Modelling long-term HFC emissions from India's residential air-conditioning sector: exploring implications of alternative refrigerants, best practices, and a sustainable lifestyle within an integrated assessment modelling framework
Bibliographic record
Abstract
India's growing role in the global climate debate makes it imperative to analyse emission reduction policies and strategies across a range of GHGs, especially for under-researched non-CO<sub>2</sub> gases. Hydrofluorocarbons' (HFCs) usage in cooling equipment and subsequent emissions are expected to increase dramatically in India with the phase-out of hydrochlorofluorocarbons (HCFCs) as coolants in air-conditioning equipment. We focus on the residential air-conditioning sector in India and analyse a suite of HFC and alternative coolant gas scenarios for understanding the implications for GHG emissions from this sector within an integrated assessment modelling framework. We find that, if unabated, HFC410A emissions will contribute to 36% of the total global warming impact from the residential air-conditioner sector in India in 2050, irrespective of the future economic growth trajectory, and the remaining 64% is from energy to power residential air-conditioners. A move towards more efficient, low global warming potential (GWP) alternative refrigerants will significantly reduce the cumulative global warming footprint of this sector by 37% during the period 2010–2050, due to gains both from energy efficiency as well as low GWP alternatives. Best practices for reducing direct emissions are important, but only of limited utility, and if a sustainable lifestyle is adopted by consumers with lower floorspace, low GWP refrigerants, and higher building envelope efficiencies, cumulative emissions during 2010–2050 can be reduced by 46% compared to the Reference scenario. <b>Policy relevance</b> Our analysis has important implications for Indian climate policy. We highlight that the Indian government's amendment proposal to the Montreal Protocol is a strong signal to the Indian market that the transition away from high GWP refrigerants towards low/zero GWP alternatives will happen sooner or later. The Bureau of Energy Efficiency should extend building energy conservation code policy to residential buildings immediately, and the government should mandate it. Government authorities should set guidelines and mandate reporting of data related to air-conditioner coolant recharge frequency and recovery of scrapped air-conditioner units. For contentious issues like flammability where there is no consensus within the industry, the government needs to undertake an independent technical assessment that can provide unbiased and reliable information to the market.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".