Working conditions in the forestry sector in Minas Gerais: an evaluation of the social life cycle
Bibliographic record
Abstract
The forestry sector plays a fundamental role in both the economy and the environment, providing raw materials for tangible goods such as paper, furniture, and even inputs for steel production, as well as contributing intangible benefits such as climate regulation and the preservation of wildlife habitats. Despite the environmental and economic relevance of this activity, its social impacts are still underexplored in scientific literature and by the sector’s own companies. This study addresses Social Life Cycle Assessment (S-LCA) in the forestry sector and is divided into two articles. The first presents a bibliometric analysis to statistically examine the evolution of publications on S-LCA in the forestry sector and its relationship with public policies between 2003 and 2024. The results identified 49 studies in the Web of Science (WOS) database related to the topics analyzed and revealed a low application of public policies in the forestry sector using the S-LCA methodology. The second article aimed to conduct a Social Life Cycle Assessment (S-LCA) of the forestry sector in Minas Gerais, Brazil. Specifically, it sought to characterize and measure the social impacts arising from working conditions in the state’s silviculture sector. The database used refers to the first quarter of 2024 and was obtained from the Continuous National Household Sample Survey (PNAD Contínua). Various social indicators were collected and analyzed under the “working conditions” impact category, such as formal employment, wages, working hours, and social security coverage, among others. To measure the impact of working conditions, a reference scale was developed to compare the observed values with the reference benchmarks. The social life cycle analysis, using the adapted Type I methodology, classified the forestry sector as “regular” in social terms, with a score of -0.75 on a scale from -4 to 4. Finally, the study analyzed the correlation between the forestry sector and the United Nations Sustainable Development Goals (SDGs). Positive impacts were found for SDG 1 (No Poverty) and SDG 3 (Good Health and Well-being), while challenges remain for SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities). This study provides the first quantitative diagnosis of social impacts in Minas Gerais’ silviculture sector through S- LCA, generating an essential baseline for corporate monitoring and public policy formulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".