Public perceptions of environmental degradation in the Arab World: evidence from surveys about water, air, and sanitation
Bibliographic record
Abstract
Many Arab countries are struggling to combat a range of environmental problems from air pollution to water salinization to overflowing garbage. Yet little is known about how people in this region perceive these environmental problems and the factors that influence their perceptions. This article analyzes surveys conducted by the Arab Barometer with 13,850 people across 12 Arab countries in 2018–19. The focus is on public perceptions about water pollution, air pollution, and trash. About 91% of respondents said that water pollution is a very serious or serious problem. About 89% and 73% feel the same way about trash and air pollution, respectively. Perceptions about environmental quality are mainly shaped by a person’s age, educational background, financial status, and how they view the current economic situation. Although perceptions about water and trash are directly connected to a national environmental quality measure, they are unconnected to specific measurements of clean water access and sanitation quality. Furthermore, perceptions about air quality are unconnected to any general or specific (national- or local-level) measurements. Instead, a person’s age, gender, educational background, financial status, and minority status are better predictors of how much they view air quality to be a problem. These findings shed light on the topic of environmental concern in a comparatively understudied area of the world, highlighting the ways that individual, local, and national factors shape how the average person evaluates environmental problems.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".