Economic growth and sustainable development: how should we discount the future?
Bibliographic record
Abstract
In 1987, the Brundtland Commission famously defined sustainable development as “development that meets the needs of the present without compromising the needs of the future”. This paper is concerned with translating this definition in the framework of the neoclassical one-sector \nmodel of economic growth. We investigate and compare three possible criteria for sustainable development. The first one, which was introduced by Chichilnisky, the second one, which was introduced by Ekeland and Lazrak, and the third one, which goes back to Ramsey himself. We define and investigate equilibrium strategies. For the Chichilnisky criterion, there is a unique \nequilibrium strategy, which is just the optimal strategy for the neoclassical model. In the other two cases, there is a continuum of equilibrium strategies. We conclude that the most satisfying candidates for sustainable development are the equilibrium strategies for the third criterion \n(H-criterion)
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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 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".