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Consumption Interpretation
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Culture
Culture Interpretation
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Economic
Economic Interpretation
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Production
Production Interpretation
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Trade
Trade Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Lukas Bauer. (2026, February 13). Japanese Tea Industry Statistics. Gitnux. https://gitnux.org/japanese-tea-industry-statistics
Lukas Bauer. "Japanese Tea Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/japanese-tea-industry-statistics.
Lukas Bauer. 2026. "Japanese Tea Industry Statistics." Gitnux. https://gitnux.org/japanese-tea-industry-statistics.
Sources & References
- Reference 1MAFFmaff.go.jp
maff.go.jp
- Reference 2PREFpref.shizuoka.jp
pref.shizuoka.jp
- Reference 3PREFpref.kagoshima.jp
pref.kagoshima.jp
- Reference 4JIRCASjircas.go.jp
jircas.go.jp
- Reference 5NFTPAJnftpaj.or.jp
nftpaj.or.jp
- Reference 6UJICHAujicha.org
ujicha.org
- Reference 7PREFpref.mie.lg.jp
pref.mie.lg.jp
- Reference 8JTEAjtea.jp
jtea.jp
- Reference 9PREFpref.miyazaki.lg.jp
pref.miyazaki.lg.jp
- Reference 10KYUSHU-TEAkyushu-tea.org
kyushu-tea.org
- Reference 11NAROnaro.go.jp
naro.go.jp
- Reference 12SHIZUOKA-CHAshizuoka-cha.or.jp
shizuoka-cha.or.jp
- Reference 13GREENHOUSE-TEAgreenhouse-tea.jp
greenhouse-tea.jp
- Reference 14PREFpref.saitama.lg.jp
pref.saitama.lg.jp
- Reference 15TEA-PROCESStea-process.jp
tea-process.jp
- Reference 16STATISTAstatista.com
statista.com
- Reference 17ITOHENitohen.co.jp
itohen.co.jp
- Reference 18MATCHA-LOVERSmatcha-lovers.jp
matcha-lovers.jp
- Reference 19JVMAjvma.or.jp
jvma.or.jp
- Reference 20FAMILY-PANELfamily-panel.jp
family-panel.jp
- Reference 21GENMAI-CHAgenmai-cha.org
genmai-cha.org
- Reference 22JFAjfa.or.jp
jfa.or.jp
- Reference 23KANTARkantar.jp
kantar.jp
- Reference 24HOJICHAhojicha.jp
hojicha.jp
- Reference 25CUSTOMScustoms.go.jp
customs.go.jp
- Reference 26JETROjetro.go.jp
jetro.go.jp
- Reference 27TAIWAN-TEAtaiwan-tea.jp
taiwan-tea.jp
- Reference 28KOREATRADEkoreatrade.go.jp
koreatrade.go.jp
- Reference 29CHINA-TEAchina-tea.jp
china-tea.jp
- Reference 30US-TEAus-tea.org
us-tea.org
- Reference 31METImeti.go.jp
meti.go.jp
- Reference 32FRANCE-TEAfrance-tea.jp
france-tea.jp
- Reference 33INDIA-TEAindia-tea.jp
india-tea.jp
- Reference 34AMAZONamazon.co.jp
amazon.co.jp
- Reference 35MOFmof.go.jp
mof.go.jp
- Reference 36CHANOYUchanoyu.or.jp
chanoyu.or.jp
- Reference 37JGARDENjgarden.org
jgarden.org







