Ecolabels and Sustainability in the Seafood Sector: Key Elements of the Debate and Shortcomings.

The seafood sector plays a key role in global nutrition but is confronted with significant sustainability challenges including overfishing, marine debris, and the impacts of climate change. In response, several measures have been implemented, such as the introduction of fishing quotas, restrictions on fishing zones, expansion of aquaculture, increased monitoring, and promotion of sustainable consumption. In this context, ecolabels are recognized as tools to encourage sustainable consumption by influencing consumer behavior.

Exploring the environmental impacts of plastic packaging: A comprehensive life cycle analysis for seafood distribution crates.

Annually, 8.3 million tonnes of mismanaged plastic waste enter oceans, prompting the food packaging industry, a major contributor, to minimize its environmental footprint. Within the seafood sector, a nascent number of studies are exploring the impacts of various packaging solutions for distribution, yet clear insights remain elusive.

Integrating the water-energy-food nexus and LCA + DEA methodology for sustainable fisheries management: A case study of Cantabrian fishing fleets.

The fishing sector constitutes an important source of economic revenue in northern Spain. In this context, various research studies have focused on the application of the five-step Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodology to quantify environmental impacts of fishing systems. However, some of them have used environmental indicators that focus on individual environmental issues, hindering the goal of achieving integrated resource management.

Methodological guidelines for the calculation of a Water-Energy-Food nexus index for seafood products.

Indicators from life cycle assessment methodologies (i.e., footprints) have emerged as useful tools for identifying and communicating the environmental impacts of a system thanks to they are accessible and intuitive and easy to understand to non-expert public.

Environmental performance of Cantabrian (Northern Spain) pelagic fisheries: Assessment of purse seine and minor art fleets under a life cycle approach.

The perpetuation of fishing activity from an environmentally, socially and economically sustainable approach is essential to guarantee not only the future of coastal populations, but also the supply of high-value seafood for society and the safeguarding of cultural heritage. This article aims to assess the environmental performance associated with fishing fleet operations in Cantabria (northern Spain) under a life cycle thinking from a holistic approach. Thus, the Life Cycle Assessment (LCA) methodology was applied under a 'cradle-to-port' approach, setting the functional unit as 1 kg of fresh fish landed.

Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain.

In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO, O, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020.

Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels.

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the "deweather" R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO, PM and O data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013-2019).