Artificial intelligence is now playing a pivotal role in addressing one of the most complex challenges in climate policy: how to increase carbon storage in land without compromising food security, biodiversity, or livelihoods. A groundbreaking initiative led by researchers from The University of Texas at Austin and Cognizant AI Labs has leveraged evolutionary AI to design smart, balanced land-use strategies. By training their AI model on 175 years of historical land-use and carbon-stock data, the team has created a decision-making tool that adapts like nature—through digital natural selection.
The system, known as evolutionary AI, mimics biological evolution to refine land management policies over many generations. It begins by generating diverse scenarios involving agriculture, forestry, grazing, and urban development. Each is evaluated for its impact on carbon sequestration, food production, and biodiversity. Poorly performing scenarios are discarded, while promising ones are recombined and mutated to generate new iterations. Over time, this process yields finely tuned strategies that maximize environmental benefits at the lowest social and economic cost.
The data powering this model come from a detailed global archive of land-use changes dating back to the mid-19th century. These are integrated with a carbon model that estimates how various land-cover types and latitudes affect carbon storage. The result is EarthSnap—an advanced AI system that not only predicts outcomes but proposes tailored interventions with real-world policy implications.
Unlike blanket approaches such as indiscriminate tree planting, evolutionary AI emphasizes nuance. For instance, it found that converting cropland to forest yields higher carbon benefits than converting rangeland but at the cost of food production. Similarly, reforesting tropical regions stores more carbon than doing so in colder climates, yet carries greater social risk by potentially displacing local agriculture. The AI’s solutions reflect these trade-offs, concentrating efforts in regions where carbon gains are greatest and societal impacts minimal.
To translate science into policy, the research team developed an interactive interface where decision-makers can manipulate factors like tax incentives and carbon pricing. The interface then uses evolutionary AI to generate optimal land-use mosaics, showing how various policy mixes affect emissions, employment, and ecosystem health. This dynamic, data-driven tool helps reduce resistance to climate policy by offering practical, incremental solutions instead of rigid mandates.
Importantly, the researchers see evolutionary AI as a powerful tool far beyond land-use planning. It could optimize vaccine distribution, allocate water during droughts, or inform crop choices under shifting rainfall patterns—essentially augmenting human decision-making in high-stakes, complex systems.
The project’s most striking outcome is its “pick-your-battle” approach. Instead of pushing global uniformity, it identifies specific regions where strategic interventions yield the highest carbon returns for the lowest cost. These high-leverage zones—often overlooked by traditional policy—represent an opportunity for transformative yet practical climate action.
As nations strive to meet the Paris Agreement and the UN Sustainable Development Goals, tools like evolutionary AI will be critical. They offer a path forward that blends ambition with realism, helping leaders navigate the fine line between ecological preservation and human development.
https://www.earth.com/news/evolutionary-ai-can-balance-climate-goals-and-economic-progress