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Supply Chain Decarbonization

A 3D icon shows a trio of descending columns.

Which suppliers we procure from affects emissions

A 3D scene shows the word "Good" in giant letters over a scene of dirty and clean factories, with trucks leaving each factory.
PROCUREMENT FROM... WHOMEVER
Embodied emissions: 2.2 tonnes CO2 per tonne steel
A 3D scene shows the word "Great" in giant letters over a scene of dirty and clean factories, with all trucks preferentially sourcing from the lower-carbon supplier.
... LOWER-CARBON SUPPLIERS
Embodied emissions: 0.5 tonnes, 4.2x better

What is it?

In some sectors, the cleanest suppliers of a given raw material are much lower-emitting than others. But many of the cleanest such facilities have excess production capacity that isn’t being fully utilized. One of the fastest, simplest ways we can all reduce emissions is to switch from normal procurement to at least using up the surplus capacity going unused at lower-emitting facilities.

This is suddenly far easier than before, because WattTime’s partner nonprofits in the Climate TRACE coalition are using satellites and AI to vastly increase transparency in global emissions factors. Unbiased, highly granular, recent emissions estimates allow for an apples-to-apples comparison between potential suppliers or regions — so buyers have the needed data insights to choose lower-carbon options or to help their dirtier suppliers do better.

Facility-specific emissions factors empower organizations to decarbonize upstream supply chains faster and reduce Scope 3 emissions.

A satellite orbits a 3D globe highlighted with clean vs. dirty steel factory locations.

Why it matters

New data are highlighting many suppliers with excess low-emitting capacity that in many cases isn’t any more expensive than other sources. If today everyone shifted to this clean excess capacity that already exists, it could reduce over 1 billion tons of CO2 emissions per year.
14x
14x EMISSIONS DIFFERENCE PER TON BETWEEN THE CLEANEST AND DIRTIEST STEEL
50%
50% DIFFERENCE PER TON OF CARGO SHIPPING ON THE CLEANEST VS. DIRTIEST VESSELS

How it works

As a co-founding member of the Climate TRACE coalition, WattTime enables access to emissions factors for assets controlled by most major tier 3 suppliers and weighted average total emissions factors at the country level.

For subsectors where electricity emissions contribute to these emissions factors (e.g., aluminum refining), WattTime integrates granular electricity grid data to accurately represent production-related emissions.

Organizations, countries, and governments can leverage these data to understand and act on source-specific emissions factors as they seek to decarbonize their upstream supply chains and reduce Scope 3 emissions. If you procure significant amounts of raw materials with high embedded emissions (like steel, aluminum, cement, beef, crops, lumber, and others), contact us today and we’ll help you understand how you can reduce more emissions, faster. For complex cases, we’ll loop in the right expert partners from our network of NGOs who share that goal.

As a nonprofit and founding member of the Climate TRACE coalition, our only goal is to help you drive down emissions faster.
A data visualization shows ~850 steel plants globally, graphed from lowest to highest emissions factor.
Emissions Factors Can Vary Widely Across Suppliers: The emissions factor difference between the cleanest and dirtiest suppliers can be as great as 10x, creating massive opportunities to decarbonize supply chains and Scope 3 emissions through more strategic procurement choices.

Partners who are doing it

Impact stories

Get started

Anyone can go to ClimateTRACE.org and access emissions factor data for any of hundreds of thousands of emitting facilities worldwide. For many companies, it’s helpful to have a guide to walk them through how to use the data, how to evaluate tradeoffs, and how to ultimately make the lowest-emitting decision that fits their business needs. WattTime provides this support free of charge, and all Climate TRACE data are free. 
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