Non-Sewered Sanitation Systems’ Global Greenhouse Gas Emissions: Balancing Sustainable Development Goal Tradeoffs to End Open Defecation
Bibliographic record
Abstract
Discharge of excreta into the environment and the use of decentralized sanitation technologies, such as septic tanks, pit latrines and ecological sanitation variants (i.e., container-based sanitation), contribute to greenhouse gas (GHG) emissions but have remained poorly quantified. The purpose of this analysis was to investigate the impacts that meeting Sustainable Development Goal (SDG) 6.2 (i.e., ending open defecation by 2030) would have on SDG 13 (i.e., combatting climate impacts). The current Intergovernmental Panel on Climate Change GHG estimation methodology was used as the basis for calculations in this analysis, augmented with improved emission factors from collected data sets for all types of on-site sanitation infrastructure. Specifically, this assessment focused on the three different service levels of sanitation (i.e., improved, unimproved and no service) as defined by UNICEF and WHO as they pertain to three Shared Socioeconomic Pathways. This analysis considered the 100-year global warming potential values in carbon dioxide equivalents of methane and nitrous oxide that can be emitted for each scenario and decentralized sanitation technology. Ultimately, six scenarios were developed for various combinations of pathways and sanitation technologies. There was significant variability between the scenarios, with results ranging from 68 Tg CO2eq/year to 7 TgCO2eq/year. The main contributors of GHG emissions in each scenario were demonstrated to be septic tank systems and pit latrines, although in scenarios that utilized improved emission factors (EFs) these emissions were significantly reduced compared with those using only standard IPCC EFs. This analysis demonstrated that using improved EFs reduced estimated GHG emissions within each SSP scenario by 53% on average. The results indicate that achieving SDG sanitation targets will ultimately increase GHG emissions from the current state but with a relatively small impact on total anthropogenic emissions. There is a need for the continued improvement and collection of field-based emission estimations to refine coarse scale emissions models as well as a better characterization of relevant biodegradation mechanisms in popular forms of on-site sanitation systems. An increase in the understanding of sanitation and climate change linkages among stakeholders will ultimately lead to a better inclusion of sanitation, and other basic human rights, in climate action goals.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".