Climate Change Mitigation as a Complex Adaptive System - Energy System Transition for Low Carbon Emission Future
Bibliographic record
Abstract
Climate change is a complex problem that impacts human lives. The 2018 Intergovernmental Panel on Climate Change (IPCC) report highlighted that keeping global warming within the 1.5°C threshold would necessitate reaching net zero carbon emissions globally by 2050. Over the past 20 years, Adaptive Systems Engineering (ASE) methods have matured. They are now being deployed to tackle complex system problems, which can help mitigate climate change using technologies. This paper’s contributions are multifold. First, to the authors’ best knowledge, this is the first work to view climate change as a complex adaptive system (CAS) using ASE’s 3-factor modeling and adaptation framework. We formulate climate change as a large CAS by reviewing major climate change factors and the available options. Second, we look into power and heat sector, which is contributing to over 42% of the global CO2 emissions, per the 2023 International Energy Agency (IEA) report. We provide the ASE methods that can be used to produce the best adaptation paths to achieve a holistic approach to the energy system transition, maintaining the stability and adaptability of the power systems. Third, the paper provides a foundation to show that IEEE and INCOSE, as leading technical communities, can contribute to mitigate climate change, using system technologies, hand in hand with other diplomatic and management efforts in international organizations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".