Charlotte Degot: A more accurate way to calculate emissions | TED

Transcriber: Leslie Gauthier Reviewer:

翻译人员: Ming Wang 校对人员: Helen Chang

几十年来，

尽管我们一直呼吁要节能减排，

碳排放量却在持续增长。

一个关键因素在于，我们没能正确测出

For decades now,

we’ve been saying we should reduce our emissions,

人类行为对气候的影响。

but they’ve kept increasing.

想象一下，当你想存钱，

One of the key reasons is we don’t measure accurately

可去买东西的时候却发现

每样东西上都没有价签。

the climate impact of our actions.

或者当你想减肥，

Imagine trying to save money,

却发现自己量不了食物的 份量大小和热量高低。

but when you go shopping,

there is no price tag on any item ...

若是如此，你将注定失败。

or trying to lose weight,

我们面对气候变化时的无知程度

but you cannot measure the portion sizes and the calories.

近似以上两种情况。

You would be bound to fail.

测量温室气体排放量是件难事。

This level of blindness is close to the one we have

它无色、

无味，

when it comes to our climate impact.

肉眼不可见。

Measuring greenhouse gas emissions is hard.

我们不能处处都装上传感器，

It has no color,

在每栋建筑物上、

it has no smell;

每条轨道上、

it’s invisible.

每片田野上、

We cannot put sensors everywhere,

每头奶牛上。

所以大多数时候，

on every building,

我们选择放弃并置之不理。

every track,

every field,

当我们确要测量的时候，

every cow --

我们只好依赖估算

so most of the time,

和转换因数。

we give up and we don’t measure.

结果就是，我们的测量工作

And when we do measure,

we are reduced to relying on estimations

以低完成率和高错误率的估算而告终。

and conversion factors.

The consequence is we end up working with highly incomplete

我们通常有30%到60%的误差幅度，

and inaccurate estimations of our emissions.

这就意味着行动计划和目标

Often we have a margin of error of 30 to 60 percent.

建立在错误的数据上。

This means targets and action plans are set

如果我们查看一些企业或公司

将自己在气候的进展汇报给CDP

based on inaccurate data.

那个运营着全球信息披露系统的 非营利性组织，

If we look at the corporations

that report their progress on climate to the CDP,

基于环境影响的考虑。

结果令人震惊：

which is a nonprofit organization that runs a global disclosure system

这些公司中多过三分之二

for environmental impacts,

没能做到正确测量碳排放，

what we see is striking:

而仅有7%的公司，

more than two-thirds of the companies

最终在某种程度上 减少了他们对气候的影响。

are not accurately measuring their emissions,

and only seven percent of those companies

如果连测量都做不到， 更别说减少碳排放。

are ultimately reducing their impact in some way.

对公司来说，关键是要能测量出各类

You cannot reduce what you cannot measure.

导致碳排放上下波动的活动和能源。

It is key for corporations to be able to measure across all activities,

在某种程度上，

这意味着让测量碳排放变得

all sources that drive carbon up or down.

和会计核算一样严格。

In a way,

that’s just putting the same rigor to carbon measurements

落实现代化、自动化的 会计核算用了100年。

that we have for financial accounting.

在气候面前，我们并没有100年的时间。

It took more than 100 years to put modern, automated financial accounting in place.

但对公司来说，设定有意义的目标

We don’t have 100 years when it comes to climate.

和成功的行动规划至关重要。

But this is crucial for corporations to set meaningful targets

我们现在拥有的一个最有力的、

能帮我们加速完成征途的工具 就是人工智能。

and successful action plans.

One of the most powerful tools we have

人工智能能自动处理数据，

to help us accelerate on this journey is artificial intelligence.

处理形形色色、杂乱无章的原始资料，

例如发票和顾客行为数据。

Artificial intelligence can process data automatically

通过建模，它能更好估算丢失的信息。

from diverse, unstructured sources

like invoices, consumer behavior data.

它能模仿排放并最终优化排放。

It can work by modeling to better estimate the missing information.

我举一个它如何工作的例子。

It can simulate and ultimately optimize emissions.

一个制红酒和烈酒的跨国公司：

有数十亿的销量，

坐拥数百个品牌，

Let me share an example of how this could work.

消费者遍布全球。

A wine and spirits international company:

当他们想要测量其对气候的影响时，

billions of sales,

需要测量出所有的排放量。

hundreds of brands,

consumers across the globe.

这包括生产设备的直接排放、

When they want to measure their impact,

they need to measure across the entire set of their emissions.

购买的电力、

原材料、

租赁资产、

IT排放、

This means direct emissions from facilities,

出差、

交通、

purchased electricity,

垃圾

和末端产品

raw materials,

等等等等。

leased assets,

要收集的数据数不胜数。

IT emissions

business travel,

transportation,

其实公司自身很难能接触到 大多数数据，

waste,

product end of life,

etcetera, etcetera.

因为这些数据 来自公司直接活动范围之外。

That’s a huge amount of information to collect.

比如说，

And most of it is actually inaccessible to the company itself

数据可能来自同样 无法计算自己排放量的供应商。

because it comes from outside its direct scope of activity.

所以当可持续发展团队 测量公司的排放量时，

For example,

from suppliers that are not yet able to calculate their emissions either.

他们没有别的选择，只能粗算。

So when the sustainability team calculates their impact,

让我们试着查查用于做瓶子的玻璃。

测量制玻排放量的方法如下：

they have no choice but to do rough estimates.

他们取上一年购买的玻璃总量——

Let’s examine the glass for bottles.

比如说是1000吨，

The way they calculate glass emissions is the following.

把它与转换因数相乘，

They take the total amount of glass bought last year --

转换因数代表了每吨玻璃的 平均二氧化碳当量——

let’s say 1,000 tons.

They multiply it by a conversion factor,

比如说是950。

950乘以1000等于950,000。

which represents the average kilos of CO2 equivalent for one ton of glass --

当然这个数字远非准确，

let’s say 950.

因为它并没有把

950 x 1000 makes 950,000.

所有影响排放量的因素纳入考虑，

Of course this is hugely inaccurate

所以设定目标和行动规划十分困难。

because it does not take into account

all the numerous factors that impact actual emissions,

可持续发展团队此时会召集数据科学家

so it’s hard to set targets and action plans.

过来处理一些详细数据，

This is where the sustainability team calls data scientists

如关于玻璃品类、

玻璃颜色、

回收份额、

to come in and process detailed data about the type of glass,

供应商原产国

和交通方式，

依据不同的品牌

和产品。

the color of the glass,

他们可以模拟并设计其供应链条，

the recycling share,

the supplier country of origin,

并在计算中加入

the transportation mode,

by brand,

玻璃颜色的重要性——

by product.

生产透明的玻璃瓶要比绿色的玻璃瓶

They can simulate the design and the supply chain

多1.5倍的排放量。

and integrate in the calculation

还有供应商原产国的重要性——

the importance of the glass color --

1.5 times more emissions for a clear bottle

一个国家可能会比另一个国家 多两倍排放量，

versus a green bottle;

依据他们能源结构的区别。

the importance of the country of origin --

以及设计本身的重要性——

twice the amount of emissions for one country versus another one,

同等重量的产品

一种设计可能会比另一种 多1.5倍的排放量。

depending on the energy mix;

the importance of the design itself --

取代了一个数额巨大的平均数，

for the same total weight,

1.5 times more emissions for one design versus another one.

你现在有一个模型 能统筹各因素并计算排放量，

Instead of having one big, average number,

以粒度级别的水平。

有了这种方法，

you now have a model which correlates and calculates emissions

通常有30%到50%的排放数据能被修正。

at a granular level.

With this type of methodology,

更重要的是，

公司能尽可能地行动起来。

the emissions figure is typically corrected by 30 to 50 percent.

首先，设定有意义的目标。

And more importantly,

其次，确定具体举措。

the company can now move to action

最后，

as they can, one, set meaningful targets,

事后重算排放量，并衡量其进展。

two, identify very concrete initiatives,

我再举一个例子，

and three,

水泥。

recalculate emissions over time and measure their progress.

水泥能产生巨量的二氧化碳。

如果水泥是一个国家，

Let me share another example:

它能排到第三大排放国，

cement.

Cement is a massive CO2 emitter.

紧跟在中国和美国之后，

If cement were a country,

后面是欧盟和印度。

it would rank as the third-largest emitter,

大多数碳排放从生产熟料的过程中来，

right after China and the US,

in front of the European Union and India.

这是水泥的重要成分。

要想生产熟料，

Most of the emissions come from the process of producing clinker,

你要将温度保持到1400摄氏度以上。

the key ingredient in cement.

这需要大量燃料，

To produce clinker,

而且所有原料的主要成分都只有碳。

you need to maintain a temperature of over 1,400 degrees Celsius.

所以秘诀就是生产更干净、 更高质量的熟料，

It requires a lot of fuel,

and it’s really just carbon containing the whole materials.

因为熟料的质量越高，

最终生产水泥时的用量就越少，

So the secret sauce is to produce cleaner and higher quality clinker,

因此产生的碳排放量就越少。

because the higher the quality of the clinker,

但生产高质量的熟料 所需的科技十分复杂。

the less of it you will need to produce cement ultimately,

这需要多种要素相互作用。

and therefore the less emissions you will generate.

But producing high-quality clinker is a complex science.

举个例子，工艺参数，

就像机器的转速一样，

It depends on multiple factors that influence each other.

你填充它的速度有多快，

用什么种类的燃料，

For example, the process parameters,

原材料和它们具体的化学成分。

like the rotation speed of the machine,

how quickly you fill it,

此时，人工智能又一次发挥巨大作用。

the type of fuel you use,

the raw materials and their exact chemical composition.

现场操作团队正试图手动维持

This is where artificial intelligence can again have an enormous impact.

一组可行的最优参数。

人工智能可以通过不同的原始资料 进行更精确的测量，

On-site operational teams are trying to manually maintain

比如直接测量法、

the best set of parameters possible.

物料平衡

诸如此类。

AI can help by measuring better through different sources,

它还模拟所有可能的决策

并向操作员推荐最优选择。

like direct measurements,

material and mass balance,

etcetera ...

这些技术已被用于水泥生产，

simulate all the potential decisions

and recommend the optimal ones to the operators.

使碳排放量大幅减少，

仅用了几个月。

These techniques implemented in a cement production process

有无数可能的应用方式。

enable a substantial emissions reduction

没有任何公司、

in a matter of months.

工厂不能通过使用人工智能

There is an infinity of applications possible.

来对气候造成显著影响。

There is no company,

no industry that cannot derive significant climate impact

我不是说单单人工智能 就能救我们于水火。

from the use of artificial intelligence.

但是人工智能

帮助我们更精确地计算、

I’m not saying artificial intelligence alone will save us.

模拟、

与优化，

But artificial intelligence,

使大量减少碳排放成为可能，

by helping us measure accurately,

以一种极其快速、经济且简单的方式。

simulate

and optimize,

enables significant emissions reduction

我们不能错失良机。

in a quite fast, cheap and easy way.

谢谢。

We cannot miss this opportunity.

Thank you.