The United States is aiming for a zero-carbon economy by 2050, a goal that requires the creation of a robust carbon management industry. This industry will tackle greenhouse gas (GHG) emissions, develop critical infrastructure and pioneer innovative technologies for the safe transport and storage of carbon dioxide (CO2). The challenge is immense and requires the rapid deployment of new solutions on an unprecedented scale, both nationally and globally. The success of this endeavor is critical to ensuring a secure, clean energy future and mitigating the growing climate risks that threaten our nation’s well-being.
Artificial intelligence (AI) and machine learning (ML) will play a central role in accelerating infrastructure development and optimizing performance. AI’s ability to quickly analyze complex systems will help understand the fundamental science related to the carbon budget. By ingesting vast data streams, AI can refine critical predictions to assess subsurface environments, mineralization processes, and the integrity of transportation networks.
To meet the 2050 GHG emissions mitigation goal, the United States must improve its carbon capture and storage infrastructure to process at least 65 million tons of CO2 per year – a capacity comparable to the he current CO2-assisted oil recovery industry, which has evolved over half a century. By 2050, storage capacity of around a billion tonnes of CO2 each year will be required, requiring around 1,000 capture facilities, a vast network of CO2 pipelines stretching thousands of kilometers and numerous injection wells and storage reservoirs.
At the same time, carbon management solutions must be developed to meet the growing demand for clean energy in the context of a decline in production from traditional fossil fuels. This includes renewable sources such as solar and wind, as well as unconventional resources such as geothermal energy, hydrogen and nuclear energy. Achieving these innovations over the next two decades requires expanding existing infrastructure, building new facilities, and developing efficient technologies, from advanced subsurface analysis tools to new transportation systems.
As the carbon management industry grows, advances in AI optimization can minimize risks associated with early projects and reduce basin-wide impacts. One of the major challenges, named “DISCO2VER,” calls for the creation of an AI-based digital twin of the planet to accelerate clean energy transitions and inform effective GHG mitigation strategies. This virtual twin will simulate and predict scenarios in real time, facilitating the transition to a carbon-neutral economy.
Another challenge is to develop a virtual earth model to exploit underground resources sustainably. By leveraging AI and high-resolution data, the goal is to transform the understanding of the subsurface to facilitate environmentally friendly resource extraction and waste storage.
Additionally, the identification and maturation of new materials for carbon capture and removal is critical to commercial-scale deployment. AI-based research will optimize critical properties to improve carbon capture efficiency in a cost-effective manner.
Finally, predicting, measuring and mitigating emissions from hard-to-electrify sectors and unknown sources pose significant challenges. AI can detect, quantify and prevent emissions more effectively, enabling a 30-40% improvement in system efficiency.
Over the coming decade, advances in AI and machine learning will be instrumental in meeting these challenges. By pooling data, developing advanced models, and fostering interdisciplinary collaborations, we can accelerate progress toward a sustainable, carbon-neutral future. The path to a carbon neutral economy by 2050 is daunting but achievable through bold investments in technology, innovation and collaborative efforts across sectors. Developing a robust carbon management industry will not only secure our energy future, but also safeguard our nation’s health, prosperity and resilience in the face of climate change.
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