Public acceptance and support for renewable energy are important determinants of the low-carbon energy transition. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform on which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Our RoBERTa-based sentiment classification model, fine-tuned with 6300 manually annotated tweets specific to solar energy, attains 80.2% accuracy for ternary (positive, neutral, or negative) classification. Analyzing 266,686 tweets during the period of January to December 2020, we find public sentiment varies widely across states (Coefficient of Variation $=164.66\%$). Within the study period, the Northeast U.S. region shows more positive sentiment toward solar energy than did the South U.S. region. Public opinion on solar energy is more positive in states with a larger share of Democratic voters in the 2020 presidential election. Public sentiment toward solar energy is more positive in states with consumer-friendly net metering policies and a more mature solar market. States that wish to gain public support for solar energy might want to consider implementing consumer-friendly net metering policies and support the growth of solar businesses.

Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.

Deaths due to synthetic opioids have increased at higher rates for Blacks and Hispanics than for Whites in the last decade. Meanwhile, Blacks and Hispanics experience lower opioid treatment rates and have less availability of medication-assisted treatment (MAT) via office-based buprenorphine in their counties compared to Whites. Racial/ethnic residential segregation is a recognized barrier to equal availability of MAT, but little is known about how such segregation is associated with opioid and substance use treatment availability over time and across Census regions and urban-rural lines. We combined data from the Substance Abuse and Mental Health Services Administration's National Survey of Substance Abuse Treatment Services for 2009, 2014, and 2019 with the 5-year American Community Surveys of 2009, 2014, and 2019 to examine associations between residential segregation indices of dissimilarity and interaction and substance use treatment facilities per 100,000 population, including those providing MAT, in US counties. Estimating county-level two-way fixed effects models and controlling for county-level covariates, we find modest evidence of associations. Despite mostly null findings, an increased likelihood of exposure of Whites to Blacks in a county is associated with fewer substance use treatment facilities per 100,000, particularly those providing MAT via buprenorphine and located in Northeastern and Midwestern counties. Also, a more unequal distribution of Hispanics is associated with fewer facilities per 100,000 providing MAT, and this association is strongest in Southern and Western counties. These associations are driven by recent years (2014–2019) when synthetic opioids became the leading cause of opioid mortality and Blacks and Hispanics began dying at faster rates than Whites. Mixed evidence, however, tempers conclusions for how residential segregation drives racial/ethnic disparities in MAT availability.