R&D tax credits can be a significant source of taxpayer support for fossil fuel innovation
Bibliographic record
Abstract
Abstract The urgent need to accelerate the transition towards low-carbon energy is well understood. Government support for energy innovation has been an increasing focus of both policy and academic attention in recent years. The debate has focused on direct spending by governments on research and development (R&D). However, governments also support R&D indirectly, through tax credits. This source of government support has been overlooked in the academic and policy debate on energy innovation, in part because publicly available data on R&D tax credit expenditures typically do not enable the identification of spending specific to energy. This article provides the first published data on R&D tax credits in the energy sector, drawing on administrative data from Australia, Canada, Norway and the UK. This data shows that indirect support through tax credits can be a large source of support for innovation in fossil fuel extraction companies, though this differs by country. As a result, publicly available data on direct R&D spending by government can significantly understate government support for innovation in fossil fuel extraction. The article also presents patent data to show, for the UK and for Norway, that less than 5% of R&D activity in fossil fuel extraction firms is devoted to low-carbon technologies. The article concludes with the recommendation that governments should consider removing tax credit support for R&D activities that facilitate the extraction of fossil fuels.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.002 | 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 teacher head, 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".