Design thinking to avoid maladaptation in building climate change resilience of urban areas
Bibliographic record
Abstract
Adaptation of settlements to the climate change effects is an urgent task for researches and multiple stakeholders interested in efficient functioning of urban systems and safety of residents. The development of adaptation plans is complicated due to lack of certainty about the results of these actions. From the other hand, cases of maladaptation are already numerous, which is why this research was aimed at defining the principles of adaptation planning, which help to avoid the risks of maladaptation. The case of the joint project developed by Master students from Canada, Iceland and Latvia for the rehabilitation of the abandoned industrial facility in the centre of Kyiv, Ukraine, was used to analyse the drivers of maladaptation and suggest the principles of efficient implementation of climate adaptation into city development initiatives. The method of multi-criteria evaluation was used to compare possible post-rehabilitation projects and determine the role of selected factors if raising probability of maladaptation. The weight of factors, affecting the choice of the alternative, was set involving developers, specialists with the experience of designing adaptation plans, non-governmental organisations and researchers. The recommendations for the mitigation of the maladaptation risks in designing adaptation plans were developed and used to reconsider the results of the joint project and abandon the alternative prone to maladaptation. It was shown that climate issues should be considered as a separate category and target instead of including it into the broad category of environmental protection. Evaluation highlighted the importance of design thinking and system structure analysis for the multidisciplinary teams working on the urban development, involving adaptation actions. The results of the research are applicable for preparation of project groups, working on urban development and post-war-reconstruction, to guarantee efficient implementation of climate adaptation needs and prospects in corresponding plans
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".