An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it