The Sustainability Imperative

We continue the trend of the last CBTJ issue with an interview of a top-notch expert whose company is helping to make great strides toward sustainability. Cutter Consortium Fellow Lou Mazzucchelli talks with Carlos Silva of Pachama, a company that uses satellite imagery and ML to measure the carbon captured by forests and how it evolves over time. This measurement allows us to determine whether a carbon credit that is traded on an exchange represents a “real” reduction in emissions. The verifiability and accuracy of such measurements form the foundation for robust carbon markets. Silva explains how the technology works to ensure the integrity of forest carbon credits.

Cutter Consortium Senior Consultant San Murugesan provides a broad overview of the many areas where we can use IoT to improve environmental sustainability, from energy management to food waste reduction. He explains that “the power of IoT lies in its efficiency, accessibility and controllability, and scalability in connecting disparate, distributed devices and appliances.” Many of the applications Murugesan describes rely on sensors to collect data in settings where previously it would have been prohibitively expensive or infeasible. These sensors then feed information to data analytics software that can optimize decisions. His article gives us a sense of how pervasive IoT already is, and how much potential it still has.

As artificial intelligence (AI) and machine learning (ML) gain attention for their potential to tackle the environmental challenges posed by climate change, their requirement of heterogeneous data takes center stage. This Advisor addresses the fact that while heterogeneous data plays a critical role in the use of AI and ML to combat climate change, there is a dual side that is not environmentally friendly.

Technology — artificial intelligence (AI) and machine learning (ML), in particular — shows enormous promise in tackling the complexity inherent in sustainability problems. However, technology itself can leave its own environmental footprint. This issue of Cutter Business Technology Journal explores the challenge of leveraging technology to move us toward a more sustainable future, while mitigating its own impact.

AI and other advanced, innovative technologies are being applied to precision agriculture to help reduce the environmental impact of such operations. This Advisor explores how one cutting edge company, Iron Ox, is using ML, machine visioning, and autonomous robots to transform the agricultural industry.

As we explore in this Executive Update, there are tangible benefits to basing corporate purpose on sustainability drivers, and sustainable business models are gaining pace. Therefore, businesses should rethink their role in the context in which they operate and adapt their strategy, organization, resources, processes, and culture accordingly.

Simon Schillebeeckx proposes a focus on regeneration as a way for small carbon footprint firms (e.g., consulting, financial services firms) to make a positive sustainability impact. He highlights that service industry firms can proactively contribute to the regeneration of common pool resources, such as forests and lakes, which often become neglected or overused. What makes regeneration different compared to more traditional donations to a conservation nonprofit is the use of digital technology that enables an organization to lay claim to the eco­system benefits it generates through its support. The digitization of benefits claims provides a transparent accounting system for environmental benefits. Schillebeeckx explains how transparency and accountability can lay the foundation for firms to work together to preserve and restore common pool resources.

Rohit Nishant and Thompson S.H. Teo explore the environmental impact of AI and ML. Specifically, the applications where these technologies add the most value are those that require heterogenous data in complex settings (e.g., optimizing smart cities, modeling climate change). In the process of creating value, these large AI and ML models require energy-intensive computing, leaving a huge carbon foot­print. To counteract these concerns, Nishant and Teo offer the “Align, Reduce, Measure” (ARM) framework for mitigating the environmental impact of AI and ML algorithms. The framework encompasses the organiz­ational structure, addresses data heterogeneity, and measures results to create accountability.