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Record W4408426750 · doi:10.5194/egusphere-egu25-10845

Cost-effective climate benefits through fluorocarbon lifecycle management in China

2025· preprint· en· W4408426750 on OpenAlexaboutno aff
Ziwei Chen, Pallav Purohit, Fuli Bai, Thomas Gasser, Yue He, Lena Höglund-Isaksson, Pengnan Jiang, Jianxin Hu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFluorocarbonChinaSystem lifecycleApplication lifecycle managementEnvironmental resource managementBusinessEnvironmental scienceEngineeringComputer scienceGeographyChemical engineering

Abstract

fetched live from OpenAlex

Achieving global climate goals requires heightened ambition and innovative measures. Banks of hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), potent non-CO2 greenhouse gases, represent a significant yet untapped mitigation opportunity. Globally, fluorocarbon refrigerant banks are estimated at 24 Gt CO2-eq and continue to grow, forming a massive and expanding reservoir of greenhouse gases that will eventually be released into the atmosphere if left unaddressed. While the Montreal Protocol and its Kigali Amendment regulate the production and consumption of fluorocarbons, emissions from existing stocks remain largely unregulated. Fluorocarbon lifecycle management (FLM) – encompassing leakage prevention, recovery, recycling, reclamation and destruction – presents a viable solution to mitigate these emissions. In China, the world’s largest producer and consumer of HCFCs and HFCs, implementing FLM could unlock substantial mitigation potential beyond current climate action, serving as a critical step toward net-zero goals. This study provides the necessary systematic evaluation to harness this opportunity.To comprehensively assess the emission profiles of banked fluorocarbons with or without FLM, we developed the Extended Lifecycle Emissions Framework (ELEF), a refined emission modeling approach rooted in IPCC methodologies. ELEF expands conventional frameworks to cover both direct and indirect emissions across the entire lifecycle of fluorocarbons in equipment/product. A bottom-up cost analysis, adapted from the widely applied Greenhouse gas and Air pollution Interactions and Synergies (GAINS) framework to capture sector- and substance-specific treatment nuances, was conducted to assess the cost-effectiveness of FLM in China. Leveraging detailed activity data and localized emission factors, we reconstructed the country’s fluorocarbon banks and emissions from 2000 and projected them through 2060. Mitigation potential was then quantified across varying ambition levels defined by abatement cost cap, with climate impacts assessed using impulse response functions (IRFs) that incorporate climate-carbon feedback.Our results reveal that China currently holds 3.6 ± 0.1 Gt CO2-eq of fluorocarbon banks, which are projected to peak at 4.5 ± 0.1 Gt CO2-eq by 2034. If unmanaged, emissions from these banks could contribute an additional 0.014℃ to global warming by mid-century. FLM, however, could prevent up to 8.0 Gt CO2-eq of cumulative emissions by 2060, reducing the peak temperature increase contribution by 62.4%. Notably, 57 out of 76 mitigation options analyzed exhibit average abatement costs below 10 USD/t CO2-eq, enabling 93.2% of the maximum mitigation potential at a total cost of 18.9 billion USD. These cost-effective measures could deliver additional mitigation equivalent to over 50% of the 13 Gt CO2-eq reductions pledged under the Kigali Amendment in China, or reduce the surface warming contribution of global HFC emissions in 2050 by more than 10%.This study introduces a robust framework for assessing the costs and benefits of FLM. By applying it to China, we demonstrate the significant mitigation scale and feasibility of national-level implementation. Our findings highlight the substantial and cost-effective climate benefits achievable through FLM, offering policymakers an actionable pathway to bridge the emission gap and echoing recent international calls for immediate action.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.297
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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