Courtesy : harved business review

Green software Without doubt, software is the backbone of virtually all the intelligent solutions designed to support the environment. It’s critical, for example, in efforts to tackle deforestation and reduce emissions. In many instances, however, software is also part and parcel of a rapidly growing carbon footprint. In fact, recent and proliferating digital technologies have begun to worsen many of the environmental problems they are aimed at solving. But companies can make software an integral part of their sustainability efforts by taking its carbon footprint into account in the way it is designed, developed, and deployed and by rethinking some aspects of how the data centers that provide cloud-based services operate. Let’s be clear: On its own, software doesn’t consume energy or emit any harmful discharge. The problem lies in the way software is developed for use — and then in the way it is used. Software runs on hardware, and as the former continues to grow, so does reliance on the machines to make it run. For example, blockchain drives some of the most advanced green solutions available such as microgrids that allow residents to trade environmentally friendly energy. And this software innovation is also behind the development of cryptocurrency. In 2019, researchers at the University of Cambridge estimated that the energy needed to maintain the Bitcoin network surpassed that of the entire nation of Switzerland. Then there’s the information and communications technology sector as whole. By 2040, it is expected to account for 14% of the world’s carbon footprint — up from about 1.5% in 2007. The very development of software can be energy intensive. For example, consider what we learned when we trained an artificial intelligence (AI) model on a small, publicly available dataset of iris flowers. The AI model achieved accuracy of 96.17% in classifying the flowers’ different species with only 964 joules of energy. The next 1.74%-point increase in accuracy required 2,815 joules of energy consumption. The last 0.08% incremental increase in accuracy took nearly 400% more energy than the first stage. Now consider that same example in the context of the bigger picture of AI overall. Training a single neural network model today can emit as much carbon as five cars in their lifetimes. And the amount of computational power required to run large AI training models has been increasing exponentially, with a 3.4-month doubling time. All that said, it wouldn’t make sense to limit reliance on software as a means to enable work, especially in the post-Covid world where work from home or remote locations could become the norm for many. Nor would limiting software-driven innovation be a viable response. However, companies can make software an integral part of their sustainability efforts by judging its performance on its energy efficiency as much as on traditional parameters (e.g., functionality, security, scalability, and accessibility) and by including green practices and targets as criteria for CIO performance reviews. Ultimately, the rewards would outweigh the challenges: The early, increased scrutiny that building green software requires translates into a higher-quality product: leaner, cleaner, and more fit for its purpose. These qualities also offset additional upfront costs. Green software will help large companies meet their ESG targets, an increasingly important performance measure for stakeholders. Finally, our research (soon to be published) has shown that newly minted computer engineers are increasingly weighing a company’s focus on sustainability when choosing an employer; a commitment to green software can be a persuasive draw. So how can companies go green with their software? It’s a three-part process that begins with articulating a strategy that sets some boundaries, then targets the software development life cycle, and makes the cloud green as well. No single company that we know of is engaged fully in this process as we describe it and reaping the full benefits of purposefully green software. However, a growing number of businesses — including Google, Volkswagen, and Rainforest (itself a software testing company) — are deploying a variety of the following approaches and techniques. Articulate a strategy that guides trade-offs and allows for flexibility. Doing this will get IT teams thinking about what the right level of tolerance should be for their software’s environmental effects. There are almost always trade-offs between business and environmental goals, and software engineers need to be able to determine where the go/no-go line is. Think back to the AI model we trained on the iris flower data set. Whether that last step to increase the accuracy is worth the energy it consumes is a business decision that requires clear guidance from the top.