Insights to accelerate place-based at scale renewable energy landscapes: An analytical framework to typify the emergence of renewable energy clusters along the energy value chain
Bibliographic record
Abstract
Renewable energy transitions depend on activities at both ends of the value-chain or lifecycle, from the development of new innovations and technologies to their widespread diffusion. Place-based at scale approaches to renewable energy landscapes create local value, incorporate multifunctionality and decentralisation, mitigate harm for ecosystems, address justice and local resilience. That the potential, demand, and production of renewable energies are place-based phenomena is not accounted for in dominant energy-economy models, requiring new methods of analysis for an energy transition. The emergence of renewable energy across landscapes is increasingly linked in practice to the concept of “renewable energy clusters” that acknowledge the emergence of renewable energy as spatially distributed, heterogeneous and place-based phenomena. Renewable energy clusters describe a range of place-based energy activities along the energy value chain, from production of technologies and innovations to their use. Despite their promise, there lacks a clear definition and typology of renewable energy clusters, and research has not yet synthesised the place-based factors that influence or inhibit their emergence, that could be used to inform place-based strategies that address local assets, actors, space, labour, knowledge issues, or localised justice issues. This work offers a first step by serving as a preliminary investigation of renewable energy clusters and the factors that may predict their emergence. First, a qualitative approach is used to identify three initial types of renewable energy clusters along the energy value chain. The fields of regional sciences , technology innovation systems, and energy geography are drawn upon to identify factors that may influence or inhibit the emergence and form of renewable energy clusters. The seven synthesised dimensions that can be tested to typify and predict renewable energy cluster emergence: actors, institutions, networks, knowledge and tools, proximity, location characteristics, and path dependency . These initial types can guide the development of a sample of empirical cases of renewable energy clusters that can be analysed through machine learning typification to identify a more nuanced articulation of vertically integrated cluster types along the energy value chain. Typification can reveal characteristics these renewable energy clusters have in common with others, and what outcomes emerge from these characteristics within the specific context of place-based energy transitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".