Getting Smart? Climate Change and the Electric Grid
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".