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Looking Ahead: Using AI to Build Dynamic Cost Models
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Looking Ahead: Using AI to Build Dynamic Cost Models

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March 2024

Navigating CBAM's Rising Costs Through Smart Supply Chain Optimization

How CBAM will drive impacts through the supply chain, beyond direct importers

In a previous post, we examined the EU's Carbon Border Adjustment Mechanism (CBAM) as it enters its transitional phase. This policy aims to address carbon leakage by applying fees to imports of emissions-intensive goods like steel and aluminum. These fees correspond to the goods' embedded emissions from manufacturing and transport. 

While measures like CBAM are crucial for the EU's climate goals, the policy heralds significant cost increases that will reverberate through global supply chains. As CBAM ramps up through the late 2020s, import fees are set to rise based on projected increases in EU carbon pricing.

The Downstream Price Implications of CBAM

Global businesses are already starting to feel the impact of CBAM. Countries like India that export, a leading exporter of steel to Europe, have historically had more lenient emissions regulations than EU manufacturers, resulting in production that is more carbon-intensive. The CBAM taxes on these imports will steadily increase through 2030 and beyond as EU carbon prices rise. EU companies now have to plan for this added tax or find cleaner suppliers. 

But CBAM's ripple effects extend far beyond import fees alone. As those taxes get factored into raw material prices, the costs will trickle down through manufacturing supply chains to finished products.

The auto industry is a prime example: consider Gestamp, an international group that supplies metal car parts to automakers like Ford. Gestamp relies heavily on steel and aluminum, which has a carbon-intensive production process. With CBAM, the materials Gestamp sources will become more expensive. These higher costs will result in pricier components for automakers and, ultimately, a more expensive product for consumers. 

To show just how significant these cost impacts could be, we looked at the door structures Gestamp used within Ram’s trucks. The predominant emission footprint of this good, unsurprisingly, is the steel material used to make it. With the implications of CBAM, the annual cost of this product line alone could grow by more than $12.5M.*  

This isn't just an EU issue; as carbon pricing initiatives expand globally, companies will face increasing pressure to reduce emissions and mitigate these added costs. Solutions like Muir AI's Carbon Origin can provide the needed supply chain visibility to identify carbon hotspots and target the most impactful interventions.

Transform Your Emission Strategies

It's important to note that CBAM is just one of many pressures suppliers are facing and need to navigate. Having in-depth supply chain visibility, especially detailed cost modeling that includes emissions, is critical for long-term planning and resilience. 

This is where Muir AI's Platform offers a strategic edge. Our solution maps out the materials and carbon footprint for your whole product portfolio, from manufacturing to shipping. We chart your supplier networks at every tier, identifying emissions hotspots and carbon pricing impacts all along the way. You get unparalleled insight into your real product costs and opportunities to cut them in the CBAM era.

Armed with this intelligence, companies can figure out the best ways to minimize future CBAM costs and taxes. That could mean shifting where you source from, negotiating with suppliers, redesigning products, or improving logistics. The same analysis pinpoints other chances to save money and boost sustainability, like reducing material waste or optimizing shipping routes.

Smart supply chain leaders see the disruption happening and are proactively modeling their CBAM risk exposure. Contact us to learn how we can help your company navigate the fast-changing landscape of carbon-taxed trade with data-driven insights.

*These insights were derived from publicly available information as well as Muir AI’s proprietary data

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