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

Transcriber: Leslie Gauthier Reviewer:

譯者: Cheng-Ying Tang 審譯者: Helen Chang

For decades now,

幾十年來，

we’ve been saying we should reduce our emissions,

我們一直呼籲人們應該 減少溫室氣體排放量。

but they’ve kept increasing.

然而，排放量卻持續增加。

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

關鍵原因之一是我們並未準確地測量

the climate impact of our actions.

我們的行為影響著氣候的變化。

Imagine trying to save money,

試想一下 : 你試著省錢，

but when you go shopping,

但當你去商店購物時，

there is no price tag on any item ...

卻發現所有商品都沒有標價。

or trying to lose weight,

或者，你嘗試減重，

but you cannot measure the portion sizes and the calories.

但你無法計算食物的份量和卡路里。

You would be bound to fail.

如此，你必定會失敗。

This level of blindness is close to the one we have

這種程度的茫然

如同我們對氣候影響的認知一樣 只是一知半解。

when it comes to our climate impact.

Measuring greenhouse gas emissions is hard.

計算溫室氣體的排放量是很困難的。

It has no color,

它沒有顏色，

it has no smell;

也沒有氣味，

it’s invisible.

甚至是無形的。

We cannot put sensors everywhere,

我們不能夠在任何地方，

on every building,

如每棟建築物、

every track,

每條鐵軌、

every field,

每塊田地、

every cow --

每頭牛身上都放置測量儀器。

so most of the time,

因此，我們多半放棄

we give up and we don’t measure.

不去測量。

And when we do measure,

而當我們進行測量時，

we are reduced to relying on estimations

我們只能根據估計值與轉換因子。

and conversion factors.

The consequence is we end up working with highly incomplete

結果導致我們以非常不完整

and inaccurate estimations of our emissions.

且不準確的數據來進行 氣體排放量的計算。

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

在計算上，我們通常有百分之三十 到六十的誤差範圍。

This means targets and action plans are set

這意味著目標與計劃的執行

都是基於不準確的資料數據。

based on inaccurate data.

If we look at the corporations

如果我們檢視那些向碳揭露專案

that report their progress on climate to the CDP,

報告其對氣候做出改善方法的企業。

which is a nonprofit organization that runs a global disclosure system

碳揭露專案是一個非營利組織， 為了環境變遷

for environmental impacts,

推動著全球碳揭露系統。

what we see is striking:

我們將會看到驚人的事實：

more than two-thirds of the companies

超過三分之二的公司

are not accurately measuring their emissions,

並未準確測量他們的碳排放量，

and only seven percent of those companies

而且，只有百分之七的公司

are ultimately reducing their impact in some way.

最終以某種方式 減少了他們對環境的影響。

You cannot reduce what you cannot measure.

你不能減少無法測量的東西。

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

關鍵在於，企業要能夠精準計算 所有導致碳排放量上升或下降的

all sources that drive carbon up or down.

所有活動及來源。

In a way,

就某種意義上來說，

that’s just putting the same rigor to carbon measurements

我們必須把碳排放量的計算

that we have for financial accounting.

以如同財務會計般嚴格的標準來看待。

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

現代自動化財務會計的實施 花了100多年的時間才完成。

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

在氣候方面，我們並沒有 100年的時間可以蹉跎。

But this is crucial for corporations to set meaningful targets

但這對於企業訂定有意義的目標 和成功的行動計劃來說

and successful action plans.

是至關重要的。

One of the most powerful tools we have

我們擁有最強大的工具之一

to help us accelerate on this journey is artificial intelligence.

能夠幫助我們加快進程， 那就是人工智慧。

Artificial intelligence can process data automatically

人工智慧能夠自動處理

from diverse, unstructured sources

來自各種非結構式來源的數據。

like invoices, consumer behavior data.

例如：發票或是消費者行為數據。

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

它可以透過數據建模 來更準確地估算缺失的訊息。

It can simulate and ultimately optimize emissions.

它可以模擬並最終優化 碳排放量的計算。

Let me share an example of how this could work.

讓我來分享一個例子來說明 這是如何運作的。

A wine and spirits international company:

一家銷售葡萄酒和烈酒的國際公司

billions of sales,

擁有幾十億的銷售額、

hundreds of brands,

幾百個品牌、

consumers across the globe.

以及遍及全球的顧客。

When they want to measure their impact,

當他們想要計算對環境的影響時，

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

他們需要估計整趟流程的碳排放量。

This means direct emissions from facilities,

這就表示需要計算來自設施、

purchased electricity,

外購電力、

raw materials,

原物料、

leased assets,

租賃資產、

IT emissions

資訊科技的碳排放、

business travel,

商務旅行、

transportation,

產品運輸、

waste,

廢棄物、

product end of life,

產品壽命中止、

etcetera, etcetera.

等等......碳的實際排放量。

That’s a huge amount of information to collect.

要蒐集的資訊量非常大，

And most of it is actually inaccessible to the company itself

且實際上大多數資訊 是公司本身無法得知的，

because it comes from outside its direct scope of activity.

因為它來自上述活動的範圍之外。

For example,

舉例來說，

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

供應商尚未能夠計算其碳排放量，

So when the sustainability team calculates their impact,

因此，當環境可持續發展團隊 計算其環境影響時，

they have no choice but to do rough estimates.

他們別無選擇，只能粗略估計數據。

Let’s examine the glass for bottles.

我們來探討瓶子的原物料：玻璃。

The way they calculate glass emissions is the following.

他們計算玻璃碳排放量的方法如下。

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

他們拿去年買的玻璃總量，

let’s say 1,000 tons.

假定為1000公噸。

They multiply it by a conversion factor,

乘上一個轉換因子，

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

此轉換因子為1公噸的玻璃產生的 平均二氧化碳公斤數。

let’s say 950.

假定為950公斤。

950 x 1000 makes 950,000.

950乘上1000等於950,000。

Of course this is hugely inaccurate

當然，這項數據是極度不準確的，

because it does not take into account

因為並未考慮到

all the numerous factors that impact actual emissions,

影響實際碳排放量的眾多因素。

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

也因此設定目標與行動計劃 是非常困難的。

This is where the sustainability team calls data scientists

這就是環境可持續發展團隊 召集數據科學家

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

來處理詳細數據的地方，

如玻璃的類型、

the color of the glass,

玻璃的顏色、

the recycling share,

回收份額、

the supplier country of origin,

供應商原產地、

the transportation mode,

運輸模式、

by brand,

品牌，

by product.

以及最終產品。

They can simulate the design and the supply chain

他們能模擬玻璃瓶的設計與供應鏈

and integrate in the calculation

並將其納入碳排放量的計算。

the importance of the glass color --

如玻璃顏色的重要性

1.5 times more emissions for a clear bottle

透明玻璃瓶的排放量

versus a green bottle;

是綠色玻璃瓶的1.5倍。

the importance of the country of origin --

或者是原產地的重要性

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

某國家的排放量是另一個國家的兩倍

depending on the energy mix;

這取決於該國的能源組合。

the importance of the design itself --

又或者是玻璃瓶設計的重要性

for the same total weight,

在總重量相同的情況下，

1.5 times more emissions for one design versus another one.

某種設計的排放量是 另一種設計的 1.5 倍。

Instead of having one big, average number,

你現在擁有一個模型

you now have a model which correlates and calculates emissions

能夠以非常具體且清晰的角度 連結並計算碳排放量

at a granular level.

而不是一個巨大的平均數值。

With this type of methodology,

使用這種計算方法，

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

碳排放數據通常會被修正 百分之三十到百分之五十。

And more importantly,

而更重要的是，

the company can now move to action

公司現在能夠採取行動。

as they can, one, set meaningful targets,

第一，設定有意義的目標。

two, identify very concrete initiatives,

第二，訂定非常具體的新措施。

and three,

第三，

recalculate emissions over time and measure their progress.

每隔一段時間重新計算碳排放量 並估算進展。

Let me share another example:

讓我來分享另一個例子：

cement.

水泥。

Cement is a massive CO2 emitter.

水泥是巨大的二氧化碳排放來源。

If cement were a country,

假如水泥是一個國家，

it would rank as the third-largest emitter,

它將成為世界第三大 二氧化碳排放國，

right after China and the US,

僅次於中國和美國，

in front of the European Union and India.

且超越歐盟與印度。

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

大部分排放來自 生產水泥熟料的過程，

the key ingredient in cement.

熟料是水泥的關鍵成分。

To produce clinker,

要生產熟料，

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

需要將溫度維持在攝氏1400度以上。

It requires a lot of fuel,

這需要大量燃料，

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

而實際上就是碳包含全部的材料。

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

而解決方法就是生產 更乾淨、更高品質的熟料，

because the higher the quality of the clinker,

因為熟料的品質越好，

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

生產水泥所需的熟料就越少。

and therefore the less emissions you will generate.

進而降低二氧化碳的排放量。

But producing high-quality clinker is a complex science.

然而，生產高品質的熟料 是一門複雜的科學，

It depends on multiple factors that influence each other.

取決於多種相互影響的因素。

For example, the process parameters,

舉例來說：生產製造的程序參數，

like the rotation speed of the machine,

如機器的轉速、

how quickly you fill it,

需要填裝多快、

the type of fuel you use,

使用何種燃料、

the raw materials and their exact chemical composition.

原物料以及它們確切的化學成分。

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

這就是人工智慧能夠 再次產生巨大影響的地方。

On-site operational teams are trying to manually maintain

現場營運團隊正嘗試手動維護

the best set of parameters possible.

可能產生的一組最佳參數。

AI can help by measuring better through different sources,

人工智慧可以透過不同的測量 來做出更好的計算，

like direct measurements,

如直接測量、

material and mass balance,

原料及質量平衡

etcetera ...

等等。

simulate all the potential decisions

模擬所有潛在可行的決策，

and recommend the optimal ones to the operators.

並推薦最佳的方案給營運者。

These techniques implemented in a cement production process

這些科技應用在水泥製造過程中，

enable a substantial emissions reduction

能夠在幾個月內

大幅減少二氧化碳的排放量。

in a matter of months.

There is an infinity of applications possible.

人工智慧有著無限應用的可能性。

There is no company,

任何公司、

no industry that cannot derive significant climate impact

任何產業都將從人工智慧的應用中

from the use of artificial intelligence.

獲取對環境產生重大影響的資訊。

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

我並不表示僅靠人工智慧 就能拯救我們。

But artificial intelligence,

但人工智慧

by helping us measure accurately,

幫助我們準確計算

simulate

模擬

and optimize,

並優化，

enables significant emissions reduction

使大量減少碳排放量

in a quite fast, cheap and easy way.

得以更快速、廉價且簡單的方式實現。

We cannot miss this opportunity.

我們不能夠再失去這次的機會。

Thank you.

謝謝。