AI in Sustainability

Here is a question with an obvious answer: Do you think Artificial Intelligence (AI) is being used to address sustainability issues?

Yes, of course! AI has embedded itself in almost every facet of our daily lives, and its applications are growing at an astonishing rate. It would be inconceivable to think that someone is not developing some AI tool to save the world… or something like that. Here is a slightly more complicated question: In what ways is AI used to promote sustainability?

While many of us are familiar with apps that help track our carbon footprint or recommend energy-saving solutions, AI's role in sustainability is much more profound and far-reaching. It goes beyond convenience tools and taps into predictive modeling, optimization, and even governance to foster a more sustainable world.

Zooming out, we see that AI can support a wide array of the UN's Sustainable Development Goals (SDGs). Established in 2015, these 17 goals form a roadmap for ending poverty, protecting the planet, and ensuring prosperity for all by 2030. While it’s beyond our scope to speculate on whether these goals will be met, it’s clear that AI has far-reaching implications (1) - both accelerating and inhibiting progress toward various SDG targets.

On the accelaratory front, evidence from research suggests that AI can significantly contribute to 134 of the 169 (79%) targets under SDGs, which is a testament to AI’s perceived usefulness on numerous different contexts such emphasis on Climate Action (SDG 13), Clean Energy (SDG 7) or Zero Hunger (SDG 2). Here are a couple of examples:

• Climate Action (SDG 13): AI is already helping improve climate models by analyzing vast amounts of environmental data, predicting extreme weather events, and assessing future climate risks. Additionally, AI aids in designing low-carbon cities, optimizing energy consumption, traffic flow, and waste management to reduce emissions.
• Clean Energy (SDG 7): AI plays a key role in enhancing the integration of renewable energy sources into power grids. AI-driven smart grids match energy demand with periods of high renewable energy production, making the most of variable resources like wind and solar. Moreover, AI-powered predictive maintenance ensures that renewable energy systems run smoothly and efficiently over time.
• Zero Hunger (SDG 2): In agriculture, AI helps monitor crop health, manage water use, and predict yields. AI-driven precision farming uses satellite imagery and real-time data to inform farmers about the exact needs of their crops, minimizing waste and maximizing production efficiency.

However, it’s not all positive. AI could also hinder progress in 35% of the SDG targets. A primary concern is the energy consumption. Training AI models and maintaining data centers require vast amounts of energy, contributing to carbon emissions, which could exacerbate climate issues rather than alleviate them.

Another drawback is inequality. Advanced AI technologies are often developed in wealthier nations, leading to a gap between the global north and south. In regions where infrastructure and resources for AI are limited, there is a risk of widening inequalities, particularly in sectors like agriculture and energy.

While the potential downsides are significant, the overall impact of AI on sustainability remains overwhelmingly positive—if managed responsibly. Ensuring that AI development considers its environmental and social impacts will be crucial in determining whether it becomes an invaluable ally or a missed opportunity in the quest for a sustainable future. Regardles, we can rest assured in knowing that Skynet is not coming for us…

Bibliography
(1) Vinuesa, Ricardo, et al. "The role of artificial intelligence in achieving the Sustainable Development Goals." Nature communications 11.1 (2020): 1-10.

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