On Abatement Services: Market Power and Efficient Environmental Regulation

In this paper, we study an eco-industry providing an environmental service to a competitive polluting sector. We show that even if this eco-industry is highly concentrated, a standard environmental policy based on a Pigouvian tax or a pollution permit market reaches the first-best outcome, challenging the Tinbergen rule. To illustrate this point, we first consider an upstream monopoly selling eco-services to a representative polluting firm. We progressively extend our result to heterogeneous downstream polluters and heterogeneous upstream Cournot competitors. Finally, we underline some limits of this result. It does not hold under the assumption of abatement goods or downstream market power. In this last case, we obtain Barnett's result

Multiview child motor development dataset for AI-driven assessment of child development

Background: Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. Results: The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. Conclusion: Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.ope

Las Vegas Daily Optic, 10-16-1900

https://digitalrepository.unm.edu/lvdo_news/3601/thumbnail.jp

Las Vegas Daily Optic, 01-04-1900

https://digitalrepository.unm.edu/lvdo_news/3364/thumbnail.jp