Methodology to identify demand-side low-carbon innovations and their potential impact on socio-technical energy systems
Bibliographic record
Abstract
The rapid diffusion of demand-side low-carbon innovations has been identified as a key strategy for maintaining average global temperature rise at or below 1.5 °C. Diffusion research tends to focus on a single sector, or single technology case study, and on a small scope of factors that influence innovation diffusion. This paper describes a novel methodology for identifying multiple demand-side innovations within a specific energy system context and for characterizing their impact on socio-technical energy systems. This research employs several theoretical frameworks that include the Energy Technology Innovation System (ETIS) framework to develop a sample of innovations; the Sustainability Transitions framework to code innovations for their potential to impact the socio-technical system; the energy justice framework to identify the potential of innovations to address aspects of justice; and how characteristics of innovations are relevant to Innovation Adoption. This coding and conceptualization creates the foundation for the future development of quantitative models to empirically assess and quantify the rate of low-carbon innovation diffusion as well as understanding the broader relationship between the diffusion of innovations and socio-technical system change. The three stages of research are:•Contextualization: surveys and desk research to identify low-carbon innovations across the ETIS;•Decontextualization: the development of a codebook of variables•Recontextualization: coding the innovations and analysis.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".