Towards more reliance on carbon pricing in India
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
The STG Climate Cluster is studying pragmatic means of promoting a wider use of carbon pricing in emerging economies, particularly those belonging to the G20. As part of their commitments under the Paris Agreement, countries are showing more interest in putting a price on carbon as this helps to cut emissions in a cost-effective manner. The focus is therefore to find pragmatic approaches to add carbon pricing tools to the domestic policy mix. At the end of 2020, UN Secretary-General Guterres pleaded to the European Council for Foreign Relations to plan for a green recovery post-COVID, stopping the financing of coal immediately and putting a price on carbon. Yet, despite the numerous second round pledges for carbon neutrality under the Paris Agreement, very few countries have consistent policies in place which would deliver both. In this respect, India offers an interesting case-study. There are many opportunities, challenges and pitfalls in the energy transition moving away from a high reliance on coal. In this policy brief, four ‘no regret’ steps towards an intersectoral carbon pricing scheme are formulated. These would gradually strengthen the institutions that support and embed carbon pricing in India. The steps include reforming existing energy policies, extending corporate climate risk disclosure, developing a sustainable finance taxonomy, and further supporting greenhouse gas monitoring, reporting and verification. Before outlining the four policy options, we offer a summary of India’s energy and climate policy context.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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