With an increased focus on climate change, green IT (environment-friendly and sustainability-related technologies) has gained prominence. This includes green data centers with low carbon footprints and environmental management systems to help firms “green” their operations.
Artificial intelligence (AI) that can help firms measure their environmental footprint or optimize their energy consumption and carbon footprint are a recent green IT addition. Because AI is a disruptive technology based on its ability to learn, it’s expected to bring impactful sustainability transformation. However, achieving such transformation will require a significant change in the system surrounding AI.
AI’s Data-Centric System
The current discourse on AI emphasizes data as the key driver of the economy. We see glimpses of this discourse in phrases such as “The world’s most valuable resource is no longer oil, but data.” Growth in AI is accelerating the growth of data centers, which store massive amounts of data. Hyperscale data center revenue is expected to exceed US $60 billion in the next five years. These data centers are the major sources of carbon footprint and are detrimental to environmental sustainability. Consequently, companies that deploy AI are faced with serious environmental challenges.
Recent studies have found that data centers’ carbon footprints are exceptionally high. An International Energy Agency estimate pegs their energy consumption at approximately 200 terawatt-hours of electricity. That equates to nearly 1% of global electricity demand or 0.3% of all global CO2 emissions.
Companies are aware of this and are responding to the issue in a number of ways. Some companies are leveraging green data centers with lower energy use and carbon footprints. Some are switching to renewable energy to power their data centers. Companies like Amazon, Google, and Microsoft that are huge corporate purchasers of renewable energy are developing onsite renewable energy sources and forming partnerships with green vendors.
Of course, these initiatives have limitations. For instance, using energy-efficient data centers might be ineffective if data consumption increases, negating the benefits from energy efficiency. The negation of benefits from energy efficiency improvement due to increased consumption (termed “Jevon’s paradox”) has been observed in contexts like steam engines. Increased data consumption can also limit the effectiveness of renewable energy in reducing data centers’ carbon footprint.
In particular, renewable energy in data centers faces several challenges: managing real-time balancing of energy demand and supply; fluctuation of data center load between peak and normal periods; and properties such as intermittency, variability, and non-dispatchability of renewable power generation.
It can also involve tradeoffs that pose challenges to sustainability. For instance, using a data center with solar energy can reduce a data center’s footprint, but solar panel manufacturing creates many environmentally harmful byproducts. Such tradeoffs, if ignored, can make data centers unsustainable. Clearly, increasing data consumption can lead to increased energy and carbon footprint both directly and indirectly, create challenges for sustainability, and make green initiatives ineffective or limit their effectiveness.
Our consumption of goods and services keeps our economic engine running, leading to high economic growth levels. IT systems like those that inform firms about their carbon footprint and aid in formulating reduction strategies form the part of this system aimed at regular growth in product and service delivery. Even initiatives such as energy analytics, in which energy consumption data is leveraged to formulate ways to improve output relative to energy consumed, aim to achieve growth in product and service delivery, albeit less energy-intensive.
This calls for a shift toward a greener, more sustainable data-centric system. Specifically, a holistic view of the interactions and interdependencies among various components (rather than a focus on individual components) is needed because a positive change in one component could negatively affect other components.
[For more from the authors on this topic, see: “Greening Data Management for AI.”]
