Scenarios for future Indian HFC demand compared to the Kigali Amendment
Bibliographic record
Abstract
Abstract Demand for hydrofluorocarbon (HFC) refrigerants used as substitutes for ozone-depleting substances is growing in India and is estimated to continue growing at a high rate through the middle of this century. HFCs, although not directly ozone-depleting, are highly potent greenhouse gases subject to a global phasedown under the 2016 Kigali Amendment to the Montreal Protocol on Substances that Deplete the Ozone Layer. As of 20 January 2022, 130 Parties have ratified the Kigali Amendment, including India. This analysis evaluates scenarios for India’s HFC demand trajectory compared to likely control obligations under the Kigali Amendment. It is based on current and projected markets for HFC-using equipment and types of refrigerants utilized now and likely to be used in the future. Sectors considered in this work include mobile air conditioning, stationary air conditioning, refrigeration, and foam blowing agents. Results suggest that India’s annual HFC demand under current market trends could reach 76 MMT CO 2 -equivalent (CO 2 e) in 2030 and 197 MMT CO 2 e in 2050, from 23 MMT CO 2 e in 2020, making no changes to the current mix of HFCs in use. The Kigali Amendment requires for compliance that India freeze its HFC consumption in 2028 at a projected level of 59–65 MMT CO 2 e and phase down progressively over the following 29 years; in that case, annual Indian HFC demand would peak in 2030 at a projected 57 MMT CO 2 e and fall to 8 MMT CO 2 e by 2050. This trajectory would avoid cumulative HFC use of 2.2 GT CO 2 e through 2050 versus the current market trends. If actions are taken to accelerate the refrigerant transition in stationary air conditioning by five years, India could peak its annual HFC demand by 2028 at 40 MMT CO 2 e and avoid additional cumulative HFC demand of 337 MMT CO 2 e between 2025 and 2050, exceeding its obligations under the Kigali Amendment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".