Improving deep decarbonization modelling capacity for developed and developing country contexts
Bibliographic record
Abstract
Energy models are essential for the development of national or regional deep decarbonization pathways (DDPs), providing the necessary analytical framework to systematically explore the system transitions that are required. However, this is challenging due to the long time horizon, the numerous data requirements and the need for transparent, credible approaches that can provide insights into complex transitions.This article explores how this challenge has been met to date, based on a review of the literature and the experiences of practitioners, drawing in particular on the Deep Decarbonization Pathways Project (DDPP), a collaborative effort by 16 national modelling teams. The article finds that there are a range of modelling approaches that have been used across different country contexts, chosen for different reasons, with recognized strengths and weaknesses. The key motivations for use of a given approach include being fit-for-purpose, having in-country capacity and the intertwined goals of transparency, communicability and policy credibility.From the review, a conceptual decision framework for DDP analysis is proposed. This three step process incorporates policy priorities, national characteristics and the model-agnostic principles that drive model choices, considering the needs and capabilities of developed and developing countries, and subject to data and analytical practicalities. Finally an agenda for the further development of modelling approaches is proposed, which is vital for strengthening capacity. These include a focus on model linking, incorporating behaviour and policy impacts, the flexibility to handle distinctive energy systems, incorporating wider environmental constraints and the development of entry-level tools. The latter three are critical for application in developing countries.Policy relevanceFollowing the Paris Agreement, it is essential that modelling approaches are available to enable governments to plan how to decarbonize their economies in the long term. This article takes stock of current practices, identifies the strengths and weaknesses of existing approaches and proposes how capacity can be strengthened. It also provides some practical guidance on the process of choosing modelling approaches, given national priorities and circumstances. This is particularly relevant as countries revisit their Nationally Determined Contributions to meet the global objective of remaining well below a 2°C average global temperature increase.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".