IntroductionQuantitative models of sustainable consumption typically treat consumption as exogenous, assuming that every green purchase improves environmental outcomes. Behavioral research on moral licensing, however, suggests that purchases perceived as virtuous can license compensatory indulgence that erodes some of the benefit. Recent formal work treats licensing as an environmental rebound mechanism in static consumer models; this study contributes the first dynamic stock-and-flow formalization, developed from a recent conceptual framework of brand-induced moral licensing.MethodsFour communication pathways (virtue attribution, identity conferral, effort signaling, and absolution framing) feed two moral accounts with opposite mechanics: credits, which deplete when spent, and credentials, which license behavior without being consumed. The model yields closed-form steady states and a licensing-resistance condition on the rebound ratio ρ> (backfire at ρ> 1). Communication parameters were calibrated through a preregistered content analysis of 120 verbatim-archived units from eight sustainable brands, with perfect dual-coder agreement on the reliability subsample; identity internalization was calibrated from a two-country survey of 3,293 adults. The behavioral conversion parameters are literature-anchored or illustrative, so all rebound magnitudes are model-derived, order-of-magnitude results rather than empirical estimates.ResultsRebound stays far below backfire: steady-state values span 0.01 to 0.19 across archetypes, preserving 81 to 99% of gross benefit, across 8,000 uncertainty samples and under structural variants allowing credential depletion, sign reversal, and alternative licensing functions. Calibration inverts the theorized risk ordering: identity-positioned brands deploy almost no identity-conferring language, decoupling the typology's conceptual categories from their communication signatures. The model further predicts that 6-month evaluations understate long-run rebound nearly five-fold, implying measurement windows of 1 to 3 years. Absolution framing is absent from the corpus, consistent with the application timeline of Directive (EU) 2024/825; in counterfactual simulation, restoring such claims forfeits up to a third of gross benefit, whereas reducing the growth signal lowers cumulative modeled benefit without reducing rebound.DiscussionLicensing rebound is projected to be real but bounded; message design, not demarketing, is the effective lever within the modeled mechanism. The model supplies an auditable communication-risk map and the design brief for the longitudinal study the field lacks.
IntroductionThis study investigated the mediating impact of environmental self-efficacy in the relationships among sustainability knowledge, sustainability attitudes, and sustainability behaviour among university students in southwestern Nigeria. Grounded in Bandura's Social Cognitive Theory (SCT), the study examined how sustainability knowledge and attitudes translate into behaviour.MethodsThe study adopted a quantitative, cross-sectional research design. We collected data using the Environmental Self-Efficacy Scale (ESE-10) and the Sustainability Consciousness Questionnaire (SCQ). A total of 251 valid responses were analysed using structural equation modelling (SEM) with maximum likelihood estimation and bootstrapping (5,000 resamples).ResultsThe study found that sustainability knowledge (β = 0.104, p = 0.121) and sustainability attitude (β = 0.133, p = 0.082) had no significant direct effects on sustainability behaviour. Environmental self-efficacy exerted significant positive effects on sustainability knowledge (β = 0.568, p < 0.001), sustainability attitude (β = 0.594, p < 0.001), and sustainability behaviour (β = 0.543, p < 0.001). The specific indirect effects of sustainability knowledge (β = 0.308, 95% CI [0.198, 0.431]) and sustainability attitude (β = 0.323, 95% CI [0.211, 0.448]) on sustainability behaviour through environmental self-efficacy were significant. Moderation analysis indicated that environmental self-efficacy did not moderate the relationship between knowledge and behaviour or attitude and behaviour.DiscussionThese results suggest that environmental self-efficacy functions as a mediating mechanism through which sustainability knowledge and attitudes are translated into sustainability behaviour. Therefore, policy programmes aimed at promoting sustainability consciousness among university students should incorporate practical, confidence-building experiences. Also, assessment strategies should measure not only what students know and feel but also their belief in their capability.
This study applies recently published life cycle assessment (LCA) guidelines for higher education institutions (HEIs) to the Icelandic context and presents the first LCA of the University of Iceland (UI), based on data from the 2024 reference year. The assessment used the Environmental Footprint 3.1 method in Sphera LCA for Experts software to calculate environmental impacts across 16 categories. For the first time in this HEI LCA framework, the use of artificial intelligence (AI) tools such as ChatGPT was also assessed. Sensitivity and scenario analyses were applied to reflect island-specific conditions. Infrastructure and transport were the main environmental hotspots. Infrastructure dominated seven impact categories, while transport led in six. Key data gaps remained in equipment stockpiles and chemical use. The results demonstrate LCA feasibility in the Icelandic university context and identify priority areas for environmental improvement. Main limitations include generic European building datasets, missing Iceland-specific district heating data, uncertainty in air travel emissions, and AI modeling based on 41 survey responses. This study identifies priority areas for environmental improvements, particularly regarding air travel reduction and spatial optimization strategies such as shared workspaces. This study presents the first LCA of an Icelandic university and is among the first to include AI tool usage in this context. It provides a foundation for future LCAs and life cycle sustainability assessments (LCSA).
Pharmaceutical manufacturing is a material contributor to the environmental footprint of healthcare through energy-intensive processes, solvent-dominated waste streams, and the discharge of active pharmaceutical ingredients and micropollutants to water ecosystems. Despite growing concern regarding the environmental impacts of pharmaceutical manufacturing, environmental sustainability is not a primary objective of conventional good manufacturing practice (GMP) frameworks, and cleaner production principles remain unevenly integrated into pharmaceutical manufacturing regulation and practice. This scoping review, conducted following JBI methodology and reported in accordance with PRISMA-ScR, mapped peer-reviewed and gray literature published from 2000 through July 2025 on environmental sustainability and climate change mitigation in pharmaceutical manufacturing. A protocol was registered with the Open Science Framework (osf.io/zyfek). A comprehensive search of MEDLINE, Embase, Global Health, Web of Science, and gray literature sources (WHO IRIS, OECD iLibrary, UN Environment databases, Google Scholar, ProQuest) was executed on 3 July 2025. Two reviewers screened records independently using Covidence (κ = 0.79 title/abstract; κ = 0.84 full-text). From 2,943 screened records, 125 met inclusion criteria. Green chemistry metrics (E-factor, Process Mass Intensity) were the most frequently reported sustainability measure (36% of the 125 records), followed by climate impact assessments (24%) and solvent-related criteria (17%). Industrial-scale operations were the most frequently reported (50%), and non-traditional manufacturing modes (continuous flow, process intensification, hybrid systems) featured in a third of records (33%). Only eight records (6.4%) explicitly referenced WHO GMP, reflecting limited engagement with regulatory organizations within the scholarly literature retrieved rather than necessarily a gap in industry practice. Barriers clustered into economic, regulatory, technical, organizational, and environmental domains. The mapped literature on cleaner production in pharmaceutical manufacturing is dominated by waste- and mass-efficiency metrics, while lifecycle and ecological safety dimensions remain underaddressed. These gaps suggest that integrating explicit environmental provisions into major GMP frameworks—grounded in the full breadth of green chemistry principles and the planetary boundaries framework—warrants further consideration as a means of supporting global harmonization and equitable implementation, particularly for low- and middle-income country manufacturers.
This study aims to construct a high-precision and dynamic carbon footprint accounting model and provide feasible emission reduction strategy combinations to address specific issues in the forest tourism supply chain, such as blurred boundaries in carbon footprint accounting, static lag in emission factors, and divergent emission reduction strategies in balancing environmental, economic, and social benefits. Firstly, this paper determines the lifecycle accounting boundaries based on the ISO 14067 (International Organization for Standardization) standard, covering upstream material supply, midstream scenic area operation, downstream waste disposal, and tourist return. Then, this paper collects and preprocesses multi-source heterogeneous data, including smart meter data and GPS (Global Positioning System) trajectories, to address data silos and noise issues. Next, this paper introduces a triple dynamic correction mechanism based on load factors, grid time series marginal emission factors, and biodynamic parameters to achieve spatiotemporal dynamic perception of carbon footprint. Also, this paper takes the dynamic accounting results as input and constructs a multi-objective optimization model with clean energy substitution rate as the decision variable. Finally, this paper uses NSGA-II (Non dominated Sorting Genetic Algorithm II) and elite strategy to solve the Pareto front, achieving closed-loop collaboration between accounting accuracy and multi-dimensional objective trading operations. The experimental results show that the average absolute percentage error of the carbon footprint time series prediction method in this paper is 2.01%; the unit emission reduction cost is 40.97 yuan/ton of carbon dioxide equivalent; the tourist satisfaction index reaches 0.87. The method proposed in this paper combines triple dynamic factor correction with three-dimensional NSGA-II multi-objective optimization, significantly improving the accuracy of carbon footprint calculation in the forestry tourism supply chain, and formulating emission reduction strategies that consider environmental, economic, and social benefits.
