每日英语练习 - 2026-07-01（周三）
📌 今日主题
科技新闻：AI 数据中心的环境热岛效应
本文来自 Al Jazeera 的科技报道，探讨了 AI 数据中心（data centres）惊人的能源消耗及其对周边环境的热影响。这是一个结合科技、环境与社会责任的现实话题。
📝 英文原文
Tech giants are racing to build the infrastructure that powers artificial intelligence. But a growing body of evidence suggests that AI hyperscalers – large-scale cloud service providers like Google, Amazon and Microsoft – are warming the ground around them as well.
A study by Cambridge-led researchers found that land surface temperatures around AI data centres rise by an average of 2 degrees Celsius (3.6 degrees Fahrenheit), with some areas recording increases as high as 9C (16.2F). Researchers have called this the “data heat island effect.”
Every time someone uses ChatGPT, Gemini or Claude, the request is handled in a data centre, a vast facility full of specialised computers that run 24 hours a day. AI data centres use powerful chips that perform thousands of calculations in parallel, making them much more energy hungry than typical servers.
According to the International Energy Agency (IEA), data centres consumed about 415 terawatt hours (TWh) of electricity in 2024 — about 1.5 percent of global supply. That figure is projected to nearly double to 945 TWh by 2030. A single 100-megawatt hyperscale data centre can consume about 2.5 billion litres of water a year — equivalent to the annual needs of 80,000 people.
The study found that more than 340 million people living within 10km (6 miles) of a data centre could be affected by the temperature increases — an impact researchers described as having a “remarkable influence on communities and regional welfare.”
🔍 重点词汇
| 单词/短语 | 音标 | 中文释义 | 例句 |
|---|---|---|---|
| infrastructure | /ˈɪnfrəstrʌktʃər/ | 基础设施 | Tech giants are racing to build AI infrastructure. |
| hyperscaler | /ˈhaɪpərˌskeɪlər/ | 超大规模云服务商 | Hyperscalers like Google and Amazon dominate cloud computing. |
| data centre | /ˈdeɪtə ˈsentər/ | 数据中心 | Data centres run 24 hours a day. |
| land surface temperature | /lænd ˈsɜːrfɪs ˈtemprətʃər/ | 地表温度 | Land surface temperatures rose by 2°C on average. |
| heat island effect | /hiːt ˈaɪlənd ɪˈfekt/ | 热岛效应 | Researchers call this the “data heat island effect.” |
| energy hungry | /ˈenərdʒi ˈhʌŋɡri/ | 高能耗的 | AI chips are much more energy hungry than typical servers. |
| terawatt hour (TWh) | /ˈterəwɑːt aʊər/ | 太瓦时（电量单位） | Data centres consumed 415 TWh of electricity in 2024. |
| projected | /prəˈdʒektɪd/ | 预计的 | That figure is projected to nearly double by 2030. |
| equivalent to | /ɪˈkwɪvələnt tuː/ | 相当于 | That is equivalent to the annual needs of 80,000 people. |
| remarkable | /rɪˈmɑːrkəbl/ | 显著的、引人注目的 | The study found a remarkable influence on communities. |
| regional welfare | /ˈriːdʒənl ˈwelfer/ | 地区福祉 | The impact affects regional welfare. |
| sustainable | /səˈsteɪnəbl/ | 可持续的 | This should be part of the conversation about sustainable AI. |
💡 实用表达
-
“A growing body of evidence suggests that…” — 越来越多的证据表明……
- 例：A growing body of evidence suggests that remote work improves productivity.
-
“…racing to build…” — 竞相建造……
- 例：Companies are racing to build the next generation of AI tools.
-
“Every time someone uses…, the request is handled in…” — 每当有人使用……，请求就会在……处理
- 例：Every time you search online, your request is handled by a server somewhere.
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“…could be affected by…” — 可能受到……的影响
- 例：Millions of people could be affected by the new policy.
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“That figure is projected to nearly double by…” — 这个数字预计到……将几乎翻倍
- 例：Global energy demand is projected to nearly double by 2050.
🎯 练习步骤
- 通读全文：先完整读一遍英文原文，理解大意
- 精读标记：逐句阅读，标记不熟悉的词汇和表达
- 跟读练习：大声朗读 3 遍，注意断句、重音和语调
- 脱稿复述：合上文本，用自己的话复述大意（中英文都可以）
- 结构化输出：用以下框架组织你的复述：
- 这段话在说什么？（一句话概括）
- 主要观点是什么？（列 2-3 个要点）
- 我的感受/看法？（一句话）
🗣️ 口才小贴士
用数据增强说服力：今天的文章大量使用了具体数字（2°C、415 TWh、3.4亿人、2.5 billion litres）。在日常口语表达中，当你想要说服别人或让你的观点更有分量时，加入一个具体的数字会让你的话立刻从”感受性陈述”升级为”事实性论证”。
比如不要说：“AI data centres use a lot of energy. It’s a big problem.” 可以改成：“AI data centres consumed 415 terawatt hours of electricity in 2024 — that’s about 1.5 percent of global supply. And it’s projected to nearly double by 2030.”
练习方法：选一个你关心的话题，先不带数字说一遍，再找 1-2 个关键数据加进去，感受两者的区别。