Evolving Interconnections: Themes and Trends in Sustainable Built Environment Responses to the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it