Overallocation and Correction of Carbon Emissions in the Evaluation of Carbon Footprint

This paper points to several carbon footprint data distortions that overallocate carbon footprints to individual companies, and to several carbon data intricacies that lead to improved data integrity. Data distortion due to the same company being listed in multiple geographical jurisdictions or through different share classes overstates Emissions Scope 1 by 4.6%, Emissions Scope 2 by 5.5%, Emissions Scope 3 by 10.6% and Reserves by 6.0%. Data distortion due to index construction by having different market capitalization in representative indices overallocates Emissions Scope 1 by 33.9%, Emissions Scope 2 by 27.6%, Emissions Scope 3 by 21.3% and Reserves by 57.2%. A significant amount of carbon data is not precise but is estimated by third-party providers through proprietary techniques. The estimated data for Scope 1 Emissions is 46.4% for the companies in the index. In addition, carbon data is stale, resulting in 94.5% of data being two years old or more. Usage of carbon data in a present format may incorrectly remove some companies from portfolios (negative screen, complete removal) or incorrectly reduce some companies’ weight in a portfolio (partial screen, fractional removal).

PREDICTING NPLs FOR CROATIA WITH MACROECONOMIC VARIABLES

This paper aims to examine some of the macroeconomic drivers of nonperforming loans (NPL) in Croatia. Unemployment rate, industrial production index, construction works volume and the number of tourist arrivals were evaluated as the drivers on a quarterly data sample from 2008q4 to 2020q4. Following quantile regression approach, unemployment rates and construction works volume appeared as significant drivers of NPL in Croatia. Furthermore, empirical findings from this paper suggest asymmetric effects on NPL from its drivers. While decrease in construction works volume and increase in unemployment rates were found to correspond with increase in NPL, an increase in construction works volume and decrease in unemployment rates were not correlated with decrease in NPL. Consequently, the paper brings implications for credit institutions in Croatia within the context of COVID-19 pandemic crises.

Cohesive Subgraph Identification in Weighted Bipartite Graphs

Cohesive subgraph identification is a fundamental problem in bipartite graph analysis. In real applications, to better represent the co-relationship between entities, edges are usually associated with weights or frequencies, which are neglected by most existing research. To fill the gap, we propose a new cohesive subgraph model, (k,ω)-core, by considering both subgraph cohesiveness and frequency for weighted bipartite graphs. Specifically, (k,ω)-core requires each node on the left layer to have at least k neighbors (cohesiveness) and each node on the right layer to have a weight of at least ω (frequency). In real scenarios, different users may have different parameter requirements. To handle massive graphs and queries, index-based strategies are developed. In addition, effective optimization techniques are proposed to improve the index construction phase. Compared with the baseline, extensive experiments on six datasets validate the superiority of our proposed methods.