Guest Editorial: Transition towards deep decarbonisation of modern energy systems
Bibliographic record
Abstract
The decarbonisation of modern energy systems is key to reducing global greenhouse gas emissions and hence mitigating climate change.While governments worldwide have taken significant initiatives towards decarbonisation and announced their carbon peaking and neutrality targets and plans, significant techno-economic challenges remain along the pathway to achieve this decarbonisation goal.Energy systems generally encompass multiple energy carriers, diverse temporal and spatial resolutions, and heterogenous energy entities.This necessitates a suitable design and control of the interfaces between electricity, natural gas, transportation, and heat networks, as well as the transportation, water and agricultural systems.Meanwhile, digital technologies such as big data, machine learning, blockchain, ICT, and IoT are receiving much attention as they can aid the decarbonisation process.Cyber-physical systems as an orchestration of these novel technologies further increases the efficiency of energy provision, thereby optimising economic feasibility and environmental impact.This IET Smart Grid special issue on Transition Towards Deep Decarbonisation of Modern Energy Systems invites a broad spectrum of contributors from universities, industry, research laboratories, and policymakers to develop and present novel solutions and technologies that will facilitate and advance the agenda of deep decarbonisation of modern energy systems.This special issue solicits original research papers that target at, but are not restricted to, the following aspects.It is worth noting that this special issue places an emphasis on addressing the mutual research interests of academics and industry.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".