Towards more reliance on carbon pricing in India
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".