Blockchain Technology in the Environmental Economics: A Service for a Holistic and Integrated Life Cycle Sustainability Assessment
Bibliographic record
Abstract
The application of blockchain technology in the field of environmental economics is still in its inception so it is not sufficiently used in a holistic and integrated life cycle sustainability assessment (HILCSA). The capability of the blockchain to provide a verifiable and transparent record can make it a good tool in environmental economics for an agile reflection in doing business and production. The research is focused on the advantages and challenges in the inclusion of blockchain technology into a holistic life cycle assessment. Based on the existing possibilities of using blockchain technology in environmental economics and life cycle assessments (LCAs), a framework and a model for applying the blockchain in the holistic life cycle sustainability assessment are proposed. A Design Science methodology was used as a research strategy. Particular emphasis in this paper is put on risk management when integrating blockchain methodologies through environmental economics into the life cycle sustainability assessment (LCSA) in order to use all the advantages of the blockchain technology optimally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".