Technology sustainability assessment to support decision making on energy production at local scale
Bibliographic record
Abstract
Considering climate issues, the Kyoto Protocol is the most important worldwide reference aimed at reducing greenhouse gases that cause climate change; going further, the recent European Climate and Energy Policy defi nes binding targets for 2020, that include increasing use of renewable energy (wind, solar, biomass, etc.) to 20% of total energy production.In order to identify the most suitable strategies to achieve this objective, an evaluation of some relevant issues that can affect the sustainability of possible renewable energy options is necessary.This study focus on technology sustainability assessment of energy production from forest biomass, defi ning a set of indicators to provide a decision support system (DSS) for local decision makers, enabling them to evaluate the environmental impacts, the resource availability and renewability, the feasibility of the technology in the local context and the social acceptance of the plant among the local community.The case study presented in this article refers to an Italian mountain community (Comunità Montana delle Alpi Lepontine) in northern Italy, where the assessment of environmental, economic and social sustainability of a plant producing electricity powered by Syngas coming from gasifi cation of forest biomass was performed.The aim of the research was to develop a multidisciplinary DSS to assess the feasibility of the application and to defi ne guidelines for sustainability assessment of technologies for energy production using forest biomass, identifying critical issues and potential areas of optimization.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".