The role of hydrogen in integrated assessment models: A review of recent developments
Bibliographic record
Abstract
Hydrogen is emerging as a crucial energy source in the global effort to reduce dependence on fossil fuels and meet climate goals. Integrating hydrogen into Integrated Assessment Models (IAMs) is essential for understanding its potential and guiding policy decisions. These models simulate various energy scenarios, assess hydrogen’s impact on emissions, and evaluate its economic viability. However, uncertainties surrounding hydrogen technologies must be effectively addressed in their modeling. This review examines how different IAMs incorporate hydrogen technologies and their implications for decarbonization strategies and policy development, considering underlying uncertainties. We begin by analyzing the configuration of the hydrogen supply chain, focusing on production, logistics, distribution, and utilization. The modeling characteristics of hydrogen integration in 12 IAM families are explored, emphasizing hydrogen’s growing significance in stringent climate mitigation scenarios. Results from the literature and the AR6 database reveal gaps in the modeling of the hydrogen supply chain, particularly in storage, transportation, and distribution. Model characteristics are critical in determining hydrogen’s share within the energy portfolio. Additionally, this study underscores the importance of addressing both parametric and structural uncertainties in IAMs, which are often underestimated, leading to varied outcomes regarding hydrogen’s role in decarbonization strategies. • Hydrogen is crucial for reducing emissions, especially in hard-to-abate sectors like industry and transport by 2050 and 2100. • IAMs show hydrogen playing a key role in future energy systems, especially in stringent climate mitigation scenarios. • Model characteristics play a vital role in determining the share of hydrogen within the energy portfolio. • Addressing parametric and structural uncertainties is essential for robust hydrogen projections and achieving neutrality strategies.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".