The upcoming release of EPA's Lead and Copper Rule is top of mind for many in the drinking water industry. As part of its rule-making process, EPA received 80,000 public comments on its proposed rule. Using natural language processing (performing data science on textual data), BlueConduit, in collaboration with the Environmental Policy Innovation Center, explored the content and tone of those comments.
This provided an opportunity to understand what topics were important to stakeholder groups and how they communicated those messages as part of their submitted comments. B
y employing the techniques of topic analysis and sentiment analysis, we were able to identify the principle themes of different types of comments and the speakers' feeling (on a positive/negative spectrum). analyzing what different typ
es of commenters had to say about the proposed regulations.
Machine learning found that service line replacement was a more frequent and detectable theme of public input than enhanced testing and sampling of drinking water.
Concern over children's safety is a central theme in comments and attachments.
The analysis found little to no evidence that financial themes were central in both comments and attachments. Additionally, there was no evidence of negative sentiments related to “unrealistic” financial demands, suggesting that the federal government should not exaggerate cost considerations in its final rule, compared to the draft.
Topic analysis found detectable differences between different commenter types, but that those differences do not always correspond to differences of opinion.
The insights that this type of analysis highlights for the Proposed Lead and Copper Rule Revisions show the use of these tools for government agencies in processing large volume of comments. The full report offers a deeper exploration of those opportunities.