AI comes with a significant carbon footprint but has the potential to support climate change initiatives
With a compound annual growth rate of 29% projected over the next four years, AI's transformative momentum is reshaping products and services at an unprecedented pace, according to IDC's latest forecast. This forecast also argues that global AI spending is set to soar, more than doubling to $632 billion by 2028, fueled by the explosive adoption of generative AI across industries. However, one lingering area of concern is its environmental impact. The development, maintenance and disposal of AI models all come with a significant carbon footprint due to computationally extensive data centers. With AI continuing to innovate, the demand for data centers is increasing and in theory, so will CO2 emissions. As an industry, we now have to be proactive in confronting the negatives that weigh down the positives of AI, otherwise, we may see AI thriving at the expense of our climate change goals.
The industry is increasingly aware of the need for environmentally sustainable energy sources to power AI-driven innovation. Several multinational technology companies, including Google, have already signed agreements to power their data centers with nuclear energy in the future. Many computational service providers now offer options to utilize sustainable energy sources as well. Besides, advancements in AI are not solely about increasing computational demands; they also involve developing practices that boost efficiency, requiring less energy to deliver even better performance.
It’s also important to recognize the potential of AI to support climate change initiatives. Sustainable AI, when implemented and executed properly, can mitigate the effects that traditional AI has on the environment without sacrificing the quality of its outputs. AI systems, for example, have been used in reforestation efforts in the Amazon by analyzing data from sensors, drones and satellites to determine likely future deforestation scenarios. In addition to sustainable operations, AI systems can be harnessed across several climate change initiatives including weather predictions, natural disaster responses and precision agriculture to manage and improve crop and livestock production. Therefore it is entirely feasible to continue AI innovation while aligning with global climate goals, not only with energy output but also sustainable use cases and thus far significant progress has been made. However, effective regulation and policy implementation will be key to striking the balance.
We are already witnessing the emergence of regulatory frameworks that incorporate environmental compliance and mandate the disclosure of environmental information for emerging technologies. Article 40 of the EU AI Act establishes a framework for developing harmonized standards aimed at improving the resource efficiency of AI systems, with a particular focus on energy efficiency. The act outlines a process whereby the European Commission will request standardization bodies to develop standards addressing the energy consumption of both high-risk AI systems and general-purpose models throughout their lifecycles. Article 40 also emphasizes the importance of global collaboration in AI standardization. By mandating that participants consider existing international standards and strive to strengthen global cooperation, the EU aims to create a unified framework for AI development that promotes interoperability and trust. Recognising AI as a global phenomenon requiring international cooperation for effective regulation has the potential to drive significant improvements in AI's environmental footprint.
However, we’ve not yet reached the stage of cohesive international collaboration so until then, multi-national tech companies will need to demonstrate pragmatism to ensure they’re meeting sustainability goals while operating across countries with varying standards. This involves two key aspects. First, companies must prioritize environmental standards by establishing best practices that meet or even exceed the highest regulatory requirements, regardless of the jurisdictions in which they operate. By consistently adhering to these standards globally, they demonstrate a commitment to sustainability that goes beyond mere compliance. Second, companies should implement robust tracking and verification systems to monitor their environmental impact accurately. This could involve leveraging technologies like blockchain to enhance transparency and auditability. These measures ensure that companies can reliably assess their progress toward sustainability goals and make necessary adjustments, thereby maintaining accountability across all operational regions.
As we approach 2025, there’s no doubt that AI will continue to be a transformative force, however, the environmental repercussions surrounding the technology are a cause for genuine concern. Despite this, we can achieve the best of both worlds between AI innovation and sustainability. By leaning into AI’s potential to support climate change initiatives, leveraging renewable energy for consumption and fostering a transparent and collaborative ecosystem, we’ll be poised to confront these challenges and strike the right balance between innovation and climate change.
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