Evolving Interconnections: Themes and Trends in Sustainable Built Environment Responses to the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has influenced the way the sustainable built environment—encompassing buildings, infrastructure, and other physical structures—is designed, managed, and utilized, as societal responses to the pandemic may have contributed to shifts in priorities and practices in these areas. Research has predominantly focused on the pandemic’s impacts on enhancing the resilience of the built environment and its role in supporting health protocols, such as reducing transmission risks. However, a critical gap persists in understanding the evolving relationship between the various stages of the COVID-19 pandemic and the sustainable built environment. Accordingly, this systematic literature review (SLR) aims to explore the major themes and trends in sustainable built environment responses to the COVID-19 pandemic and identify gaps in existing studies. The authors employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically search four databases for English-language journal articles published between 2020 and 2023. A total of 331 articles were analyzed using descriptive and thematic methods. The findings reveal that research priorities shifted during different stages of the pandemic, with particular attention given to key areas of the sustainable built environment: healthy outdoor spaces, such as urban green spaces (UGS); energy efficiency and urban planning; and urban mobility and transportation. This SLR contributes to advancing risk reduction strategies that address the intricate interdependencies between major health emergencies and long-term sustainability imperatives for the built environment.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".