Toward Sustainability: Technology Transition and Endogenous Population Growth
Bibliographic record
Abstract
In order to reach the state of economic sustainability, the problem of technology transition emphasizes the possibility of substituting for the exhaustible resource with an everlasting source of energy input. This paper aims at providing an analysis of this problem in an overlapping-generation model where the population is not a datum, but endogenous in the sense that it results from fertility decisions made by economic agents. First, we provide a new proof of the existence of competitive equilibrium under infinite time horizon. Here the difficulty lies in the fact that the market size is itself endogenous, because fertility - hence the population - is an individual decision at every point in time. Second, and perhaps most interestingly, the oil stock might not be entirely depleted, and the unused part in situ may serve the role of storing value for wealth transmission over time, just as money. But in contrast with paper money, which has no intrinsic value, leaving productive oil in situ as a bubble certainly adds another dimension to the inefficiency of overlapping-generation model. In this case, there are infinitely many equilibria as well as many steady states, depending on the data that characterize the initial state of the economy. Moreover, the convergence to some steady state, far from being simply monotone, might exhibit cyclical behavior, such as damped oscillation, limit cycles, etc.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".