Multi-label feature selection (MLFS) is a critical task for handling high-dimensional datasets where labels exhibit complex interdependencies. While embedded methods effectively identify discriminative features by optimizing predictive performance through structured sparsity, they often overlook the underlying topological structure of the label space. Conversely, spectral methods capture label correlations via similarity structures but lack direct optimization for prediction. To bridge this gap, this paper introduces CFIFS (Choquet Fuzzy Integral Feature Selection), a framework that models MLFS as a non-additive, score-level information fusion problem. The proposed pipeline integrates two complementary perspectives: a group-sparse embedded score for predictive relevance and a spectral score—combining Dirichlet smoothness and the Hilbert-Schmidt Independence Criterion (HSIC)—for topological consistency. Rather than relying on traditional additive aggregation, CFIFS employs a two-source Choquet fuzzy integral to fuse these rankings. The fuzzy capacity is learned through an automated data-driven procedure, allowing the framework to adaptively weigh the synergy or redundancy between input scores. Furthermore, the model provides an interpretable interaction index that quantifies the relationship between the predictive and topological evidence. Extensive experiments on 20 benchmark datasets demonstrate that CFIFS significantly outperforms seven state-of-the-art MLFS baselines. Rigorous statistical validation via Friedman and Holm tests confirms the robustness and superior generalization of the proposed fusion approach across diverse application domains.
Livestock systems represent a considerable environmental challenge. In response, various scientists, non-governmental organisations, and policy makers claim that Western populations in particular need to sharply reduce meat consumption. Given people’s attachment to meat, many of these actors favour hard policy interventions based on a range of systemic financial and legal reforms that would go beyond mere nudging and the formulation of recommendations, including the top-down imposition of meat taxes and bans, as well as herd size reductions, which would lead to sharply higher prices. However, arguments in support of such policies tend to oversimplify the issue, ignoring regional variations, mitigation potential, and broader ecological and nutritional contexts. The focus of this article is on dietary greenhouse gas (GHG) emissions as a main target for environmental policymaking, with all livestock production in the West contributing 2.6
Innovation is a central driver of competitiveness, resilience, and sustainability in the agri-food sector, particularly among small and medium-sized enterprises (SMEs). However, traditional science- and technology-based models may not fully grasp the innovation dynamics in this domain, and research explicitly addressing agri-food SMEs remains limited. This study adapts, integrates, and extends existing Innovation Capability (IC) and related constructs into a unified WI-PI-IA framework (Willingness to innovate-Propensity to innovate-Innovation Ability) for agri-food SMEs. The framework is empirically tested through a sectoral quantitative case-study based on structured questionnaires administered to twenty SMEs operating in the Sardinian sheep dairy industry. The findings confirm the framework's validity, highlighting the role of contextual factors and revealing distinct innovation patterns between cooperatives and private firms. This study is, to our knowledge, the first to conceptualise IC in agri-food SMEs as the outcome of the three above constructs and offers a comprehensive and context-sensitive approach that contributes to academic research and directs policymakers towards factors that affect agri-food SME innovation outcomes, considering their unique structures and specific challenges they face.
Aim Foundation species sustain biodiversity by providing habitat, resources, and protection from environmental stress. However, their influence on environmental severity and variability and their role in shaping diversity across spatial scales remain poorly understood. We conducted a large-scale study to assess how environmental conditions and community diversity vary between areas with and without canopy-forming macroalgae across local to regional spatial scales.Location Latitudinal range from 28 degrees 34 ' N to 54 degrees 11 ' N, encompassing both the Northeast Atlantic and Western Mediterranean regions.Time Period 2019-2020.Major Taxa Studied Marine macroalgae and benthic invertebrates.Methods Using a standardised nested sampling design across 13 sites in two marine regions (the Northeast Atlantic and Western Mediterranean) we compared the intensity and variability of temperature and light intensity, as well as community alpha- and beta-diversity, between intertidal rocky areas with and without canopy-forming macroalgae. Data were analysed by means of linear mixed-effects models.Results There were regional differences in variations of environmental conditions between habitats: NE Atlantic macroalgal canopies were characterised by lower environmental extremes and variability-especially in summer-than open rock areas, whereas differences between habitats were small in the W Mediterranean. Likewise, differences in alpha- and beta-diversity between habitats were region-specific and scale-dependent: in the NE Atlantic, alpha-diversity was higher in the presence of canopies than in open rock areas from local to regional scales, while beta-diversity decreased in canopy stands with increasing spatial scale. In the W Mediterranean, alpha-diversity was higher under canopies than on open rock at the regional scale, whereas beta-diversity was higher under canopies at small to intermediate scales.Main Conclusions Our results support the view that key environmental conditions and biodiversity on intertidal rocky shores differ between areas characterised by the presence or absence of canopy-forming macroalgae acting as foundation species, across a hierarchy of spatial scales.
Introduction Afforestation is increasingly recognized as a key strategy to address climate change and ecological degradation, offering multiple ecosystem services. However, strategic planning is needed to ensure that afforestation actions are ecologically effective and economically efficient by targeting areas where ecosystem service provision is most significant.Objectives In this study, we provide a workflow to prioritize afforestation areas at national scale tailored to four distinct ecosystem service goals: ecological connectivity, human health, climate mitigation, and water regulation.Methods Using Italy as a case study, we applied goal-specific analysis to areas considered suitable for afforestation, integrating geospatial datasets of environmental and social factors. For each objective, spatial indicators were combined into a priority map at 1 km resolution, subsequently aggregated into composite maps. The top 20% priority areas for each objective were identified to inform national and regional planning.Results High-priority areas to improve ecological connectivity were focused around urban centers and extensive agricultural plains. Water regulation benefits were most relevant in urbanized and vineyard landscapes, while urban areas also emerged as priorities for human health. Climate mitigation potential was highest in moist temperate and mountain regions. Overlap among goals was limited, revealing significant trade-offs across ecosystem services.Conclusions The workflow provides a replicable approach to identify goal-specific afforestation priorities across diverse landscapes. It supports coordinated strategies to enhance ecosystem service provision and restoration effectiveness at national and regional levels.
