Understanding and Mitigating AI’s Ecological Footprint

May 21, 2024 | In-depth

Artificial Intelligence (AI) has undeniably transformed various aspects of daily life, bringing unprecedented convenience and innovation. However, its environmental footprint is becoming an increasingly critical issue and demands urgent attention. New technologies development and maintenance require vast amounts of resources, significantly impacting the environment. Kate Crawford, in her insightful book “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” (2021), meticulously outlines these costs and and its journey from mineral extraction to the high energy demands of data centers reveals a grim reality.

 

 

AI’s environmental impact

 

The “Grey Gold”

Lithium, often dubbed “grey gold,” has become indispensable for AI technologies. The Silver Peak lithium mine in the U.S. is a prime example, drawing interest from tech giants like Elon Musk due to Tesla’s substantial demand for lithium hydroxide. With Tesla requiring around 28,000 tons annually, nearly half of the global demand, the environmental implications of such extraction are profound.

The Energy Consumption of Data Centers

The carbon footprint of AI, particularly from data centers, is staggering. In 2018, Chinese data centers, which predominantly rely on coal (73%), emitted approximately 96 million tons of CO2. As AI continues to evolve, these emissions are expected to increase, underscoring the urgent need for sustainable practices.

Understanding AI’s Carbon Emissions

Researcher Sasha Luccioni has extensively studied AI’s carbon emissions. Her work, “Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning” provides a comprehensive overview of methods to estimate AI’s carbon output. Factors such as hardware power consumption, training duration, and energy grid carbon intensity are pivotal. Luccioni’s research on BLOOM, a 176B parameter language model, compares the energy use and carbon emissions of various large language models (LLMs). Training a LLM in 2022 can emit 25 metric tons of CO2 equivalents with renewable energy, but this figure can soar to 500 metric tons with high-carbon intensity sources like coal or natural gas.

The Water Footprint

AI’s environmental impact extends beyond carbon emissions. In “Making AI Less ‘Thirsty’: Uncovering and Addressing the Secret Water Footprint of AI Models” an insightful paper by Pengfei LiJianyi YangMohammad A. IslamShaolei Ren it is revealed that training models such as GPT-3 in U.S. Microsoft data centers consumes around 700,000 liters of fresh water. This is equivalent to producing 370 BMWs or 320 Teslas, highlighting the substantial water usage associated with AI development.

 

 

On the Other hand… Innovative Solutions for a Greener Future

 

Despite its undeniable negative impact, AI can also become a game-changer in the quest for energy efficiency and sustainability and, ultimately, balance their negative impact. Nowadays, new technologies have crucial role in driving sustainability and environmental protection, and, as they continue to evolve, their application can lead to significant environmental benefits. Here some examples:

Industrial Energy Efficiency – AI’s role in improving energy efficiency in buildings and industries is profound. By analyzing energy usage patterns, AI systems can optimize consumption, resulting in significant energy savings and reduced carbon footprints.

Renewable Energy –  AI is instrumental in advancing renewable energy sources such as wind and solar power. It enhances the efficiency of these energy sources by predicting energy production, optimizing performance, and streamlining maintenance processes.

Energy Grids – AI’s contribution to developing smarter energy grids is another significant benefit. By analyzing data from sensors, meters, and other devices, AI helps utilities better manage electricity demand and supply. This leads to reduced energy waste and improved reliability.

Sustainable Agriculture – In agriculture, AI supports sustainable practices by analyzing soil data, predicting crop yields, and identifying pest and disease outbreaks. This helps farmers optimize production while minimizing the use of pesticides and fertilizers.

Waste Management –  AI also improves efficiency in waste management by analyzing data on waste production, collection, and disposal. This allows cities and municipalities to optimize their waste management systems, reduce waste, and increase recycling rates.

 

Make a Virtue of Necessity: the Good Players

 

Addressing the environmental impact of AI necessitates the adoption of sustainable practices by both companies and consumers. Awareness and conscientious actions can pave the way for responsible AI usage. As the demand for AI grows, prioritizing environmentally friendly methods is crucial to mitigating its negative effects on our planet. Several innovative solutions are emerging to address AI’s environmental impact, offering hope for a more sustainable future. Like…

Plantiverse

Plantiverse is a groundbreaking digital platform designed to help users create and manage virtual gardens. This application enables users to explore various plant species, learn about their care requirements, and receive maintenance reminders. By integrating community features, Plantiverse fosters connections among plant enthusiasts, making gardening more accessible and engaging for everyone.

IBM Environmental Intelligence Suite

IBM’s Environmental Intelligence Suite is a comprehensive software solution that helps businesses manage environmental risks and opportunities. Leveraging advanced AI and data analytics, this suite provides insights into environmental conditions, weather patterns, and climate-related events. It aids organizations in predicting and responding to natural disasters, optimizing resource usage, and ensuring regulatory compliance.

GEP Green

GEP Green is a sustainable procurement and supply chain management solution offered by GEP. It focuses on helping organizations achieve their sustainability goals by optimizing supply chains for environmental efficiency. This includes reducing carbon footprints, managing waste, and improving energy efficiency. The platform integrates sustainability into procurement processes, enabling companies to make eco-friendly purchasing decisions and track their environmental impact.

Greyparrot Sync

Greyparrot Sync is an AI-powered waste management solution developed by Greyparrot. Utilizing advanced AI and machine learning algorithms, Greyparrot Sync analyzes waste streams, identifies recyclable materials, and improves sorting accuracy. This technology enhances recycling rates, reduces contamination, and provides real-time data on waste composition, supporting more informed decisions about waste management.

 

It’s true: the ecological impact of Artificial Intelligence is a pressing concern that cannot be ignored. From the resource-intensive nature of its development to its substantial energy and water consumption, AI poses significant environmental challenges. However, through sustainable practices and innovative solutions, we can mitigate these impacts and pave the way for a greener future. As AI continues to evolve, it is imperative to prioritize environmental stewardship and ensure that technological advancements do not come at the expense of our planet.

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