Siting bioenergy facilities in the United States: Measuring participation in decisions and distribution of effects
Bibliographic record
Abstract
Indicators are crucial for measuring progress in sustainability, community development, and energy equity objectives. Thus, indicators are vital for siting or repurposing energy facilities, revealing benefits and adverse effects on underserved communities compared to those under baseline or alternative conditions. However, the use of quantitative metrics can reduce the assessment of progress to a technical exercise of data collection, frequently lacking citizen participation. In this paper, we emphasize the importance of incorporating procedural and distributional justice into the siting process, through the identification of indicators aimed at avoiding the perpetuation of or increase in socioeconomic disparities. More specifically, we describe the process through which a diverse committee of US agriculture, energy, and environmental justice stakeholders and experts, along with US energy researchers, collaboratively developed a list of indicators reflecting justice objectives for siting bioenergy. Stakeholders emphasized categories of procedural justice indicators such as trust, influence, informed consent, and private property rights, whereas energy burden, for example, was identified as an important distributional justice indicator. The proposed indicators can be selected or modified to reflect local needs and priorities. This paper demonstrates that indicators can be developed through participatory processes to guide stakeholders to make informed decisions regarding siting and permitting of biorefineries or biopower facilities. These indicators enable the comparison of siting options; early identification of key problems, concerns, or priorities; and tracking of progress toward justice-related targets. Ultimately, this approach contributes to a more equitable and sustainable energy transition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".