India’s Climate Finance Requirements: An Assessment
Bibliographic record
Abstract
This study assesses India’s climate finance requirement from 2022-2030 to decarbonise its four major carbon-emitting sectors—cement, steel, power, and road transport. Climate finance or additional capital expenditure (capex) for transitioning to a low-carbon economy, i.e., over and above the capex already planned in the business-as-usual (BAU) scenario, has been estimated at US$467 billion for 2022-2030 or 1.3 per cent of India’s gross domestic product (GDP) annually. This comprises US$251 billion for the steel sector, followed by US$141 billion for cement, US$57 billion for power and US$18 billion for road transport. The estimated investment in the four sectors will reduce the use of 291 million tonnes of coal and 72 billion litres of petrol and diesel, mitigating 6.9 billion tonnes of CO2 emissions (excluding road transport). The study also evaluated the macroeconomic consistency of India’s estimated climate finance requirement. Overall, capital and financial flows net of the projected current account deficit (CAD) for India are estimated at US$530 billion during 2023–2030 as against the projected expansion of US$474 billion in monetary base. Thus, India would need to skilfully manage both (i) capital flows in the BAU; and (ii) climate finance from external sources. India may have to strategically widen its CAD, subject to a maximum of 2.5 per cent of GDP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".