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Record W4393983873 · doi:10.54612/a.6cbejge10k

Socio-economic indicators for the assessment of sustainability in the Swedish forest sector, and linkages with the national environmental quality objectives

2024· report· en· W4393983873 on OpenAlexaboutno aff
Ronju Ahammad, Francisco X. Aguilar

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityQuality (philosophy)Environmental resource managementBusinessEnvironmental qualityEnvironmental planningNatural resource economicsEnvironmental economicsGeographyEnvironmental scienceEconomicsPolitical scienceEcology

Abstract

fetched live from OpenAlex

Sweden’s Environmental Quality Objectives (EQOs) have been adopted to help describe the environment the country wishes to achieve, and are a promise to future generations of clean air, a healthy living environment, and rich opportunities to enjoy nature. Here, we assessed selected socio-economic indicators adapted from the Montréal Process for the Conservation and Sustainable Management of Temperate and Boreal Forests (MP) to examine trends in the Swedish forest sector of direct relevance to the EQOs. We did this with the aim of raising awareness about important socio-economic dimensions related to the EQOs, and to explore the linkages between the EQOs and the forest bioeconomy. We focused on the forest sector because of its central importance to meeting the EQOs, and fundamental social and economic roles it plays in Swedish society. The MP was chosen as our guiding framework because it was developed to assess national-level sustainable forest conservation and management, thus, incorporating critical economic, environmental and social dimensions. We applied a mixed methods approach based on a literature review, analyses of national and multilateral databases, and consultation with experts to identify and interpret selected indicators. We identified forest sector socio-economic indicators relevant to the EQOs related to forest property and ownership, economic value and consumption of wood and wood products, employment, wood energy, access to greenery, per capita forest availability, and cultural values. Interpretation of national-level indicators estimated for the 2000-2020 period point to overall progress toward maintaining forest conservation and production areas and a sector that has added substantial economic value through the processing of wood and wood products. Forests are an importance source of renewable energy and increasingly support the location of non-wood energy sources through the placement of wind power mills across forested lands. Downward trends were observed in fewer forest owners, a shrinking workforce, and per capita forest area which might be explained by processes of bequeathing, higher industry efficiencies and continued population growth. Selected indicators related to production forests, wood energy, per capita protected forests and cultural importance suggest these can directly support relevant EQOs including living forests, limited climate impact, rich plant and animal life. Through exports and hiring foreign workers, the Swedish forest sector has kept a direct linkage with the consumption of wood products abroad and in supporting economic wellbeing in lesser-developed nations through wages from forestry and non-wood seasonal employment, respectively. There is limited current information on cultural aspects such as heritage values and reindeer herding. Available data suggest a declining trend in damages to cultural remains within forest felling areas. We recommend regular and periodic assessment of the cultural and conservation values for Swedish forests to strengthen the ability to assess social and ecological sustainability relevant to the EQOs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0270.026
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.338
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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