Data from a poll conducted by MIT Technology Review Insights in 2024 suggests growing momentum for this dynamic duo: 38% of executives polled say that cloud and AI are key components of their company's sustainability initiatives, and another 35% say the combination is making a meaningful contribution to sustainability goals (see Figure 1). Consider that 45% of respondents identified energy consumption optimization as their most relevant use case for AI and cloud in sustainability initiatives. And organizations are backing these priorities with investment--more than 50% of companies represented in the poll plan to increase their spending on cloud and AI-focused sustainability initiatives by 25% or more over the next two years. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff.

This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development of a custom prompt based on the standard definitions and its application to two different datasets: 527 projects from the Paris Participatory Budget and 398 activities from the PROBONO Horizon 2020 project. The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. The approach is particularly promising when it comes to breaking down silos in urban planning by providing a holistic view of the impact of projects. The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. However, it also points out the importance of human expertise in interpreting results and ethical considerations. This study hopefully can contribute to the growing body of work on AI applications in urban planning and provides a novel method for operationalising standardised sustainability frameworks in different urban contexts.

As CIOs and other executives look for ways to expand sustainability initiatives, there's a growing awareness that initiatives can't stop at the four walls of the data center or office building. Today's structures can contain hundreds of thousands of components that consume energy and add to an organization's carbon footprint. In fact, buildings consume one-third of all energy globally and produce one-quarter of all greenhouse gas emissions (GHGs), according to The World Resources Institute. What's more, business and IT leaders are often narrowly focused on improving sustainability in data centers and procuring greener computing systems. Yet they overlook critical ways that technology can shrink a carbon footprint. "There is a growing awareness that buildings and workspaces are a crucial part of sustainability initiatives," states Bryon Carlock, National Real Estate Practice Leader for consulting firm PwC.

We introduce the novel task of detecting sustainability initiatives in company reports. Given a full report, the aim is to automatically identify mentions of practical activities that a company has performed in order to tackle specific societal issues. As a single initiative can often be described over multiples sentences, new methods for identifying continuous sentence spans needs to be developed. We release a new dataset of company reports in which the text has been manually annotated with sustainability initiatives. We also evaluate different models for initiative detection, introducing a novel aggregation and evaluation methodology. Our proposed architecture uses sequences of five consecutive sentences to account for contextual information when making classification decisions at the individual sentence level.