Considering the role of tourism in promoting sustainable practices in destinations, this study aims to map the scientific literature on footprint calculators in the last three years (2020–2023) with a focus on the tourism context. The method adopted is a scoping review with a qualitative and exploratory approach, using the Scopus database. The originality of this research lies in the study of publications related to footprint calculators with a focus on the tourism sector. Based on the analysis carried out, the main results show that the study of footprint calculators applied to the tourism sector has had little prominence in the indexed research in the Scopus database during the specific period considered for this study. Consequently, the conclusion of the study highlights the marginality of the tourism sector in the discussion of footprint calculators in the last 3 years of scientific publications.

The area of lake surface water is shrinking rapidly in Central Asia. We explore anthropogenic and climate factors driving this trend in Shalkar Lake, located in the Aral Sea region in Kazakhstan, Central Asia. We employ the Landsat satellite archive to map interannual changes in surface water between 1986 and 2021. The high temporal resolution of our dataset allows us to analyze the water surface data to investigate the time series of surface water change, economic and agricultural activities, and climate drivers like precipitation, evaporation, and air temperature. Toward this end, we utilize dynamic linear models (DLM). Our findings suggest that the shrinking of Shalkar Lake does not exhibit a systemic trend that could be associated with climate factors. Our empirical analysis, adopted to address local conditions, reveals that water reduction in the area is related to human interventions, particularly agricultural activities during the research period. On the other hand, the retrospectively fitted values indicate a semi-regular periodicity despite anthropogenic factors. Our results demonstrate that climate factors still play an essential role and should not be disregarded. Additionally, considering long-term climate projections in environmental impact assessment is crucial. The projected increase in temperatures and the corresponding decline in lake size highlights the need for proactive measures in managing water resources under changing climatic conditions.

 Public-private partnerships (PPPs) are vital for infrastructure development in developing countries, integrating private efficiency with public oversight. However, PPP models often face risks, particularly in Indonesia’s water sector, due to its unique geographical and regulatory challenges. This study aims to identify and evaluate risk factors specific to drinking water PPP projects in Indonesia. Using a quantitative approach, structured questionnaires were distributed to experts in the sector, and the data was analyzed using a fuzzy evaluation method. Risks were categorized into location, design and construction, financial, operational, revenue, and political. The study emphasizes that effective risk management, including identification, analysis, and mitigation, is essential for project success. It highlights the importance of stakeholder involvement and flexible risk management strategies. Comprehensive and proactive risk management is key to the success of drinking water infrastructure projects. The research suggests that an integrated and collaborative approach among stakeholders can enhance risk management effectiveness. These findings provide valuable insights for policymakers, project managers, investors, and other stakeholders, underscoring the necessity for adaptable regulatory frameworks and robust policy guidelines to improve the sustainability and efficacy of future water-related PPPs.

This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city's northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model's predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.