TL;DR
This paper draws a sharp and necessary distinction between superficial greenwashing and genuine sustainability, arguing that meaningful progress requires grounding AI systems and services in four non-negotiable operational principles: Redesign, Anticipate, Redistribute, and Envision.
As organizations leverage advanced digital technologies to drive sustainability – a convergence recognized as the “twin transition” – they face heterogeneous and conditional risks. This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes. It aims to provide executives with a “Responsible Intersectional Techno-Leadership” (RITL) playbook to govern these socio-technical complexities. Rather than a general literature review, this conceptual paper employs a targeted synthesis of recent discourse across twin transition dynamics, AI ethics, and strategic management. It integrates intersectionality theory into upper echelons logic to construct an actionable, context-aware leadership methodology suitable for diverse global environments. The analysis identifies a critical governance blind spot: the integration of digital and green strategies is not uniformly beneficial. Without intersectional oversight, AI tools can reproduce systemic historical inequalities, leading to what is formalized here as “algorithmic greenwashing.” The proposed RITL framework offers three operational levers for executives: Cognitive Audits (Strategic Awareness), Intersectional Talent Architecture (Structural Participation), and The Accountability Loop (Process Governance). To address the operational ambiguity of the twin transition, the paper provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers. It offers concrete mechanisms to audit their AI supply chains and foster intersectional STEM ecosystems, ensuring that digital sustainability efforts mitigate, rather than exacerbate, corporate ESG risks across varying organizational contexts. Moving beyond the established consensus that digitalization and sustainability are linked, this paper contributes by problematizing the twin transition. It reframes intersectional inclusion not as a compliance cost, but as a strategic asset and risk-management firewall, offering a nuanced approach to AI governance that ensures sustainable futures are equitable.
Transitioning toward a circular and regenerative economy requires more than technological innovation; it demands a shift in the underlying systems that shape how value, knowledge, and legitimacy are defined [Meadows D, Randers J, Meadows D, The limits to growth: the 30-year update, Routledge (2012); Raworth K, Doughnut economics: seven ways to think like a 21st-century economist, Chelsea Green Publishing (2017)]. While the circular economy (CE) is widely promoted as a pathway to sustainability, its implementation across industrial sectors remains uneven and frequently limited to efficiency-driven, technical interventions [Voulvoulis N et al., Glob. Environ. Change 75:102544 (2022)]. This paper examines the systemic conditions that constrain the adoption of circular economy principles through a case study of the craft distilling industry in regional Victoria, Australia. Guided by Sposito’s (2021) Rational–Holistic Model of Planning and Decision-Making, the study applies a multimethodological systems approach, integrating thematic analysis, boundary critique, and soft systems tools (PQR and CATWOE). This enables an exploration of how system structures - particularly those relating to motivation, control, knowledge, and legitimacy - shape what is considered feasible and acceptable in practice. The findings reveal that barriers to circular economy implementation are not solely technical or economic but are embedded within broader institutional and cultural frameworks. Regulatory fragmentation, compliance-driven definitions of legitimacy, and economically dominant worldviews constrain the uptake of more regenerative practices. By contrasting existing (“is mode”) and alternative (“ought mode”) system configurations, the study highlights the importance of reconfiguring underlying assumptions about system purpose and governance. The paper contributes methodologically by demonstrating how systems thinking combined with thematic analysis can reveal structural constraints, and conceptually by positioning CE as a pathway dependent on a broader shift toward a sustainability-oriented worldview.
As artificial intelligence (AI) becomes deeply embedded in human and organizational life, debates about Sustainable AI demand renewed ethical reflection. Current approaches often remain fragmented—separating AI for Sustainability, such as climate-mitigation applications, from the Sustainability of AI, including the intensive resource use and discriminatory effects of AI systems themselves—and lack a coherent moral foundation. This article addresses Sustainable AI as an integrated ethical concept, rather than focusing narrowly on either strand, and reframes it through the lens of virtue ethics, emphasizing human flourishing, moral agency, and the pursuit of the common good. Complementing this perspective with the Human-Centered AI (HCAI) paradigm, we develop an integrated governance framework that incorporates MacIntyrean categories of practices, institutions, and traditions while explicitly addressing the environmental, socio-cultural, economic, and organizational dimensions of the common good. In this framework, these four dimensions identify the substantive goods that Sustainable AI should protect and promote, while the micro (practices), meso (organizational and governance structures), and macro (cultural-historical traditions) levels specify the constructive conditions through which such goods are discerned, coordinated, and sustained over time. The framework illustrates how virtues can guide the responsible design, deployment, use, and oversight of AI systems. Our contribution is threefold: we advance Sustainable AI ethics by grounding it in virtue ethics and the common good; we extend business ethics by introducing a virtue–HCAI approach to governance; and we bridge theory and practice through a multi-level model that offers conceptual clarity and ethical orientation for long-term human flourishing.
It is argued that considerations of AI ethics must extend beyond models and data to encompass the hardware infrastructures on which they depend, and that embedding stakeholder reflection is critical for anticipatory governance in the physical infrastructure of AI.
The rapid proliferation of electronic devices has made electronic waste (e-waste) one of the fastest-growing waste streams globally, yet only a fraction is formally collected and recycled. The circular economy (CE) offers a transformative framework for rethinking e-waste systems, but a persistent implementation gap separates theoretical potential from on-the-ground reality. This paper provides a narrative thematic review that synthesizes cross-disciplinary evidence to map the co-evolution of e-waste management practices, business models, and policy instruments under a CE lens. Drawing on academic literature, policy documents, and industry reports from 2010 to 2025, the review examines current practices across collection systems, reverse logistics, and formal-informal sector dynamics. It identifies systemic barriers spanning product design, collection inefficiencies, and processing limitations, revealing how failures in one domain, such as weak eco-design or fragmented regulation, amplify challenges elsewhere, creating self-reinforcing lock-ins. The analysis then explores emerging enablers, including technological innovations (hydrometallurgy, bio-hydrometallurgy, automated disassembly, digital product passports), policy instruments (extended producer responsibility with eco-modulated fees, right-to-repair legislation), circular product design strategies, and economic incentives (deposit-refund schemes, blended finance, public procurement). The central finding is that isolated interventions are insufficient; coordinated, systems-level approaches are required to bridge implementation gaps. The paper concludes with actionable recommendations for policymakers, recyclers, and consumers, alongside a forward-looking research agenda to advance CE-based e-waste management.
Despite growing policy interest, global circularity is declining — falling to just 6.9% in 2025. This paper argues that a key reason is the absence of a concrete vision of what a circular economy would look like as a physical place. To provide that vision, the argument traces a line across three bodies of work. Bauwens et al.’s (2020) scenarios of bottom-up sufficiency and peer-to-peer circularity offer the greatest potential for community empowerment. When pursued through a systematic application of circular economy principles to food, water, energy, and housing, these scenarios converge on a settlement model: the Circular Economy Village (CEV). Instantiating bottom-up sufficiency, the CEV is a walkable, mixed-use precinct in which resources circulate locally as a self-reinforcing system — eliminating waste, regenerating nature, and providing food, water, energy, and housing through integrated, community-governed infrastructure. Embodying peer-to-peer circularity, a network of CEVs can scale to constitute a global circular economy consistent with Herman Daly’s steady-state economy and the economy in equilibrium anticipated by the Limits to Growth Report (1972). The paper introduces a three-dimensional model of circularity — temporal, spatial, and relational — and proposes that a circular future can look like a network of Circular Economy Villages.