Integrating environmental impacts into Cost-Benefit Analysis using emergy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Evaluating projects relies on economic analysis with environmental impact viewed separately. • We wished to show environmental impact as cash flow to include in economic cost-benefit analysis. • Emergy analysis was used to express environmental impacts as cash flow, calculate present value. • This method applied in a case study changed the project viability from a net loss to a net benefit. • Projects with a significant environmental impact would benefit from this type of analysis. Evaluating project viability often relies on economic analysis with environmental impacts either evaluated subjectively in a separate analysis or not at all. The concept of the study was to express environmental impacts as cash flow equivalents through the life of the project to directly compare environmental and economic impacts in a standard cost-benefit analysis. Embodied energy accounting methodologies have been used as a novel method for converting environmental impacts of a project to monetary terms. With environmental impacts expressed in monetary terms on an annual basis, the present value of the environmental impacts could be calculated using an appropriate interest rate based on environmentally-focused investment for a similar project. Application of this method to a case study based on a recent wetland restoration project in eastern Canada demonstrated that inclusion of environmental impact valuation in the analysis of the project changes the project from a net-cost (CAN$-23.5 million) to a net-benefit (CAN$3.7 million) and providing a clear justification for the project. This method is applicable to projects with significant environmental impacts and can be used in a project approval process or for selecting between project approaches.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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 it