2 research outputs found
Generative AI and Discovery of Preferences for Single-Use Plastics Regulations
Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these single-use plastics regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulations becomes evident in social data only when a bill is effectively enacted
Reliability of electric vehicle charging infrastructure: A cross-lingual deep learning approach
Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector. Deployment of charging infrastructure is needed to accelerate technology adoption; however, managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions. In this article, we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese. We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available. We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest. This evidence contrasts with predictions in the U.S. and European markets, where the performance is closer to parity. We also find that networked stations with communication protocols provide a relatively higher quality of charging services, which favors policy support for connectivity, particularly for underserved or remote areas