Economic recessions and decarbonisation: analysing green stimulus spending in Canada and the US
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
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.
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