On the Role of Construction in Achieving the SDGs
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
Construction and real estate have been central to the debates on sustainable development. However, the dominant definition of sustainability in construction and real estate remain centred on the environmental dimension. The 2030 Agenda and its Sustainable Development Goals (SDGs) offer new opportunities for the building sector to expand its focus. The available literature utilizes the existing green ratings, sustainability assessment tools and standards as the basis for investigating how construction and buildings can contribute to the 2030 Agenda for Sustainable Development. However, less focus was placed on exploring the broad intersection between the building sector, on the one hand, and the SDGs and their targets on the other. This paper uses a multi-step methodology to analyze the potential role of construction and real estate in the 2030 Agenda. The paper identifies SDG targets that depend (directly or indirectly) on construction and real estate activities, and reveals that 17% of the SDG targets are directly dependent and 27% of the targets are indirectly dependent on these sectorsâ activities. The identified targets are analyzed and are found to be related to all 17 goalsâwith the largest contributions to SDGs 11, 6, and 7. The results of the analysis are mapped and illustrated in order to provide insights to academics, practitioners and governments. This research contributes to the literature on the implementation of the 2030 Agenda. It also exposes the synergistic possibilities, and the partnerships required, to make use of the potential role of construction and real estate in the implementation of the UN Agenda.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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