Peer effects and inequalities in technology uptake. Evidence from a large-scale renovation subsidy programme
Bibliographic record
Abstract
The energy transition's success in addressing climate change depends on several factors, including the affordability of new technologies and the influence of peers within communities. However, concerns about affordability raise questions about how economic inequalities shape peer effects and whether they create barriers to equitable adoption. To this end, we explore how inequalities influence peer effects in the uptake of renewable heating sources. We leverage over 260,000 observations from unique and unpublished microdata from the Polish Clean Air Priority Programme – one of the largest retrofit schemes in Europe. Our results show that peer effects accelerate technology uptake, with each additional installation increasing the likelihood of subsequent adoption by 0.014 pp. This amounts to a 7.7 % aggregate increase in the probability of installations in the average 1-km grid cell attributable to peer spillovers. Peer influence is affected by economic inequality. In more economically homogeneous areas, affluent individuals considerably impact their peers. In areas with higher economic disparities, this influence diminishes. Our findings highlight the role of heating technology type and adopter wealth in shaping peer effect magnitude. Less wealthy adopters of biomass stoves emerge as a significant driver of peer influence, especially in areas with lower income inequality. We advise direct transfers to address technology adoption inequalities, leveraging social capital in low-inequality areas and adopting individualised strategies in high-inequality areas. • Economic inequality weakens peer effects in technology adoption decisions. • Peer influence on technology adoption varies by income inequality and wealth. • Progressive policies are needed to reduce disparities in technology uptake.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".