Preparation of Brownfields Success Stories
Description
Preparing draft Brownfields success stories involves compiling and summarizing extensive data from technical reports about site history and environmental assessment/cleanup ads well as financial information about cleanup costs, grant funds, leveraged public/private investment, and jobs leveraged. this information comes from many different sources and can be much more quickly collected and compiled by using AI.
Detailed example
First draft of text for a Brownfields success story ultimately intended for public viewing on our regional Brownfields program web page. Prompt engineering includes specified format, word count, reading level, writing style, and other parameters to ensure consistency and quality.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
By leveraging AI to do the heavy lifting of collecting and compiling the data, we can focus our efforts more on the narrative aspect of the success stories and shorten the overall amount of time it takes to prepare stories and publish them on our website, where they can provide inspiration and ideas to other communities who may be struggling with brownfield-related challenges of their own.
Audit / financial statement impact
The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
public data from EPA's Assessment Cleanup and Redevelopment Exchange System (ACRES)