AI's Carbon Footprint
Artificial intelligence is transforming our world, but at what cost to the planet? Examine the growing environmental impact and data-driven opportunities for innovation to build a greener future.
Consider this: the daily power usage of ChatGPT, just one of many AI applications, is nearly equal to 180,000 U.S. households as of March 2024 - that’s about 200 million requests. A single query uses significantly more energy than a Google search, ranging anywhere from 10 to 25 times more. But is it 10 to 25 times more useful?
We’ve already established a substantial carbon footprint with our daily technological usage, and with the escalating growth of AI, the environmental and human impacts of this technology deserve critical attention along with praise for its benefits.
AI’s Energy Hunger
As artificial intelligence technology gets smarter, it uses exponentially more energy. Data centres are the backbone of AI, powering everything from simple chats to climate forecasting models - in 2023, they produced about 2.5% to 3.7% of global carbon emissions and could use as much energy as countries like the Netherlands by 2027.
While initially built for large-scale networking, storage, and cloud computing, data centres are undergoing a major transformation to meet the escalating energy demands of AI. This shift towards more power-hungry applications has prompted the development of green data centres, which prioritize energy efficiency and renewable energy sources to mitigate their environmental impact.
Power Grids
Beyond data centres, our power grids are under immense pressure - the growing adoption of electric vehicles, the rise of cryptocurrency mining, and increased demand for air conditioning due to climate change are just some of the many factors contributing to additional strain on existing infrastructure, which is unsustainable.
Upgrading our power grids to accommodate an electric future and divert from fossil fuels is essential. This will require billions of dollars in new investments (1, 2) to develop and implement regionally tailored solutions, including new engineering designs for machinery and building materials, as well as advanced analytical models for optimizing electricity distribution.
Training AI
Training AI models is an energy-intensive process, requiring massive amounts of data to be fed into the models over several months to 'teach' them complex tasks such as language understanding, image and speech recognition, and decision-making. This demands immense computational power, resulting in significant carbon emissions that can vary greatly depending on the location of data centres and the type of energy used for training.
“Training GPT-3 once on a clean grid is equal to driving 1 car for one year, and training on a dirty grid (one reliant on fossil fuels) is equal to driving 40 cars for one year.”
The energy consumption of AI training is largely determined by the complexity of models, often defined by billions of ‘parameters’ – numerical values that fine-tune the AI's ability to learn and perform tasks. Many top performing models, developed by major tech companies, keep their exact number of parameters confidential for competitive advantage, while open-source models are transparent about their parameter count, enabling easier comparison and analysis.
Physically Resource Intensive
Data centres are not just energy-intensive, but also incredibly resource-hungry, relying on a wealth of materials for their construction and operation, including precious metals like gold, silver, and copper - some of which are classified as critical raw materials (CRMs).
Moreover, continuous upgrading and decommissioning of hardware like servers and cooling systems generates massive amounts of electronic waste, which can leach harmful substances like lead and mercury into soil and water if not handled correctly.
The Hidden Water Footprint of AI
The intensified computing power of AI-powered data centres generate significant heat, requiring vast amounts of freshwater for cooling to prevent infrastructure damage. For instance, training one large language model like Chat-GPT3 can use about 700,000 litres of freshwater - that’s enough to fill over 280 backyard swimming pools.
With a global freshwater crisis, it’s important to be mindful of how and where we use this scarce resource. AI’s growing water footprint deserves more attention, particularly in arid or drought-prone regions where data centres strain local water supplies, competing with residents and agriculture. Increased transparency is essential for understanding the full extent of water usage and taking steps to mitigate its impact.
AI as a Sustainability Catalyst
Electricity and heat production are the largest contributors to global greenhouse gas emissions, and AI has the potential to redefine how we generate and consume energy. Through innovation and increased adoption of regenerative practices, we can minimize resource depletion and identify efficiencies in virtually every domain — developing effective solutions that not only mitigate climate change but also deliver positive financial outcomes.
Innovating for Energy Efficiency
With growing awareness, the tech industry is working on ways to make AI more energy-efficient, like compressing models and optimizing how the next word is predicted (‘inference’).
As the market matures, consumers will have greater access to smaller, open-source models that can be run locally on personal computers; this allows customization tailored to user needs, privacy, security, and cost savings by reducing the need for expensive cloud-based processing.
Accelerated computing is another key innovation, utilizing specialized hardware like Graphics Processing Units (GPUs) and Google’s Tensor Processing Unit (TPUs), designed for machine learning, to offload work from the central processing unit (CPU) and improve overall energy efficiency.
Infrastructure and Manufacturing
In addition to optimizing power grids, AI is playing a crucial role in transforming the way we design and operate buildings. For example, AI-generated concrete formulas are a promising innovation that can lower emissions in the construction industry, which is particularly important for rapidly growing populations.
AI-driven smart buildings can optimize resource use by adjusting lighting, heating, and cooling based on occupancy and weather patterns, leading to reduced carbon emissions. In addition, intelligent IoT solutions are being deployed in commercial and industrial buildings to collect and analyze data on energy usage, occupancy patterns, and equipment performance, leading to increased cross-sector collaboration and more efficient energy management.
Global Logistics and Supply Chains
With the interconnected nature of our world, AI has the potential to revolutionize the way we move goods and people to make transportation more efficient and sustainable. By leveraging data analytics and predictive modeling, supply chains can optimize routes, schedules, and resource allocation, leading to reduced fuel consumption and emissions. However, the complexity of global supply chains requires thoughtfully designed solutions to avoid exacerbating existing inequalities and biases.
We’re already optimizing shipping routes for cargo ships to minimize their fuel consumption, which is vital for protecting our oceans. In aviation, airlines are optimizing flight paths and reducing delays, resulting in less fuel burn and unnecessary emissions — and on the roads, applications like Google Maps utilize AI to provide drivers with more fuel-efficient routes, effectively removing an estimated 250,000 fuel-based cars off the road for a year (which might finally be self-driving in a few decades).
Agriculture
With food production accounting for 40% of the world’s greenhouse gas emissions and a global population projected to increase by two billion by 2050, the need for sustainable agriculture is increasingly urgent.
Precision farming utilizes AI-powered drones and sensors to customize the management of individual crops and land areas, boosting overall yields and optimizing resources to meet the demands of a growing and hotter world. AI-driven research is also contributing to the development of new climate-resilient crop varieties, while tools for enhanced pest and disease detection, soil analysis, and livestock monitoring are further improving agricultural sustainability and productivity.
The agricultural sector's heavy reliance on water, which accounts for 70% of global freshwater withdrawals, makes efficient water use absolutely necessary, given the increasing demands of data centres and the ongoing global freshwater crisis.
Flood and Disaster Management
Warmer temperatures induced by climate change disrupt the natural water cycle, leading to an increase in extreme weather events. Among these, floods are the most common natural disaster, accounting for almost 10 million people displaced in 2023 alone.
AI is proving invaluable in predicting and responding to these devastating events, as well as other natural disasters such as earthquakes, hurricanes, and wildfires. By analyzing vast amounts of data from weather patterns, satellite imagery, and social media, early warnings can be identified that allow communities to prepare and evacuate in advance, potentially saving countless lives and reducing economic losses. In the immediate aftermath, AI-assisted satellite imaging and remote sensors can quickly assess damage and streamline the often challenging recovery process.
Achieving Equilibrium
At the recent COP28 climate change conference event, global leaders made a landmark declaration: the beginning of the end of fossil fuels. A historic agreement was reached among all nations to achieve net-zero global emissions by 2050, prioritizing a rapid transition to renewable energy and significant emission reductions. Notably, there was also recognition of AI’s essential role in tackling climate change, while ensuring it doesn’t widen the digital divide caused by unequal access to technology.
While international agreements set ambitious goals, the responsibility for achieving sustainability ultimately falls on national and local governments, as well as individual companies and consumers. Governments at all levels need to enact stronger regulations and incentives for renewable energy adoption, while big tech companies must prioritize transparency and invest in sustainable AI research and development, while prioritizing decarbonization efforts across all scopes of emissions.
The Path Forward
Collaboration and accountability across all stakeholders are necessary to ensure the responsible implementation of artificial intelligence; these technologies must be accessible, reliable, and beneficial for everyone while minimizing harm to both our communities and the planet.
In the short term, AI’s rapid growth presents major challenges in balancing competitive technological advancement with sustainability goals, despite many companies having made climate pledges in recent years. For example, Microsoft's greenhouse gas emissions were actually around 30 percent higher in 2023. Consumers can play a crucial role by demanding transparency from companies and prioritizing sustainability when choosing AI products and services, using accountability mechanisms like model cards, which function like nutrition labels to provide transparency into large language and machine learning models. However, most model cards currently lack carbon emission reporting.
The convergence of technological advancements in areas like IoT, materials science, renewable energy, and smart grid infrastructure, alongside increased awareness and policy action, offers cautious optimism for a sustainable AI future.