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    Efficiency analysis and CO2 emission reduction strategies in the US forest sector: a data envelopment analysis approach
    (Springer, 2024) Amirteimoori, Alireza; Zadmirzaei, Majid; Susaeta, Andres; Amirteimoori, Arash
    Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5-10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.
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    Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach
    (Elsevier science INC, 2024) Amirteimoori, Alireza; Cezar, Asunur; Zadmirzaei, Majid; Susaeta, Andres
    This study addresses the global concern about undesirable outputs in the Forest Sector. We propose two innovative models, namely a directional weak disposable DEA model and an extended stochastic DEA model, to measure environmental efficiency. These models make a significant contribution to the field by specifically assessing the uncertain environmental efficiency of the forest sector. We validated our proposed models by conducting an empirical application using the United Nations Economic Commission for Europe (UNECE) forest sector dataset. The study examines important outputs such as above ground biomass stock, export unit prices of industrial roundwood, wood removals (desirable outputs), and CO2 emissions from wildfires (undesirable output). The results demonstrate that our novel stochastic weak disposability DEA model outperforms traditional approaches when the second scenario is applied. Specifically, the average technical efficiency (TE) score decreases to 0.92, and the number of efficient units reduces to 27, representing an approximate improvement of 55 %. Furthermore, the reduction rate of CO2 emissions is 4.09 % lower than the benchmark. Hence, our extended novel stochastic weak disposability DEA approach enhances the assessment of efficiency and inefficiency in decision-making units, contributing to the mitigation of risk and uncertainty. It also improves overall environmental performance in forest management.
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    Exploring technical efficiency in the European forest sector: a two-stage chance-constrained data envelopment analysis
    (Elsevier b.v., 2025) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, Majid
    This study analyses the technical efficiency of the forestry sector in Europe which comprises 40 countries. The novelty of this study is the stochasticity of the data and the existence of contextual variables in the two-stage production process of the forest sector. We first developed a two-stage chance-constrained data envelopment analysis model in which the forestry and exploitation stages occur at country-specific levels within the European forest production sector. It was found that the forest management stage is generally more efficient than the exploitation stage and total production at the country-specific level. Contextual variables have a significant impact on efficiency scores, which means that efficiency calculations in the subsequent stage need to be adjusted to take these influences into account. By mitigating these contextual effects, the study improved technical efficiency scores, highlighting top performers like the Russian Federation (DMU31 in North zone), Switzerland (DMU37 in Central-West zone), and Iceland (DMU16 in North zone) with TE scores of 1.0322, 1.0209, and 1.0198 respectively, while also identifying areas for enhancement in countries such as Turkey (DMU38 in South-East zone), Slovakia (DMU33 in Central-East zone), and Romania (DMU30 in Central-East zone) which fall into the lowest three ranks based on their performance with TE scores of 0.5583, 0.5058, and 0.4482 respectively. An important conclusion is that these findings are crucial for policymakers and stakeholders in Europe when developing strategies to improve efficiency and sustainability in the forest sector.
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    Exploring technical efficiency in the European forest sector: A two-stage chance-constrained data envelopment analysis
    (Elsevier B.V., 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, Majid
    This study analyses the technical efficiency of the forestry sector in Europe which comprises 40 countries. The novelty of this study is the stochasticity of the data and the existence of contextual variables in the two-stage production process of the forest sector. We first developed a two-stage chance-constrained data envelopment analysis model in which the forestry and exploitation stages occur at country-specific levels within the European forest production sector. It was found that the forest management stage is generally more efficient than the exploitation stage and total production at the country-specific level. Contextual variables have a significant impact on efficiency scores, which means that efficiency calculations in the subsequent stage need to be adjusted to take these influences into account. By mitigating these contextual effects, the study improved technical efficiency scores, highlighting top performers like the Russian Federation (DMU31 in North zone), Switzerland (DMU37 in Central-West zone), and Iceland (DMU16 in North zone) with TE scores of 1.0322, 1.0209, and 1.0198 respectively, while also identifying areas for enhancement in countries such as Turkey (DMU38 in South-East zone), Slovakia (DMU33 in Central-East zone), and Romania (DMU30 in Central-East zone) which fall into the lowest three ranks based on their performance with TE scores of 0.5583, 0.5058, and 0.4482 respectively. An important conclusion is that these findings are crucial for policymakers and stakeholders in Europe when developing strategies to improve efficiency and sustainability in the forest sector. © 2024 Elsevier B.V.
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    Managerial ability and productivity growth in the European forest sector
    (Springer, 2023) Amirteimoori, Alireza; Banker, Rajiv D.; Zadmirzaei, Majid; Susaeta, Andres
    This paper aims to examine how the data envelopment analysis (DEA) technique can be applied to evaluate managerial ability and productivity growth for 29 European forest sectors over the period 2011-2020. Toward this end, we first applied DEA to evaluate the technical efficiency (TE) from both periodical-frontier and met-frontier perspectives in which results showed that the average TE was 0.645 and the annual operating efficiency of the years studied was reduced by 35%. A modified regression test is secondly developed in order to determine the effect of contextual variables on the log of TE. The findings showed that the regional density, time series and gross domestic product had the highest positive influence on improving the TE results, respectively. In the following, by considering the explanatory variables, a modified DEA-based Malmquist productivity index is used to calculate the productivity growth over the period 2011-2020. The results indicated that there was a decline of 12% for total factor productivity in 2019-2020 compared to 2014-2015 and 7% compared to 2015-2016 which is due to the uniform growth of technological change (TC) and efficiency change recession compared to previous periods. Hence, productivity growth is mainly due to frontier shift (TC).
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    On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms
    (Pergamon-Elsevier Science Ltd, 2023) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, Majid; Hasanzadeh, Fahimeh
    Greenhouse gases (GHG) remain in the atmosphere for a very long-time causing alarmingly fast warming worldwide (global warming); especially Carbon dioxide (CO2) emissions have become a worldwide concern because of their harmful effects on the climate, and they are considered as an undesirable product of a lot of production systems. Various models dealing with undesirable outputs for measuring environmental efficiency have been employed to control greenhouse gas emissions via forecasting and/or optimizing their emissions. In this regard, this study proposes a novel modified Fuzzy Undesirable Non-discretionary DEA (FUNDEA) model to Measure environmental efficiency, and combine it with some novel artificial intelligence algorithms (Artificial Neural Network (ANN), Gene Expression Programming (GEP) and Artificial Immune System (AIS)) in order to predict optimal values of inefficient Decision-Making Units (DMUs) for being more efficient and mitigating their Co2 emissions in the uncertain environment for the first time herein. The model is applied to a dataset of 24 Iranian forest management units. Although our findings show that 17 DMUs are inefficient with a weak efficiency dispersion, these inefficient DMUs could improve their efficiency border by following the combined approaches (FUNDEA-ANN, FUNDEA-GEP and FUNDEA-AIS). As a consequence, the applied FUNDEA- artificial intelligent approaches are performed very well in predicting the optimal values of CO2 emissions and, hence increasing the total environmental efficiency.
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    Scale elasticity and technical efficiency analysis in the European forest sector: a stochastic value-based approach
    (Springer, 2023) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, Majid
    This paper is the first to examine both technical efficiency and scale elasticity of the forest sector across different European (EU) regions in an uncertain environment. In doing so, a dataset from 29 EU forest sectors have been collected; where the input and output data have been assumed to be random while their prices (or importance weights) were known. A chance-constrained data envelopment analysis approach was derived from several directional value-based models aimed at evaluating technical efficiency in the first stage. The results indicated that the number of efficient EU forest sectors were reduced substantially with extremely weak efficiency dispersion (about 18%), and the total average technical efficiency scores were moderately reduced by 0.74 as well. However, in the second stage, the findings of the modified cost/revenue-based scale elasticity model interestingly revealed that almost 7% of the EU forest sectors are in increasing returns to scale regions, which means they are still attractive acquisition targets and have the opportunity to increase the level of outputs and shift to the fully efficient border and finally increase their overall efficiency. As the major conclusion, all of our applied stochastic data envelopment analysis models (specially the cost/revenue-based approach) have high discriminating power in order to clearly distinguish the efficiency of the EU forest sector.