Bibliographic record
Abstract
The fossil fuel-based energy systems require accelerated transitioning towards renewable energy provisions in order to reduce carbon emissions. Urban energy systems are commonly called sociotechnological systems, that have interconnections with the political, environmental, and economic landscape of the urban areas. These inter-sectoral linkages, the constant evolution of stakeholder's priorities and relationships, and their conflicting objectives in the urban energy landscape make urban energy systems a complex system. Asserting the need to comprehend the challenges of transitioning towards sustainable energy systems, it appears desirable to view urban energy systems as complex systems. Based on recent literature on urban energy systems and complex systems thinking, the paper initially discusses the characteristics of urban energy systems. It aims to demonstrate the relationship of urban energy systems with social, technological, environmental, political, and economic aspects of urban areas. It further emphasizes the need and the approaches to recognise urban energy systems as complex systems due to the presence of factors, such as multiple stakeholders, the interconnectedness of the agents, changing dynamics, and adaptive processes in the systems. This paper takes the case study of the city setting of Bhopal, Madhya Pradesh, and considers its urban Solar City Master Plan to better understand the essence of complex energy systems. Against this background, the aim of the paper is to understand the application of complexity economics and systems thinking to the transition of urban energy systems from fossil fuels to renewables. In addition, the paper intends to explore how examining the urban energy systems through the lens of complexity economics and systems thinking can be valuable in formulating policy interventions towards sustainable urban energy transitions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".