Why It Matters
Machine learning (ML) presents both opportunities and challenges in the fight against climate change. While it can support sustainability initiatives, its intensive energy demands and use in industries with high carbon footprints pose concerns, as highlighted by Priya Donti of Climate Change AI.
Machine learning plays a complex role in addressing climate change. On one hand, it enhances sustainability efforts through improved efficiency and data analysis. On the other, its high computational needs and applications in certain industries could contribute to environmental degradation.
To maximize its benefits, organizations must embrace a balanced approach—leveraging ML’s capabilities while mitigating its energy consumption and unintended consequences, according to MIT professor Priya Donti. Speaking at the 2024 MIT Sustainability Conference, she emphasized the importance of critically assessing ML’s impact on broader sustainability goals.
How Machine Learning Supports Sustainability
ML contributes to climate solutions in multiple ways, including optimizing energy use, advancing climate science, and improving operational efficiency. A 2022 study by Donti and her colleagues outlined key areas where ML can drive positive environmental change:
Transforming Raw Data into Actionable Insights
Gathering precise climate data is often challenging, particularly in remote or inaccessible areas. ML can bridge this gap by analyzing satellite and aerial imagery, producing insights applicable on a larger scale. For example, Climate Trace, a coalition of nonprofit organizations, uses ML to compile independent emissions inventories based on satellite data.
Enhanced Forecasting
By identifying patterns in historical climate and energy data, ML can improve predictions for weather patterns and renewable energy output. This, in turn, helps with better grid management and resource allocation.
Automated Decision-Making
ML algorithms enable real-time adjustments to optimize energy efficiency. Systems can automatically regulate temperatures in buildings, data centers, or refrigeration units, ensuring minimal waste of resources.
Predictive Maintenance
Preventing system failures before they occur can significantly enhance efficiency. For instance, Germany’s Deutsche Bahn employs ML to detect potential railway infrastructure issues, enabling preemptive maintenance. Similarly, some natural gas providers use ML-driven sensors to identify methane leaks before they happen.
Accelerating Scientific and Engineering Discoveries
ML aids in the rapid analysis of experimental data, accelerating discoveries in fields such as carbon capture technology and materials science. It can also simulate time-consuming climate models, enhancing their precision and efficiency.
Data Integration for Climate Research
ML facilitates the merging and analysis of data from multiple sources, improving the accuracy of climate models and decision-making processes.
As climate challenges are diverse, it is essential to cultivate a broad ecosystem of ML applications rather than relying on a single dominant approach, Donti advised.
The Challenges: High Energy Consumption
Despite its benefits, ML consumes vast amounts of energy and water. Large-scale data processing and model training require substantial computational power, which contributes to carbon emissions. Additionally, manufacturing, transporting, and disposing of hardware further exacerbate ML’s environmental impact.
Studies indicate that data centers and information technology systems accounted for up to 2% of global greenhouse gas emissions in 2020. While the precise contribution of AI is uncertain, its energy demands continue to rise.
Moreover, newer AI models tend to be more resource-intensive. Larger, multi-purpose ML models require significantly more computation than their predecessors. Selecting the wrong ML approach can lead to unnecessary energy consumption, underscoring the need for thoughtful implementation.
Transitioning to renewable energy sources and optimizing energy use in data centers are essential steps, but they alone cannot resolve ML’s environmental impact. Transparency in data collection and reporting will be critical to addressing these challenges.
The Uncertainties: Potential Climate-Related Risks of ML
ML is not only used for climate-friendly initiatives but also in industries that contribute to environmental degradation. For example:
- The oil and gas sector leverages ML to enhance extraction and production efficiency, intensifying carbon emissions.
- Precision agriculture, powered by ML and IoT devices, enables large-scale livestock management, which may increase greenhouse gas emissions.
- Self-driving vehicles, although more energy-efficient individually, could lead to increased car usage, potentially hindering efforts to transition toward sustainable transportation models.
- Personalized advertising driven by ML encourages consumerism, often leading to increased carbon-intensive consumption patterns.
Recommendations for Responsible ML Use
To mitigate these risks and harness ML’s potential for good, Donti suggests the following actions:
- Diversify ML applications to ensure that organizations are not confined to a single approach or vendor.
- Prioritize sustainability-driven applications with demonstrated environmental benefits.
- Adopt measures to minimize emissions at every stage, from model training to deployment.
- Communicate the real-world impact of AI transparently, avoiding exaggerated claims.
“As AI continues to evolve, we must proactively consider both its direct environmental footprint and the broader consequences of its applications,” Donti stated. “By directing its use toward sustainability, we can maximize its positive impact while mitigating harm.”
