In summer 2024, as part of Arizona State University’s collaboration with OpenAI, the ASU Library launched a pilot project using the AI tool ChatGPT. This project aims to enhance the discoverability and curation of digital collections within the library’s repository ecosystem. The use of AI in libraries is gaining attention, with many institutions exploring AI for generating descriptive metadata. ASU Library’s extensive repository platforms, including an institutional repository, data repository, and a digital collections platform, hold approximately over 10,000 objects, with numbers expected to grow. The library lacks a dedicated position for creating metadata, with the responsibility distributed among various units already tasked with other duties. This project aims to determine whether ChatGPT can effectively generate accurate metadata that meets best practices. The library will use an existing archival collection of government documents, which already has human-created metadata, as a benchmark, in comparing the generated metadata for the fields Title, Description, and Keywords. By comparing ChatGPT-generated metadata to the existing metadata, the library will assess the relevance of AI outputs and the level of oversight required. If the AI-generated metadata shows minimal variance from the human-created metadata, the workflow could expand to other collections and reduce the backlog of unpublished archival collections that require descriptive metadata.

Carbon dioxide removal is necessary to mitigate climate change, but not all methods will be fit-for-purpose. Some can be unethical, unsafe, counterproductive, and ecologically damaging. But, because fitness is a value judgment, it is critical to have a clear definition of its meaning. We propose to define fit-for-purpose as an attribute of a carbon removal method that indicates that its detrimental side effects are sufficiently small to be acceptable. A method is not fit for purpose if it risks unsustainable environmental or societal damages. We then identify six criteria that can be used to judge a method’s fitness-for-purpose based on those chosen by other organizations, including carbon negativity, measurability, additionality, safety, and low environmental risks. We compare our perspectives on these criteria to those presented by six entities including Microsoft, Carbon Direct, Frontier, California’s Air Resources Board, the United Nations Development Program, and the Accountability Framework. This work reflects our thinking in 2023 with some updates in 2024 and intends to be a starting point for a more thorough development process that ought to be adopted by the international carbon removal community in an inclusive process.

Heat exposure for urban populations has become more prevalent as the temperature and duration of heat waves in cities increase. Occupational exposure to heat is a major concern for personal health, and excessive heat exposure can cause devastating outcomes. While occupational heat exposure studies have traditionally focused on environmental temperature, work intensity, and clothing, little is known about the daily exposure profile of workers, including their daily travel and working patterns. This study developed a novel measure of exposure and reprieve dynamics, the moving average hourly exposure (MAHE) to balance short-duration but high-exposure events and capture the inability to reprieve from exposure events. MAHE was assessed by combining an activity-based travel model (ABM) and the Occupational Requirement Survey to simulate urban workers' total daily heat exposure. The simulation considers daily travel, work schedules, and outdoor working frequency. The simulation was conducted for 1 million workers in Phoenix, Arizona, using Mean Radiant Temperature (MRT). The results show that 53% to 89% of workers in Phoenix's construction, agriculture, transportation, raw material extraction, and entertainment industries will likely experience MAHE over 38°C for at least an hour. These industries also have up to 34% of the laborers exposed to over 7 hours of continuous 38°C and above MAHE exposure. The location of the most intense heat exposure was identified near the downtown and central business districts, significantly different from the home locations of the workers in suburban and rural areas. Formulating the MAHE balances heat risk events with cooling benefits and aids in identifying individuals with prolonged high heat exposure.