The optimal timing of clean technology adoption: A stochastic cost–benefit analysis
Bibliographic record
Abstract
This paper develops a quantitative framework to determine the optimal timing for transitioning to clean technologies, which is crucial for sustainable development and climate action. We propose a stochastic model using optimal stopping theory, analyzing the dynamic cost advantages of clean versus conventional technologies. The model derives explicit timing solutions adaptable to market trends and user-specific factors. To illustrate the model’s practical application, we apply it to an empirical case study focused on the adoption of electric vehicles (EVs). Our results indicate that users with higher usage intensity or greater anticipated improvements in cost advantages related to future running costs tend to adopt EVs earlier. In contrast, factors such as increased volatility in cost advantages - often affected by fluctuating energy prices - or unpredictable negative jumps in initial EV costs can delay adoption decisions. This finding highlights the role of stable energy markets, potentially supported by policies like renewable energy investments, grid stabilization, and price guarantees, in promoting EV adoption. Additionally, our results underscore the importance of technological advancements in accelerating cost reductions. Policies that establish financial incentives to reduce initial EV costs can significantly lower adoption barriers, encouraging broader and earlier EV uptake, particularly among high-mileage users.
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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 | 0.002 |
| 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) | 0.000 | 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".