Global University Rankings (GURs) intend to measure the performance of universities worldwide. Other rankings have recently appeared that evaluate the creation of environmental policies in universities, e.g., the Universitas Indonesia (UI) GreenMetric. This work aims to analyze the interaction between the Top 500 of such rankings by considering the geographical location of universities and their typologies. A descriptive analysis and a statistical logistical regression analysis were carried out. The former demonstrated that European and North American universities predominated the Top 500 of GURs, while Asian universities did so in the Top 500 of the UI GreenMetric ranking, followed by European universities. Older universities predominated the Top 500 of GURs, while younger ones did so in the Top 500 of the UI GreenMetric ranking. The second analysis demonstrated that although Latin American universities were barely present in the Top 500 of GURs, the probability of them appearing in the Top 500 of the UI GreenMetric ranking was 5-fold. We conclude that a low association exists between universities’ academic performance and their commitment to the natural environment in the heart of their institutions. It would be advisable for GURs to include environmental indicators to promote sustainability at universities and to contribute to climate change.

The companies are aware of the impact that disseminating their corporate social responsibility (CSR) performance has on how shareholders or investors perceive them. This work analyses if disseminating CSR results affects their economic–financial results, their scores in open‐access sustainability ranking, their brand values, and also the credit ratings that agencies S&P and Moody's confer them. For this purpose, the only 13 companies of the IT sector occupying a top 100 brand rankings position were selected. The results reveal that large companies come over as being more transparent in terms of sustainability, but this transparency is not related to their financial behaviour. Brand rankings collect socio‐economic and environmental information, but only the transparency in social and environmental aspects explains the public‐access CRS rankings. Finally, the results also show that this transparency affects credit ratings.

This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. MethodsThe database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used or predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. ResultsModels based on multimorbidity-related variables better explained integrated healthcare expenditure.In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R 2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. ConclusionsThe expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size.

Nowadays, companies have more freedom on how they can report their corporate social responsibility (CSR) actions and outcomes, despite them being increasingly important for how investors and shareholders can obtain knowledge about companies’ non-financial aspects. This is why more importance is being attached to sustainability rankings as an additional tool to seek excellence and distinguish between companies. The main objective of the present research was to analyze the degree of similarity in sustainability valuations among the most important open-access sustainability rankings that have appeared in the last decade (Green Ranking, RepTrack, Global 100 most sustainable corporations, and Finance Yahoo Sustainability). The secondary objective was to study whether these rankings incorporated the most de facto prestigious brands, and the third objective was to learn of the influence of the level of controversy in Finance Yahoo Sustainability scores in technological companies. Our results reveal wide variability among open-access CSR rankings. Not all the most valued brands appear in the sustainability rankings, which indicates the differences between the rankings of brands and CSR rankings. Finally, the level of controversy was found to be an important aspect in companies’ CSR scores.

BackgroundPharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending.MethodsDescriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts.ResultsThe risk adjustment tool proved to have an acceptable level of prediction (R2 ≥ 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established.ConclusionThe model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.