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Countries and regions国家与地区

Where can AI data centres be built, and what shapes their cooling?AI 数据中心能建在哪里？什么在影响冷却选择？

Power is the biggest constraint in ten of the eleven regions.十一个地区中有十个的最大约束是电力。

In mainland China, it is where and how efficiently you may build. Six regions set a PUE requirement or target of 1.2–⁠1.4.中国大陆则是建在哪里、按多高能效建。六个地区设有 1.2–⁠1.4 的 PUE 要求或目标。

Eleven regions, each with a country page; climate from hourly observations 2020–2024.十一个地区，每个都有国家页；气候来自 2020–2024 年逐时观测。

China (mainland)中国大陆≤ 1.2≤ 1.2Taiwan台湾≤ 1.3 / 1.4≤ 1.3 / 1.4South Korea韩国none found未找到Japan日本≤ 1.3≤ 1.3Malaysia马来西亚1.41.4Singapore新加坡1.251.25Indonesia印度尼西亚none found未找到Australia澳大利亚1.251.25New Zealand新西兰none found未找到United States美国no federal limit无联邦上限India印度none found nationally未找到全国规则

Number: PUE requirement or target. Legal status varies; see the table.数字：PUE 要求或目标，法律效力不同，见下表。

Biggest constraint is power最大约束是电力

Biggest constraint is not power: China (mainland)最大约束不是电力：中国大陆

Constraints are our reading of the sourced rules.约束是本站对有来源规则的解读。

Boundaries follow Natural Earth and are not a position on any dispute.边界依据 Natural Earth，不代表本站对任何争议边界的立场。

At a 10 K approach, a dry cooler alone makes 32 °C water for 2 hours a year in Singapore, and 41 °C water for 7,990.在新加坡，接近温差 10 K 时，干冷器每年只有 2 小时能单独供出 32 °C 的水，41 °C 的水则有 7,990 小时。

Singapore新加坡

2h

0% of the year at W32, 10 KW32、10 K 时占全年 0%

Dry-cooler approach干冷器接近温差

Hours a year a dry cooler alone can make the water at the chosen approach (2020–2024 average, 24 stations, NOAA hourly observations). Other hours need chillers or another way to reject heat. This assumes the dry cooler supplies facility water directly. A glycol loop with a plate heat exchanger adds its own approach (often 2–3 K) and leaves fewer hours. These are shares of the year; chillers are still sized for the hottest hours. W20, W25 and W41 are this site's labels, not ASHRAE classes.所选接近温差下仅靠干冷器即可供水的年小时数（2020–2024 年平均，24 个站点，NOAA 逐时观测）。其余小时需冷机或其他排热方式。此处假设干冷器直接供设施水。若中间设乙二醇回路和板式换热器，需叠加其接近温差（常见约 2–3 K），小时数更少。这里是全年占比，冷机仍按最热时段选型。W20、W25、W41 是本站标签，不是 ASHRAE 等级。

At a 10 K approach, a dry cooler alone makes 32 °C water for 2 hours a year in Singapore, and 41 °C water for 7,990.在新加坡，接近温差 10 K 时，干冷器每年只有 2 小时能单独供出 32 °C 的水，41 °C 的水则有 7,990 小时。

Table表格

City (weather station)城市（气象站）Station ID站号Supply water and approach供水温度与接近温差Dry-bulb limit, °C干球温度上限，°CHours a year, average年均小时数Lowest and highest year最低与最高单年Hours observed, lowest year观测覆盖（最低单年）99.6th-percentile dry-bulb, °C干球温度 99.6 百分位，°C
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W17, 5 K≤ 123,6383,472–3,803100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W17, 10 K≤ 72,2362,003–2,497100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W17, 15 K≤ 21,010673–1,402100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W20, 5 K≤ 154,4904,216–4,693100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W20, 10 K≤ 103,1683,006–3,288100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W20, 15 K≤ 51,7941,496–2,133100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W25, 5 K≤ 205,8755,692–6,058100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W25, 10 K≤ 154,4904,216–4,693100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W25, 15 K≤ 103,1683,006–3,288100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W32, 5 K≤ 277,8457,702–7,947100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W32, 10 K≤ 226,4126,226–6,555100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W32, 15 K≤ 174,9534,669–5,140100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W41, 5 K≤ 368,7488,732–8,760100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W41, 10 K≤ 318,4588,393–8,498100%34.2
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）72403093738W41, 15 K≤ 267,6117,447–7,737100%34.2
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W17, 5 K≤ 121,8991,642–2,149100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W17, 10 K≤ 7902696–1,228100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W17, 15 K≤ 2288154–518100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W20, 5 K≤ 152,6972,379–2,912100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W20, 10 K≤ 101,5401,252–1,831100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W20, 15 K≤ 5641492–968100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W25, 5 K≤ 204,0823,684–4,484100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W25, 10 K≤ 152,6972,379–2,912100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W25, 15 K≤ 101,5401,252–1,831100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W32, 5 K≤ 276,3886,101–6,712100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W32, 10 K≤ 224,6044,232–5,000100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W32, 15 K≤ 173,1392,804–3,380100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W41, 5 K≤ 368,5018,236–8,684100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W41, 10 K≤ 317,5897,250–7,882100%38.6
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）72259003927W41, 15 K≤ 265,9965,727–6,329100%38.6
Phoenix (Sky Harbor)凤凰城（天港）72278023183W17, 5 K≤ 12943793–1,070100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W17, 10 K≤ 7129109–147100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W17, 15 K≤ 200–0100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W20, 5 K≤ 151,8281,606–1,967100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W20, 10 K≤ 10597457–693100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W20, 15 K≤ 54524–54100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W25, 5 K≤ 203,1603,050–3,245100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W25, 10 K≤ 151,8281,606–1,967100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W25, 15 K≤ 10597457–693100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W32, 5 K≤ 274,8434,648–5,011100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W32, 10 K≤ 223,6033,547–3,685100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W32, 15 K≤ 172,2912,095–2,410100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W41, 5 K≤ 367,2626,935–7,632100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W41, 10 K≤ 315,8365,475–6,195100%45
Phoenix (Sky Harbor)凤凰城（天港）72278023183W41, 15 K≤ 264,5714,456–4,670100%45
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W17, 5 K≤ 12887808–1,02296%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W17, 10 K≤ 7239–3296%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W17, 15 K≤ 200–096%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W20, 5 K≤ 152,2202,082–2,37496%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W20, 10 K≤ 10339292–39796%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W20, 15 K≤ 500–096%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W25, 5 K≤ 205,3284,872–5,70996%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W25, 10 K≤ 152,2202,082–2,37496%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W25, 15 K≤ 10339292–39796%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W32, 5 K≤ 278,4178,314–8,55496%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W32, 10 K≤ 226,7106,217–7,17096%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W32, 15 K≤ 173,3733,151–3,59896%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W41, 5 K≤ 368,7468,727–8,76096%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W41, 10 K≤ 318,6948,643–8,75196%32.8
Sydney (Sydney Airport)悉尼（悉尼机场）94767099999W41, 15 K≤ 268,2508,076–8,40296%32.8
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W17, 5 K≤ 121,7231,475–1,95081%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W17, 10 K≤ 7407268–51381%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W17, 15 K≤ 290–1981%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W20, 5 K≤ 153,0112,777–3,25781%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W20, 10 K≤ 101,055851–1,25581%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W20, 15 K≤ 515685–21181%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W25, 5 K≤ 205,7885,313–6,18881%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W25, 10 K≤ 153,0112,777–3,25781%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W25, 15 K≤ 101,055851–1,25581%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W32, 5 K≤ 278,1507,950–8,41181%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W32, 10 K≤ 226,8206,386–7,15681%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W32, 15 K≤ 174,0293,728–4,32381%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W41, 5 K≤ 368,7248,700–8,76081%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W41, 10 K≤ 318,5688,473–8,67581%35.9
Western Sydney (Penrith)悉尼西部（彭里斯）94763099999W41, 15 K≤ 267,9767,756–8,25281%35.9
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W17, 5 K≤ 123,3642,957–3,66696%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W17, 10 K≤ 7651515–75396%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W17, 15 K≤ 22510–4596%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W20, 5 K≤ 155,2614,812–5,71096%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W20, 10 K≤ 102,0391,655–2,32796%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W20, 15 K≤ 5224158–29896%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W25, 5 K≤ 207,4947,247–7,70696%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W25, 10 K≤ 155,2614,812–5,71096%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W25, 15 K≤ 102,0391,655–2,32796%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W32, 5 K≤ 278,4498,342–8,50396%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W32, 10 K≤ 227,8977,705–8,05996%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W32, 15 K≤ 176,3645,989–6,76296%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W41, 5 K≤ 368,7448,731–8,75496%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W41, 10 K≤ 318,6598,592–8,69196%34
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）94866099999W41, 15 K≤ 268,3688,234–8,43896%34
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W17, 5 K≤ 121,7381,490–1,85296%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W17, 10 K≤ 7187143–21296%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W17, 15 K≤ 200–096%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W20, 5 K≤ 153,9903,643–4,20296%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W20, 10 K≤ 10781698–81496%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W20, 15 K≤ 54424–6696%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W25, 5 K≤ 207,4877,195–7,65096%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W25, 10 K≤ 153,9903,643–4,20296%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W25, 15 K≤ 10781698–81496%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W32, 5 K≤ 278,7538,742–8,76096%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W32, 10 K≤ 228,2107,910–8,40496%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W32, 15 K≤ 175,5365,177–5,86796%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W41, 5 K≤ 368,7608,760–8,76096%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W41, 10 K≤ 318,7608,760–8,76096%26.2
Auckland (Auckland Airport)奥克兰（奥克兰机场）93119099999W41, 15 K≤ 268,7378,709–8,75996%26.2
Johor (Senai)柔佛（士乃）48679099999W17, 5 K≤ 1200–096%34.3
Johor (Senai)柔佛（士乃）48679099999W17, 10 K≤ 700–096%34.3
Johor (Senai)柔佛（士乃）48679099999W17, 15 K≤ 200–096%34.3
Johor (Senai)柔佛（士乃）48679099999W20, 5 K≤ 1500–096%34.3
Johor (Senai)柔佛（士乃）48679099999W20, 10 K≤ 1000–096%34.3
Johor (Senai)柔佛（士乃）48679099999W20, 15 K≤ 500–096%34.3
Johor (Senai)柔佛（士乃）48679099999W25, 5 K≤ 2000–096%34.3
Johor (Senai)柔佛（士乃）48679099999W25, 10 K≤ 1500–096%34.3
Johor (Senai)柔佛（士乃）48679099999W25, 15 K≤ 1000–096%34.3
Johor (Senai)柔佛（士乃）48679099999W32, 5 K≤ 275,3014,964–5,55896%34.3
Johor (Senai)柔佛（士乃）48679099999W32, 10 K≤ 22100–3696%34.3
Johor (Senai)柔佛（士乃）48679099999W32, 15 K≤ 1700–096%34.3
Johor (Senai)柔佛（士乃）48679099999W41, 5 K≤ 368,7608,760–8,76096%34.3
Johor (Senai)柔佛（士乃）48679099999W41, 10 K≤ 317,6787,458–7,82596%34.3
Johor (Senai)柔佛（士乃）48679099999W41, 15 K≤ 264,3433,801–4,72296%34.3
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W17, 5 K≤ 1200–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W17, 10 K≤ 700–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W17, 15 K≤ 200–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W20, 5 K≤ 1500–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W20, 10 K≤ 1000–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W20, 15 K≤ 500–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W25, 5 K≤ 2000–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W25, 10 K≤ 1500–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W25, 15 K≤ 1000–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W32, 5 K≤ 274,1743,710–4,45996%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W32, 10 K≤ 2210–296%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W32, 15 K≤ 1700–096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W41, 5 K≤ 368,7608,759–8,76096%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W41, 10 K≤ 317,1386,923–7,34296%34.9
Kuala Lumpur (Subang)吉隆坡（梳邦）48647099999W41, 15 K≤ 262,7952,127–3,21496%34.9
Singapore (Changi)新加坡（樟宜）48698099999W17, 5 K≤ 1200–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W17, 10 K≤ 700–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W17, 15 K≤ 200–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W20, 5 K≤ 1500–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W20, 10 K≤ 1000–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W20, 15 K≤ 500–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W25, 5 K≤ 2000–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W25, 10 K≤ 1500–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W25, 15 K≤ 1000–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W32, 5 K≤ 273,2392,363–3,98596%33.2
Singapore (Changi)新加坡（樟宜）48698099999W32, 10 K≤ 2220–896%33.2
Singapore (Changi)新加坡（樟宜）48698099999W32, 15 K≤ 1700–096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W41, 5 K≤ 368,7608,760–8,76096%33.2
Singapore (Changi)新加坡（樟宜）48698099999W41, 10 K≤ 317,9907,727–8,16896%33.2
Singapore (Changi)新加坡（樟宜）48698099999W41, 15 K≤ 261,7541,034–2,54496%33.2
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W17, 5 K≤ 1200–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W17, 10 K≤ 700–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W17, 15 K≤ 200–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W20, 5 K≤ 1500–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W20, 10 K≤ 1000–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W20, 15 K≤ 500–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W25, 5 K≤ 2000–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W25, 10 K≤ 1500–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W25, 15 K≤ 1000–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W32, 5 K≤ 274,2173,706–4,68196%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W32, 10 K≤ 2221–696%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W32, 15 K≤ 1700–096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W41, 5 K≤ 368,7598,754–8,76096%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W41, 10 K≤ 317,4537,085–7,80896%34
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）96749099999W41, 15 K≤ 262,9512,311–3,51296%34
Tokyo (Haneda)东京（羽田）47671099999W17, 5 K≤ 122,7032,513–2,89096%33.6
Tokyo (Haneda)东京（羽田）47671099999W17, 10 K≤ 71,062825–1,42296%33.6
Tokyo (Haneda)东京（羽田）47671099999W17, 15 K≤ 27128–11596%33.6
Tokyo (Haneda)东京（羽田）47671099999W20, 5 K≤ 153,5963,371–3,88696%33.6
Tokyo (Haneda)东京（羽田）47671099999W20, 10 K≤ 102,0501,840–2,36496%33.6
Tokyo (Haneda)东京（羽田）47671099999W20, 15 K≤ 5503335–73796%33.6
Tokyo (Haneda)东京（羽田）47671099999W25, 5 K≤ 205,2964,910–5,55996%33.6
Tokyo (Haneda)东京（羽田）47671099999W25, 10 K≤ 153,5963,371–3,88696%33.6
Tokyo (Haneda)东京（羽田）47671099999W25, 15 K≤ 102,0501,840–2,36496%33.6
Tokyo (Haneda)东京（羽田）47671099999W32, 5 K≤ 277,6407,234–8,05196%33.6
Tokyo (Haneda)东京（羽田）47671099999W32, 10 K≤ 226,0365,657–6,33796%33.6
Tokyo (Haneda)东京（羽田）47671099999W32, 15 K≤ 174,2703,921–4,53696%33.6
Tokyo (Haneda)东京（羽田）47671099999W41, 5 K≤ 368,7598,758–8,76096%33.6
Tokyo (Haneda)东京（羽田）47671099999W41, 10 K≤ 318,5388,402–8,64396%33.6
Tokyo (Haneda)东京（羽田）47671099999W41, 15 K≤ 267,3146,877–7,74496%33.6
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W17, 5 K≤ 12216180–28796%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W17, 10 K≤ 740–1196%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W17, 15 K≤ 200–096%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W20, 5 K≤ 15844645–1,11596%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W20, 10 K≤ 107656–10196%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W20, 15 K≤ 500–096%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W25, 5 K≤ 202,9052,687–2,99496%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W25, 10 K≤ 15844645–1,11596%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W25, 15 K≤ 107656–10196%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W32, 5 K≤ 275,9695,648–6,26196%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W32, 10 K≤ 223,6763,324–3,90896%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W32, 15 K≤ 171,6561,451–1,83296%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W41, 5 K≤ 368,7488,742–8,75596%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W41, 10 K≤ 317,8867,763–7,95596%35.8
Taoyuan (Taoyuan Airport)桃园（桃园机场）46686099999W41, 15 K≤ 265,4555,053–5,82996%35.8
Seoul (Incheon Airport)首尔（仁川机场）47113199999W17, 5 K≤ 123,9693,642–4,21396%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W17, 10 K≤ 72,8332,636–2,97296%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W17, 15 K≤ 21,6351,435–1,95196%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W20, 5 K≤ 154,7184,419–4,99896%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W20, 10 K≤ 103,5153,286–3,74396%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W20, 15 K≤ 52,3812,133–2,48896%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W25, 5 K≤ 206,0715,837–6,35596%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W25, 10 K≤ 154,7184,419–4,99896%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W25, 15 K≤ 103,5153,286–3,74396%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W32, 5 K≤ 278,2657,922–8,54996%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W32, 10 K≤ 226,6776,327–7,09996%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W32, 15 K≤ 175,2245,009–5,51196%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W41, 5 K≤ 368,7608,760–8,76096%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W41, 10 K≤ 318,7058,652–8,74596%31.6
Seoul (Incheon Airport)首尔（仁川机场）47113199999W41, 15 K≤ 268,0137,555–8,40096%31.6
Beijing (Capital Airport)北京（首都机场）54511099999W17, 5 K≤ 123,8893,777–4,09496%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W17, 10 K≤ 72,9412,840–3,09096%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W17, 15 K≤ 22,0061,843–2,19196%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W20, 5 K≤ 154,4784,355–4,67396%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W20, 10 K≤ 103,4903,384–3,64496%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W20, 15 K≤ 52,5902,450–2,72096%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W25, 5 K≤ 205,6025,434–5,77396%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W25, 10 K≤ 154,4784,355–4,67396%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W25, 15 K≤ 103,4903,384–3,64496%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W32, 5 K≤ 277,6127,431–7,87696%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W32, 10 K≤ 226,1625,958–6,37496%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W32, 15 K≤ 174,8924,763–5,04696%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W41, 5 K≤ 368,7248,643–8,75296%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W41, 10 K≤ 318,3658,199–8,55596%36.2
Beijing (Capital Airport)北京（首都机场）54511099999W41, 15 K≤ 267,3567,167–7,61196%36.2
Shanghai (Hongqiao)上海（虹桥）58367099999W17, 5 K≤ 122,5652,482–2,62796%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W17, 10 K≤ 71,232984–1,49896%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W17, 15 K≤ 2272147–35996%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W20, 5 K≤ 153,3103,200–3,43096%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W20, 10 K≤ 102,0611,860–2,21196%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W20, 15 K≤ 5754515–97096%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W25, 5 K≤ 204,7564,629–4,95496%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W25, 10 K≤ 153,3103,200–3,43096%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W25, 15 K≤ 102,0611,860–2,21196%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W32, 5 K≤ 276,9776,701–7,33796%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W32, 10 K≤ 225,3695,176–5,55296%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W32, 15 K≤ 173,8593,746–3,93396%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W41, 5 K≤ 368,6958,618–8,75696%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W41, 10 K≤ 318,1407,880–8,33696%36.8
Shanghai (Hongqiao)上海（虹桥）58367099999W41, 15 K≤ 266,6576,450–7,02996%36.8
Hokkaido (Sapporo)北海道（札幌）47412099999W17, 5 K≤ 124,8124,687–5,13282%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W17, 10 K≤ 73,6243,315–3,86082%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W17, 15 K≤ 22,4792,005–2,87082%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W20, 5 K≤ 155,5305,414–5,74082%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W20, 10 K≤ 104,3364,222–4,58582%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W20, 15 K≤ 53,1562,761–3,42182%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W25, 5 K≤ 206,8366,534–7,05182%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W25, 10 K≤ 155,5305,414–5,74082%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W25, 15 K≤ 104,3364,222–4,58582%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W32, 5 K≤ 278,4748,264–8,63582%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W32, 10 K≤ 227,4026,969–7,60182%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W32, 15 K≤ 176,0185,894–6,08982%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W41, 5 K≤ 368,7608,760–8,76082%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W41, 10 K≤ 318,7268,699–8,75382%30.7
Hokkaido (Sapporo)北海道（札幌）47412099999W41, 15 K≤ 268,3388,055–8,52482%30.7
Kyushu (Fukuoka)九州（福冈）47807099999W17, 5 K≤ 122,4972,162–2,64482%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W17, 10 K≤ 7866602–1,25282%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W17, 15 K≤ 26311–10882%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W20, 5 K≤ 153,4433,275–3,74382%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W20, 10 K≤ 101,8151,474–2,11582%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W20, 15 K≤ 5402177–62582%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W25, 5 K≤ 204,9834,732–5,27282%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W25, 10 K≤ 153,4433,275–3,74382%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W25, 15 K≤ 101,8151,474–2,11582%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W32, 5 K≤ 277,1856,891–7,52382%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W32, 10 K≤ 225,5915,272–5,77582%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W32, 15 K≤ 174,0373,840–4,47782%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W41, 5 K≤ 368,7568,747–8,76082%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W41, 10 K≤ 318,3218,064–8,45082%34.6
Kyushu (Fukuoka)九州（福冈）47807099999W41, 15 K≤ 266,8666,672–7,23182%34.6
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W17, 5 K≤ 1200–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W17, 10 K≤ 700–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W17, 15 K≤ 200–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W20, 5 K≤ 1520–595%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W20, 10 K≤ 1000–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W20, 15 K≤ 500–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W25, 5 K≤ 2013096–18195%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W25, 10 K≤ 1520–595%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W25, 15 K≤ 1000–095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W32, 5 K≤ 273,0932,685–3,37795%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W32, 10 K≤ 22372283–50395%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W32, 15 K≤ 17164–2595%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W41, 5 K≤ 368,7258,711–8,75095%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W41, 10 K≤ 317,2847,109–7,39695%36.3
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）43003099999W41, 15 K≤ 262,0101,710–2,26995%36.3
Chennai (Chennai Airport)金奈（金奈机场）43279099999W17, 5 K≤ 1200–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W17, 10 K≤ 700–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W17, 15 K≤ 200–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W20, 5 K≤ 1500–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W20, 10 K≤ 1000–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W20, 15 K≤ 500–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W25, 5 K≤ 2040–1296%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W25, 10 K≤ 1500–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W25, 15 K≤ 1000–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W32, 5 K≤ 272,7732,481–3,02696%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W32, 10 K≤ 2210318–17896%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W32, 15 K≤ 1700–096%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W41, 5 K≤ 368,5718,501–8,62796%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W41, 10 K≤ 316,7126,519–6,82496%38.6
Chennai (Chennai Airport)金奈（金奈机场）43279099999W41, 15 K≤ 261,9071,613–2,14996%38.6
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W17, 5 K≤ 1240–1596%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W17, 10 K≤ 700–096%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W17, 15 K≤ 200–096%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W20, 5 K≤ 1511540–20396%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W20, 10 K≤ 1000–296%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W20, 15 K≤ 500–096%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W25, 5 K≤ 201,6811,279–2,00196%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W25, 10 K≤ 1511540–20396%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W25, 15 K≤ 1000–296%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W32, 5 K≤ 277,2207,040–7,49496%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W32, 10 K≤ 223,8033,474–4,13196%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W32, 15 K≤ 17430240–58496%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W41, 5 K≤ 368,7558,736–8,76096%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W41, 10 K≤ 318,4408,292–8,53296%34.4
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）42705699999W41, 15 K≤ 266,6376,408–6,89596%34.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W17, 5 K≤ 1260–2095%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W17, 10 K≤ 700–095%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W17, 15 K≤ 200–095%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W20, 5 K≤ 156040–11495%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W20, 10 K≤ 1020–695%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W20, 15 K≤ 500–095%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W25, 5 K≤ 20963793–1,07995%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W25, 10 K≤ 156040–11495%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W25, 15 K≤ 1020–695%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W32, 5 K≤ 275,7465,612–5,79295%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W32, 10 K≤ 221,8601,619–2,00895%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W32, 15 K≤ 17232168–34095%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W41, 5 K≤ 368,5948,517–8,65895%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W41, 10 K≤ 317,7557,653–7,83295%38.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）43128599999W41, 15 K≤ 265,1084,939–5,18495%38.4
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W17, 5 K≤ 1200–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W17, 10 K≤ 700–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W17, 15 K≤ 200–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W20, 5 K≤ 1560–1887%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W20, 10 K≤ 1000–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W20, 15 K≤ 500–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W25, 5 K≤ 20323208–40287%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W25, 10 K≤ 1560–1887%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W25, 15 K≤ 1000–087%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W32, 5 K≤ 273,7873,454–3,97487%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W32, 10 K≤ 22739560–83787%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W32, 15 K≤ 17411–9387%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W41, 5 K≤ 368,6958,600–8,73887%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W41, 10 K≤ 317,2536,994–7,47687%37
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）43149099999W41, 15 K≤ 262,7742,489–2,90987%37
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W17, 5 K≤ 12708551–89496%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W17, 10 K≤ 75924–7696%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W17, 15 K≤ 200–096%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W20, 5 K≤ 151,3531,127–1,54496%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W20, 10 K≤ 10369263–49396%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W20, 15 K≤ 550–1496%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W25, 5 K≤ 202,4802,344–2,83696%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W25, 10 K≤ 151,3531,127–1,54496%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W25, 15 K≤ 10369263–49396%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W32, 5 K≤ 274,6254,365–4,86996%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W32, 10 K≤ 222,9752,817–3,32696%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W32, 15 K≤ 171,7991,576–2,07396%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W41, 5 K≤ 368,2197,947–8,48396%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W41, 10 K≤ 316,6756,360–7,02296%42.6
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）42181099999W41, 15 K≤ 264,1863,945–4,46696%42.6

Sources来源

  1. NOAA National Centers for Environmental Information, Integrated Surface Database (global hourly); hourly observations 2020–2024, US government data, public domainNOAA National Centers for Environmental Information, Integrated Surface Database (global hourly)；2020–2024 年逐时观测，美国政府数据，公有领域
  2. ASHRAE TC9.9: facility water classes W17–W45 and W+ASHRAE TC9.9：设施水温等级 W17–W45 及 W+

Method and notes方法和说明

No compressor is needed in an hour when the outdoor dry-bulb temperature is at or below the supply water temperature minus the dry cooler's approach (5, 10 or 15 K). W17, W32, W40 and W45 are ASHRAE facility water classes; W20 and W25 mean 20 and 25 °C supply, the range where chilled-water plant design is moving, and W41 means 41 °C supply, enough for 45 °C water at the GPU after a 4 K CDU approach (this site's labels, not ASHRAE classes). The dry cooler is assumed to supply the facility water directly. One value per UTC hour (the first valid observation); hours are scaled from the observed share to a year of 8,760 hours (leap years included) and averaged over 2020–2024; the range is the lowest and highest single year. 5 K is conventional commercial practice; data centres increasingly use 10–15 K to reduce the number and footprint of dry coolers. Each year counts as 8,760 hours. A wider approach means fewer, smaller dry coolers but more hours the dry cooler alone cannot cover, met by chillers or another way to reject heat; where chillers run, a warmer condenser loop lowers their efficiency and raises PUE. Where footprint is the binding limit, designers accept that. The 99.6th-percentile column is the average over the five years of each year's 99.6th-percentile hourly dry-bulb temperature (about 35 hours a year are hotter). Missing hours are assumed to follow the same temperature mix as the observed ones; where gaps are uneven (Penrith 2020: about two-thirds of hours observed from January to June, nearly all from August; Sapporo and Fukuoka 2023: in May and June only eight observations a day, every three hours; 2024: about 65–76% of hours observed from August to November; Visakhapatnam 2023: more gaps in April to June and in August), that year is less certain. Airport stations can be cooler or warmer than a data-centre site nearby.当室外干球温度不高于“供水温度减干冷器接近温差（5、10 或 15 K）”时，该小时不需要压缩机。W17、W32、W40、W45 为 ASHRAE 设施水温等级；W20 和 W25 指 20 °C 和 25 °C 供水，是冷站冷冻水设计正在移向的区间；W41 指 41 °C 供水，经 CDU 4 K 接近温差后可在 GPU 处提供 45 °C 水（本站标注，不是 ASHRAE 等级）。假设干冷器直接供设施水。每个 UTC 小时取第一个有效观测值；按观测到的比例折算为 8,760 小时的全年（闰年同样按 8,760 小时），取 2020–2024 年平均；区间为单年最低与最高。常规商用接近温差 5 K，数据中心越来越多采用 10–15 K 以减少干冷器数量和占地。每年按 8,760 小时计。接近温差越大，干冷器越少、占地越小，但干冷器单独无法覆盖的小时更多，需由冷机或其他排热方式承担；冷机运行时，较高的冷凝水温会降低其效率、使 PUE 变差。当占地是硬约束时，设计会接受这一代价。99.6 百分位一列为五年中各年逐时干球温度 99.6 百分位的平均值（每年约 35 小时更热）。缺测小时假设与已观测小时的温度分布相同；缺测不均匀时（彭里斯 2020 年：1–6 月约三分之二的小时有观测，8 月起几乎全部有观测；札幌和福冈 2023 年：5、6 月每天只有 8 次、每三小时一次观测；2024 年：8–11 月约 65–76% 的小时有观测；维沙卡帕特南 2023 年：4–6 月和 8 月缺测较多），该年结果的不确定性更大。机场气象站与附近数据中心场址的气温可能有差别。

Eleven regions: PUE requirement or target, power build-out and the biggest constraint十一个地区：PUE 要求或目标、电力建设规模和最大约束
Region地区Biggest constraint (our reading)最大约束（本站解读）PUE requirement or targetPUE 要求或目标Power build-out, with its basis电力规模及其口径
China (mainland)中国大陆Location and PUE rules选址与能效规则new large sites go first to eight hub nodes, held to PUE 1.2新建大型项目优先进 8 个枢纽节点，PUE 不得高于 1.2Second: electricity price, Beijing surcharges existing sites above PUE 1.35 from 2026其次：电价，北京自 2026 年起对 PUE 高于 1.35 的存量数据中心加价≤ 1.2≤ 1.2Action-plan rule, national hub-node projects行动计划要求，国家枢纽节点项目≈19 GW约 19 GWaverage load of computing centres in 2025 (170 TWh ÷ 8,760 h; NEA data, this site's conversion)2025 年算力中心平均负荷（1,700 亿千瓦时 ÷ 8,760 小时；国家能源局数据，本站换算）. Actual use, converted to average load实际用电，换算为平均负荷
Taiwan台湾Power电力Taipower holds back loads of 5 MW or more north of Taoyuan台电暂缓桃园以北 5 MW 以上用电Second: own power and green power, users of 5 MW or more owe a 10% renewable duty; new large users must add own generation or storage (law passed August 2026, thresholds pending)其次：自备电源与绿电，5 MW 以上用户须承担 10% 再生能源义务；新设大用户须自备发电或储能（2026 年 8 月修法通过，门槛待定）≤ 1.3 / ≤ 1.4≤ 1.3 / ≤ 1.4Rule, new builds of 5 MW or more; 1.3 hyperscale, 1.4 colocation规定，5 MW 以上新建；超大型 1.3，主机代管 1.4303.5 MW303.5 MWcontract capacity at four energised transmission-level data centres (Taipower statistics as reported by the press, December 2025)4 座已送电输电级数据中心的契约容量（媒体转述的台电统计，2025 年 12 月）. Contracted; 4.76 GW applied for已签约；另有 4.76 GW 申请
South Korea韩国Power电力any load of 10 MW or more needs a grid assessment用电 10 MW 及以上须先过电网评估Second: water, no data-centre water rule found; operators report their own WUE其次：水，未找到数据中心用水规则；运营方自报 WUENone found未找到Voluntary certification only仅有自愿性认证6.2 GW6.2 GWdata-centre peak demand in 2038, 11th Basic Plan (operating sites drew 1,762 MW in 2022)第 11 次基本计划预计的 2038 年数据中心峰值需求（2022 年运营站点 1,762 MW）. Forecast, peak demand预测，峰值需求
Japan日本Power电力grid connection can take ten years or more in some cases并网在部分情况下可能需要十年以上Second: land and water, hub criteria include industrial water and 30 ha or more of industrial land其次：土地和水，枢纽选址标准包括工业用水和 30 公顷以上产业用地≤ 1.3≤ 1.3Law, new builds from FY2029法定，2029 财年起新建+6.6 GW+6.6 GWgrid demand added by FY2035 (OCCTO, January 2026)到 2035 财年新增的电网需求（OCCTO，2026 年 1 月）. Forecast预测
Malaysia马来西亚Power电力in Johor, TNB must approve the supply first在柔佛须先获 TNB 批准供电Second: water, new sites should avoid water-stressed areas其次：水，新站点应避开缺水地区1.41.4Design target, tax-incentive condition设计目标，税收优惠条件8.35 GW8.35 GWsecured by TNB (61 projects), June 2026TNB 已锁定（61 个项目），2026 年 6 月. Secured pipeline, maximum demand已锁定管线，最大需求
Singapore新加坡Power电力new capacity comes mainly through government calls新增容量主要通过政府征集分配Second: land, all 2026 awards are on Jurong Island (our observation)其次：土地，2026 年分配全部位于裕廊岛（本站观察）1.251.25Call condition征集条件1.6 GW1.6 GWmaximum IT load of all data centres, October 2026全部数据中心最大 IT 负荷，2026 年 10 月. Includes the 200 MW awarded in 2026含 2026 年分配的 200 MW
Indonesia印度尼西亚Power电力a 1 GW order needs a 500 kV line and 3-5 years1 GW 订单需 500 kV 线路和 3-5 年Second: water, a 640 MW project at Jatiluhur is halted pending permits, with water use in focus其次：水，Jatiluhur 的 640 MW 项目因许可未发停工，用水受到关注None found未找到No rule found in the sources read here所读来源中未找到规则5.2 GW5.2 GWexpected data-centre power demand in 2034 (energy ministry, from 1.1 GW in 2025)能源部预计的 2034 年数据中心用电需求（2025 年为 1.1 GW）. Forecast, electricity demand (ESDM, as reported)预测，用电需求（ESDM，经媒体报道）
Australia澳大利亚Power电力new large sites must underwrite new supply (announced)新建大型站点须为新增电源提供担保（已宣布）Second: water, NSW and Victoria push cooling to recycled water其次：水，新州和维州推动冷却改用再生水1.251.25NSW guideline新州指引67.1 GW67.1 GWat all stages advised to AEMO, March 2026向 AEMO 报送的各阶段容量，2026 年 3 月. Mostly proposed or at enquiry大部分处于提议或咨询阶段
New Zealand新西兰Power电力large-load connection rules and added generation expected (announced)大负荷并网规则和新增发电要求将出台（已宣布）Second: water, Datagrid's consents allow over 600,000 L a day of groundwater其次：水，Datagrid 的许可允许每天取用逾 60 万升地下水None found未找到No PUE rule in the sources read here; sector average about 1.3 (NZTech)所读来源中未找到 PUE 规则；行业平均约 1.3（NZTech）104 MW104 MWdeployed capacity in 2025 (NZTech estimate); baseline forecast 315 MW by 20302025 年已部署容量（NZTech 估计）；基线预测 2030 年 315 MW. Industry estimate, deployed capacity行业估计，已部署容量
United States美国Power电力large loads must clear grid processes such as ERCOT's大负荷须走 ERCOT 等电网接入流程Second: water, California makes new sites disclose water use (signed 2026-09)其次：水，加州要求拟建站点披露用水（2026-09 签署）No federal limit无联邦上限LBNL average 1.4 in 2023 (estimate)LBNL 估计 2023 年平均 1.4~50 GW约 50 GWadded electricity use by 2030, DOE planning midpoint到 2030 年新增用电，美国能源部规划中值. Planning assumption规划假设
India印度Power电力industry consultations rank power first; Navi Mumbai's 6 GW allocation is nearing its limit行业访谈把电力排在首位；新孟买 6 GW 分配容量已接近上限Second: water, a court is questioning a Visakhapatnam campus's water use, and Bengaluru draws far more groundwater than it recharges; no project was found stopped其次：水，法院质询维沙卡帕特南园区的用水，班加罗尔地下水开采量远超补给；未发现项目因此停工None found未找到State incentives only (Uttar Pradesh, Karnataka)仅有邦级激励（北方邦、卡纳塔克邦）1,575 MW1,575 MWinstalled capacity, August 2026 (about 17 GW of demand estimated up to 2031-32)装机容量，2026 年 8 月（至 2031-32 年需求估计约 17 GW）. Government figure, drawn from industry estimates政府数字，来自行业估计

Eleven regions: PUE requirement or target, power build-out and the biggest constraint十一个地区：PUE 要求或目标、电力建设规模和最大约束

Table表格

Region地区Status状态PUE requirement or targetPUE 要求或目标Power build-out电力建设规模Biggest constraint (our reading)最大约束（本站解读）Monthly-report policy records月报政策记录Monthly-report projects月报项目Monthly-report investment records月报投资记录
1. China (mainland)中国大陆Country page live国家页已上线≤ 1.2 (Action-plan rule, national hub-node projects)≤ 1.2（行动计划要求，国家枢纽节点项目） source来源≈19 GW, average load of computing centres in 2025 (170 TWh ÷ 8,760 h; NEA data, this site's conversion) (Actual use, converted to average load)约 19 GW，2025 年算力中心平均负荷（1,700 亿千瓦时 ÷ 8,760 小时；国家能源局数据，本站换算）（实际用电，换算为平均负荷） source来源 source来源Location and PUE rules: new large sites go first to eight hub nodes, held to PUE 1.2; next: electricity price选址与能效规则：新建大型项目优先进 8 个枢纽节点，PUE 不得高于 1.2；其次：电价 source来源 source来源 source来源 source来源501
2. Taiwan台湾Country page live国家页已上线≤ 1.3 / ≤ 1.4 (Rule, new builds of 5 MW or more; 1.3 hyperscale, 1.4 colocation)≤ 1.3 / ≤ 1.4（规定，5 MW 以上新建；超大型 1.3，主机代管 1.4） source来源 source来源 source来源303.5 MW, contract capacity at four energised transmission-level data centres (Taipower statistics as reported by the press, December 2025) (Contracted; 4.76 GW applied for)303.5 MW，4 座已送电输电级数据中心的契约容量（媒体转述的台电统计，2025 年 12 月）（已签约；另有 4.76 GW 申请） source来源Power: Taipower holds back loads of 5 MW or more north of Taoyuan; next: own power and green power电力：台电暂缓桃园以北 5 MW 以上用电；其次：自备电源与绿电 source来源 source来源 source来源000
3. South Korea韩国Country page live国家页已上线None found (Voluntary certification only)未找到（仅有自愿性认证） source来源6.2 GW, data-centre peak demand in 2038, 11th Basic Plan (operating sites drew 1,762 MW in 2022) (Forecast, peak demand)6.2 GW，第 11 次基本计划预计的 2038 年数据中心峰值需求（2022 年运营站点 1,762 MW）（预测，峰值需求） source来源 source来源 source来源Power: any load of 10 MW or more needs a grid assessment; next: water电力：用电 10 MW 及以上须先过电网评估；其次：水 source来源 source来源 source来源 source来源200
4. Japan日本Country page live国家页已上线≤ 1.3 (Law, new builds from FY2029)≤ 1.3（法定，2029 财年起新建） source来源+6.6 GW, grid demand added by FY2035 (OCCTO, January 2026) (Forecast)+6.6 GW，到 2035 财年新增的电网需求（OCCTO，2026 年 1 月）（预测） source来源Power: grid connection can take ten years or more in some cases; next: land and water电力：并网在部分情况下可能需要十年以上；其次：土地和水 source来源 source来源 source来源278
5. Malaysia马来西亚Country page live国家页已上线1.4 (Design target, tax-incentive condition)1.4（设计目标，税收优惠条件） source来源8.35 GW, secured by TNB (61 projects), June 2026 (Secured pipeline, maximum demand)8.35 GW，TNB 已锁定（61 个项目），2026 年 6 月（已锁定管线，最大需求） source来源Power: in Johor, TNB must approve the supply first; next: water电力：在柔佛须先获 TNB 批准供电；其次：水 source来源 source来源267
6. Singapore新加坡Country page live国家页已上线1.25 (Call condition)1.25（征集条件） source来源1.6 GW, maximum IT load of all data centres, October 2026 (Includes the 200 MW awarded in 2026)1.6 GW，全部数据中心最大 IT 负荷，2026 年 10 月（含 2026 年分配的 200 MW） source来源Power: new capacity comes mainly through government calls; next: land电力：新增容量主要通过政府征集分配；其次：土地 source来源 source来源322
7. Indonesia印度尼西亚Country page live国家页已上线None found (No rule found in the sources read here)未找到（所读来源中未找到规则） source来源 source来源5.2 GW, expected data-centre power demand in 2034 (energy ministry, from 1.1 GW in 2025) (Forecast, electricity demand (ESDM, as reported))5.2 GW，能源部预计的 2034 年数据中心用电需求（2025 年为 1.1 GW）（预测，用电需求（ESDM，经媒体报道）） source来源Power: a 1 GW order needs a 500 kV line and 3-5 years; next: water电力：1 GW 订单需 500 kV 线路和 3-5 年；其次：水 source来源 source来源134
8. Australia澳大利亚Country page live国家页已上线1.25 (NSW guideline)1.25（新州指引） source来源67.1 GW, at all stages advised to AEMO, March 2026 (Mostly proposed or at enquiry)67.1 GW，向 AEMO 报送的各阶段容量，2026 年 3 月（大部分处于提议或咨询阶段） source来源Power: new large sites must underwrite new supply (announced); next: water电力：新建大型站点须为新增电源提供担保（已宣布）；其次：水 source来源 source来源 source来源 source来源445
9. New Zealand新西兰Country page live国家页已上线None found (No PUE rule in the sources read here; sector average about 1.3 (NZTech))未找到（所读来源中未找到 PUE 规则；行业平均约 1.3（NZTech）） source来源104 MW, deployed capacity in 2025 (NZTech estimate); baseline forecast 315 MW by 2030 (Industry estimate, deployed capacity)104 MW，2025 年已部署容量（NZTech 估计）；基线预测 2030 年 315 MW（行业估计，已部署容量） source来源 source来源Power: large-load connection rules and added generation expected (announced); next: water电力：大负荷并网规则和新增发电要求将出台（已宣布）；其次：水 source来源 source来源 source来源011
10. United States美国Country page live国家页已上线No federal limit (LBNL average 1.4 in 2023 (estimate))无联邦上限（LBNL 估计 2023 年平均 1.4） source来源 source来源~50 GW, added electricity use by 2030, DOE planning midpoint (Planning assumption)约 50 GW，到 2030 年新增用电，美国能源部规划中值（规划假设） source来源Power: large loads must clear grid processes such as ERCOT's; next: water电力：大负荷须走 ERCOT 等电网接入流程；其次：水 source来源 source来源 source来源 source来源62232
11. India印度Country page live国家页已上线None found (State incentives only (Uttar Pradesh, Karnataka))未找到（仅有邦级激励（北方邦、卡纳塔克邦）） source来源 source来源 source来源1,575 MW, installed capacity, August 2026 (about 17 GW of demand estimated up to 2031-32) (Government figure, drawn from industry estimates)1,575 MW，装机容量，2026 年 8 月（至 2031-32 年需求估计约 17 GW）（政府数字，来自行业估计） source来源 source来源 source来源 source来源Power: industry consultations rank power first; Navi Mumbai's 6 GW allocation is nearing its limit; next: water电力：行业访谈把电力排在首位；新孟买 6 GW 分配容量已接近上限；其次：水 source来源 source来源 source来源 source来源133

Sources来源

  1. China (mainland): cn-pue-hub中国大陆：cn-pue-hub 2025-12-31
  2. China (mainland): main series中国大陆：主序列 2026-05-27
  3. China (mainland): cn-power-2025中国大陆：cn-power-2025 2025
  4. China (mainland): cn-hub-siting中国大陆：cn-hub-siting 2024-07-03
  5. China (mainland): cn-green-80中国大陆：cn-green-80 2025-12-31
  6. China (mainland): cn-occupancy-rule中国大陆：cn-occupancy-rule 2024-07-03
  7. Taiwan: tw-pue-rule台湾：tw-pue-rule 2025-11-03
  8. Taiwan: tw-pue-definition台湾：tw-pue-definition 2025-11-03
  9. Taiwan: tw-pue-applies-new台湾：tw-pue-applies-new 2025-11-04
  10. Taiwan: main series台湾：主序列 2025-12-16
  11. Taiwan: tw-hold-5mw台湾：tw-hold-5mw 2025-12-16
  12. Taiwan: tw-hold-still-2026台湾：tw-hold-still-2026 2026-08-19
  13. Taiwan: tw-easing-signal台湾：tw-easing-signal 2026-07-15
  14. South Korea: kr-green-dc韩国：kr-green-dc 2026-10-30
  15. South Korea: kr-11plan-dc韩国：kr-11plan-dc 2025-02-21
  16. South Korea: kr-12plan-dc韩国：kr-12plan-dc 2026-08-20
  17. South Korea: kr-target-2035韩国：kr-target-2035 2026-06-29
  18. South Korea: kr-grid-10mw韩国：kr-grid-10mw 2025-05-12
  19. South Korea: kr-assessment-draft韩国：kr-assessment-draft 2026-07-09
  20. South Korea: kr-assessment-score韩国：kr-assessment-score 2026-07-09
  21. South Korea: kr-aidc-exempt韩国：kr-aidc-exempt 2026-05-07
  22. Japan: jp-pue-newbuild日本：jp-pue-newbuild 2029-04-01
  23. Japan: main series日本：主序列 2026-01-21
  24. Japan: jp-grid-10y日本：jp-grid-10y 2025-12-22
  25. Japan: jp-hubs-2023日本：jp-hubs-2023 2023-05
  26. Japan: jp-grid-release日本：jp-grid-release 2027-04
  27. Malaysia: my-pue马来西亚：my-pue
  28. Malaysia: my-tnb-pipeline马来西亚：my-tnb-pipeline 2026-06
  29. Malaysia: my-johor-power马来西亚：my-johor-power 2024-04
  30. Malaysia: my-cress马来西亚：my-cress 2025-08-29
  31. Singapore: sg-cfa2-pue新加坡：sg-cfa2-pue 2025-12-01
  32. Singapore: sg-it-2026新加坡：sg-it-2026 2026-10-07
  33. Singapore: sg-cfa-primary新加坡：sg-cfa-primary 2026-10-07
  34. Singapore: sg-cfa2-green新加坡：sg-cfa2-green 2025-12-01
  35. Indonesia: id-no-specific-regulation印度尼西亚：id-no-specific-regulation 2026-10-06
  36. Indonesia: id-iesr-pue印度尼西亚：id-iesr-pue 2026-10-01
  37. Indonesia: id-esdm-dc-demand印度尼西亚：id-esdm-dc-demand 2025-11-19
  38. Indonesia: id-pln-500kv印度尼西亚：id-pln-500kv 2026-09-03
  39. Indonesia: id-batam-ppa印度尼西亚：id-batam-ppa 2026-04-17
  40. Australia: au-nsw-pue澳大利亚：au-nsw-pue 2026-08
  41. Australia: au-aemo-pipeline-growth澳大利亚：au-aemo-pipeline-growth 2026-03
  42. Australia: au-federal-obligation澳大利亚：au-federal-obligation 2026-07-15
  43. Australia: au-vic-plan澳大利亚：au-vic-plan 2026-09-22
  44. Australia: au-national-cabinet澳大利亚：au-national-cabinet 2026-08-26
  45. Australia: au-aemc-advice澳大利亚：au-aemc-advice 2026-08-05
  46. New Zealand: nz-pue-average新西兰：nz-pue-average 2025-09
  47. New Zealand: nz-nztech-deployed新西兰：nz-nztech-deployed 2025-09
  48. New Zealand: nz-nztech-2030新西兰：nz-nztech-2030 2025-09
  49. New Zealand: nz-transpower-large-load新西兰：nz-transpower-large-load 2026-10-30
  50. New Zealand: nz-pm-additionality新西兰：nz-pm-additionality 2026-07-27
  51. New Zealand: nz-datagrid-substation新西兰：nz-datagrid-substation 2026-08-12
  52. United States: us-federal-pue美国：us-federal-pue 2026-07-05
  53. United States: us-lbnl-pue美国：us-lbnl-pue 2023
  54. United States: us-doe-50gw美国：us-doe-50gw 2025-07-07
  55. United States: us-ercot-large-load美国：us-ercot-large-load 2026-07-05
  56. United States: us-tx-sb6美国：us-tx-sb6 2025-06-20
  57. United States: us-ferc-206美国：us-ferc-206 2026-07-05
  58. United States: us-ca-laws美国：us-ca-laws 2026-09-21
  59. India: in-up-pue印度：in-up-pue 2026-07
  60. India: in-karnataka-pue印度：in-karnataka-pue 2026-09-30
  61. India: in-bis-bee印度：in-bis-bee 2026-08-07
  62. India: in-installed-1575印度：in-installed-1575 2026-08-07
  63. India: in-no-official-record印度：in-no-official-record 2026-09-25
  64. India: in-cea-17gw印度：in-cea-17gw 2026-08-07
  65. India: in-mop-26gw印度：in-mop-26gw 2026-07-27
  66. India: in-ceew-hurdles印度：in-ceew-hurdles 2026-02
  67. India: in-navi-6gw印度：in-navi-6gw 2026
  68. India: in-gov-transmission-ready印度：in-gov-transmission-ready 2026-03-13
  69. Counts of adopted records in the monthly report databases (policy and investment), placed by country in phase 2月报数据库（政策和投资）中已采用记录的数量，第 2 阶段按国家归位
  70. Map: Natural Earth Admin 0 countries, via world-atlas 2.0.2 (countries-50m.json); Natural Earth: public domain (natural earth terms of use); world-atlas: ISC licence, Copyright 2013-2019 Michael Bostock地图：Natural Earth 国家边界（经 world-atlas 2.0.2）；Natural Earth 为公有领域；world-atlas 采用 ISC 许可，Copyright 2013-2019 Michael Bostock

Method and notes方法和说明

Singapore and Taiwan are drawn as dots. Regions in scope: United States, Australia, New Zealand, Malaysia, Singapore, Indonesia, Japan, Taiwan, South Korea, China mainland, India. Boundaries are simplified for this scale.新加坡和台湾以圆点表示。覆盖范围：美国、澳大利亚、新西兰、马来西亚、新加坡、印度尼西亚、日本、台湾、韩国、中国大陆、印度。边界按此比例简化。

Go deeper继续深入

Hours table: all 24 stations, every supply water and approach, with the 99.6th-percentile dry-bulb temperature小时数表：全部 24 个站点、各供水温度和接近温差，以及干球温度 99.6 百分位24 stations24 个站点
City (weather station)城市（气象站）W17W20W25W32W4199.6th-percentile dry-bulb, °C干球温度 99.6 百分位，°C
5 K10 K15 K5 K10 K15 K5 K10 K15 K5 K10 K15 K5 K10 K15 K
Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）3,6382,2361,0104,4903,1681,7945,8754,4903,1687,8456,4124,9538,7488,4587,61134.2
Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）1,8999022882,6971,5406414,0822,6971,5406,3884,6043,1398,5017,5895,99638.6
Phoenix (Sky Harbor)凤凰城（天港）94312901,828597453,1601,8285974,8433,6032,2917,2625,8364,57145
Sydney (Sydney Airport)悉尼（悉尼机场）8872302,22033905,3282,2203398,4176,7103,3738,7468,6948,25032.8
Western Sydney (Penrith)悉尼西部（彭里斯） lowest year: 81% of hours observed最低单年：观测到 81% 的小时1,72340793,0111,0551565,7883,0111,0558,1506,8204,0298,7248,5687,97635.9
Melbourne (Melbourne Airport)墨尔本（墨尔本机场）3,364651255,2612,0392247,4945,2612,0398,4497,8976,3648,7448,6598,36834
Auckland (Auckland Airport)奥克兰（奥克兰机场）1,73818703,990781447,4873,9907818,7538,2105,5368,7608,7608,73726.2
Johor (Senai)柔佛（士乃）0000000005,3011008,7607,6784,34334.3
Kuala Lumpur (Subang)吉隆坡（梳邦）0000000004,174108,7607,1382,79534.9
Singapore (Changi)新加坡（樟宜）0000000003,239208,7607,9901,75433.2
Jakarta (Soekarno-Hatta)雅加达（苏加诺-哈达）0000000004,217208,7597,4532,95134
Tokyo (Haneda)东京（羽田）2,7031,062713,5962,0505035,2963,5962,0507,6406,0364,2708,7598,5387,31433.6
Taoyuan (Taoyuan Airport)桃园（桃园机场）216408447602,905844765,9693,6761,6568,7487,8865,45535.8
Seoul (Incheon Airport)首尔（仁川机场）3,9692,8331,6354,7183,5152,3816,0714,7183,5158,2656,6775,2248,7608,7058,01331.6
Beijing (Capital Airport)北京（首都机场）3,8892,9412,0064,4783,4902,5905,6024,4783,4907,6126,1624,8928,7248,3657,35636.2
Shanghai (Hongqiao)上海（虹桥）2,5651,2322723,3102,0617544,7563,3102,0616,9775,3693,8598,6958,1406,65736.8
Hokkaido (Sapporo)北海道（札幌） lowest year: 82% of hours observed最低单年：观测到 82% 的小时4,8123,6242,4795,5304,3363,1566,8365,5304,3368,4747,4026,0188,7608,7268,33830.7
Kyushu (Fukuoka)九州（福冈） lowest year: 82% of hours observed最低单年：观测到 82% 的小时2,497866633,4431,8154024,9833,4431,8157,1855,5914,0378,7568,3216,86634.6
Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）000200130203,093372168,7257,2842,01036.3
Chennai (Chennai Airport)金奈（金奈机场）0000004002,77310308,5716,7121,90738.6
Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）400115001,68111507,2203,8034308,7558,4406,63734.4
Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）60060209636025,7461,8602328,5947,7555,10838.4
Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场） lowest year: 87% of hours observed最低单年：观测到 87% 的小时000600323603,787739418,6957,2532,77437
Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）7085901,35336952,4801,3533694,6252,9751,7998,2196,6754,18642.6

Assumes the dry cooler supplies the facility water directly. With a glycol loop and a plate heat exchanger in between, add the exchanger's approach (often 2–3 K) to the dry cooler's: 10 K plus 3 K falls between the 10 K and 15 K columns, so there are fewer hours than the 10 K column shows. 假设干冷器直接供设施水。若中间有乙二醇回路和板式换热器，应把换热器的接近温差（常见约 2–3 K）加到干冷器的接近温差上：10 K 加 3 K 介于 10 K 和 15 K 两列之间，小时数少于 10 K 一列。W17 and W32 are ASHRAE facility water classes; W20 and W25 mean 20 °C and 25 °C supply, where chilled-water plant design is moving, and W41 means 41 °C supply, enough for 45 °C at the GPU after a 4 K CDU approach (this site's labels, not ASHRAE classes). Hours a year (average of 2020–2024; every year counted as 8,760 hours). Darker = a larger share of the year: W17 和 W32 为 ASHRAE 设施水温等级；W20 和 W25 指 20 °C 和 25 °C 供水，是冷站冷冻水设计正在移向的温度；W41 指 41 °C 供水，经 CDU 4 K 接近温差后 GPU 处为 45 °C（本站标注，不是 ASHRAE 等级）。每年小时数（2020–2024 年平均；每年按 8,760 小时计）。颜色越深 = 占全年比例越大：<25% 25–50% 50–75% 75–95% ≥95%

The same hours on eleven country maps同一小时数的十一张国家地图11 maps11 张地图
Supply water供水温度Dry-cooler approach干冷器接近温差

Hours a year a dry cooler alone can make the water (2020–2024 average); the rest need chillers or another way to reject heat. Share of the year: 仅靠干冷器即可制水的年小时数（2020–2024 年平均）；其余小时需冷机或其他排热方式。占全年比例：<25% 25–50% 50–75% 75–95% ≥95%

China (mainland)中国大陆

12
  1. Beijing (Capital Airport)北京（首都机场）6,162 h · 70%6,162 小时 · 70%
  2. Shanghai (Hongqiao)上海（虹桥）5,369 h · 61%5,369 小时 · 61%

Taiwan台湾

1
  1. Taoyuan (Taoyuan Airport)桃园（桃园机场）3,676 h · 42%3,676 小时 · 42%

South Korea韩国

1
  1. Seoul (Incheon Airport)首尔（仁川机场）6,677 h · 76%6,677 小时 · 76%

Japan日本

123
  1. Tokyo (Haneda)东京（羽田）6,036 h · 69%6,036 小时 · 69%
  2. Hokkaido (Sapporo)北海道（札幌）7,402 h · 84%7,402 小时 · 84%
  3. Kyushu (Fukuoka)九州（福冈）5,591 h · 64%5,591 小时 · 64%

Malaysia马来西亚

12
  1. Johor (Senai)柔佛（士乃）10 h · 0%10 小时 · 0%
  2. Kuala Lumpur (Subang)吉隆坡（梳邦）1 h · 0%1 小时 · 0%

Australia澳大利亚

123
  1. Sydney (Sydney Airport)悉尼（悉尼机场）6,710 h · 77%6,710 小时 · 77%
  2. Western Sydney (Penrith)悉尼西部（彭里斯）6,820 h · 78%6,820 小时 · 78%
  3. Melbourne (Melbourne Airport)墨尔本（墨尔本机场）7,897 h · 90%7,897 小时 · 90%

New Zealand新西兰

1
  1. Auckland (Auckland Airport)奥克兰（奥克兰机场）8,210 h · 94%8,210 小时 · 94%

United States美国

123
  1. Northern Virginia (Washington Dulles)北弗吉尼亚（杜勒斯）6,412 h · 73%6,412 小时 · 73%
  2. Dallas (Dallas/Fort Worth)达拉斯（达拉斯-沃斯堡）4,604 h · 53%4,604 小时 · 53%
  3. Phoenix (Sky Harbor)凤凰城（天港）3,603 h · 41%3,603 小时 · 41%

India印度

123456
  1. Mumbai (Chhatrapati Shivaji Airport)孟买（贾特拉帕蒂·希瓦吉机场）372 h · 4%372 小时 · 4%
  2. Chennai (Chennai Airport)金奈（金奈机场）103 h · 1%103 小时 · 1%
  3. Bengaluru (Kempegowda Airport)班加罗尔（肯佩戈达机场）3,803 h · 43%3,803 小时 · 43%
  4. Hyderabad (Rajiv Gandhi Airport)海得拉巴（拉吉夫·甘地机场）1,860 h · 21%1,860 小时 · 21%
  5. Visakhapatnam (Visakhapatnam Airport)维沙卡帕特南（维沙卡帕特南机场）739 h · 8%739 小时 · 8%
  6. Delhi (Indira Gandhi Airport)德里（英迪拉·甘地机场）2,975 h · 34%2,975 小时 · 34%

Shown: W32 at a 10 K approach (default). Assumes the dry cooler supplies the facility water directly; with a glycol loop and a plate heat exchanger, add its approach (often 2–3 K), so there are fewer hours. These are shares of the year: chillers are still sized for the hottest hours.当前显示：W32，接近温差 10 K（默认）。假设干冷器直接供设施水；若中间有乙二醇回路和板式换热器，需再加其接近温差（常见约 2–3 K），小时数会更少。这里是占全年的比例：冷机仍要按最热时段选型。

What a country page answers, and how many records sit behind this page国家页回答什么，本页背后有多少记录

Every country page uses the same fields: build scale, power, water, efficiency rules, main policies and climate. Regions can be compared field by field without a combined score. Its thermal meaning is an HVAC reading of what these limits mean for the cooling plant.每个国家页采用同一套字段：建设规模、电力、水、能效要求、主要政策和气候。各地区可逐项比较，不做综合评分。热管理含义是从 HVAC 角度判断这些限制对冷站意味着什么。

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Monthly-report policy records placed on a country or state; Taiwan and New Zealand have none yet已归位到国家或州的月报政策记录；台湾、新西兰尚无记录

Monthly report database月报数据库
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Country pages with a PUE requirement or target (China (mainland), Taiwan, Japan, Malaysia, Singapore and Australia); all 11 give a sourced power figure in MW or GW有 PUE 要求或目标的国家页（中国大陆、台湾、日本、马来西亚、新加坡、澳大利亚）；11 个国家页都给出有来源的电力数字（MW 或 GW）

Counted from the table above按上表计数
Database view: record counts by region数据库视图：各地区记录数11 rows11 行
Showing显示 11
Region地区Code代码Status状态Monthly-report policy records月报政策记录Monthly-report projects月报项目Monthly-report investment records月报投资记录
United States美国USCountry page live国家页已上线62232
Australia澳大利亚AUCountry page live国家页已上线445
New Zealand新西兰NZCountry page live国家页已上线011
Malaysia马来西亚MYCountry page live国家页已上线267
Singapore新加坡SGCountry page live国家页已上线322
Indonesia印度尼西亚IDCountry page live国家页已上线134
Japan日本JPCountry page live国家页已上线278
Taiwan台湾TWCountry page live国家页已上线000
South Korea韩国KRCountry page live国家页已上线200
China (mainland)中国大陆CNCountry page live国家页已上线501
India印度INCountry page live国家页已上线133