Health and economic effects of increased taxation on tobacco, alcohol, and sugar-sweetened beverages in China: a modelling study
Bibliographic record
Abstract
BACKGROUND: China faces substantial burdens from tobacco, alcohol, and sugar-sweetened beverage (SSB) consumption, yet tax increases on these products have been stagnant. Existing evidence on the health, economic, and fiscal effects of such taxes in China is limited, which hinders tax reforms on tobacco and alcohol and implementation of SSB excise taxes. We aim to quantify the potential effects of increasing these taxes in China. METHODS: We modelled the health, macroeconomic, and fiscal consequences under different tax increase scenarios in China between 2026 and 2050. Years of life gained (YLGs), deaths averted, and additional fiscal revenue were estimated using a cohort state-transition model incorporating age-specific, sex-specific, and income-specific demographic projections, consumption patterns, price elasticities, and relative risk estimates. Macroeconomic benefits were calculated through enhanced labour supply and reduced health-care expenditures using a health-augmented macroeconomic model. Sensitivity analyses were conducted to account for parameter and structural uncertainties. FINDINGS: From 2026 to 2050, under 20% price increases through tax hikes, taxation of tobacco, alcohol, and SSBs would generate 20·58 million (95% uncertainty interval 12·53-29·19), 9·02 million (5·61-12·92), and 3·67 million (2·40-5·05) YLGs, respectively, and avert 0·86 million (0·53-1·23), 0·36 million (0·22-0·51), and 0·13 million (0·09-0·19) deaths, respectively. Health benefits were concentrated among lower-income groups and males. These health improvements would translate into macroeconomic gains equivalent to 0·043% (0·031-0·060), 0·034% (0·024-0·047), and 0·0016% (0·0013-0·0019) of total gross domestic product, respectively. Additional fiscal revenues would total ¥4·53 trillion (3·70-5·50) for tobacco, ¥2·00 trillion (1·75-2·29) for alcohol, and ¥295·5 billion (227·9-377·1) for SSBs. Higher taxes would yield greater health, economic, and equity gains, but fiscal revenues would decline beyond certain tax share levels (72% for tobacco, 59% for alcohol, and 40% for SSBs). INTERPRETATION: Increasing excise taxes on tobacco, alcohol, and SSBs in China can simultaneously generate health benefits, macroeconomic gains, and additional fiscal revenues, as well as improve equity. FUNDING: National Social Science Fund of China, the Taikang Yicai Public Health and Epidemic Control Fund, and Bill & Melinda Gates Foundation. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 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".