IRES working paper series. Number 2013-04
Bibliographic record
Abstract
In recognition of the long-term impacts resulting from financial decisions, a growing number of campaigns are advocating divestment from companies responsible for high levels of carbon emissions. However, a systematic understanding of divestment is needed for an examination of the social, economic and environmental impacts that extend beyond the currently stated political motivations to divest. We have developed the Shadow Impact Calculator (SIC) based on economic input-output life cycle assessments (EIO-LCA) as a tool to examine broader impacts of investment decisions. A portfolio’s “shadow footprint” represents the economic, social and environmental impacts underlying an investor’s decision to hold equities in particular companies, economic sectors or nations. We show which sectors of the economy have particularly large or small carbon shadows. To demonstrate the use of SIC we examine the endowment investments of a Canadian university. We also show how the immediate economy-wide impacts of divestment are often much smaller than would otherwise be expected.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".