Getting Smart? Climate Change and the Electric Grid
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
Interest in the potential of smart grid to transform the way societies generate, distribute, and use electricity has increased dramatically over the past decade. A smarter grid could contribute to both climate change mitigation and adaptation by increasing low-carbon electricity production and enhancing system reliability and resilience. However, climate goals are not necessarily essential for smart grid. Climate change is only one of many considerations motivating innovation in electricity systems, and depending on the path of grid modernization, a future smart grid might do little to reduce, or could even exacerbate, risks associated with climate change. This paper identifies tensions within a shared smart grid vision and illustrates how competing societal priorities are influencing electricity system innovation. Co-existing but divergent priorities among key actors’ are mapped across two critical dimensions: centralized versus decentralized energy systems and radical versus incremental change. Understanding these tensions provides insights on how climate change objectives can be integrated to shape smart grid development. Electricity system change is context-specific and path-dependent, so specific strategies linking smart grid and climate change need to be developed at local, regional, and national levels. And while incremental improvements may bring short term gains, a radical transformation is needed to realize climate objectives.
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.
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 | 0.000 |
| 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