Prospects and challenges for sustainable sanitation in developed nations: a critical review
Bibliographic record
Abstract
Serious sanitation challenges are not confined to developing nations. Infrastructure costs, nutrient losses and related ubiquitous surface water impairments, energy demands, and sludge disposal problems have led many to question the long-term sustainability of conventional centralized sanitation in developed nations. Such concerns have resulted in the development of a body of literature and practices aimed at making sanitation more sustainable. This paper has two major aims: (i) provide a representative multidisciplinary (including engineering, natural sciences, and social sciences) synthesis of literature on sustainable sanitation in developed nations over the past 15 years, and (ii) raise consciousness of sustainable sanitation approaches in regions and disciplines where this area of inquiry has at least partly been neglected. Literature included in the review was identified by searching the ISI Web of Knowledge and by “mining” key review papers that already synthesized literature on topics such as ecological sanitation, excreta as fertilizer, and wastewater reuse. The sustainability challenges faced by conventional sanitation are discussed first, followed by a review of efforts in two major areas of sustainable sanitation: (i) incremental improvements to conventional centralized sanitation, and (ii) source separation and decentralization (SSD). Based on the literature review, there does not appear to be a perfect solution to the sanitation challenges (e.g., water use and pollution, greenhouse gas emissions, nutrient and micropollutant releases) faced by society. Compared with SSD, there is a much broader knowledge base on the construction, operation, and maintenance of conventional sanitation systems. And there are significant opportunities for incremental improvements (e.g., reduction of water, energy use), but also significant drawbacks such as the costs of maintaining ageing infrastructure and the mixing of domestic, storm, and industrial effluents. On the other hand, SSD is conceptually promising, and significant progress has been made over the last two decades in basic research and in exploring various SSD models. But SSD is still in its relative infancy: literature and pilot projects are regionally limited and significant logistical, behavioral, regulatory, and other challenges remain. Contributions that meaningfully address sanitation “software” or social perspectives are, therefore, urgently needed from social science fields including economics, policy, and behavioral psychology. Progress toward achieving sustainable sanitation can be aided by addressing four major areas: (i) defining sustainability goals and benchmarks, (ii) raising societal awareness of sanitation problems, (iii) broadening the societal and geographical scope of the research, and (iv) more thoroughly exploring barriers to adapting or changing sanitation systems.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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 teacher head, 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".