Eco-Efficiency of Forestry Companies Around the World: A Data Envelopment Analysis
Bibliographic record
Abstract
Objective: to evaluate and compare the economic, environmental, and eco-efficiency of companies operating in the forestry sector and fill the gap in relation to the lack of information about the theme. Theoretical framework: increased awareness of society around the traditional productive model and its impact on the environment demands that companies associate business competitiveness with environmental responsibility. Therefore, the disclosure of information and the analysis of it using methods such as the eco-efficiency approach are important to monitor the performance of companies. Methods: the Data Envelopment Analysis method was used with three proposed models: economic (desirable output); environmental with undesirable variables; and eco-efficiency model with desirable and undesirable variables. Results and conclusions: results show that the years 2009-2010 and 2016-2017 were more favorable to technical efficiency, while ecological efficiency was higher from 2010 to 2012. Meanwhile, the highest average eco-efficiency score was registered in 2013. In the 11 years analyzed, Portugal, Canada, United Kingdom, Australia, South Africa, and Spain stood out as the most eco-efficient countries. Research implications: the eco-efficiency approach demonstrated and discussed in this research can be used to monitor forestry companies’ performance taking into account their productivity and environmental impact reduction. Originality/value: this study presents a comprehensive evaluation of silviculture companies worldwide using the technical, environmental, and eco-efficiency models. Analyses are based on financial and environmental data of 82 publicly listed companies from 23 countries, including developed and developing economies and cover the period of 11 years (2009 to 2019).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".