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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 climate 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 regulation becomes evident in social data only when a bill is effectively enacted. 

Evidence from a policy experiment shows that public safety bans on electric scooters and electric bikes can generate unintended traffic congestion in city centres. The studied ban is found to increase travel times by 9–11% for daily evening commutes and by 37% following stadium events. 

Micromobility, such as electric scooters and electric bikes—an estimated US$300 billion global market by 2030—will accelerate electrification efforts and fundamentally change urban mobility patterns. However, the impacts of micromobility adoption on traffic congestion and sustainability remain unclear. Here we leverage advances in mobile geofencing and high-resolution data to study the effects of a policy intervention, which unexpectedly banned the use of scooters during evening hours with remote shutdown, guaranteeing near perfect compliance. We test theories of habit discontinuity to provide statistical identification for whether micromobility users substitute scooters for cars. Evidence from a natural experiment in a major US city shows increases in travel time of 9–11% for daily commuting and 37% for large events. Given the growing popularity of restrictions on the use of micromobility devices globally, cities should expect to see trade-offs between micromobility restrictions designed to promote public safety and increased emissions associated with heightened congestion.

 

Problems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

 

Human and machine readable replication dataset for "Housing Policies Accelerate Energy Efficiency Participation" Omar I. Asensio, Olga Churkina, Becky Rafter, Kira E. O'Hare 

Mobile applications have become widely popular for their ability to access real-time information. In electric vehicle (EV) mobility, these applications are used by drivers to locate charging stations in public spaces, pay for charging transactions, and engage with other users. This activity generates a rich source of data about charging infrastructure and behavior. However, an increasing share of this data is stored as unstructured text—inhibiting our ability to interpret behavior in real-time. In this article, we implement recent transformer-based deep learning algorithms, BERT and XLnet, that have been tailored to automatically classify short user reviews about EV charging experiences. We achieve classification results with a mean accuracy of over 91% and a mean F1 score of over 0.81 allowing for more precise detection of topic categories, even in the presence of highly imbalanced data. Using these classification algorithms as a pre-processing step, we analyze a U.S. national dataset with econometric methods to discover the dominant topics of discourse in charging infrastructure. After adjusting for station characteristics and other factors, we find that the functionality of a charging station is the dominant topic among EV drivers and is more likely to be discussed at points-of-interest with negative user experiences. 

For a growing class of prediction problems, big data and machine learning analyses can greatly enhance our understanding of the effectiveness of public investments and public policy. However, the outputs of many machine learning models are often abstract and inaccessible to policy communities or the general public. In this article, we describe a hands-on teaching case that is suitable for use in a graduate or advanced undergraduate public policy, public affairs or environmental studies classroom. Students will engage on the use of increasingly popular machine learning classification algorithms and cloud-based data visualization tools to support policy and planning on the theme of electric vehicle mobility and connected infrastructure. By using these tools, students will critically evaluate and convert large and complex datasets into human understandable visualization for communication and decision-making. The tools also enable user flexibility to engage with streaming data sources in a new creative design with little technical background. 

There is a growing interest in applying computational tools to the automatic discovery of social and economic behavior. For example, with decisions involving resource allocation related to public infrastructure, the ability to predict failures can allow for more efficient policy responses. In this paper, we use social data from a popular electric vehicle (EV) driver app to characterize the emerging EV charging station infrastructure. We introduce a typology of EV charging experiences collected from user reviews and deploy text classification algorithms, including convolutional neural networks (CNN), to automatically learn about potential failures. We use machine learning techniques as a pre-processing tool for econometric analyses on the quality of service delivery. After classifying the reviews into 9 main user topics and 34 subtopics, we find that the dominant issues in EV charging relate to station functionality and availability, which drive negative consumer experience. Contrary to the public discourse about EVs, range anxiety was not of large concern to existing EV drivers. Based on our findings, we move towards automated identification of failures in public charging infrastructure that can significantly reduce research evaluation costs through relatively simple computational solutions.