Navigating Responsible AI for Climate Action
The United Nations’ 1987 Brundtland Report defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This definition emphasizes the importance of striking a balance between current demands and the protection of future resources, particularly in the era of artificial intelligence (AI).
AI today presents a clear dichotomy: while it facilitates efficiency and innovative solutions to pressing environmental and societal challenges, it also imposes increased resource demands that are approaching the energy consumption needs of entire countries.
The Role of AI in Sustainable Development
As AI becomes more prevalent across industries and regions, numerous environmentally focused use cases have emerged. The AI for Good movement, supported by institutions like the United Nations, illustrates how AI can help achieve the Sustainable Development Goals (SDGs), many of which address climate change, such as Goal 13. The European Parliament’s Think Tank suggests that AI could potentially reduce global greenhouse gas emissions by 1.5–4% by 2030, aiding in the realization of Goal 13.
However, the environmental implications of AI necessitate a significant responsibility to mitigate its impacts. The high energy consumption required to train and maintain sophisticated machine learning models is one of the many environmental costs associated with AI systems.
AI’s Environmental Footprint
AI’s environmental footprint can be assessed through several key factors:
- Energy Consumption: AI models require substantial processing power due to their complexity. This leads to significant energy consumption, especially during the training phase, which can be lengthy for more intricate models. The choice of model type can greatly influence an AI system’s overall environmental impact. For instance, deep learning, natural language processing (NLP), and generative AI (GenAI) models typically demand more energy than simpler categorization models.
- Greenhouse Gas (GHG) Emissions: The substantial energy needed to operate AI systems—especially from non-renewable power sources—often triggers significant greenhouse gas emissions.
- Water Consumption: Large data centers, essential for training and deploying advanced AI models, require water-intensive cooling systems to prevent overheating. This demand can exacerbate water scarcity in vulnerable regions.
- Hardware and E-Waste: The production and disposal of servers, GPUs, and other specialized technology contribute to environmental degradation through resource extraction, manufacturing emissions, and electronic waste, which pollutes ecosystems.
A Sustainable Path Forward
One promising avenue for sustainability involves deploying large AI models on edge devices like wearables, smart speakers, and smartphones. These devices, limited in processing power, cannot run complex models with billions of parameters, thus reducing operational costs and energy consumption associated with cloud computing. AI models on edge devices tend to be more energy-efficient compared to their cloud-based counterparts, mitigating their environmental impact.
End users also face environmental costs from the continuous operation of GenAI tools. For example, generating images typically consumes more energy than producing text, and large language models inherently require more energy than smaller models designed for specific tasks.
To foster a sustainable AI ecosystem, ongoing research at the intersection of AI and sustainability is essential. Companies must be deliberate about the how, why, and when of GenAI applications.
Mobilizing for Climate Action
Recent climate events—including unprecedented heatwaves, devastating wildfires, and catastrophic floods—underscore the urgency of addressing climate emergencies. The Responsible AI Working Group (RAI WG) of the Global Partnership on AI (GPAI) has established a Committee on Climate Action and Biodiversity Preservation to explore how AI can support climate action.
Given that AI is a versatile tool, it must be developed responsibly across all applications. Key principles include fairness, accountability, safety, privacy, security, and robustness, which are critical for effective policy recommendations.
Data quality also plays a crucial role in responsible AI. The accuracy, timeliness, and completeness of datasets significantly affect the reliability and performance of AI systems. Promoting accountability and transparency is vital to build trust and address ethical concerns surrounding AI technology.
AI is integral to integrating renewable energy (RE) into the energy sector. Variable renewable energy (VRE) sources like solar and wind present unpredictable demand and supply patterns compared to traditional energy networks. However, AI can accurately predict these patterns, facilitating smoother transitions to renewable energy and progressively lowering emissions.
Recommendations for Governments
To mitigate AI’s adverse effects on climate, governments should:
- Refrain from directly supporting applications that conflict with climate objectives.
- Prioritize climate change when promoting the development of AI-enabled technologies.
- Ensure that reporting and carbon pricing regulations adequately reflect cloud computing.
- Only procure AI and computing services from companies committed to achieving net-zero emissions.
In conclusion, harnessing AI’s potential to combat climate change and enhance global collaboration requires a coordinated approach focused on ethics, transparency, and accountability. By adhering to ethical standards and promoting responsible AI practices, stakeholders can effectively address climate challenges using the transformative power of AI.
