This study examines the degree of corporate social responsibility (CSR) in the European banking sector in terms of commitment to the 2030 Agenda Sustainable Development Goals (SDGs). It also explores whether gender diversity on the board of directors can be used to differentiate between companies with different degrees of engagement with the SDGs. This question is important, given that achieving equal opportunities for women is a CSR priority for today’s companies given stakeholders’ demands. Descriptive and inferential statistical analyses are conducted using a sample of the 30 largest banks in Europe in terms of market capitalization as of 15 February 2019. Key conclusions are that most of the analyzed banks target at least one of the SDGs and that the banks that are most committed to Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action) of the 2030 Agenda have greater gender diversity on their boards of directors.

Climate change is one of the greatest challenges facing humanity today. Therefore, all segments of society must act together to stop the deterioration of the planet and the depletion of its resources. The business sector must play an active role in acting responsibly toward the environment. Given the importance of this issue, major efforts have been made to analyze the environmental performance of the most polluting sectors. In contrast, other sectors that are also of great interest due to their contribution to sustainable development, such as the banking sector, have been overlooked. Notable factors conditioning performance include aspects of corporate governance such as gender diversity. However, the empirical evidence reveals a lack of consensus regarding the influence of women directors on corporate environmental performance. This background motivates the study of the commitment of the banking sector to reducing their environmental impact and the analysis the influence of board gender diversity on environmental performance. Data for the period 2009 to 2018 on 52 banks from the most polluting Western regions were studied using descriptive statistics and fixed effects econometric estimation to test the relationship between a selection of relevant variables. The key conclusions are that banks are committed to protecting the environment and that there are no significant differences between banks’ commitment to the planet on the basis of board gender diversity.

The tourism sector is a driver of economic development characterised by its environmental impact. It is a prevalent part of the 2030 Agenda, given its potential to help meet the Sustainable Development Goals (SDGs). At the same time, board gender diversity is considered essential for companies to implement environmentally sustainable initiatives. However, analysis of the relationship between the role of women on boards and environmental performance has been neglected in the tourism literature. This paper adopts a novel approach to the study of this sector by analysing the relationship between gender diversity on the board of directors and companies’ environmental practices. A fixed effects model is estimated using an international sample of 120 listed tourism companies for the period 2002 to 2019. The results show that boards that are more gender diverse and have a greater female presence are associated with poorer environmental performance and a weaker implementation of policies and practices to reduce resource use and emissions. However, board gender diversity aids performance in environmental innovation.

Purpose The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector. Design/methodology/approach The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies. Findings The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems. Originality/value This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

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