Technology Innovation as a Response to Climate Change: The Case of the Climate Change Emissions Management Corporation of Alberta
Bibliographic record
Abstract
Abstract Innovation is the central element of climate change policy in many jurisdictions. Reduced to technology development and linked to market‐driven priorities, innovation accommodates the interests of large emitters in the energy sector and underpins a sustainable development discourse that denies ecological limits to economic growth. This study examines the use of innovation as a key component of climate change policy in the case of Alberta's Climate Change Emissions Management Corporation, utilizing a political economy approach to explain the drivers of government funding priorities. An analysis of this technology fund's investments over nine years, under two different governments, revealed that nearly half of the revenue has been used to subsidize R&D in the fossil fuels industry in the name of clean energy development, and that this priority has continued despite recent government commitments under the Paris CoP agreement. The carbon levy system that generates revenue for the fund has been unsuccessful in incentivizing facility reductions, pointing to the need for more stringent regulation. Innovation as a framework for transition to a post‐carbon economy is severely limited by its exclusion of the roles of social knowledge and citizen participation in envisaging and designing paths for change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".