Modelling the diffusion of multiple demand-side low-carbon energy innovations within a 1.5°C scenario
Bibliographic record
Abstract
Decarbonizing the energy sector is a critical component in meeting global climate change mitigation commitments in a 1.5°C scenario. In order accelerate the transition to a low-carbon energy system, solutions will need to be deployed at all stages of the energy system, including the diffusion and adoption of innovations by energy users. If deployed at scale (achieving market shares above 15%), disruptive demand-side low-carbon innovations have the potential to accelerate a low-carbon energy transition through the destabilization of the established socio-technical regime. However, demand-side innovations tend to be overlooked in favor of supply-side energy solutions. Moreover, many of the innovations needed to achieve sizable emission reductions already exist, yet experience slow rates of diffusion. Diffusion of innovation studies that attempt to address these issues often assess a single technology or a small scope of factors in isolation, which limits the application of the research findings. This empirical study investigates the factors that influence the diffusion of 132 demand-side low-carbon energy innovations in the Canadian province of Ontario that have the potential to contribute to a low-carbon energy transition. A framework was developed for analyzing and evaluating low-carbon innovations based on their potential contribution to system change. Each innovation was coded in accordance with the model framework. This research found that there is currently limited potential for low-carbon demand-side energy innovations to create a system transformation through disruptive innovation in Ontario. This research also found that legitimacy is a necessary but not sufficient condition for influencing system disruption. More empirical studies that apply the model framework presented in this analysis are needed in order to effectively map the range and combination of factors that can facilitate a low-carbon energy transition in Canada through system disruption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".