28.pdf.

  • UN Comtrade. (2019). International Trade Statistics Database. Retrieved 27 June, 2019, from https://comtrade.un.org/.

  • Upadhyay, T. P., Shahi, C., Leitch, M., & Pulkki, R. (2012). An application of data envelopment analysis to investigate the efficiency of lumber industry in northwestern Ontario. Canada, Journal of Forestry Research, 13, 675–684.

    Article  Google Scholar 

  • Viitala, E. J., & Hänninen, H. (1998). Measuring the efficiency of public forestry organizations. Forest Science, 44, 298–307.

    Google Scholar 

  • World Bank. (2019a). National accounts data. Retrieved 27 June, 2019, from http://www.worldbank.org/.

  • World Bank. (2019b). Climate change knowledge data. Retrieved 27 June, 2019, from http://sdwebx.worldbank.org/climateportal/index.cfm?page=downscaled_data_download&menu=historical.

  • Yang, H., Yuan, T., Zhang, X., & Li, S. (2016). A decade trend of total factor productivity of key state-owned forestry enterprises in China. Forests, 7, 97.

    Article  Google Scholar 

  • Yin, R. (1998). DEA: A new methodology for evaluating the performance of forest products producers. Forest Products Journal, 48, 29–34.

    Google Scholar 

  • Yin, R. (1999). Production efficiency and cost competitiveness of pulp producers in the Pacific Rim. Forest Product Journal, 49, 43–49.

    Google Scholar 

  • Yin, R. (2000). Alternative measurements of productive efficiency in the global bleached softwood pulp sector. Forest Science, 46, 558–569.

    Google Scholar 

  • Zadmirzaei, M., Limaei, S. M., Olsson, L., & Amirteimoori, A. (2017). Assessing the impact of the external non-discretionary factor on the performance of forest management units using DEA approach. Journal of Forest Research, 22(3), 144–152.

    Article  Google Scholar 

Download references

Acknowledgements

This research was carried out with the financial support of the Spanish Ministry of Science and the European Regional Development Fund (ERDF), Grant DPI2017-85343-P. The authors wish to thank the Ibero-American Program for the Development of Science and Technology, and the Red Iberoamericana BigDSSAgro (Reference No. P515RT0123) for their support of this work and Professor Kristof de Witte (KU Leuven) for providing the code of the conditional efficiency framework. Finally, the authors also appreciate the constructive comments and suggestions of the reviewers, which have helped to improve the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ester Gutiérrez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gutiérrez, E., Lozano, S. Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach. Ann Oper Res 314, 471–496 (2022). https://doi.org/10.1007/s10479-020-03756-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03756-9

Keywords

Navigation