听力课堂TED音频栏目主要包括TED演讲的音频MP3及中英双语文稿，供各位英语爱好者学习使用。本文主要内容为演讲MP3+双语文稿:如何准确计算温室气体排放量 ，希望你会喜欢！

【主讲人】Charlotte Degot
波士顿咨询集团（BCG）常务董事兼合伙人
气候变化科学家（人工智能与数据科学方向）

【演讲主题】A more accurate way to calculate emissions
温室气体的无色无味性为许多企业的测量过程带来了困难。然而人工智能技术却可以通过处理大量的碳排放数据，帮助企业设定有意义的气候目标。最重要的是，能够随着时间推移帮助其减少污染。

【中英文稿】

For decades now, we’ve been saying we should reduce our emissions, but they’ve kept increasing. One of the key reasons is we don’t measure accurately the climate impact of our actions. Imagine trying to save money, but when you go shopping, there is no price tag on any item ... or trying to lose weight, but you cannot measure the portion sizes and the calories. You would be bound to fail.

几十年来，我们一直呼吁人们应该减少温室气体排放量。然而，排放量却持续增加。关键原因之一是我们并未准确地测量 我们的行为影响着气候的变化。试想一下：你试着省钱，但当你去商店购物时，却发现所有商品都没有标价。或者，你尝试减重，但你无法计算食物的份量和卡路里。如此，你必定会失败。

This level of blindness is close to the one we have when it comes to our climate impact.

这种程度的茫然如同我们对气候影响的认知一样只是一知半解。

Measuring greenhouse gas emissions is hard. It has no color, it has no smell; it’s invisible. We cannot put sensors everywhere, on every building, every track, every field, every cow -- so most of the time, we give up and we don’t measure. And when we do measure, we are reduced to relying on estimations and conversion factors. The consequence is we end up working with highly incomplete and inaccurate estimations of our emissions. Often we have a margin of error of 30 to 60 percent. This means targets and action plans are set based on inaccurate data.

计算温室气体的排放量是很困难的。它没有颜色，也没有气味，甚至是无形的。我们不能够在任何地方，如每栋建筑物、每条铁轨、每块田地、每头牛身上都放置测量仪器。因此，我们多半放弃 不去测量。而当我们进行测量时，我们只能根据估计值与转换因子。结果导致我们以非常不完整且不准确的数据来进行气体排放量的计算。在计算上，我们通常有百分之三十到六十的误差范围。这意味着目标与计划的执行都是基于不准确的数据数据。

If we look at the corporations that report their progress on climate to the CDP, which is a nonprofit organization that runs a global disclosure system for environmental impacts, what we see is striking: more than two-thirds of the companies are not accurately measuring their emissions, and only seven percent of those companies are ultimately reducing their impact in some way. You cannot reduce what you cannot measure. It is key for corporations to be able to measure across all activities, all sources that drive carbon up or down. In a way, that’s just putting the same rigor to carbon measurements that we have for financial accounting. It took more than 100 years to put modern, automated financial accounting in place. We don’t have 100 years when it comes to climate. But this is crucial for corporations to set meaningful targets and successful action plans.

如果我们检视那些向碳揭露项目报告其对气候做出改善方法的企业。碳揭露项目是一个非营利组织，为了环境变迁推动着全球碳揭露系统。我们将会看到惊人的事实：超过三分之二的公司并未准确测量他们的碳排放量，而且，只有百分之七的公司最终以某种方式减少了他们对环境的影响。你不能减少无法测量的东西。关键在于，企业要能够精准计算所有导致碳排放量上升或下降的所有活动及来源。就某种意义上来说，我们必须把碳排放量的计算以如同财务会计般严格的标准来看待。现代自动化财务会计的实施花100多年的时间才完成。在气候方面，我们并没有100年的时间可以蹉跎。但这对于企业订定有意义的目标和成功的行动计划来说是至关重要的。

One of the most powerful tools we have to help us accelerate on this journey is artificial intelligence. Artificial intelligence can process data automatically from diverse, unstructured sources like invoices, consumer behavior data. It can work by modeling to better estimate the missing information. It can simulate and ultimately optimize emissions.

我们拥有最强大的工具之一能够帮助我们加快进程，那就是人工智能。人工智能能够自动处理来自各种非结构式来源的数据。例如：发票或是消费者行为数据。它可以透过数据建模来更准确地估算缺失的讯息。它可以模拟并最终优化碳排放量的计算。

Let me share an example of how this could work. A wine and spirits international company: billions of sales, hundreds of brands, consumers across the globe. When they want to measure their impact, they need to measure across the entire set of their emissions. This means direct emissions from facilities, purchased electricity, raw materials, leased assets, IT emissions business travel, transportation, waste, product end of life, etcetera, etcetera. That’s a huge amount of information to collect. And most of it is actually inaccessible to the company itself because it comes from outside its direct scope of activity. For example, from suppliers that are not yet able to calculate their emissions either. So when the sustainability team calculates their impact, they have no choice but to do rough estimates.

让我来分享一个例子来说明这是如何运作的。一家销售葡萄酒和烈酒的国际公司拥有几十亿的销售额、几百个品牌、以及遍及全球的顾客。当他们想要计算对环境的影响时，他们需要估计整趟流程的碳排放量。这就表示需要计算来自设施、外购电力、原物料、租赁资产、信息科技的碳排放、商务旅行、产品运输、废弃物、产品寿命中止、等等......碳的实际排放量。要搜集的信息量非常大，且实际上大多数信息是公司本身无法得知的，因为它来自上述活动的范围之外。举例来说，供货商尚未能够计算其碳排放量，因此，当环境可持续发展团队计算其环境影响时，他们别无选择，只能粗略估计数据。

Let’s examine the glass for bottles. The way they calculate glass emissions is the following. They take the total amount of glass bought last year -- let’s say 1,000 tons. They multiply it by a conversion factor, which represents the average kilos of CO2 equivalent for one ton of glass -- let’s say 950. 950 x 1000 makes 950,000. Of course this is hugely inaccurate because it does not take into account all the numerous factors that impact actual emissions, so it’s hard to set targets and action plans. This is where the sustainability team calls data scientists to come in and process detailed data about the type of glass, the color of the glass, the recycling share, the supplier country of origin, the transportation mode, by brand, by product. They can simulate the design and the supply chain and integrate in the calculation the importance of the glass color -- 1.5 times more emissions for a clear bottle versus a green bottle; the importance of the country of origin -- twice the amount of emissions for one country versus another one, depending on the energy mix; the importance of the design itself -- for the same total weight, 1.5 times more emissions for one design versus another one. Instead of having one big, average number, you now have a model which correlates and calculates emissions at a granular level.

我们来探讨瓶子的原物料：玻璃。他们计算玻璃碳排放量的方法如下。他们拿去年买的玻璃总量，假定为1000公吨。乘上一个转换因子，此转换因子为1公吨的玻璃产生的平均二氧化碳公斤数。假定为950公斤。950乘1000等于950,000。当然，这项数据是极度不准确的，因为并未考虑到影响实际碳排放量的众多因素。也因此设定目标与行动计划是非常困难的。 这就是环境可持续发展团队召集数据科学家来处理详细数据的地方，如玻璃的类型、玻璃的颜色、回收份额、供货商原产地、运输模式、品牌，以及最终产品。他们能模拟玻璃瓶的设计与供应链并将其纳入碳排放量的计算，如玻璃颜色的重要性，透明玻璃瓶的排放量 是绿色玻璃瓶的1.5倍。或者是原产地的重要性，某国家的排放量是另一个国家的两倍，这取决于该国的能源组合。又或者是玻璃瓶设计的重要性，在总重量相同的情况下，某种设计的排放量是另一种设计的 1.5 倍。你现在拥有一个模型能够以非常具体且清晰的角度连结并计算碳排放量而不是一个巨大的平均数值。

With this type of methodology, the emissions figure is typically corrected by 30 to 50 percent. And more importantly, the company can now move to action as they can, one, set meaningful targets, two, identify very concrete initiatives, and three, recalculate emissions over time and measure their progress.

使用这种计算方法，碳排放数据通常会被修正百分之三十到百分之五十。而更重要的是，公司现在能够采取行动。第一，设定有意义的目标。第二，订定非常具体的新措施。第三，每隔一段时间重新计算碳排放量并估算进展。

Let me share another example: cement. Cement is a massive CO2 emitter. If cement were a country, it would rank as the third-largest emitter, right after China and the US, in front of the European Union and India. Most of the emissions come from the process of producing clinker, the key ingredient in cement. To produce clinker, you need to maintain a temperature of over 1,400 degrees Celsius. It requires a lot of fuel, and it’s really just carbon containing the whole materials. So the secret sauce is to produce cleaner and higher quality clinker, because the higher the quality of the clinker, the less of it you will need to produce cement ultimately, and therefore the less emissions you will generate. But producing high-quality clinker is a complex science. It depends on multiple factors that influence each other. For example, the process parameters, like the rotation speed of the machine, how quickly you fill it, the type of fuel you use, the raw materials and their exact chemical composition. This is where artificial intelligence can again have an enormous impact. On-site operational teams are trying to manually maintain the best set of parameters possible. AI can help by measuring better through different sources, like direct measurements, material and mass balance, etcetera ... simulate all the potential decisions and recommend the optimal ones to the operators. These techniques implemented in a cement production process enable a substantial emissions reduction in a matter of months.

让我来分享另一个例子：水泥。水泥是巨大的二氧化碳排放来源。假如水泥是一个国家，它将成为世界第三大二氧化碳排放国，仅次于中国和美国，且超越欧盟与印度。大部分排放来自生产水泥熟料的过程，熟料是水泥的关键成分。要生产熟料，需要将温度维持在摄氏1400度以上。这需要大量燃料，而实际上就是碳包含全部的材料。而解决方法就是生产更干净、更高质量的熟料，因为熟料的质量越好，生产水泥所需的熟料就越少。进而降低二氧化碳的排放量。然而，生产高质量的熟料是一门复杂的科学，取决于多种相互影响的因素。举例来说：生产制造的程序参数，如机器的转速、需要填装多快、使用何种燃料、原物料以及它们确切的化学成分。这就是人工智能能够再次产生巨大影响的地方。现场营运团队正尝试手动维护可能产生的一组最佳参数。人工智能可以透过不同的测量来做出更好的计算，如直接测量、原料及质量平衡等等。模拟所有潜在可行的决策，并推荐最佳的方案给营运者。这些科技应用在水泥制造过程中，能够在几个月内大幅减少二氧化碳的排放量。

There is an infinity of applications possible. There is no company, no industry that cannot derive significant climate impact from the use of artificial intelligence. I’m not saying artificial intelligence alone will save us. But artificial intelligence, by helping us measure accurately, simulate and optimize, enables significant emissions reduction in a quite fast, cheap and easy way. We cannot miss this opportunity.

人工智能有着无限应用的可能性。任何公司、任何产业都将从人工智能的应用中获取对环境产生重大影响的信息。我并不表示仅靠人工智能就能拯救我们。但人工智能帮助我们准确计算模拟并优化，使大量减少碳排放量得以更快速、廉价且简单的方式实现。我们不能够再失去这次的机会。

Thank you.

谢谢。