Search

The forest products industries play a vital role in the economic, social, and environmental well-being of the Lake States in the United States. While various economic contribution analyses of forest products industries have been conducted to highlight the importance of such industries to regional economies, little effort has yet been made to parse out the contribution of activities in the value chain. The value chain is a series of steps involved in producing goods or services. This study used a matrix decomposition approach to estimate the economic contribution along the value chain through multiple pathways of four forest resource-based industries using wood as inputs: biomass power generation, sawmills, paper mills, and the construction of new single-family residential structures in the Lake States. The direct and indirect economic output values in 2017 resulting from the construction of new single-family residential structures were $19.1 billion, sawmills were $2.5 billion, paper mills were $17.6 billion, and the biomass power generation industry was $759 million. Of the direct and indirect economic output contributed by each industry, the highest percentage of output attributable to the logging industry was observed from the sawmills industry (12%), followed by biomass power generation (9%), paper mills (1.4%), and the construction of new single-family residential structures (<1%), respectively. The percentage of total economic output attributable to the stumpage industry in the region followed a similar trend as commercial logging for all value-chain industries. The relative economic contribution of the value-chain industries to the total economic contribution of the final industry varied based on whether the industry was a primary or secondary forest products industry and the pathways used for sourcing wood inputs. [ABSTRACT FROM AUTHOR]

This paper analyses carbon emissions and value-added embodied in trade between two large developed countries, South Korea and the United States, during 2000-2014. Using multi-regional input-output (MRIO) tables, our analysis reveals that carbon emissions and value-added embodied in exports grew by 19% and 101% for South Korea but shrank by 43% and 7% for the United States. As a result, South Korea experienced a 40% increase in net carbon exports and 243% increase in net valueadded exports. At the industry level, the primary drivers of changes in carbon exports were electricity and basic materials. The majority of industries in witnessed improvements in carbon intensities suggesting improved environmental efficiency. While both countries achieved a decoupling of carbon emissions from value-added exports, substantial year-to-year and sectoral variations were observed. Finally, structural decomposition analysis indicates that domestic supply-side factors played a role in decreasing emissions whereas foreign demand-side factors contributed to emissions increases. In line with the main findings, various implications for policy and future research are discussed. [ABSTRACT FROM AUTHOR]

For over eighty years, Rule 11 of the Federal Rules of Civil Procedure has authorized sanctions to combat frivolous legal arguments and factual allegations in some paper presentations. Since 1993, however, the rule has not applied to discovery. This article demonstrates how Rule 11 should be used to deter advocacy based on discovery that has "gone bad" since its initial disclosure. Disciplinary actions, malpractice claims, the discovery rules, and even the inherent sanctioning authority of the court are insufficient to deter lawyers from relying on now-bad discovery papers. Rule 11 would hold lawyers to a duty of reasonable inquiry before relying on prior discovery, including discovery conducted or presented by a prior lawyer or firm. This article proceeds in six parts. First, it generally demonstrates why it matters that Rule 11 apply to now-rotten discovery papers. Second, it employs a 2001 Fourth Circuit opinion to show how a Rule 11 application to gone-bad discovery would help deter frivolity in civil litigation. Third, it reviews the current rule's exclusion of "discovery process" violations, arguing that it does not encompass presentations of discovery once good, but now gone bad. Fourth, it briefly reviews how federal case practices on pleadings gone bad can apply to discovery gone bad. Fifth, it reviews comparable state laws and precedents on presentations of civil litigation papers gone bad. Finally, it concludes with suggestions on how Rule 11 sanctions should be assessed for advocacy of now-bad discovery papers. [ABSTRACT FROM AUTHOR]

Using daily data since 2017, we disentangle China-specific structural shocks driving Chinese financial markets and examine spillovers across global markets. The novelty of this paper consists of simultaneously identifying China shocks with shocks emanating from the United States and shocks to global risk sentiment - two major forces driving global financial markets - to ensure that China spillover estimates do not reflect common factors. Our results show that shocks originating in China have material impacts on global equity markets, although spillovers are much smaller than those following shocks in the United States, or those triggered by shifts in global risk sentiment. By contrast, shocks from China account for a significant proportion of variation in global commodity prices, more on a par with those of the United States. Nevertheless, spillovers from China can be significantly amplified in an environment of heightened global volatility, or when the shocks are large. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Scandals are momentous events with far-reaching consequences for organizations and society, but we still know relatively little about the contextual conditions that enable them. In this paper, we develop and test a theory of the publicization of organizational misconduct—that is, of how an act of wrongdoing by an organization becomes widely publicized, thereby giving rise to a scandal; or receives little coverage, with little or no consequence for the offending organization. We argue that communities have structural features that affect the social cost of making information public as well as its dissemination, shaping the likelihood of organizational misconduct being publicized in the process. We test and find evidence in support of our arguments using data on sex abuse in the U.S. Catholic Church between 1980 and 2010, and exploring the heterogeneity in the publicization of patterns of misconduct by Catholic clergy across dioceses and over time. We conclude by discussing the implications of our findings for the scholarly understanding of organizational misconduct and scandals across settings. [ABSTRACT FROM AUTHOR]

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas. [ABSTRACT FROM AUTHOR]