Diffusion of demand-side low-carbon innovations and socio-technical energy system change
Bibliographic record
Abstract
To mitigate climate change in an accelerated time frame, more research is needed to understand how to achieve effective large-scale diffusion of low-carbon innovations. The conceptualization of sectoral socio-technical system transitions requires extending beyond an economic and technological focus, towards a wider system view that combines societal, behavioural, and institutional elements alongside the natural environments and infrastructures. Any socio-technical system reconfiguration will be shaped by the diffusion of multiple innovations. This study employs a novel empirical and quantitative framework that integrates considerations of system actors, behaviours, innovations, and infrastructure simultaneously. Based on a review of socio-technical literature, the framework scores demand-side, low-carbon innovations on a scale from regime reinforcing to disruptive across the dimensions of decarbonization, democratisation and decentralisation. It also scores the innovations according to the policy (economic, regulatory, informational) and legitimacy (actors, discourse) factors that support or inhibit their diffusion. This allows for the investigation of the relationship between the diffusion of innovations and socio-technical energy system change, including whether a relationship exists, its strength, and direction. In analysing 80 innovations that diffused to the demand-side between 1998-2018 in Ontario, Canada, diffusion is found to be negatively correlated with system disruption and decarbonization. Although economic supports tend to be a focus of mainstream policymaking, this study found that economic instruments, legitimacy through discourse, and combined policy and legitimacy supports are important to the systemic diffusion of demand-side low-carbon innovations.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".