Economic recessions and decarbonisation: analysing green stimulus spending in Canada and the US
Bibliographic record
Abstract
Existing research has demonstrated that government policies often prioritise growth over climate during economic downturns. Yet government stimulus spending during economic downturns also offers an opportunity for decarbonisation through long-term investments in infrastructure, transportation electrification, building efficiency, and clean energy technologies able to reduce emissions and sustainably shift the economy away from fossil fuels. We study the size and distribution of green stimulus spending in response to two recent economic downturns – the 2008 financial crisis and the 2020 Covid-19 pandemic. Focusing on Canada and the US – two major economies with strong incumbent fossil fuel interests – we explore the determinants of green stimulus spending. Counter to conventional wisdom, our findings provide little evidence to support the notion that institutional permeability to industry lobbying influenced the share of green stimulus spending. Instead, drawing on a novel dataset on green recovery spending and lobbying, we show that the strength of liberal parties in the legislatures shapes the distribution of stimulus funds. Our analysis suggests that liberal parties committed to decarbonisation can leverage economic crises to align economic and climate policy making, even in the face of strong lobbying efforts by the fossil fuel sector.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".