In recent years, Artificial Intelligence (AI) has been leveraged across a wide range of industries to drive innovation, enhance efficiency, and pioneer new approaches to growth and development. However, as we advance in adopting this technology, it is essential to consider its environmental impact and how it can be leveraged to address current climate challenges. This article explores the relationship between AI and climate change, exploring both the negative impacts of the technology and the potential solutions it offers to help mitigate these effects.

The dual nature of AI in climate change

AI represents both an opportunity and a challenge in the fight against climate change. Its reliance on resource-intensive computing power and energy-heavy infrastructure creates a significant environmental cost, especially as models grow increasingly complex. However, AI also holds significant potential in the fight against global warming, providing solutions for emissions monitoring, resource optimization, and the creation of more efficient systems. This duality calls for effective AI governance that aligns technological advancement with sustainable and ethical practices, reducing its environmental impact.

How AI can contribute to climate change

As promising as it may be, AI notably impacts climate change. The demand for computing power and the large-scale operations of data centers are directly linked with increased carbon emissions. Large-scale AI models require massive processing capabilities, consuming vast amounts of electricity and causing servers to heat up, requiring cooling systems.

Data centers, for instance, are essential infrastructure for AI but are also highly energy-intensive, accounting for over 1% of global electricity consumption—a figure that continues to grow. The environmental cost increases even more when we consider the lifecycle of electronic devices, from raw material extraction to their disposal.

Another relevant factor is the “algorithmic footprint” of AI models. Developing and training complex algorithms generates significant emissions, with some large-scale models estimated to have an environmental impact up to five times that of a car¹ over its entire lifespan. As the sector rapidly expands, the carbon footprint associated with AI algorithms and infrastructure has become a central point in discussions about technological sustainability.

AI as a tool for climate change mitigation

Despite its negative environmental impact, AI presents numerous opportunities to address climate change by driving sustainability efforts across various sectors. For instance, AI can optimize industrial processes, predict climate patterns, and monitor ecosystem health, paving the way for more sustainable practices. In emissions monitoring, AI plays a critical role by enabling real-time data analysis to track emissions across industries such as agriculture, transportation, and manufacturing, helping to identify areas for improvement.

When it comes to natural resource management and conservation, AI supports the sustainable use of vital resources like water and soil. A notable example is precision agriculture, which optimizes irrigation and fertilizer application to minimize waste and pollution. Additionally, AI is transforming energy efficiency and grid optimization. Smart energy management systems, for instance, monitor and adjust electricity usage in real time, enhancing efficiency and reducing unnecessary consumption in buildings and electrical grids.

Environmental impact of AI

The evolution of AI promises to transform industries and drive remarkable advancements across various fields, but its environmental impact raises concerns. From high energy consumption to challenges within the hardware supply chain, AI presents several environmental issues that require attention and regulation.

Energy consumption & water usage in AI model training

Training complex AI models, particularly those based on deep learning, consumes substantial energy. According to a study available here², it’s estimated that training ChatGPT-3, one of the world’s most widely used generative AI applications, required energy equivalent to powering around 121 homes in the USA for an entire year. Additionally, this process generated CO₂ emissions equivalent to 33 flights between Australia and the UK. Considering that the subsequent version, GPT-4, was trained with 570 times more parameters, its energy consumption was undoubtedly even higher. It’s important to note that the environmental impact continues beyond the training phase, persisting during the ongoing use of these technologies.

In addition to energy consumption, cooling the servers used in AI training requires significant water usage in regions where data centers rely on water-based cooling systems. This dependency on water represents an additional challenge, as AI can exacerbate water scarcity, sometimes in already vulnerable regions.

Carbon footprint of large-scale data centers

Data centers are the backbone of AI, responsible for processing, storing, and transmitting vast volumes of data. These facilities consume massive amounts of electricity, with usage growing rapidly. According to Bloomberg, the number of operational data centers worldwide has surpassed 7,000, a significant increase from the 3,600 recorded in 2015. Together, these data centers consume an annual amount of electricity comparable to the entire consumption of a country like Italy. Although some technology companies have made strides in using renewable energy sources, many still rely on electrical grids powered by fossil fuels, exacerbating AI’s carbon footprint.

Supply chain emissions from AI hardware production

Producing AI hardware—servers, graphics processing units (GPUs), and machine learning-specific chips—significantly contributes to global emissions. This supply chain involves raw material extraction, manufacturing, transportation, and assembly, each carrying its environmental impact. The extraction of raw materials and international transportation increase carbon emissions, while manufacturing electronic components consumes substantial energy, often derived from non-renewable sources. Tracking emissions throughout the production chain remains limited, making it challenging to control this environmental impact effectively.

Electronic waste and its environmental consequences

The rapid evolution of technology and the short lifecycle of electronic devices generate a large volume of electronic waste. According to the World Economic Forum, approximately 50 million tons of electronic and electrical waste (e-waste) are produced globally yearly, and only 20% are recycled. If left unaddressed, this amount could double by 2050, reaching 120 million tons annually. As new AI devices are introduced and older ones become obsolete, the e-waste problem worsens. Many of these discarded electronics contain toxic and hazardous materials that, if not properly recycled or disposed of, can contaminate soil and water resources, negatively impacting ecosystems and local communities. Improving recycling systems and promoting the reuse of electronic components are essential steps to reduce AI-related pollution.

AI and resource depletion for rare earth minerals

Many AI hardware components rely on rare earth minerals such as lithium, cobalt, and neodymium, essential for producing batteries, processors, and other electronic devices. However, the extraction and exploitation of these minerals presents a dual challenge: environmental impact due to soil erosion, water contamination, and scarcity risk, as these resources are limited. As demand for these minerals rises, we will likely face increasing shortages, compounding sustainability and supply chain security issues. Developing alternative technologies and adopting circular economy practices can help reduce dependence on rare minerals.

Strategies for sustainable AI development

Given the environmental impact associated with AI development and usage, there is a growing need for strategies that support a more sustainable approach. These strategies aim to reduce AI’s ecological footprint and promote responsible practices within the sector, ensuring that technological progress aligns with global sustainability goals.

Implementing green AI Practices in organizations

Implementing Green AI practices ensures that artificial intelligence contributes positively to sustainability by minimizing its environmental impact. This requires an approach that prioritizes efficiency at every stage, from algorithm development to the physical infrastructure supporting it.

Optimizing algorithm design is a key strategy, allowing AI models to perform complex calculations with lower energy consumption. Various techniques can be used to reduce memory and computational complexity, making processing more efficient. In addition to software, hardware choices also play a crucial role.

On the other hand, data centers, which are among the largest energy consumers, should operate with a focus on efficiency and carbon footprint reduction. Locating data centers in low-carbon regions and adopting renewable energy sources and advanced cooling technologies can effectively mitigate their environmental impact.

Designing energy-efficient algorithms and models

An essential approach to AI development is designing algorithms and models that require less energy without compromising accuracy or efficiency. This can include selecting less complex models for tasks that don’t require complex or data-intensive models, thereby reducing processing demands. Tools such as model compression and federated learning can also help reduce the energy needed to train and operate AI models while ensuring security and efficiency.

Adoption of renewable energy in AI operations

A straightforward and impactful step is transitioning AI infrastructure operations to renewable energy sources. Many technology companies are already adopting renewable energy, such as solar or wind, to reduce their carbon footprint, contributing to more sustainable operations. Additionally, strategically locating data centers in areas with easy access to clean energy sources can maximize the effectiveness of this effort.

Advances in quantum computing for energy reduction

Quantum computing represents a groundbreaking opportunity for the future of sustainable AI. With its potential to perform complex calculations far more efficiently than classical computers, quantum computing can drastically reduce the time and energy required for training AI models. While still in the early stages of development, quantum computing promises to transform computational efficiency, allowing complex algorithms to be processed with a fraction of the energy currently required. However, for this technology to significantly impact the sustainability of AI, advances need to be accompanied by practical and scalable adoption.

Policy and regulation shaping sustainable AI

The growing awareness of AI’s environmental impact has led governments and regulatory bodies to consider specific policies and regulations to ensure that technological development aligns with sustainability goals. These efforts aim to establish standards that encourage Green AI practices and promote responsible innovation.

Government policies on AI environmental impact

Governments worldwide are beginning to recognize the need to regulate AI’s environmental impact. Measures such as setting energy efficiency standards for data centers, establishing carbon emission limits for technological infrastructures, and encouraging green energy solutions are examples of policies that can drive the AI sector toward greater sustainability. In addition, policies that promote transparency regarding AI’s energy consumption and carbon footprint allow for greater accountability, motivating companies to adopt more eco-friendly practices.

Another key initiative is requiring corporate sustainability reporting from large-scale AI companies. By obliging companies to disclose the environmental impact of their models and algorithms, these policies foster a culture of environmental responsibility and awareness, encouraging the adoption of an ESG (Environmental, Social, and Governance) strategy in AI development and deployment.

Incentives for research in green AI technologies

Many governments are introducing incentives for research and development (R&D) in Green AI technologies to foster innovation in sustainable AI. These incentives can include grants for research projects focused on energy-efficient algorithms, developing AI models that consume fewer resources, and advances in low-impact computing. Beyond grants, long-term funding programs enable universities and companies to explore sustainable technologies, such as quantum computing, which promise to reduce AI’s environmental impact in the future.

AI’s role in promoting a sustainable future

Despite its environmental challenges, Artificial Intelligence is critical in advancing sustainable and ethical practices across various industries. Through targeted innovations and collaborative efforts between the technological and environmental sectors, AI can significantly contribute to building a greener future by offering practical solutions for conservation and global sustainability.

AI innovations driving environmental conservation

AI innovations have enabled remarkable progress in environmental conservation. By using machine learning models and advanced algorithms, it’s now possible to monitor entire ecosystems in real time, predict environmental changes, and take proactive measures to preserve biodiversity. AI technologies monitor endangered species, track forest health, and control overfishing, providing scientists and environmental organizations with accessible, detailed insights.

Another example of AI in conservation is satellite image analysis, which helps detect illegal deforestation, pollution, and changes in vegetation cover. These insights enable faster and more effective responses to environmental damage and support the development of more targeted policies. In addition, AI facilitates waste management and recycling by optimizing processes that reduce the environmental impact across various industries.

Collaborative efforts between tech and environmental sectors

Promoting a sustainable future through AI requires collaboration between technology and the environmental sector. These partnerships can include the co-development of technologies aimed at specific environmental goals, such as reducing carbon emissions, conserving water, and promoting a circular economy. Technology companies working with NGOs and environmental organizations are creating solutions to address critical issues, combining technological expertise with environmental knowledge.

These collaborative efforts also involve building knowledge networks and best practices that can be shared and applied globally. Intergovernmental organizations and public-private partnerships are essential for scaling sustainable AI solutions, ensuring that technologies developed in specific contexts can be adapted and implemented across different regions and environments. This collaboration is key to creating a positive and lasting impact, helping to align technological progress with global environmental sustainability goals.

Still have some questions about AI and Sustainability?

What are energy-efficient algorithms?

Energy-efficient algorithms are designed to perform data processing tasks using the least amount of energy possible without compromising the accuracy or quality of the results. These algorithms use optimization methods, which can include adjusting the complexity of models, compressing data, and implementing neural networks that are less data- and computation-intensive. The core idea is to reduce energy consumption during model training and execution. Energy efficiency in algorithms is essential for reducing the environmental impact of AI.

What policies regulate AI’s environmental impact?

Several policies and regulations are being developed to address AI’s environmental impact. Common measures include energy efficiency standards for data centers, policies that encourage using renewable energy in technological operations, and regulations requiring transparency around the carbon footprint of AI projects. The European Union, for example, is leading some of these initiatives, promoting laws and directives that support green technologies and imposing requirements for environmental impact reporting.

What is Green AI?

Green AI refers to the development and use of Artificial Intelligence technologies in ways that minimize their environmental impact. Green AI encompasses practices that reduce the energy consumption of AI models and infrastructures and use AI to support environmental initiatives, such as ecosystem conservation and industrial process optimization. This approach aims to align technological innovation with sustainability goals, ensuring that progress in AI occurs without compromising the environment. Green AI is essential for advancing artificial intelligence in harmony with global initiatives to address climate change and promote sustainable development.

References:

1. Hao, K. (2019, June 6). Training a single AI model can emit as much carbon as five cars in their lifetimes. MIT Technology Review. ↩︎
2. Verónica Bolón-Canedo, Laura Morán-Fernández, Brais Cancela, Amparo Alonso-Betanzos (2024). A review of green artificial intelligence: Towards a more sustainable future. ↩︎