Search

The African Growth Opportunity Act (AGOA) has been a crucial trade and development initiative, offering preferential access to qualified Sub-Saharan African (SSA) countries to the United States market since its enactment in 2000. This paper presents a comprehensive review of scholarly articles and policy reports that analyze the impact of AGOA on the economic performance of SSA countries. Employing various econometric methods and data analysis techniques, researchers have investigated the effects of AGOA on trade flows, foreign direct investment (FDI) inflows, employment, economic growth, and poverty levels. The findings reveal that AGOA has positively affected the region's trade, particularly in apparel, textiles, and agriculture. However, its influence on promoting export diversification and attracting FDI is nuanced, with substantial heterogeneity among the beneficiary countries and industries within each country. While some SSA countries have experienced substantial export growth and FDI inflows, others have not fully leveraged the benefits of AGOA due to absorptive capacity constraints and governance challenges. AGOA's effectiveness in promoting broad-based employment, GDP growth, and poverty reduction remains an active area of inquiry, necessitating further research to understand the policy's sustained impact and inform future trade policy designs for SSA countries. [ABSTRACT FROM AUTHOR]

When a nuclear power plant closes, its host community often faces a number of negative economic impacts, such as the loss of skilled jobs and a major source of tax revenue. Even after decommissioning is completed, many former plant sites remain undeveloped, further inhibiting economic recovery. In this paper, we use data from seven decommissioned and decommissioning plants as well as estimates from industry professionals to calculate an approximate opportunity cost for each host community. This cost represents how much potential revenue has been lost while the plant site has remained undeveloped as compared to an alternate scenario in which it has been redeveloped into a solar power plant. Additionally, this paper discusses the presence of spent nuclear fuel and the challenges this presents for the redevelopment of former nuclear plant sites. This paper demonstrates the importance of efficient decommissioning and post-decommissioning redevelopment and examines solar power generation as one potential redevelopment pathway. [ABSTRACT FROM AUTHOR]

As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are "good" or "bad." This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like "season" or "variability," and that simple decompositions can be combined to represent more complex concepts, like "seasonal variability," creating an expressive language through which to interrogate models and data. Plain Language Summary: Models are essential scientific tools for explaining and predicting phenomena ranging from weather and climate, to health outcomes, to economic development, to the origins of the universe, and testing competing models is one of the most basic scientific activites. Yet, how scientists evaluate and justify their models can be inconsistent or even arbitrary. Traditionally, one performance metric—such as mean squared error—is used to identify the best model, but one metric provides little insight into what aspects of a model are "good" or "bad." This paper proposes a basic language for expressing different aspects of a model's performance. On one hand, this is useful for determining which aspects of model may require revision, but it also allows the modeler to separate out the best elements among several models and combine them to form an ensemble, analogous to how an audio engineer mixes together multiple tracks to form the best rendition of a musical piece. Key Points: Mean squared error (MSE) is an objective but somewhat enigmatic measure of model performanceMSE can be decomposed into components that quantify specific aspects of model performance, such as bias and varianceMixing components among models yields a form of ensemble prediction [ABSTRACT FROM AUTHOR]