An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy
Bibliographic record
Abstract
Identifying policy levers to accelerate the adoption of household energy technologies requires an integrative perspective, yet energy models have so far focused on the adoption of single technologies and single policies rather than co-adoption and policy mixes, respectively. Furthermore, experimental consumer data are underutilized in this field, limiting the capacity to study heterogeneous consumer responses to policies. Here, we report an interdisciplinary study addressing this gap by proposing an agent-based model on co-adoption of photovoltaic systems, electric vehicles, and heat pumps up to 2050. The model incorporates realistic consumer decision making and, importantly, is empirically grounded in experimental data of a large sample including 1,469 respondents. We simulate 16,834 policy mixes, which show that, even with decreasing investment costs, accelerating diffusion depends to a large extent on the specific policy mix. The findings moreover illustrate significant variation in adoption levels under identical policy conditions depending on income and political orientation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".