The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the availability of environmental, social and governance (ESG) indicators are attracting investors to socially responsible investment decisions. Furthermore, whereas the strategic importance of ESG metrics has been particularly studied for private enterprises, little attention have received public companies. To address this gap, the present work has three aims—1. To predict the accuracy of main financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) of public enterprises in Europe based on ESG indicators and other economic metrics; 2. To identify whether ESG initiatives affect the financial performance of public European enterprises; and 3. To discuss how ESG factors, based on the findings of aims #1 and #2, can contribute to the advancements of the current debate on Corporate Social Responsibility (CSR) policies and practices in public enterprises in Europe. To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (i.e., ordered logistic regression) model. The former predicts the accuracy of ROE and ROA on several ESG and other economic metrics and fulfils aim #1. The latter is used to test whether any causal relationships between ESG investment decisions and ROA and ROE exist and, whether these relationships exist, to assess their magnitude. The inferential analysis fulfils aim #2. Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators. In addition, the existing relationship appears more evident when companies invest in environmental innovation, employment productivity and diversity and equal opportunity policies. As a result, to fulfil aim #3 useful policy insights are advised on these issues to strengthen CSR strategies and sustainable development practices in European public enterprises.

Protected areas play an important role in the conservation and protection of biodiversity of particular territories, especially of ecosystems that provide resources for living organisms, including human beings. Different studies highlight the importance of biodiversity and its associated benefits in terms of ecosystem services of protected areas. The economic assessment of ecosystem services and biodiversity becomes a viable solution to help the policy maker to make decisions on the environmental preservation of these areas according to the Agenda 2030 for Sustainable Development. Nonetheless, very few studies provide an economic evaluation of the benefits of protected areas. To advance the current debate on the economic evaluation of the benefits provided by protected areas, the present paper purposes an integrated approach. It presents an overview of main ecosystem services’ mapping techniques currently available to researchers and policy makers and offers a systematic review carried out for the period 2015–2020 at an international level. The main findings are particularly attractive for the Gargano National Park (GNP) in the south of Italy, which is recognised as being a biodiversity hot spot at global level. The current study provides useful guidance for the assessment of trade-offs, the support to policy makers, and the provision of efficient allocation of public resources for protected areas.