Fossil gas decarbonization with low-carbon fuels: A system dynamics modelling approach
Bibliographic record
Abstract
The current study aims to develop a dynamic model for determining the future viability of RNG and hydrogen subjected to varying policy efforts, technology progress, and demand. System dynamics modelling was employed to evaluate the future viability of RNG and hydrogen through changes in fuel supply availability, fuel price, return on investment, and emission reduction simulations under different government subsidy scenarios, including investment support, tax reliefs, subsidy intensity, and duration. The model results indicated the ability to achieve over 10 % return on investments. Further, with increased supply capacity, the simulation indicated the potential to displace FNG 100 % by 2045 with surplus RNG and hydrogen. Moreover, RNG and hydrogen selling prices can be reduced to 0.009 $/ MJ and 4.8 $/kg H 2 by 2050. The results demonstrate that subsidy policies and tax incentives facilitate the development and market diffusion of RNG and hydrogen. The findings highlight the potential of policy-driven market mechanisms to accelerate gas network decarbonization beyond North America. The study also emphasizes the socio-economic benefits, such as improved energy security, reduced emissions, and greater community resilience. The developed model serves as a useful tool for policymakers and investors to identify effective long-term strategies for decarbonization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".