Aug 2026· Strategy & Leadership· pp. 1-16· 0 citations· 40 references
TL;DR
This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes, and provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers.
Abstract
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.
The convergence of artificial intelligence (AI) and global climate action has generated a transformative "AI–climate nexus" that is reconfiguring the architecture of international relations. This study investigates how AI redefines climate diplomacy, accelerates the energy transition, and reshapes geopolitical power dynamics. Utilizing a qualitative methodology comprising systematic document analysis of 127 policy and academic sources alongside four comparative case studies, the research identifies three core dynamics. First, while AI enhances emissions verification and renewable grid optimization, it introduces new vulnerabilities, including digital infrastructure lock-in and the substantial carbon footprint of computation. Second, AI drives geopolitical restructuring by shifting leverage toward states with computational supremacy and control over critical mineral supply chains. Third, it exposes severe institutional fragmentation and equity deficits, where the Global South faces acute risks of data colonialism and algorithmic marginalization. Concluding that current polycentric governance regimes are ill-equipped to manage these intersecting crises, the paper advocates for a dedicated, equity-centered multilateral framework, such as a UN AI–Climate Governance Interface. Ultimately, the study argues that aligning emerging technology governance with the Paris Agreement requires embedding the principle of common but differentiated responsibilities into global AI policy.
Hina Ahmad, Kinza Kamran, Fizzah Muhammad· Journal of Global Social Tra...· 1 citation
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.
Dulce M. Redín, M. C. Ames· Journal of Business Ethics· 0 citations
This paper addresses the strategic tensions arising from the convergence of Generative AI (GenAI) and global multipolarity. It proposes a conceptual framework integrating holistic cultural wisdom to navigate the opacity and contextual challenges of modern algorithmic governance.
Adopting a conceptual approach, this study synthesizes literature on strategic leadership, explainable AI (XAI) and cross-cultural cognition. It juxtaposes the efficiency-driven, linear logic of algorithmic decision-making with high-context, holistic epistemologies to construct a “Symbiotic Stewardship” model.
Traditional Western-centric leadership models are insufficient for managing the “black box” of GenAI in a culturally fragmented global landscape. The proposed framework argues that indigenous and holistic cultural wisdom serves as a critical ethical governor. It enables leaders to balance human–AI augmentation, mitigate technological opacity and achieve contextual resonance across diverse institutional environments.
Responding to the call for phenomenon-based research, this paper bridges high-tech algorithmic strategy with high-touch cognitive sociology. It introduces the concept of “Algorithmic Stewardship,” redefining executives not merely as resource orchestrators, but as cultural translators who harmonize machine computation with human relational ethics.
Zhiyin Xiao, Guangfei Wu· Journal of Strategy and Mana...· 0 citations
The purpose of this study is to examine the complex challenges to sustainability in the built environment and uncover their interlinked causal relationships.
This study applies grey influence network analysis (GINA) and its novel interpretative extension (iGINA) to model, quantify and prioritise the interrelationships among barriers using expert judgements.
The iGINA analysis revealed misaligned stakeholder incentives as the most influential barrier, followed by high initial cost, while technological readiness ranked lowest, challenging tech-centric assumptions.
Expert-driven data limits generalisability, but advances grey systems theory for uncertain, large-scale networks.
This study prioritises realigning incentives via performance-based contracts, green bonds, life cycle costing and regulatory sandboxes − breaking economic barriers for scalable green adoption.
iGINA uncovers feedback loops and leverage points overlooked by traditional methods, offering a blueprint for managers, policymakers and researchers.
Drisya Murali, S. M., R. Raman· Construction Innovation· 0 citations
Existing AI governance frameworks address technical alignment, data protection, and regulatory compliance, but not the interior developmental capacity of the humans who govern AI-influenced decisions: the human readiness problem. This paper presents the Emergence Mirror Framework (EMF), an architecture that integrates Kegan’s constructive-developmental theory (1994), Kohlrieser’s secure base theory (2006), and Scharmer’s Theory U (2009) into a unified seven-layer developmental scaffold oriented toward discernment as the apex governance capacity. The EMF advances a specific, testable claim: Kegan’s fourth-order self-authoring mind constitutes the minimum developmental threshold for effective AI stewardship, operationalising what human rights and legal scholarship has theorised but not rendered actionable. The framework was developed through a seven-month phenomenological inquiry and a structured proof-of-concept study with 22 participants across four cohorts (n = 22; descriptive, not statistically generalisable), using multi-source triangulation of developmental surveys, AI dialogue transcripts, and facilitator observations. Participants ranged from late third-order to mid fourth-order profiles, testing the framework across the readiness gap it identifies. Nineteen of 22 participants retained decision sovereignty, 18 of 22 demonstrated early interrogation of AI outputs, and 20 of 22 articulated sovereignty boundaries, consistent with the moral performance indicators the EMF predicts (Kristjánsson et al., Rev Gen Psychol 25:239-255, 2021). A participant at the third-to-fourth order transition experienced the predicted developmental friction whilst governance integrity remained intact. Individual posture profiles, visualised through a Leadership Posture Heat Map (Sect. 4.3.4), reveal variation aggregate statistics alone cannot capture. The paper further demonstrates that an AI mirror as reported by Vallor (The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking, Oxford University Press, Oxford, 2024) can function as a developmental instrument: relationally grounded engagement oriented toward resonance, rather than replacement, can deepen discernment and strengthen sovereign decision-making rather than producing moral deskilling.
Arulnageswaran Aruleswaran, Thaatchaayini Kananatu· AI and Ethics· 1 citation
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.
Naira Paola Arnez-Jordan, Chiara Ullstein, Michel Hohendanner et al.· 0 citations