Marco Marto

Environmental, social, and governance (ESG) reports are increasingly important for maritime ports and large service and industrial companies, as they measure sustainability, social responsibility, and integration with society. This study aims to strengthen collaboration between port administrations and academia by identifying differences between industry needs and academic focus. The research analyzes ESG indicators from sustainability reports of 23 port administrations and keywords from 34 SCOPUS publications (2020–2025). Indicators and keywords were grouped into ESG dimensions and thematic clusters, generating two criteria: the number of indicators used by ports and the number of keywords in academic research. Two multicriteria decision analysis methods, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), were applied to rank and compare topics across clusters and criteria. Results show similarities in rankings but also reveal differences in priorities between ports and academia, highlighting the need for stronger collaboration and common ESG indicators. Key priorities identified include data and innovation, ranked 1st and 2nd in the governance pillar, employment and job benefits, ranked 1st in the social pillar, and energy efficiency and management, ranked 1st in the environmental pillar. Differences in some environmental and social topics suggest further efforts are needed to align academic research with port administration priorities.

Recently, several unexpected events of various types (geopolitical, economic, health-related, and weather-related) have had a significant global impact, affecting the lives of millions of people and businesses. Disruptions in supply chains highlight the importance of planning and managing them at various hierarchical levels of decision-making. This paper presents and analyzes supply chain networks (SCNs) for the distribution of soybean and corn in Europe and North Africa, considering the uncertainty of demand and costs along intermodal routes. Transportation and distribution costs for these commodities are minimized by considering the uncertainty of demand and transportation costs. Starting from a deterministic model to establish the SCN, uncertainty is then introduced through stochastic modeling, with the optimization goals redefined accordingly, more specifically in terms of expected cost (EC) and conditional value at risk (CVAR) constraints. The solutions of the bi-objective model, including equivalent average emissions CO₂, highlight the strategic positions of the port of Itaqui on the supply side and of the port of Sines as distribution hubs (transshipment) in the design of the networks according to the different objectives. The advantageous position of the port of Sines is analyzed in detail, according to the strategic objective of designing a more efficient intermodal SCN for the distribution of soybean and corn.

1. As consequence of the ongoing climate changes and global warming, several regions of the globe are facing more extreme temperature events in the hottest seasons of the year, leading to a greater...

Long-term planning is a crucial tool for addressing forest harvest scheduling problems, particularly when dealing with complex future scenarios. When the planning horizon extends beyond the rotation period, stands can be harvested multiple times. The complexity of models and solution techniques for harvest scheduling problems, particularly those with constraints on clearcut areas (referred to as area-restricted model, ARM), increases significantly in a multiple-harvest context. This study aims to solve the ARM for a highly combinatorial case study where a 100-year planning horizon allows, on average, six harvests per stand. The forest, located in northern Portugal, consists of 1926 stands spread across two non-contiguous regions. Existing studies on multiple harvests often either fail to assess the quality of the solutions or are generally unsuitable for the case study. We propose a branch-and-cut-based heuristic, that incorporates a variable fixing process, to provide a measure of solution’s quality. While this method cannot solve the problem for the entire forest, it yields good solutions for the two regions.

Urban parks are vital components of city ecosystems, enhancing biodiversity, climate resilience, air and water quality, health, socialization, and economic benefits for citizens in urban areas. This paper examines urban parks in Novi Sad by gathering opinions on their qualities and functions through a questionnaire. The respondents were students enrolled in the landscape architecture course at the University of Novi Sad. To analyze their responses, multivariate statistical analysis techniques, including ANOVA, MANOVA, and contingency tables, were applied. The results highlight the primary reasons for visiting urban parks in general, as well as specific parks in Novi Sad. The paper offers insights into visitor behavior, including the frequency and length of their stays, etc., and provides an assessment of the parks’ educational functions, which were expected to be highly relevant for the respondent group. The results can be relevant for further urban park development and serve as a starting point for applying multi-criteria (MC) analysis. Specifically, the results can be used to define a set of criteria, goals, and other essential elements necessary for conducting Analytic Hierarchy Processes or similar MC analysis methods.

This work aims to provide a more complete characterization of coastal erosion mitigation and climate change adaptation measures by presenting a participatory approach that integrates medium- to long-term perspectives, considering simultaneously social, environmental, economic and engineering dimensions to help decision makers implement sustainable climate change adaptation (CCA) strategies. The work lists, explains and characterizes existing climate change mitigation and adaptation measures as well as their costs and positive and negative social, environmental and economic impacts, in three distinct databases. These databases are discussed, complemented and validated in participatory moments with local stakeholders of the Ovar Municipality, Portugal, which represents the case study to support the proposed methodology. Although Ovar is a pilot case, the integrated framework for resilient CCA has a global application with respect to methodologies and concepts. The proposed approach is useful to help coastal management entities to engage in more efficient, effective and beneficial planned action to mitigate coastal erosion and adapt to future climate change effects. The open-source databases and the participatory approach facilitate decision makers and coastal communities to navigate the complexity of solutions and build consensus around collective actions for coastal areas.

The scientific literature and decision makers debate and explore education’s influence on regional development. However, differences among EU regions remain to be explained. The present article proposes to measure these disparites in terms of the gross domestic product (GDP) per capita and understand how tertiary education can explain that performance. A data envelopment analysis (DEA) optimization technique was applied along with spatial econometric models for all EU NUTS 2 regions (as defined by Eurostat for regional policies application), using the year 2020 as reference. The case of Portugal as a particular set of (seven) NUTS 2 regions included in the EU is detailed and analyzed in-depth. The two-stage least squares regression seems to explain well the differences in GDP per capita with the independent (and instrumental) variables which include the percentage of tertiary education and the spatial lags of this variable. The DEA optimization can support and help to explain most of the spatial regression results. The study identifies the NUTS 2 regions with the best favorable relationships among GDP per capita and percentages of tertiary education, predominantly located in the central and northern European countries and some in Ireland. The south EU regions, as expected, were identified as the regions with the poorest performances for GDP per capita and percentage of tertiary education, as well as some regions in eastern Europe. The positive and significant impact of the percentage of people with tertiary education on the values of GDP per capita given by the spatial econometric model suggests that special priority must be given to education and science in public policies agenda and government budget.