Addressing biodiversity impacts of land use in life cycle assessment of forest biomass harvesting
Bibliographic record
Abstract
Forests are an increasingly important source of feedstock for bioenergy as global efforts to mitigate atmospheric CO2 concentrations increase. In keeping with the principles of sustainable forest management, it is important that feedstock procurement not have negative impacts on the environment, including biodiversity. Impacts of land use, including forest management, can be evaluated along all stages in the production of these goods and services, using life cycle assessment (LCA), which is a potentially powerful tool for organizing and evaluating the impacts of production. There is growing recognition of the need to integrate land‐use impacts into LCA for forest products such as bioenergy, especially on biodiversity. Integrating quantitative indicators of biodiversity into LCAs of biomass production systems is particularly challenging because biodiversity is a multidimensional concept that can never be fully represented by a single number, and yet many proposed approaches rely on this. Reliance on a single metric oversimplifies ‘biodiversity’ and might lead to inappropriate conclusions on local land management practices. LCA is not suited to providing reliable site‐specific assessment of forest product systems in regard to the complexities of biodiversity. Nevertheless, the global and comprehensive nature of LCA makes it a useful tool for preventing a shift in environmental problems or burdens across the value chain because of local land management decisions. In this context, complementary site‐specific and/or regional studies or analyses may help mitigate against inaccurate conclusions being drawn from LCA. WIREs Energy Environ 2016, 5:670–683. doi: 10.1002/wene.211 This article is categorized under: Bioenergy > Climate and Environment Energy and Development > Climate and Environment
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".