SURPRISE 3

　　We will build an entire New York City every month.

　　今后四十年，全世界每个月将新建一个纽约市。

　　I wish more people fully understood what it will take to stop climate change. Electricity accounts for only a quarter of all the greenhouse gases emitted around the world. Manufacturing isn’t far behind, at 21 percent.

　　我希望更多的人能充分了解如何才能阻止气候变化。电力只占全球温室气体排放总量的四分之一。制造业也没有落后，占21%。

　　As the urban population continues to grow in the coming decades, the world’s building stock is expected to double by 2060—the equivalent of adding another New York City monthly between now and then. That’s a lot of cement and steel. We need to find a way to make it all without worsening climate change.

　　在未来几十年，随着城市人口的持续增长，全球建筑物到2060年预计将会翻倍，相当于从现在开始每月新建一个纽约市。这需要大量钢铁水泥。我们需要想办法在不加剧气候变化的前提下实现这一切。

　　The larger point is that if we’re going to solve climate change, we need to get to near-zero emissions on all the things that drive it—agriculture, electricity, manufacturing, transportation, and buildings. I call these five areas the grand challenges in climate change.

　　重要的是，我们如果要解决气候变化，就要让农业、电力、制造、交通、建筑等所有导致气候变化的行业都实现近零排放。我把这五个领域称为气候变化的五大挑战。

　　But we need to do a much better job of informing people about the challenges. It would help if media coverage matched the breadth of the problem. Solar panels are great, but we should be hearing about trucks, and cement too.

　　我们需要更好地努力让公众了解这些挑战，而媒体对这些问题的广泛关注将对此大有帮助。太阳能电池板，但我们也应该了解卡车、水泥等方面的信息。

　　SURPRISE 4

　　Data can be sexist.

　　数据也存在性别歧视。

　　I spend big chunks of my day studying data on health and development. I’m amazed at how little data we have on women and girls. I think the main reason is that we create this artificial divide where some issues are “women’s” issues and others aren’t, and the women’s issues don’t get as much in-depth study. That blocks progress for everyone. You can’t improve things if you don’t know what’s going on with half the population. There’s no good reason for that, now that technology makes it so much easier to gather data.

　　我每天花大量的时间研究有关健康和发展的数据。关于妇女和女童的数据如此之少，让我非常惊讶。我想主要原因在于我们人为地将某些问题划分为“女性”问题和其他问题，而女性问题通常得不到深入研究。这将妨碍每个人的进步。如果不了解全球半数人口的情况，就无法改善事情。更何况科技让数据收集更加简单，我们没有理由这样。

　　How much income did women in developing countries earn last year? How much property do they own? How many more hours do girls spend on household chores than boys? I don’t know. Neither does anyone else. The data just doesn’t exist.

　　发展中国家的妇女去的年收入多少?她们拥有多少财产?女孩花在家务上的时间比男孩多多少?我不知道，其他人也不知道。这些数据不存在。

　　The problem isn’t only that some women are missing from the record altogether. It’s also that the data we do have—data that policymakers depend on—is bad. You might even call it sexist. We like to think of data as being objective, but the answers we get are often shaped by the questions we ask. When those questions are biased, the data is too.

　　问题不只是数据统计忽略了部分女性，而且我们拥有的，决策者们所依据的现有数据质量很糟糕，甚至可以说是带有性别歧视的。我们通常认为数据是客观的，但我们得到的答案往往受我们提出的问题的影响。如果问题本身就带有偏见，得到的数据也会如此。

　　When such flawed data is all you have to go on, it’s easy to undervalue women’s economic activity—and difficult to measure whether women’s economic condition is improving.

　　如果我们必须以这些有缺陷的数据为依据，就容易低估女性经济活动的价值，也难以衡量女性的经济状况是否得到改善。