Data for decarbonisation: How AI can secure a hydrogen future

The use of AI to measure the carbon intensity of hydrogen and verify its provenance could help revolutionise carbon management in a complex supply chain. This would provide hydrogen importers and end users with the confidence that the product they purchase complies with regulation and low emissions credentials, and to report against their own carbon-emissions reduction strategies.

Certifications for sustainable forestry, social impact organisations and fair trade have been commonplace for some time, designed to instil consumer confidence that products and services meet promised standards or a guarantee of origin. 

A low-emissions hydrogen ‘proof of provenance’ process could work in much the same way and will likely be an important tool to link carbon intensity with hydrogen production. Implementing this process, however, is challenging, due to the complexities inherent in hydrogen supply chains, including varying production methods, derivative products, transportation logistics and regulatory differences across regions. 

Green hydrogen, produced by splitting water into hydrogen and oxygen using renewable electricity, stands out as one of the cleanest forms of hydrogen. Alternative pathways include hydrogen derived from fossil fuels like gas and coal, in which the byproduct – carbon dioxide – is captured and stored. 

To be branded as ‘low emissions’, hydrogen and its derivative products will require robust emissions tracing and verification mechanisms throughout the supply chain. This is important as hydrogen exports are anticipated to contribute to a prosperous Asia Pacific region in trade and investment outcomes. 

Tracking carbon intensity will be required, from production – whether via electrolysis, pyrolysis, gasification, or reformation – through to conversion into synthetic fuels or ammonia, product shipping and its end use in various industries such as feedstock or fuel. As Lord Kelvin, an eminent 19^th-century scientist, was reported to exclaim, "To measure is to know". 

Digital certification and AI’s potential

More than 40 countries have either published or are drafting hydrogen strategies. To be able to prove the authenticity of certificates for hydrogen, exporters will be required to account for the carbon pricing mechanisms in the importing countries, as well as the daily fluctuations in those carbon prices. 

In Australia, for instance, the Hydrogen Guarantee of Origin (GO) Scheme is currently under development as a means of certification. With an initial focus on hydrogen production, it intends to trace emissions intensity through the upstream component of the hydrogen supply chain. While the ‘what’ has been established, the ‘how’ of the GO scheme, including technologies for measuring, tracking and verifying emissions, remains unresolved. 

The strength of AI lies in its ability to manage and analyse the complex datasets associated with accumulating data at each stage of the supply chain. Take, for example, a refrigerator. Imagine if, as a consumer, a ‘tick’ certification indicated that the steel used in its manufacture was produced using low emissions hydrogen. Achieving this level of assurance requires an incredibly complex process of tracking and verifying emissions across the entire supply chain, such as:

• Renewable energy certifications;
• The process used to produce the low-emissions hydrogen;
• Guarantee of Origin (GO) certificates;
• Calculation of real emissions from hydrogen and steel transportation;
• Optimisation of the manufacturing process;
• Ensuring that steel production meets quality and emissions standards; and
• Tracking the amount of steel used in the manufacture of the refrigerator.

AI could analyse all of the data and calculate the total emissions intensity (and compliance with regulations) to authorise the use of the ‘tick’ certification.

In this example, AI may ensure the integrity of this process by continuously monitoring and verifying data throughout the supply chain. It may increase the operational efficiency of complex supply chains and help by achieving higher levels of compliance and transparency that are critical in complex regulatory environments. 

Its power is in being able to track and measure the low-emissions hydrogen supply chain, providing a guarantee of origin for end users.

Transparency and credibility to proliferate an industry 

While the application of AI for supply chain provenance is not novel, the complexity lies in how it is applied to a developing industry (hydrogen) in which the permutations for the final product are significant.

The integration of AI in the low-emissions hydrogen sector would represent a giant leap forward in streamlining proof of provenance. Its ability to handle vast amounts of data with precision would ensure that each stage of the supply chain is meticulously documented and verified, maintaining the credibility of low-emissions hydrogen as a truly clean energy source.

It’s important to note that the successful implementation of AI in this context will require careful consideration of ethical implications, data privacy concerns and the need for international cooperation in establishing standardised protocols. 

However, as countries around the world increasingly adopt hydrogen strategies and aim for significant emissions reductions, AI may offer a powerful way to ensure that low-emissions hydrogen lives up to its promise as a cornerstone of the clean energy future.