Global Bottled Water Industry: A Review of Impacts and Trends
Bibliographic record
Abstract
The report examines facts and perceptions about bottled water in the global context.It analyses the geography, structure, trends, and drivers of the global bottled water market.It examines the existing knowledge on the quality of bottled water, its impacts on water resources, and its role in plastic pollution.It raises the question of the bottled water industry's contribution to the sustainable development goal on universal access to safe drinking water.The analysis considered only those types of bottled water that have little or no difference in taste from the tap water provided by regular municipal water supply.It is shown that bottled water is widely consumed in the both Global North and South although prices can be orders of magnitude higher than tap water.The current global bottled water sales are estimated at almost 270 billion US$ and 350 billion liters.The report maps and ranked the top 50 countries in the world by total and per capita bottled water sales both in dollars and liters.The Asia-Pacific region constitutes about half of the global bottled water market, and the Global South countries together about 60%.The USA, China and Indonesia combined comprise half of the global market.Germany is the biggest market in Europe, Mexico in the LAC region and South Africa in Africa.Singapore and Australia stand out as the leaders in both annual revenue and volume of bottled water sold per capita, with the USA and China per capita indicators being much smaller.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".