The Technological Dimension of Sustainability: A Conceptual Perspective on Governability and Resilience Under Tech4.0

Abstract

Technology is increasingly central to sustainability, yet frameworks built around the environmental–social–economic (E–S–Ec) triad and ESG disclosure regimes do not fully capture the governance problems created by interconnected digital and cyber–physical infrastructures. In this conceptual paper, Tech4.0 is used in a deliberately narrow working sense, focusing on AI-mediated decision systems, data/platform/cloud infrastructures, software dependency chains, and cyber–physical control environments in which opacity, infrastructural dependence, interdependence, and cascading failures create distinctive problems of governability and resilience. Against this background, the paper examines whether making the technological dimension explicit adds analytical value within sustainability architecture. It examines the case for treating Technological Sustainability (T) as a distinct analytical dimension/pillar insofar as it foregrounds system properties of the Technosphere that tend to be diluted when distributed across environmental, social, and economic categories. The paper then discusses the hierarchy T → Corporate Technological Responsibility (CTR) → Corporate Digital Responsibility (CDR) as a possible corporate-level operational pathway and outlines an exploratory measurement agenda structured around exposures, capabilities, and outcomes. Rather than offering empirical proof or a validated reporting architecture, the article provides a conceptual research program for later empirical inquiry into technological accountability under Tech4.0 conditions.

1. Introduction

The concept of sustainable development emerged as a response to the post-war industrial trajectory of accelerated economic growth and corporate expansion, which was accompanied by a sharp increase in anthropogenic pressure on biophysical systems (the Great Acceleration) while institutions for environmental regulation and accountability mechanisms developed more slowly [1,2,3]. Against this background, the normative core of sustainability was articulated in the Brundtland Commission’s definition: development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” [4], thereby establishing a framework of intergenerational justice and development constraints.

Subsequent corporate operationalization of sustainability led to methodological simplification through the triad of environmental, social, and economic dimensions and to the institutionalization of a balancing logic via the triple bottom line approach [5] and its associated management and reporting regimes. As a result, E–S–Ec became the dominant framework for articulating sustainability: it stabilized the language of trade-offs and externalities, enabled comparability of disclosures, and translated sustainability into a manageable agenda for corporate and public governance.

At the same time, technology has always evolved alongside civilization as a distinct domain of practices and artifacts—from tools of production and housing infrastructure to computing and communication systems. The Third Industrial Revolution (the internet, mobile communications, and large-scale data transmission and storage) substantially intensified the technological mediation of economic and social processes: coordination and scaling became faster and more complex, and new dependencies on infrastructures and standards emerged [6,7]. In this context, technology is no longer a neutral set of tools; it increasingly functions as an environment—the Technosphere—through which core functions of the economy and social institutions are conducted [8,9,10,11].

The shift to Tech4.0 (AI, big data, platforms, cloud infrastructure, IoT, and robotics) amplifies this trajectory: the Technosphere is increasingly described as a quasi-autonomous, complex, open, non-equilibrium dynamic system with high connectivity, nonlinear effects, and rapid replication and scaling of solutions [12,13]. In this paper, Tech4.0 is used in a deliberately narrow working sense. It refers not to all digitalization-related phenomena, but to interconnected digital and cyber–physical infrastructures whose governance relevance arises from opacity, infrastructural dependence, interdependence, and the potential for cascading failures. In this sense, the focus is on AI-mediated decision systems, data/platform/cloud infrastructures, software and dependency chains, and cyber–physical control environments, rather than on “everything digital” associated with contemporary technological change. Crucially, the unit of analysis is not individual technologies but their networked interdependence and infrastructural criticality, which generate systemic externalities. Accordingly, the paper does not attempt to cover all technology-related sustainability issues. It focuses only on those socio-technical configurations in which opacity, interdependence, and infrastructural dependence create distinctive problems of governability and resilience. Consequently, technological development introduces new classes of systemic constraints and risks. Algorithmically mediated decisions make it harder to reconstruct causal chains and validate assumptions, reducing the observability and controllability of outcomes [14,15,16]. Networked and interdependent infrastructures also increase the likelihood of cascading failures and shock propagation across interconnected systems, while attacks and disruptions can travel through supply chains and digital dependencies [13,17,18]. The technological environment therefore strengthens interlinkages among the environmental, social, and economic pillars and is shaped by feedback from them; sustainability is increasingly determined by whether these interactions remain compatible over the long run [12].

In response to these dynamics, new regulatory and governance regimes that treat “trustworthy” and “human-centric” technologies as prerequisites for the legitimacy and governability of digital solutions are emerging. The European Union, in particular, has institutionalized a risk-based approach to AI [19], which aims to protect health, safety, and fundamental rights while promoting reliable, human-centric AI systems. In parallel, the cyber-resilience framework has been strengthened through NIS2 [20], which sets common requirements for cybersecurity risk management and incident response in critical sectors. At the same time, the governance of data ecosystems is being developed through the Data Governance Act [21], designed to increase trust in data sharing and re-use within European data spaces. At the level of management tools, standards for AI risk control are also advancing, including the NIST AI Risk Management Framework, which proposes a functional structure for risk governance (govern–map–measure–manage) to embed a trustworthiness approach across the AI system lifecycle [22].

Despite these strengthening—yet still fragmented—AI, data, and cyber governance regimes across jurisdictions, mainstream sustainability frameworks continue to reproduce the E–S–Ec triad, while technological issues remain dispersed across separate themes (digital ethics, privacy, cybersecurity, and AI governance). The EU provides one of the most institutionalized examples of this regulatory direction, combining a risk-based regime for AI [19], a legal framework for data protection [23], and reinforced cybersecurity requirements for critical sectors, thereby illustrating an expanding EU policy mix for sustainability transitions [20,24].

At the same time, even as sustainability reporting in the EU becomes more stringent and institutionalized through the Corporate Sustainability Reporting Directive [25] and the introduction of the European Sustainability Reporting Standards [26], technological impacts are not framed as a distinct, conceptually coherent pillar of sustainability. In practice, they typically appear as cross-cutting themes within existing disclosure structures rather than as a separate, measurable domain with clear boundaries, criteria, and verification procedures. This creates a methodological and managerial gap: environmental, social, and economic dimensions have relatively mature descriptive languages and accountability instruments [25,27,28], whereas technological effects lack a comparably specified architecture that enables responsibility contours, auditability, and consistent assessment. This paper refers to this mismatch as the Tech4.0 governance gap (see Section 3.4). It comprises interlinked conceptual and institutional dimensions, reflecting, respectively, insufficient specification of the technological dimension of sustainability and the lag of governance, metrics, and accountability regimes behind the pace and scale of Technosphere dynamics.

Against this background, the research problem addressed in this paper is not whether technology can be separated from environmental, social, and economic sustainability concerns in a strict and fully non-overlapping sense. Rather, it is whether making the technological dimension explicit adds analytical value by foregrounding system properties of socio-technical configurations that become increasingly consequential under Tech4.0—namely governability, resilience, infrastructural dependence, and cascading socio-technical risks [29]. On this basis, the paper develops a conceptual perspective that goes beyond existing CDR-oriented discussions by linking digital responsibility to a broader sustainability architecture rather than treating it only as a specialized corporate ethics domain.

The aim of this paper is to clarify the analytical role and contours of the technological dimension of sustainability under Tech4.0 conditions and to discuss its possible translation into more structured contours of accountability and measurement. Accordingly, the paper is guided by the following research question: To what extent does the explicit recognition of the technological dimension add analytical value for understanding governability, resilience, infrastructural dependence, and cascading socio-technical risks under Tech4.0 conditions?

The paper makes three main contributions. First, it clarifies why the technological dimension may merit more explicit analytical recognition within sustainability debates under Tech4.0 conditions, particularly in relation to governability and resilience. Second, it develops a conceptual bridge between the system-level technological dimension and corporate-level responsibility through the T → CTR → CDR architecture. Third, it outlines an exploratory agenda for further research and measurement by distinguishing exposures, capabilities, and outcomes as analytically separate but related elements.

The remainder of the paper is structured as follows. Section 2 outlines the conceptual and methodological approach. Section 3 reconstructs the evolution of sustainability and develops the problem framing around the Tech4.0 governance gap. Section 4 advances the argument for treating T as a distinct analytical pillar/dimension and introduces the architecture T → CTR → CDR. Section 5 presents exploratory propositions, measurement cues, implications, and directions for future research.

2. Conceptual and Methodological Approach

This paper is conceptual and exploratory. It relies on theory-driven conceptualization supported by a structured reading of conceptually and institutionally foundational sources. Bibliometric mapping is used only as a navigation and diagnostic tool to reveal field fragmentation and thematic dispersion. The starting point is an analytical problem identified in the literature and in current reporting practices: the classic E–S–Ec triad still operates “above” the Technosphere, while a substantial share of Tech4.0 effects and risks is difficult to capture without analytical dilution when treated only within environmental, social, or economic categories. Instead, they relate to the governability/controllability and resilience of socio-technical systems as such. In this paper, governability is used as an umbrella category that includes observability, auditability, correctability, and procedural accountability; its operational components are specified in Section 4. Accordingly, the paper does not claim to empirically establish the existence of an objectively separate fourth pillar. Its methodological purpose is to examine whether the explicit recognition of the technological dimension improves conceptual clarity and supports a more coherent discussion of governability- and resilience-related issues under Tech4.0 conditions.

The conceptual argument is developed through the following analytical sequence.

First, we conceptually reconstruct the evolution of sustainability as an expanding set of goals, norms, and tools for corporate governance and reporting—from an environmental focus to the integration of social and governance dimensions and the institutionalization of an ESG architecture (Section 3.1).

Second, we develop the problem framing around the Tech4.0 governance gap. We explain why technological risks and impacts—including, for example, algorithmic opacity, cyber resilience, and platform dependence—are dynamic, scalable, and characterized by network externalities. These properties suggest limits in how fully the traditional E–S–Ec triad can represent them without analytical loss (Section 3.2, Section 3.3 and Section 3.4).

Third, we examine the case for treating T as a distinct analytical dimension within an expanded sustainability framework (Section 4.1). The paper draws a clear distinction between (i) sustainability architecture as a system of long-term viability domains (E–S–Ec and the proposed T), and (ii) the architecture of accountability and disclosures (ESG), where “G” functions as a procedural mechanism for control and assurance. Within the proposed perspective, T is discussed at the first level (as a sustainability dimension/domain), while its possible corporate operationalization is explored at the second level through CTR/CDR.

Fourth, we clarify the core analytical focus of T and discuss the contours of the T → CTR → CDR architecture as a possible multi-level pathway of operationalization. Here, T provides the system-level frame; CTR is discussed as a corporate responsibility contour for technological impacts (policies, processes, controls, disclosure and assurance); and CDR as a specialized digital sub-layer within CTR (Section 4.2).

Finally, we move toward exploratory operationalization and practical interpretation. Rather than claiming empirical validation within this paper, we outline a preliminary research and measurement agenda for subsequent work and for possible use in governance and reporting (i) by formulating exploratory propositions and the capability logic of CTR, (ii) by developing initial measurement cues (operationalization logic), (iii) by discussing managerial and regulatory implications in conditional terms, and (iv) by outlining directions for future research (Section 5).

The evidence base is formed at the intersection of three streams: (i) research on sustainability/ESG and corporate responsibility and reporting; (ii) studies on digital transformation, AI and data governance, and technology-related risks; (iii) standards, regulatory acts, and methodological documents that define accountability and disclosure requirements in the technological domain. Given the high variability in search yields, we apply a combined strategy that clearly separates contextual field mapping from the conceptual argument and from the selection of a core reference corpus.

First is field mapping. We use Scopus to map large publication sets (TITLE-ABS-KEY searches) in order to identify intersections and areas of high and low research density at the interface of E/S/Ec with technology-related topics and accountability (see Supplementary Materials). Quantitative outputs (publication counts for queries and overlaps) are treated as contextual indicators of maturity or fragmentation, not as evidence for theoretical claims. Accordingly, the mapping exercise is used to illustrate fragmentation, thematic concentration, and the relative rarity of certain formulations in the literature, but not to infer the objective existence of a fourth pillar or to prove a governance gap in a strict empirical sense.

Second, selection of core references. Some queries returned hundreds or thousands of publications. We therefore use explicit but interpretive selection principles to clarify why specific works are included in the conceptual argument: (i) conceptual relevance: the presence of definitions, typologies, and accountability frameworks applicable to T/CTR/CDR; (ii) influence and representativeness: highly cited and/or methodologically foundational works within the relevant streams; where needed, “anchor” conceptual texts that structure the field; (iii) mechanisms and measurability: sources that specify governance mechanisms (governance, controls, audit/assurance) and/or elements of operationalization (indicators, disclosure requirements).

The conceptual perspective developed in this paper is structured through three analytical procedures: (i) concept analysis: clarifying the boundaries of T, CTR, and CDR; specifying the object of responsibility, relevant actors, accountability mechanisms, expected disclosure evidence, and potential metrics; (ii) framework alignment: aligning existing ESG approaches with technological effects and examining whether T is best understood not as reducible to E/S/Ec, but as a complementary analytical dimension focused on the governance of technological impacts; (iii) conceptual modeling: outlining CTR domains and the digital sub-layer CDR, as well as implementation levels (strategy → policies → processes → controls → disclosure/assurance) that may support later operationalization and measurability.

The unit of analysis is differentiated across levels. T refers to macro-level requirements for the Technosphere as an environment, while CTR/CDR capture the micro-level of reproducible corporate accountability contours. The linkage between these levels is specified through propositions and a measurement agenda. First, propositions link the maturity of CTR/CDR to observable governance and sustainability outcomes, including the quality of technology governance and accountability, stakeholder trust, investor transparency, and business-model resilience to technology-related risks. Second, the measurement agenda translates CTR/CDR domains into measurable variables using the logic exposures → capabilities → outcomes, where (i) exposures describe the structural risk profile (connectivity, criticality, dependency, and related features), (ii) capabilities capture reproducible CTR/CDR contours (governance, controls, audit/assurance, contestability, and related mechanisms), and (iii) outcomes reflect observable dynamics of technology incidents and failures, accountability quality, trust-related effects, and similar indicators.

3. Evolution of Sustainability and the Tech4.0 Governance Gap

3.1. From Environmental Concern to a Triadic Sustainability Architecture: Institutionalization Without Strict Theoretical Closure

The post-war trajectory of economic growth and corporate expansion was accompanied by the accumulation of negative externalities and a rising public demand to limit harm and reallocate responsibility [1]. In the initial stages, these expectations were articulated mainly through the discourse of corporate responsibility and through the gradual development of environmental and social regulation [30,31]. Until the end of the twentieth century, however, sustainability remained largely an umbrella idea: a set of normative expectations and partial practices that had not yet converged into a unified architecture of goals, criteria, and comparable management tools.

The normative core of sustainability was institutionalized in the Brundtland Commission report, which defined sustainable development as development that meets the needs of the present without compromising the ability of future generations to meet their own needs [4,32]. Importantly, this formulation shifts the problem from “local ecology” to a universal principle of constraint and responsibility. It establishes a framework in which development is treated as a managed balance between goals, resource limits, and intergenerational obligations.

The next stage in this evolution was the operationalization of sustainability for management and corporate settings. In the 1990s and early 2000s, a key methodological simplification took place: sustainability was framed through three interrelated dimensions—environmental, social, and economic—crystallized in the triple bottom line approach [5,33]. The strength of the triadic scheme was that it made a normative idea governable. At the same time, the rise of the three-pillar model should not be read as the outcome of a single canonical text that definitively fixed final and non-overlapping boundaries between the environmental, social, and economic domains. Rather, the triad gained prominence because it offered a workable heuristic and managerial architecture for organizing sustainability discourse, reporting, and trade-off analysis, even as its theoretical demarcation remained open and contested [32]. It helped structure externalities, clarify trade-offs, set priorities, and, critically, support comparable practices of disclosure and control. As a result, E–S–Ec became less a fixed theoretical map than a widely used coordinate system for translating sustainability into corporate policies, strategies, and reporting.

As reporting infrastructures developed, the triad also gained institutional strength. Sustainability became increasingly tied to formal disclosure regimes, ratings, indices, and standards that enabled comparability and reinforced external accountability [34,35,36]. In this institutionalized form, environment, society, and economy were often represented as separate though connected domains. Yet, in practice, these domains remained internally heterogeneous and multi-layered, which helps explain why the triadic model retained managerial usefulness while also attracting recurring theoretical criticism [32]. Against this background, ESG assessment practices expanded and integrated sustainability into financial and managerial decision processes. They also broadened the set of stakeholders for whom sustainability became an object of evaluation [31]. At the same time, critical scholarship highlighted limitations of the triadic logic and disclosure practices. It pointed to risks of reducing complex systemic interdependencies to a simplified “balance of three dimensions,” difficulties of causal attribution, and incentives for selective disclosure [37]. Another line of critique concerns ESG rating infrastructures, where substantial divergence across providers and limited cross-provider comparability have been documented [38]. Nevertheless, despite unresolved conceptual ambiguities, the triadic architecture provided practical reproducibility. It enabled metrics, comparisons across firms and industries, and the integration of sustainability into corporate governance routines.

In the 2010s and 2020s, sustainability evolved further toward “hard” institutionalization. It increasingly became a matter of accountability and formal obligations rather than voluntary responsibility. A clear signal is the tightening of reporting regimes, including the move toward standardized metrics and compliance-oriented logics in major jurisdictions [39]. In this phase, E–S–Ec became firmly embedded as the dominant framework. It functions as the baseline matrix for categorizing impacts and risks, onto which specialized topics and tools are layered. In this form, sustainability shifts from a normative concept to an institutionalized governance infrastructure—with its own language, procedures, indicators, and external verification regimes [40].

The institutional strength of E–S–Ec lies in its ability to translate diverse externalities into comparable domains of goals, metrics, and accountability. Yet that strength also produces path dependence. Once the triad becomes the default architecture, new sustainability phenomena tend to be translated into its existing vocabulary, even when their underlying mechanisms cut across or exceed the established categories. In the Tech4.0 era, this architecture increasingly encounters systematic limitations. The technological environment is not only a channel for pursuing E/S/Ec objectives; it is also a source of risk dynamics and system properties with high scalability and network-based effects. Accordingly, the problem addressed in the next subsection is not that the classical pillars were ever perfectly pure or non-overlapping, but that important socio-technical properties become analytically diluted when treated only as scattered subtopics within the triad.

3.2. Why Tech4.0 Technological Risks and Impacts Are Not Easily Reducible to E–S–Ec

As used in this paper, Tech4.0 does not denote all forms of digitalization. It refers more specifically to those infrastructural and socio-technical configurations in which AI-mediated decisions, data/platform/cloud dependencies, software supply chains, and cyber-physical control systems become deeply interdependent and governance-relevant. Under these conditions, the technological environment is no longer an external add-on; it increasingly constitutes part of the core operational layer through which markets, organizations, and institutions function. In this sense, the Technosphere can be understood as a complex, dynamically evolving system [8,12,41]. The Technosphere is described as a “global assemblage of human-created buildings, infrastructure, machinery, and other artifacts [12] and as a system in which humans are “essential but… subordinate parts” [8].

In this setting, sustainability is increasingly determined not only by the magnitude and distribution of environmental, social, and economic effects, but also by whether socio-technical systems remain governable, observable, and correctable as connectivity and complexity grow [13,42]. Two patterns are particularly salient. First, oversight and regulatory mechanisms often lag behind the pace of Technosphere dynamics [43]. Second, existing measurement and reporting regimes have limited capacity to identify and represent technological risks and impacts in a comparable, disclosure-ready format [44].

The core reason is that a substantial share of technology-induced risks and impacts have properties that do not fit the E–S–Ec triad as a classifier of externalities. For the purposes of this paper, the emphasis is placed not on every downstream consequence of digitalization, but on a narrower class of system properties: distributed causality, opacity of mediated decisions, infrastructural dependence, interdependence across critical layers, and cascade-prone vulnerability. These properties stem from endogenous digital dynamics, network propagation mechanisms, and limited observability of causal links.

First, the Technosphere exhibits a degree of autonomization and internal dynamics. In digital environments, a substantial share of change is reproduced endogenously through software update cycles, the co-evolution of platform ecosystems, the standardization of interfaces, data migration, and dependence on computing resources [45,46,47]. As a result, technological risks arise (i) as side effects of discrete managerial decisions; (ii) as outcomes of persistent infrastructural and ecosystem trajectories characterized by distributed control and limited capacity for rapid reconfiguration within a single organization [48].

Second, technological effects are highly scalable and characterized by network externalities. Data- and algorithm-based solutions replicate easily and diffuse through platform ecosystems, where network effects intensify inter-organizational interdependence [13,49]. Consequently, even minor changes in model architecture, data-access rules, or platform parameters can generate “ripple effects” that quickly extend beyond a single organization and affect connected sectoral structures [49,50]. In such environments, risks and impacts often emerge nonlinearly and cascade through interdependencies, reflecting network structure rather than simply the “average” level of exposure [13,17].

Third, Tech4.0 intensifies governability challenges by weakening the observability of causality. Algorithmically mediated decisions complicate attribution because causal chains become distributed between humans and systems—some steps are performed by an algorithm, others by a person—while the validation of assumptions and the assessment of inference quality become less transparent [14,16]. Algorithmic opacity and systematic bias therefore represent not only social concerns, but also constraints on technological controllability: when decisions cannot be reliably explained and checked, risk management becomes less effective and system correctability harder to ensure [15,51]. Similarly, data and privacy issues exceed formal compliance: violations can scale through infrastructures and erode trust, which undermines the viability of digital arrangements [52,53].

Fourth, platform dependence and the concentration of infrastructural power reshape the conditions of sustainability. In Tech4.0, the performance and reproducibility of business models increasingly depend on external digital infrastructures—cloud services, platforms, data providers, standards, and tool stacks. Sustainability therefore depends not only on internal firm processes, but also on the structure of technological dependencies and on how control over critical digital components is distributed [54,55]. Within a triadic framing, such dependencies tend to be allocated across Ec (efficiency and competition), S (trust, rights, fairness), and E (resource burdens). The systemic risk, however, lies in the potential loss of governability when providers change rules, restrict access, or experience disruptions, because even modifications to interfaces and access rules (e.g., APIs) can trigger failures and “ripple effects” across ecosystems of dependent applications and services [50].

Fifth, cyber resilience is a system-level property of networked environments. Cyber risks are inherently transboundary and cascade-prone, with incidents spreading through supply chains, connected services, and interdependent systems [56,57]. In such cases, harm is not limited to direct losses; it also includes functional disruption, process stoppages, increased uncertainty, and secondary effects across adjacent sectors [18]. Sustainability in this domain therefore refers to the capacity to maintain critical functions and recover quickly after shocks [58,59], which is difficult to operationalize within standard E/S/Ec metrics without losing essential system-level meaning.

Sixth, Tech4.0 produces social restructuring as a dynamic effect of technological trajectories. It changes occupational structures, skill requirements, and coordination mechanisms [60,61], with uneven impacts across sectors and worker groups. These shifts can intensify polarization and generate new inequalities related to access to skills, data, and infrastructures [62,63,64,65,66]. Social consequences therefore cannot be treated as a static parameter of the S pillar; they depend on the pace, governability, and predictability of transition and on the adaptive capacity of socio-technical systems facing the Technosphere’s dynamic change.

Seventh, the Technosphere also has a material and energy footprint. In this paper, however, this aspect is treated as a supporting boundary condition rather than as the analytical core of T. Digital infrastructures remain physically grounded in computing energy use, hardware demand, supply chains, and electronic waste [67,68,69,70], and these constraints shape the feasible trajectories of socio-technical systems [71,72]. Yet the primary focus here remains on governability- and resilience-related system properties rather than on reclassifying environmental performance indicators as belonging to T.

Eighth, institutional lag and fragmented metrics further constrain reducibility to E–S–Ec. The triad and associated ESG reporting regimes historically evolved in response to industrial externalities and relied on relatively stable measurement domains where indicators, methods, and assessment boundaries could be standardized over time [4,5,34,44]. In Tech4.0, the digital environment changes faster, exhibits stronger network effects, and operates across borders, so existing metrics and disclosure procedures often fail to capture technological risks in a comparable and auditable form [43,73]. While reporting increasingly addresses specific topics—data, privacy, cybersecurity, and digital ethics [74]—these themes remain dispersed and rarely form a coherent accountability contour with boundaries and measurement logic comparable in clarity to E–S–Ec.

Taken together, these properties of the Technosphere under Tech4.0 highlight a structural limit of the E–S–Ec triad. Technology-induced risks and impacts form a distinct class of system properties—governability, observability, correctability, and functional resilience—that cannot be adequately represented when they are allocated only to E/S/Ec. The next subsection shows that the literature is developing partial contours of technology governance and “digital responsibility,” yet these remain insufficiently consolidated into a coherent pillar-based architecture.

3.3. Signals in the Academic Literature: Digital Sustainability and a “Fourth Pillar”

Scopus-based mapping indicates that the intersection of sustainability with Tech4.0 topics has already generated a large and rapidly expanding research corpus. As of 5 January 2026, queries capturing the broad background of digital transformation and Tech4.0 in a sustainability context return on the order of 9–11 thousand documents. For example, there are more than 9 thousand records on digital transformation, around 9 thousand on Industry 4.0/the Fourth Industrial Revolution, and about 11 thousand on the twin transition/green-and-digital agenda (see Supplementary Materials, Figures S1–S3). This pattern suggests that technological issues have become firmly embedded in the sustainability discourse and are increasingly treated as a material driver of changes in sustainability goals and instruments. For the purposes of the present argument, however, this broad corpus serves only as background context. The analytical core of the paper is narrower and centers on those technological configurations in which interdependence, opacity, and infrastructural dependence raise distinctive governability and resilience issues.

Once the focus shifts from this broad background to questions of governability and accountability, a characteristic field structure becomes visible. The technological dimension is represented mainly through partial governance contours organized around specific risk classes, rather than through a coherent pillar architecture comparable to E–S–Ec. This pattern is particularly clear in the case of AI and algorithmic systems. The body of work that links AI and machine learning to sustainability through the language of governance, risk, ethics, and responsibility exceeds 11.5 thousand documents. Meanwhile, more “strict” branches that address algorithm governability in terms of transparency, explainability, accountability, and audit (e.g., “algorithmic transparency”, “algorithmic accountability”, “AI audit”, “model audit”) form a much smaller segment (over 2.1 thousand), as do studies focusing on bias, fairness, and discrimination in the “AI + sustainability” nexus (over 1.5 thousand) (see Supplementary Materials). This decomposition suggests that even with strong overall interest in AI-for-sustainability, governability and verifiability of algorithmic decisions are institutionalized as a set of specialized topics rather than as a unified sustainability contour.

A second major domain linked to Technosphere governability concerns data, privacy, and data governance. The broad body of work connecting data governance, data rights, privacy, and data protection (including GDPR) to sustainability exceeds 8.3 thousand documents. The overlap between privacy/data protection and the ESG disclosure logic (e.g., “ESG”, “sustainability reporting”, “disclosure”) is about 11.5 thousand. In contrast, the segment in which data governance is connected to a “hard” language of accountability, assurance, and audit in a sustainability context remains narrow—222 documents (see Supplementary Materials). This indicates that data and privacy are actively institutionalized at the level of topics and norms, but less often translated into verifiable procedures for comparable control and disclosure.

Despite large literature on Tech4.0 and on specific governance themes, direct attempts to conceptualize a technological dimension as a fourth pillar of sustainability remain rare. As of 5 January 2026, the query “fourth pillar” in combination with sustainability yields 85 documents, and “tech* pillar” yields 45; the combination of “tech* sustainab*” and “fourth pillar” returns no results (0), while links between “technological/digital sustainability” and “fourth pillar/four pillars” appear only once (1).

Corporate “digital” frameworks also point to fragmentation. Corporate Digital Responsibility appears as a distinct line (156 documents), but its overlap with sustainability/ESG/corporate responsibility is much smaller (65), and the linkage between “technolog*/AI/digital responsibility” and disclosure/reporting/assurance/accountability remains compact (40). This suggests that corporate digital responsibility contours exist, but they have not yet converged into a disclosure and obligation regime comparable in structure to E–S–Ec.

Overall, Scopus mapping reveals a stable configuration: (i) a large and growing body of work at the intersection of Tech4.0 and sustainability; (ii) a concentration of technological issues in specific governance risk domains (AI; data/privacy) with limited translation into auditable procedures; (iii) a rarity of explicit “fourth pillar” language. This implies that the literature typically treats technology either as a cross-cutting driver within the triad or as a set of partial contours (AI ethics, digital rights, cyber resilience), without elevating them into a distinct pillar with clear boundaries, criteria, and a coherent measurement logic.

3.4. The Tech4.0 Governance Gap: Analytical Problem Framing

As discussed in Section 3.3, technologies are often described as a cross-cutting driver that shapes the environmental, social, and economic parameters of sustainability. Yet this “cross-cutting” status also encourages analytical dilution: technology-induced impacts are routinely allocated across E/S/Ec without isolating the Technosphere’s distinctive mechanisms as an operating environment—nonlinearity and cascades, limited observability of causality, and infrastructure vulnerability. This makes it difficult to formulate comparable analytical criteria and sufficiently coherent management obligations. The gap identified in Section 3.1, Section 3.2 and Section 3.3 has two interrelated dimensions.

The first is conceptual (theoretical and methodological). Although several studies already argue that the role of technology in sustainable development requires rethinking [75,76], the field still lacks a sufficiently explicit and coherent treatment of the technological dimension within sustainability architecture. In particular, the literature remains underdeveloped with respect to (i) how the technological dimension should be specified analytically, (ii) which system properties of the Technosphere are most relevant for sustainability analysis, and (iii) how this dimension can be integrated into the broader sustainability architecture without being diluted into compliance language or a checklist of loosely connected “digital topics.”

The second dimension is practical and managerial (institutional). Governance and reporting regimes continue to lag the scale and speed of technological dynamics. At the level of corporate governance and external accountability, comparable and institutionalized mechanisms for consistently addressing technology-induced systemic risks within sustainability practice remain limited [77]. This is reflected in persistent difficulties in designing corporate policies and procedures that cover the full life cycle of technological solutions, maintain observability and control under algorithmic opacity, and rely on metrics of the technological environment that are suitable for disclosure and verification [68]. Thus, critical technological contours scale faster than the mechanisms that could ensure accountability and correctability, which increases the likelihood of cascading effects and the temporal and stakeholder redistribution of risks.

Against this backdrop, this section concludes by formulating three analytical questions that guide the transition to Section 4:

• Conceptual question: How can the technological dimension be specified so as to foreground those properties of the Technosphere—especially governability, observability, and resilience—that tend to become diluted when treated only within E–S–Ec categories?
• Architectural question: How can the technological dimension be incorporated into the broader sustainability architecture without reducing it to a dispersed set of “digital topics” or to a narrow governance overlay?
• Operationalization question: Which corporate-level governance contours and disclosure mechanisms may help translate this analytical perspective into more structured, though still exploratory, accountability practice?

Taken together, the results of Section 3.1, Section 3.2 and Section 3.3 show that (i) the E–S–Ec triad has consolidated as the dominant infrastructure of goals, metrics, and reporting, (ii) Tech4.0 generates a class of risks and impacts tied to the governability and resilience of socio-technical systems, and (iii) while these issues are widely represented in the literature, they are structured as thematic governance contours and are only rarely articulated as an explicit analytical dimension within sustainability architecture.

The Tech4.0 governance gap should therefore be understood as a dual analytical problem. First, a conceptual problem—insufficient explicit treatment of the technological dimension as a coherent analytical locus within sustainability architecture. Second, an institutional problem—the lag of governance regimes, metrics, and accountability mechanisms behind the pace and scale of Technosphere dynamics. Section 4 develops the argument for treating T as a distinct analytical pillar/dimension and introduces the possible corporate operationalization T → CTR → CDR.

4. Technological Sustainability and Corporate Technological Responsibility

Section 4.1 develops the analytical case for treating the technological axis as a distinct sustainability dimension by examining its object of governance, characteristic causal properties, normative relevance, and institutional salience. Rather than seeking strict separation, it shows why T may add analytical value beyond treating technological issues only as fragmented digital/governance topics within E–S–Ec.

Section 4.2 clarifies the core analytical focus of T and develops the argument further. It specifies the object of analysis (the Technosphere as a socio-technical environment), identifies the key system properties highlighted in this paper (especially governability/controllability and resilience), and introduces corporate operationalization through the hierarchy T → CTR → CDR.

4.1. Why the Technological Dimension May Merit Explicit Analytical Recognition

4.1.1. Pillar Criteria and the Place of the Technological Axis

In the literature, the E–S–Ec triad is often used as a convenient architecture for aggregating sustainability goals and trade-offs, yet its “pillar” nature historically developed more as a normative and managerial heuristic than as a strictly defined theoretical construct [32]. This helps explain why triad-based logic is easily institutionalized in corporate practice through standards and disclosures, while also generating persistent methodological critiques, ranging from conceptual ambiguity to fragmented measurement and limited comparability [31,37,39,44].

Accordingly, the issue is less whether T can be demonstrated as a perfectly separate and universally bounded pillar in a strict ontological sense, and more whether making the technological dimension explicit adds analytical and institutional value within sustainability architecture under Tech4.0 conditions [78]. In this paper, the argument is developed through four narrower grounds. First, sustainability architectures have historically remained open, heuristic, and revisable rather than theoretically closed once and for all. Second, making the technological dimension explicit helps foreground system properties of the Technosphere—especially governability, observability, correctability, and resilience—that tend to be diluted when treated only as scattered subtopics within E/S/Ec. Third, under Tech4.0, the technological axis increasingly operates as a cross-pillar causal domain whose effects propagate bidirectionally across environmental, social, and economic trajectories. Fourth, the growing institutionalization of AI/data/cyber governance suggests increasing practical relevance for treating these issues in a more integrated and analytically coherent way.

Section 4.1.2, Section 4.1.3, Section 4.1.4 and Section 4.1.5 develop these four grounds in turn, while also acknowledging that many technological effects overlap with environmental, social, and governance concerns in their consequences.

4.1.2. The Technosphere as a Distinct Analytical Focus: Infrastructure, Scale, and Mediated Agency

The first step in the argument is not to claim full ontological separation of technology from society, economy, or nature. Rather, it is to show that under Tech4.0 the Technosphere becomes a sufficiently distinct analytical focus because infrastructural scale, network interdependence, and mediated agency generate system properties that are not easily captured when technology is treated only as an auxiliary instrument of other domains. What matters here is not autonomy in an absolute sense, but the growing analytical salience of technological infrastructures and socio-technical configurations as objects of governance and sustainability analysis.

First, technology is material and infrastructural. From a geological perspective, the Technosphere can be understood as a system of material artifacts, infrastructures, and processes embedded in planetary flows of energy and matter [8]. This point is analytically important: the Technosphere may be approached as a large-scale infrastructural environment within the Earth system rather than only as a secondary instrument of “the economy” or “society”. Such a view shifts technology from an instrument of economic activity to a component of the Earth system that shapes constraints on human agency and exhibits inertial properties due to network interdependence and accumulated infrastructural stock [8,9]. As large-scale infrastructure, the Technosphere generates path dependence and “installed base” inertia that is difficult to reconfigure quickly. As technological systems expand and interconnect, they acquire technological momentum, while infrastructures operate as an embedded environment that remains largely invisible in everyday life and becomes visible mainly through breakdowns and conflict [79,80].

Second, the Technosphere has a measurable physical scale. Geologically oriented assessments describe the physical Technosphere as having an immense mass—on the order of tens of trillions of tons—and a heterogeneous composition that constitutes a distinct layer of anthropogenic reality [81]. Similarly, macro-level estimates of anthropogenic mass show that the cumulative mass of human-made objects (infrastructure, buildings, roads, machinery, and related artifacts) has exceeded the mass of all living biomass. This highlights not only environmental pressure, but also the structural role of the material technological layer in contemporary economies and societies [82]. In other words, modernity has a measurable material constitution. At this scale, the Technosphere becomes a carrier of long-term risks and constraints related to maintenance, wear, renewal, and dependence on supply chains, which reinforces the case for treating the technological dimension as analytically explicit rather than leaving it entirely implicit within E/S/Ec metrics.

Third, technology encodes behavioral rules and creates “artificial actors.” In Tech4.0, a large share of social and economic interaction is mediated by algorithms, platforms, and cyber-physical systems that (i) shape access rules, ranking, and resource allocation, (ii) produce decisions or recommendations at scales beyond human monitoring capacity, and (iii) operate under opacity and limited observability for external observers (opacity and black-box effects) [14,15,83,84]. Empirical research also reports that contemporary LLMs can exhibit behavioral strategies, such as deception, which further supports treating algorithmic systems as objects of behavioral analysis and governance rather than as neutral tools [85].

As a result, technologies increasingly function as institutional intermediaries and partially delegated agents within governance and decision-making systems. This does not require treating them as fully autonomous from society; however, it does support the need for more explicit ethical, legal, and managerial framing of their role within socio-technical environments. In this sense, modern AI systems can be analyzed as entities that display observable behavior and participate in reproducible interaction patterns within socio-technical environments. This, in turn, justifies specialized regimes for studying and governing their actions that differ from traditional procedures for controlling “tools” [86,87].

4.1.3. Analytical and Governance Relevance: Why Tech4.0 Raises More than Additional ESG Topics

The second step in the argument concerns the analytical and governance relevance of Tech4.0. As shown in Section 3.2, the core challenge is to preserve governability, observability, and correctability of socio-technical systems as connectivity and complexity increase. Within the E–S–Ec logic, technological impacts are typically addressed through their contribution to environmental, social, and economic outcomes and related trade-offs. Under Tech4.0 conditions, however, this often proves insufficient because the viability of those outcomes increasingly depends on system properties of the technological environment itself. It is also necessary to specify the procedural and architectural conditions under which such outcomes remain sustainably achievable and verifiable—namely, admissibility principles that secure governability, observability, and correctability of the Technosphere as an operating environment. Below, these principles are framed as persistent design dilemmas and admissibility criteria. They correspond to the risk categories in Section 3.2, but they are formulated in explicitly normative terms.

First, there is the governability of autonomous contours and the attribution of responsibility. As functions are delegated to models, platforms, and cyber–physical systems, the central questions become the following: (i) how system behavior can be controlled and corrected; (ii) where the boundaries of acceptable autonomy lie; (iii) how responsibility is distributed across developers, operators, infrastructure owners, and user organizations. Literature conceptualizes this as the need to ensure accountability and due process in algorithmic decision-making and as a critique of treating the technical system as separable from its social context [15,16].

Second, Tech4.0 raises requirements for observability, interpretability, and contestability under algorithmic opacity. Black-box effects, distributed causal chains, and information asymmetries between decision-makers and affected parties are common features of the digital environment. The normative focus therefore shifts toward procedural safeguards: auditability, the ability to provide meaningful explanations, and the ability to contest decisions. In high-stakes domains, interpretability is often treated as a stronger requirement than attempts to “explain” a black box post hoc [14,83].

Third, there are limits to acceptable behavioral influence and requirements to protect individual autonomy. Recommendation and targeting systems turn the digital environment into a space of design-based influence on choice, where the boundary between personalization and covert manipulation becomes normatively ambiguous. This creates a distinct class of requirements: behavioral stimuli should be identifiable and controllable; hidden erosion of autonomy and informed consent should be treated as unacceptable; and responsibility should extend to interface design and influence-oriented algorithms [88,89].

Fourth, there is the admissibility of data and models in terms of fairness, quality, and documentation. A substantial share of Tech4.0 “social” risks has technical sources embedded in data and modeling choices, including proxies, metric selection, and distribution shift. Fairness and non-discrimination therefore function not only as downstream outcomes, but also as requirements for data, models, and procedures: bias should be detectable, errors should be manageable, and data provenance and applicability limits should be transparent. This is supported by work on disparate impacts in data-driven decisions and by structured documentation practices—datasheets and model cards—as conditions for verifiability [90,91].

Fifth, resilience and system-level security become central in interdependent infrastructures. High internal connectivity makes the relevant question not only the “level of risk,” but also the system’s capacity to maintain functions under failures, attacks, and unforeseen interactions. This resonates with social–ecological resilience, where sustainability is defined as the ability of systems to absorb disturbances and reorganize without losing core functions, and as a combination of resilience, adaptability, and transformability [92,93,94]. The normative specificity of pillar T emerges here as a requirement to design and operate interdependent networks with cascading effects, failure thresholds, and inter-infrastructure dependencies in view. In other words, it implies an obligation to sustain the resilience of the Technosphere as the foundational environment for economic activity and institutions [17,95].

Taken together, these dilemmas indicate that Tech4.0 is not simply expanding the list of ESG topics. It creates a distinct class of requirements for socio-technical systems—requirements for governability, verifiability, and robustness to disruption. For this reason, the technological axis provides the normative content of pillar T and, in Section 4.1.4, is further examined as an independent causal line of systemic transformations and shocks.

4.1.4. Bidirectional Coupling T ↔ E/S/Ec: Synchronized Effects, Constraints, and Feedback

The third argument for defining Technological Sustainability as a distinct pillar follows from the cross-pillar character of the technological axis. In the context of Tech4.0, technologies function not only as tools for advancing environmental, social, and economic goals. They also function as an independent source of system-wide transformation that triggers synchronized shifts across E, S, and Ec. This reflects the Technosphere’s nature as a highly connected socio-technical environment, where effects propagate through network channels, are amplified by nonlinearities, and appear as systemic risks and cascades rather than as isolated “externalities” within a single domain [13,17].

First, Tech4.0 changes the operational substrate of economic and social processes. Digital infrastructures, platform ecosystems, and cyber–physical loops increasingly function as the environments through which transactions are coordinated, opportunities distributed, and flows governed. They therefore shape economic productivity and market structure (Ec), forms of participation and actor dependence (S), and environmental parameters through infrastructure demand and shifts in value-chain organization [6,55,96]. This infrastructural status matters because changes in Technosphere architecture—availability, compatibility, protocols, interfaces, and data—do not affect only one sustainability dimension. They reshape bundles of E/S/Ec outcomes simultaneously.

Second, high connectivity increases cascading dynamics and the synchronicity of consequences. Interdependent networks—digital, energy, transport, financial, and logistics—create conditions in which local failures, attacks, or node disruptions can propagate across adjacent infrastructures and quickly generate economic losses, social disruptions, and secondary environmental effects simultaneously (e.g., emergency operating modes, logistics breakdowns, shifting loads) [13,17,95,97]. In this sense, Tech4.0 shifts sustainability toward system properties: resilience, recovery capacity, and the ability to manage inter-network dependencies.

Third, technologies function as mechanisms that encode rules and distribute resources. Algorithmic ranking and recommendation and optimization systems shape access, visibility, and prioritization. They influence competitive dynamics and rents (Ec) while also affecting participation structures and inequality of opportunity (S). This influence is cross-pillar by nature: it changes not only outcomes—who wins and who loses—but also the logic of institutional intermediation, as interaction rules become partially embedded in system architectures and parameters [84,88].

What distinguishes the T axis from merely being a “strong driver” is the bidirectional structure of these effects. Technologies do trigger synchronized changes across E/S/Ec, but technological trajectories are also shaped by constraints and conditions generated within the environmental, social, and economic domains. A rigorous conceptualization of T therefore requires explicit attention to feedback from E/S/Ec to T, not only to channels through which technology “impacts” sustainability.

First, environmental and resource–energy constraints set boundaries for Technosphere scaling [98]. Computing, data storage, and network infrastructures have a material and energy base. Energy availability, carbon intensity, and efficiency requirements therefore become structural conditions for digital infrastructure architectures and for acceptable trajectories of growth in computational workloads. Research highlights both the rising significance of energy use in data centers and networks and the role of technological and organizational efficiency measures in shaping the real environmental profile of digital infrastructures [69,70,71]. The E domain thus acts (i) as an object affected by T, and (ii) as a constraint and selector of technological choices.

Second, economic incentives and market power structures shape Technosphere design. Competitive regimes, scale effects, platform dependence of complementors, and time-to-market pressures influence (i) architecture choices (closure versus interoperability), (ii) priorities for investment in reliability and resilience, and (iii) judgments about which risks are acceptable in exchange for speed of scaling. Studies of platform power and dependence emphasize that economic asymmetries can become embedded in infrastructural interfaces and ecosystem rules, turning economic conditions into determinants of technological development trajectories [54].

Third, social requirements—trust, rights, and the acceptability of influence and control—operate as conditions for legitimacy and applicability. They shape architectural choices and deployment practices. A socio-technical perspective shows that the “technical” and the “social” are inseparable in such systems: constraints derived from requirements of fairness, autonomy, and accountability translate into design trade-offs and stable development trajectories for the Technosphere [16,43].

In sum, the third justification criterion for pillar T is that, in Tech4.0, technology constitutes a cross-cutting causal axis that (i) synchronously reshapes E/S/Ec through infrastructural mediation, cascading dependencies, and algorithmic allocation, while (ii) being shaped in turn by E/S/Ec constraints through resource–energy limits, economic incentives, and social conditions of legitimacy. This bidirectional coupling makes the technological dimension of sustainability conceptually distinct and necessary for explaining systemic transformations and trade-offs in the Tech4.0 era.

4.1.5. Institutional Convergence: A Distinct Layer of Regulation and Corporate Governance for Technologies

The fourth argument for defining Technological Sustainability as a separate pillar is institutional convergence around technology as an autonomous object of governance. As Tech4.0 turns digital and cyber–physical arrangements into a foundational environment for economic activity and institutional functioning, a structural mismatch becomes more pronounced between the pace of technological dynamics and the capacity of traditional public governance and accountability regimes to keep up—a classic pacing problem [42,99,100]. In response, a distinct layer of rules and organizational practices has begun to take shape. Its primary focus is not the aggregated E/S/Ec effects, but the governability, verifiability, and resilience of technological systems across their life cycles [101].

At the level of public policy, this is visible as regulatory stratification: specialized regimes are emerging around major technological domains (AI/data/cyber), each with its own obligation logic. The EU AI Act institutionalizes a risk-based approach to AI and introduces requirements that target system architecture and development/deployment processes—risk management, data requirements, technical documentation, transparency, human oversight, and related controls [19]. The emphasis is on system properties that ensure governability and controllability in high-stakes contexts.

In the cyber domain, a comparable institutional shift is reflected in NIS2, which establishes obligations for cyber risk management and incident response for a broadened set of “essential” and “important” entities in critical sectors [20]. Importantly, cybersecurity is treated not as an optional IT function, but as a minimum condition for the resilience of interdependent infrastructures and for functional continuity.

In the data domain, the Data Governance Act creates a distinct data governance contour as an institutional infrastructure for trusted data sharing and reuse, including rules for data intermediaries and mechanisms for data altruism [21]. Trust, access, and intermediation in data are thus framed as a standalone regulatory object with its own procedural logic, which further supports treating the technological axis as a separate sustainability dimension.

A key conceptual point is that across these regimes the focus is not limited to downstream consequences. Regulation increasingly targets “internal” properties and processes of the Technosphere itself: risk-management requirements, documentation duties, and expectations of observability, controllability, verifiability, and system robustness. This reflects an institutional shift from ex post-evaluation of outcomes to governance of the conditions under which socio-technical contours can function in a reproducible and correctable way.

In parallel, corporate practice is converging as organizations build stable AI/data/cyber governance contours: system inventory and classification, risk assessment and monitoring procedures, data and access management, incident readiness and resilience planning, and controls for supplier risk and infrastructure dependence. At the international level, this trend is captured by anticipatory governance frameworks that emphasize initiative-taking governance of emerging technologies through a combination of value embedding, foresight and technology assessment, public engagement, adaptive regulation, and international coordination [101].

Finally, the EU sustainability reporting architecture (CSRD and ESRS) demonstrates a high degree of institutionalization of ESG disclosures (E–S–G), yet it does not create a technological dimension that is comparable in coherence or in the “strength” of obligations. Technological issues appear in a fragmented manner—through individual topics—rather than as an integrated system of principles, indicators, and accountability focused on the governability and resilience of the Technosphere [25,26]. This implies that institutional convergence around technology is already underway, but it still lacks a conceptually coherent “support” within the sustainability framework, which is precisely what makes the analytical and methodological case for defining T.

A counterargument should be acknowledged explicitly. Many technology-related effects remain environmental, social, or governance-relevant in their consequences: energy use belongs to environmental assessment, bias and exclusion have social implications, and oversight procedures are often embedded in governance structures. The claim advanced here is therefore narrower than strict separation. It is that making the technological dimension explicit improves the visibility of system properties—especially governability, resilience, infrastructural dependence, and cascading vulnerability—that tend to become diluted when addressed only indirectly through existing categories.

4.1.6. Synthesis: From Pillar T to CTR and CDR

To synthesize the argument developed in Section 4.1, the Tech4.0 trajectory cannot be treated as a simple “add-on” to environmental, social, or economic objectives. The technological axis has an independent analytical status because the Technosphere constitutes a material, infrastructure-heavy socio-technical environment with substantial inertia. Within this environment, algorithmic intermediaries and digital infrastructures reshape coordination architectures, redistribute responsibility, and weaken the verifiability of causal chains. As a result, technology-induced impacts take on a system character: they propagate through network dependencies and simultaneously affect E/S/Ec, generating cross-pillar externalities, trade-offs, and risks of cascading disruption.

From a governance perspective, this manifests as the Tech4.0 governance gap articulated in Section 3.4. An architecture of sustainability built exclusively around the E–S–Ec triad therefore remains methodologically incomplete. It supports goal-setting and disclosure across the classical domains, yet it does not capture the technological conditions of governability and resilience that increasingly determine whether those goals remain feasible in a Tech4.0 environment.

This leads to the paper’s central conceptual claim: under Tech4.0 conditions, the technological dimension may merit explicit recognition as a distinct analytical pillar/dimension alongside E–S–Ec because it helps capture system properties that are otherwise only partially visible within the classical architecture. To make this perspective more operationally meaningful, the paper then discusses Corporate Technological Responsibility (CTR), with Corporate Digital Responsibility (CDR) positioned as a digital sublayer within CTR. In the literature, CDR is typically framed as a set of corporate values and norms for responsible engagement with digital technologies and data, but it is rarely linked to a broader sustainability architecture in this way [102].

Section 4.2 therefore develops the argument further by clarifying the analytical focus of T, specifying the key system properties emphasized in this paper, and introducing the corporate hierarchy T → CTR → CDR as a possible pathway of operationalization. This, in turn, provides the basis for the exploratory propositions and the preliminary measurement agenda that follow.

4.2. Clarifying the Analytical Focus of T and a Possible Corporate Operationalization: T → CTR → CDR

4.2.1. Definition and Scope of Technological Sustainability

Technological Sustainability is the long-term capacity of the Technosphere—as a highly interconnected socio-technical environment—to preserve (i) governability (the observability, auditability, correctability, and accountability of technology-mediated processes) and (ii) resilience (the capacity to prevent, absorb, and recover from failures, attacks, and cascades) as infrastructural interdependence, opacity of mediated decision processes, and cascade-prone vulnerability intensify under Tech4.0 conditions. T operates as a feasibility condition for E–S–Ec goals: technological development should not push systemic externalities and risks beyond the limits of governability, thereby undermining the attainability of environmental, social, and economic sustainability objectives.

The definition is anchored in a deliberate choice of the object of analysis. The Technosphere is treated not as a collection of discrete digital technologies, but as a highly connected environment of infrastructural and algorithmic artifacts and practices in which causal chains become distributed, effects are amplified through nonlinearities, and risks emerge as systemic cascades and infrastructure vulnerabilities. It is precisely this “environment” status that justifies introducing T as a standalone pillar rather than as a cross-cutting topic embedded within E–S–Ec.

This definition also highlights a core implication: in the Tech4.0 context, “sustainability” increasingly depends on the institutional and organizational capacity to keep the technological environment within controllable development regimes. The goal is to avoid cascading breakdowns, opaque and difficult-to-correct technology-mediated decisions, and transboundary risks that can shift sustainability trajectories faster than classical corporate governance and regulatory mechanisms can adapt.

Unlike the biosphere, which relies on evolutionarily developed mechanisms of self-regulation, the Technosphere remains largely sustained through institutional and organizational governance contours—corporations, states, standards, and markets—while also displaying increasing complexity through agentic systems, algorithmic coordination, and self-adjusting digital infrastructures. This combination of high interdependence and limited controllability helps explain why Technosphere governability and resilience warrant explicit analytical attention in this paper.

First, governability refers to the properties and governance contours that ensure technology-mediated processes remain: (i) observable, meaning they can be monitored and their critical causal relationships can be reconstructed; (ii) auditable, meaning they allow internal and external verification and comparable control; (iii) correctable, meaning meaningful intervention is possible, decisions can be revisited, and operating modes can be adjusted when risks or deviations are detected. Together, these properties provide the foundation for a measurable accountability contour developed in the sections that follow.

Second, resilience within the T logic refers to the ability of socio-technical systems to maintain critical functions under disturbances and to recover rapidly after incidents. The key point is that this is not a claim of “zero risk.” It is a requirement for robust operating and recovery regimes under inevitable failures, attacks, and cascading disruptions—phenomena that, under Tech4.0, become systemic and large-scale.

4.2.2. Core Analytical Delimitation of T

Clarifying the analytical delimitation of T requires distinguishing it from narrower “digital” domains—ethics, privacy, cybersecurity, AI governance, and related areas—which are typically treated in a fragmented way within current architectures and therefore do not, by themselves, provide a sufficiently coherent frame for the Technosphere as an operating environment (see Section 3.4).

The proposed pillar T neither replaces these domains nor collapses into compliance. It operates at a higher level of abstraction by focusing on the Technosphere’s system properties—governability (including verifiability and correctability) and resilience—and on those classes of risks that arise specifically from infrastructural interdependence, opacity of mediated decisions, dependency on external digital layers, and the potential for cascading disruption. For this reason, the boundaries of T are not defined by a list of “digital topics.” They are defined by criteria of system-level governability and resilience that apply across technological subsystems and sectors. This is what makes pillar T conceptually stable and comparable over time and across industries.

Against this backdrop, it is useful to specify four classes of “unacceptable trajectories” as analytical limiting conditions that help clarify what T is intended to capture in this paper. A Technosphere development trajectory is treated here as analytically unacceptable when there is sustained drift (lock-in) toward at least one of the following regimes, that is, when technological dynamics increasingly undermine governability and resilience as core system properties.

• Governability loss. The trajectory is unacceptable when increasing complexity, interdependence, and mediated decision opacity outpace the development of observability–auditability–correctability–accountability contours. As a result, critical technology-mediated decisions become systematically unobservable, unauditable, and/or uncorrectable within reproducible procedures, including responsibility attribution and the possibility of meaningful intervention.
• Systemic fragility and cascades. The trajectory is unacceptable when interdependencies and single points of failure create conditions in which local failures, attacks, or errors repeatedly escalate into cross-sector cascades, and recovery of critical functions depends on external platform or infrastructure nodes that are outside the control of the risk-bearing actors.
• Institutional erosion and loss of legitimacy. The trajectory is unacceptable when technological intermediaries—algorithms, platforms, and digital contours—systematically undermine contestability, the reproducibility of rules, and procedural due process. This produces persistent accountability gaps and weakens institutional accountability and trust in oversight arrangements.
• Incompatibility with biophysical limits. The trajectory is unacceptable when Tech4.0 scaling locks in rising material and energy demands of digitalization and technology externalities in ways that shift or mask environmental costs until they accumulate to levels that raise the likelihood of hard corrections and undermine the long-term feasibility of E-domain objectives.

Accordingly, “unacceptable trajectories” define the boundaries of T not as a requirement of “zero risk,” but as a criterion for preventing the systemic entrenchment of regimes that lead to (i) loss of governability, (ii) cascading degradation of functions, (iii) institutional erosion of accountability, and (iv) biophysically unsustainable digital expansion.

4.2.3. Conditions Under Which T Becomes Salient

Under the narrower Tech4.0 scope adopted in this paper, T becomes most salient when organizations and infrastructures are tightly linked through platform, cloud, software, data, and cyber–physical dependencies. In such settings, failures do not remain local: they propagate through critical interfaces and dependency chains and appear as systemic risks, degraded governability, and cascading disruption rather than as isolated externalities.

In parallel, attribution becomes more difficult and the verification of decision premises becomes less reliable. Causal chains are increasingly distributed, while the correctness of outputs and the possibility of reproducible control become harder to sustain. This is the point at which the technological dimension becomes analytically salient: it draws attention not only to sustainability “goals” (as in E/S/Ec), but to those system properties that condition whether such goals remain governable and robust under disruption.

The salience and relative weight of capability clusters A–D (see Section 5.1) are not expected to be uniform across sectors. They will vary with infrastructural criticality, compute intensity, platform dependence, regulatory exposure, and the extent to which business processes rely on high-stakes algorithmic mediation.

4.2.4. Bidirectional Coupling: Complementarity and the Integration Principle

Pillar T is introduced not as a competitor to E–S–Ec, but as a complementary dimension that treats the technological environment as a condition for governability and for the feasibility of sustainability goals. Within this logic, technologies operate simultaneously (i) as instrumental means for advancing environmental, social, and economic objectives, and (ii) as an independent source of systemic transformations and shocks that trigger synchronized changes across E, S, and Ec.

This implies a specific integration principle. Sustainability strategies may be methodologically “complete” at the level of E/S/Ec goals yet remain operationally incomplete if they do not specify the technological constraints, trade-offs, and governability regimes that determine whether those goals can be realized. For this reason, T functions as a separate support within the overall architecture: it provides criteria for assessing whether technological development remains governable and resilient, and whether technological regimes refrain from undermining E/S/Ec sustainability trajectories.

4.2.5. A Possible Corporate-Level Operational Pathway: T → CTR → CDR

If T captures the system level—the properties of the Technosphere as an operating environment—then one possible next step is to ask how this perspective may be translated into organizational practice without claiming that corporate-level arrangements can substitute for systemic governance. In this paper, this possible pathway is discussed through the hierarchy T → CTR → CDR. CTR is defined as a corporate contour of technological accountability: a system of corporate duties, policies, and procedures for managing technology-induced impacts and risks, including governance roles, management processes, controls, and disclosure/assurance mechanisms. Within CTR, CDR is treated as a specialized digital sublayer—particularly in relation to responsibility for data, platforms, and digital stakeholder interactions—allowing digital domains to be articulated in greater detail without losing the broader coherence of T.

The T → CTR → CDR linkage is treated here not as a completed translation from macro to micro, but as a heuristic multi-level decomposition. At the organizational level, it helps identify reproducible accountability contours and possible disclosures; at the sectoral and infrastructure level, it points to the relevance of standard compatibility, interfaces, and incident-readiness regimes; and at the broader Technosphere level, it indicates how organizational practices may contribute—only partially and indirectly—to limiting cascades and governability degradation. This decomposition allows the paper to retain T as a system-level analytical framework while discussing CTR/CDR as organizational-level building blocks along the sequence strategy → policies → processes → controls → disclosure/assurance. To clarify the analytical levels and roles of the proposed framework,

Table 1. Analytical levels and roles in the proposed framework.

Element	Level	Object of Analysis	Main Analytical Function	Governance/Accountability Expression
E–S–Ec	Baseline sustainability architecture	Environmental, social, and economic domains	Organizes sustainability goals, impacts, and trade-offs	Reporting categories, disclosure metrics, policy and management targets
T	System/macro level	Technosphere as socio-technical environment	Foregrounds governability, resilience, infrastructural dependence, and cascade-prone risks	Possible analytical sustainability dimension/pillar
CTR	Corporate/organizational level	Technological impacts and risks within the firm	Provides a possible corporate accountability pathway linked to T	Governance roles, policies, processes, controls, audit/assurance
CDR	Specialized digital sublayer	Data-, platform-, and algorithm-related domains	Clarifies digital responsibilities within the broader CTR architecture	Data governance, privacy, AI/digital responsibility, transparency
T → CTR → CDR	Cross-level linkage	Relationship between system logic and firm-level practice	Provides an exploratory linkage between the technological dimension and organizational operationalization	Heuristic pathway for later propositions and measurement logic

Source: compiled by authors.

summarizes the distinction between the baseline E–S–Ec architecture, the system-level technological dimension (T), and its possible corporate-level operationalization through CTR and CDR.

For the purposes of this conceptual paper, the proposed operational pathway is linked only tentatively to the paper’s propositions and measurement agenda (Section 5). CTR/CDR maturity is discussed as potentially associated with observable governance and sustainability outcomes—such as the quality of technological governance and accountability, stakeholder trust, investor transparency, and business-model robustness to technology risks. Accordingly, measurement is approached in preliminary terms along the sequence exposures → capabilities → outcomes, serving as a heuristic structure for subsequent empirical work rather than as a validated reporting architecture. Section 5 therefore treats the resulting propositions and measurement cues as exploratory extensions of this conceptual pathway, not as proof that the macro-level and corporate-level dimensions have already been fully reconciled.

Importantly, CTR and CDR are not presented here as sufficient substitutes for public regulation, sectoral coordination, or infrastructure-level governance. They should be understood more modestly—as organizational-level building blocks within a broader multilevel governance architecture whose effectiveness depends on alignment with legal, sectoral, and infrastructural regimes.

5. Discussion

5.1. Exploratory CTR Capability Clusters and Illustrative Propositions

5.1.1. The Capability Logic in CTR

In this paper, CTR is treated as a bundle of organizational capabilities that may help keep technology-intensive contours in more governable regimes under disturbance, uncertainty, and Technosphere nonlinearities. In this sense, CTR capabilities are discussed not as proven micro foundations of T, but as organizational correlates through which governability, auditability, correctability, and resilience may become more visible and more operationally tractable in corporate practice.

Conceptually, these capabilities are organized around the four classes of unacceptable trajectories defined in Section 4.2.2: (i) governability loss; (ii) cascading risks and systemic fragility; (iii) erosion of institutions and legitimacy contours; (iv) incompatibility with biophysical limits. This structuring choice is intended to reduce excessive normativity. The aim is not to issue moral judgments about technology, but to provide an exploratory analytical link between system-level concerns and possible organizational responses.

Below, four CTR capability clusters are proposed. Each cluster primarily addresses one class of unacceptable trajectories and serves as a basis for exploratory propositions rather than for standalone empirical proof. The ten propositions that follow should be read as an illustrative analytical catalog rather than as a cumulative proof structure. Their role is to translate the conceptual argument into possible directions for later empirical inquiry, not to claim that the T → CTR → CDR pathway has already been validated across settings.

5.1.2. Capability Cluster A: Algorithmic Governability, Transparency, and Auditability

This cluster covers organizational practices that keep algorithmic and digital contours governable, verifiable, and accountable across the full technology life cycle—from design and training to operation, updates, and retirement. Its conceptual foundation is the need to address structural opacity and the resulting constraints on observability and control in socially consequential algorithmic systems [14]. Operationally, A-capability includes: (i) inventories of models, data assets, and critical digital components (registries/catalogs, criticality, ownership, dependency chains); (ii) documentation and transparent communication of system characteristics, including standardized model documentation (model cards) and dataset documentation (datasheets) that specify purpose, deployment context, limitations, and conditions for correct interpretation [91]; (iii) internal and independent audit/assurance procedures across the life cycle, designed to surface risks before deployment and to trace sources of problems after deployment [15,103]; (iv) monitoring of performance degradation and data/context drift, with triggers for review, retraining, or retirement; (v) correctability mechanisms, including predefined intervention rights, escalation protocols, and procedures for switching systems into safe or degraded modes (fail-safe shutdown/rollback) during incidents and anomalies; (vi) contestability mechanisms and responsibility allocation across risk functions, IT/IS, compliance, and business units as a condition for reproducible accountability [104].

In terms of the boundary conditions in Section 4.2.2, Cluster A primarily mitigates governability loss and—through contestability and verifiability—partly addresses risks of legitimacy erosion in algorithmically mediated decisions. Importantly, “full transparency” (e.g., code disclosure) is not a universally effective accountability mechanism. What matters more are reproducible procedures for verification, documentation, and control that ensure decisions remain within defined standards and acceptable operating regimes [15,103].

The propositions below are exploratory and heuristic. They are intended to indicate plausible directions for later empirical research rather than to claim demonstrated causal relationships at the present stage of analysis. Exploratory propositions:

P1. Higher maturity of algorithmic transparency, documentation, and auditability practices is associated with a lower expected probability and severity of technology-related sustainability failures, reflected in legal, reputational, and operational incidents driven by opaque, non-contestable decisions and/or systematic bias in high-stakes domains. Capability examples: availability and coverage of model cards/model passports, datasheets for datasets, model/data lineage, decision logging, results of regular bias/fairness tests by subgroup. Outcome examples: number/severity of regulatory actions, lawsuits, public complaints, and high-severity incidents; share of models found to have systematic violations in post-audit reviews [90,91,103].

P2. The presence of formalized independent oversight and correctability procedures may reduce detection and mitigation time and lowers aggregate damage from technological incidents by improving causal traceability, accelerating corrective action, and enabling reproducible managerial intervention. Capability examples: existence of independent assurance/audit for AI/digital controls, frequency of red-team/control testing, presence of kill-switch/rollback procedures and testing protocols, availability of post-incident root-cause analyses (RCAs). Outcome examples: MTTD/MTTR, time-to-mitigation, share of incidents resolved within SLA, financial/operational losses [22,27,103].

P3. The effect of A-capability is moderated by regulatory and societal sensitivity: where accountability, disclosure, and external monitoring requirements are stricter, the relationship between transparency/auditability maturity and lower incident frequency/severity is expected to be stronger. Capability examples: share of operations/revenue in highly regulated jurisdictions or domains, presence of mandatory impact assessments/audits, intensity of external monitoring (number of regulator requests, frequency of public reviews). Outcome examples: gradients in incident reduction across groups with various levels of regulatory stringency [22,27].

5.1.3. Capability Cluster B: Cyber Resilience, Data Governability, and Dependency Management

The second CTR capability cluster targets the prevention of cascading risks and systemic fragility (see Section 4.2.2). Under Tech4.0, technological failures and attacks are best understood not as “perimeter” events, but as systemic disturbances in a network of interdependent digital contours. They propagate through infrastructure and platform linkages and can trigger cascading functional disruptions [17]. Substantively, B-capability integrates organizational and architectural practices that preserve and restore critical functions and governability under attacks, failures, and degradation of digital components. It includes: (i) architectural robustness of infrastructure (segmentation/isolation of critical contours, redundancy, fault-tolerant configurations, and degraded-service plans); (ii) detect–respond–recover contours and their regular validation (exercises, stress tests, scenario-based simulations); (iii) management of software supply chains and components through secure development and delivery practices aligned with frameworks such as SSDF [105]; (iv) management of critical external dependencies on platforms and cloud providers (dependency mapping, concentration points, switching conditions); (v) mature data governance as a prerequisite for reliable models and processes (quality, provenance, access, change control), which may reduce the risk of output degradation and supports recovery and incident investigation [106,107].

Compared with Cluster A (transparency/auditability and correctability of decisions), Cluster B emphasizes the infrastructure-and-ecosystem layer. Functional robustness depends on whether the organization can reproducibly detect, localize, and restore critical contours while managing dependencies, supply chains, and data [17]. Exploratory propositions:

P4. Higher maturity of cyber resilience and dependency management is associated with smaller functional degradation and faster restoration of critical processes after major cyber incidents and failures, as well as with lower volatility in relevant non-financial indicators during incident periods. Capability examples: maturity under NIST CSF 2.0/ISO 27001, presence of SBOM/dependency registers, patching cadence, segmentation, redundancy, and recovery testing. Outcome examples: time-to-recovery, downtime of critical processes, deviations in service KPIs/operational metrics during incidents [22,108].

P5. Standardized and timely disclosure of material technological incidents may reduce information asymmetry and may improve trust among key stakeholders. This should be reflected in more moderate market reactions and smaller long-run reputational losses relative to partial or delayed disclosure strategies. Capability examples: existence of a formal incident disclosure policy, observed lag between event and disclosure, disclosure completeness (scope, impact, remediation), and external verification. Outcome examples: CAR/abnormal returns around disclosure, duration of negative media sentiment, customer trust/retention metrics, subsequent regulatory costs [74,109,110,111].

P6. The effect of B-capability is a nonlinear function of digital connectedness and platform integration. With high dependence on external ecosystems, the marginal effect of cyber-resilience maturity may increase because the likelihood and scale of cascading functional disruptions are highest under such configurations. Capability examples: share of critical functions hosted on external platforms/cloud, concentration of key providers, number of critical APIs/integrations, and a dependency index. Outcome examples: number of supply-chain/partner spillover incidents, share of incidents with cascading degradation, multi-sector downtime [17,18].

5.1.4. Capability Cluster C: Institutional Contours, Trust, and Human-in-the-Loop Design

The third CTR capability cluster addresses the risk of institutional erosion and weakened legitimacy contours (see Section 4.2.2). It highlights that technological sustainability depends not only on technical reliability and cyber resilience (Clusters A–B), but also on the reproducibility of procedural accountability in settings shaped by algorithmic intermediation. The central challenge is that digital contours reshape decision architectures, redistribute power and responsibility, and alter the conditions of autonomy and contestability for affected stakeholders [112]. Cluster C includes: (i) contestability and review mechanisms for automated decisions (appeal channels, documentation of grounds, and responsibility allocation) [113,114]; (ii) meaningful human oversight and human-in-the-loop design in critical processes, which prevents responsibility gaps and limits the tendency to shift accountability from management to the algorithm [115]; (iii) procedures that protect trust and autonomy in digital interactions, including constraints on manipulative design mechanisms and opaque influence regimes [89].

A boundary with the S pillar should be made explicit. The focus here is not “social policy” as such, but technology-induced institutional effects—cases where the design of digital intermediaries undermines rule predictability, decision contestability, and the legitimacy of governance contours [89]. In this sense, C-capability provides institutional conditions for Technosphere governability and directly operationalizes the governability component in the definition of T. Exploratory propositions:

P7. Higher maturity of CTR’s institutional contours—contestability, meaningful human oversight, and responsibility assignment—is associated with lower regulatory/legal conflict risk and higher stakeholder trust in domains where automated decisions are deployed. Capability examples: existence of contestability/appeal procedures, documented human-oversight roles, RACI/responsibility matrices, logs of human interventions, and the share of decisions subject to sampled human-in-the-loop review. Outcome examples: number of regulatory investigations/lawsuits, share of successful appeals, trust/acceptance indicators among affected groups [16,27].

P8. Workforce transition measures embedded in CTR governance (reskilling, role redesign, internal skill markets) are associated with a lower likelihood of organizational “social cascades” (turnover, competence loss, localized conflict), thereby supporting long-run corporate resilience in terms of functional continuity and the governability of technological contours. Capability examples: share of staff in reskilling (hours/year), presence of skill taxonomy and an internal talent marketplace, share of openings filled through internal mobility, training budgets. Outcome examples: turnover in critical roles, time-to-fill for digital vacancies, engagement/conflict indicators, and stability of operational functions during transformation [116,117,118].

5.1.5. Capability Cluster D: Biophysical Compatibility and Management of Technological Externalities

Cluster D is treated here not as a pure T-only domain, but as an interface between the technological and environmental dimensions. Its purpose is not to reclassify environmental indicators as technological ones, but to ensure that the resource and energy implications of digital infrastructures are not analytically ignored when assessing the governability and scalability of socio-technical systems.

The fourth capability cluster connects technological dynamics to biophysical limits without conflating the E and T pillars (see Section 4.2.2). Its purpose is to prevent a governance mode in which digitalization and AI are treated as “immaterial,” while their resource-, energy-, and infrastructure-related costs are displaced across time and space and remain outside accountability contours. At the corporate level, D-capability is reflected in the ability to measure and manage the resource-and-energy “shadow” of digital solutions (compute, storage, networks, data centers) and to incorporate associated infrastructure and material dependencies when scaling Tech4.0 [69,70]. At the sectoral level, structured policy instruments can materially influence energy-intensity and cost trajectories, underscoring the need to keep energy parameters within clearly delineated governance and measurement contours [119]. Substantively, D-capability includes: (i) standardized measurement and disclosure of the energy use and carbon footprint of computational workloads and digital services [72]; (ii) integration of these metrics into CTR contours (design-to-energy practices, energy-efficiency requirements, and managerial thresholds/triggers as workloads grow); (iii) explicit accounting for the infrastructure-and-material aspects of digital solutions (including the embodied component of digital infrastructure), so that technological externalities do not “disappear” into supply chains [120]. Exploratory propositions:

P9. Firms that incorporate the resource-and-energy implications of digital technologies into CTR-related decision routines may exhibit more robust environmental performance trajectories as digitalization and AI scale than firms that treat digital transformation as inherently environmentally neutral. Capability examples: tracking energy use from IT/AI workloads, PUE and compute-energy intensity metrics, carbon accounting for data centers/cloud services, and optimization policies (model efficiency, workload shifting). Outcome examples: trajectories of Scope 2/3 emissions, energy intensity of digital services, carbon intensity per unit of digital output/transaction [67,71].

P10. The relevance of D-capability is likely to be moderated by a sector’s computational intensity and infrastructure dependence. It may be stronger where demand for computing and data-center capacity grows most rapidly and where the “hidden” costs of digitalization are material (including the energy use of AI workloads and related systemic externalities). Capability examples: share of IT/compute in OPEX, growth rate of cloud/AI capacity, dependence on data centers/network infrastructure, provider concentration. Outcome examples: differences in the D-capability effect on environmental metrics between high-compute and low-compute sectors, and sensitivity of ESG metrics to rising compute demand [121].

Taken together, capability clusters A–D provide an exploratory map of CTR as a set of managerial capabilities that may help translate aspects of T into observable organizational practices and metrics. This decomposition is intended to support later comparative inquiry and the preliminary measurement agenda that follows.

5.2. Measurement Agenda for Corporate Technological Responsibility

Section 2 established that the purpose of this conceptual article is not to deliver a validated measurement framework, but to outline how the T → CTR → CDR perspective might later be translated into more systematic empirical inquiry. In this context, the measurement agenda is framed as a preliminary research program: which observable variables may help researchers and practitioners (i) describe variation in CTR maturity across organizations and sectors, (ii) examine the exploratory propositions in Section 5.1, and (iii) improve the comparability and verifiability of technological accountability over time. Importantly, measuring CTR is not intended to “replace” the ESG architecture; rather, it may provide a way to complement it in areas where high digital connectedness, infrastructural dependence, and opacity create accountability questions that are only partially visible within the E–S–Ec triad.

A central requirement of the measurement agenda is to distinguish three levels, so that CTR measurement does not degrade into a compliance checklist and does not substitute dynamic governability with the mere presence of formal policies. This is why the agenda separates policy presence from control performance: having policies and procedures is necessary but insufficient for CTR maturity. In this sense, disclosure quality is only one component of outcomes, while the core of performance is reflected in the observable dynamics of technological incidents, detection and response times, and the results of corrective interventions (including along dependency chains).

Level 1. Exposures and structural risk drivers: the complexity and connectedness of digital contours; the criticality of digital functions for operations; dependence on external platforms/cloud providers and software supply chains; computational intensity; and the concentration of technological dependencies.

Level 2. Control capabilities and governance artifacts: governance structures and roles; model/data lifecycle procedures; internal and external audit and assurance; stress testing and exercises; accountability and contestability contours; processes for managing dependencies and software supply chains (including practices comparable to SSDF); and monitoring and corrective-intervention procedures [105].

Level 3. Observable outcomes and effects: incidents and failures (including near-misses and tail events); regulatory and legal consequences; reputational effects; volatility in relevant non-financial indicators; functional resilience under disturbances (time-to-detection/time-to-mitigation/time-to-recovery); and the dynamics of the material–energy parameters of digitalization.

This separation aligns with the capability logic in Section 5.1 and provides a preliminary analytical structure for later empirical inquiry in which exposures, capability contours, and outcomes are not conflated. Exposures describe the structural risk profile; CTR contours reflect managerial maturity (policy presence and control performance); and outcomes capture actual resilience and governability in dynamics (including incidents, detection/response speed, and recovery of functions). Methodologically, this also matters because—even within ESG—comparability is often constrained by differences in measurement choices, assumptions, and weighting schemes, making assurance and methodological transparency critical for reducing information asymmetry [38].

The proposed measurement framework is organized around capability clusters A–D (see Section 5.1) and suggests a set of indicator types that could, in later empirical work, be assembled into a CTR maturity profile or a more formalized index. Below, we outline indicator types (without claiming an exhaustive list) that can be operationalized empirically using corporate disclosures, internal documents and external incident databases (see

Table 2. Proposed indicators by capability clusters A–D.

Capability Cluster	Disclosure-Oriented Indicator Options
Algorithmic governability and auditability	–Existence of a model/algorithmic-system inventory and criticality classification; –Share of critical models with documented data lineage and drift-control procedures; –Frequency of independent reviews (audit/assurance) and their coverage of key contours; –Existence of stop/escalation procedures and the average time to decide on switching the system to a safe mode.
Cyber resilience, data governance, and dependencies	–Infrastructure resilience metrics: mean time to detect/respond/recover (MTTD/MTTR) for material incidents; –Coverage by exercises and stress tests, including cascade scenarios and external-provider failures; –Share of critical suppliers/platforms with a formal assessment of technological dependence and a substitution plan; –Data governance maturity (quality, access, rights, and controls over data use in critical processes).
Institutional contours and human-in-the-loop design	–Existence of decision-contestability procedures and “rights to explanation” in critical processes; –Share of high-impact decisions operating under formal human-in/on-the-loop arrangements; –Trust/conflict metrics (complaints, regulatory/litigation cases in automated-decision domains); –Workforce transition metrics (reskilling coverage, internal mobility/skills marketplace, role redesign).
Biophysical compatibility and management of technological externalities	–Energy and carbon intensity metrics for digital solutions (including computational workloads); –Accounting for the infrastructure footprint (data centers, networks) and value-chain effects; –Indicators of “environmental displacement” (temporal/spatial), assessed via impacts in supply chains and during use/operation.

Source: compiled by authors.

). For a range of metrics, the priority is not absolute values but normalized and dynamic indicators (per unit of digital revenue/operations, per critical process, per computational workload), because it is precisely the dynamics that reveal whether digital growth remains aligned with governability and boundary conditions.

As shown in Section 3.1, Section 3.2 and Section 3.3, contemporary sustainability has already been institutionalized through disclosure standards and non-financial reporting.

The CTR measurement agenda fits into this logic as a complementary layer. First, it delineates technology-related disclosures as a distinct block focused on the governability and resilience of socio-technical systems. Second, it consolidates previously fragmented themes of digital responsibility into a coherent map of CTR domains anchored in the capability clusters. Third, it strengthens the role of assurance, since the verifiability of technology disclosures, incidents, and methodological comparability, making it a necessary condition for reducing information asymmetry and reinforcing stakeholder trust. Moreover, linking CTR to risk management and secure development practices makes it possible to use established risk-management regimes and secure software practices as operational “anchors,” without reducing CTR to compliance and without weakening its connection to T as governability and resilience.

In this way, CTR-oriented measurement is positioned as a mechanism for improving the informativeness of non-financial reporting—not by increasing the volume of declarations, but by introducing comparable indicators of governability, resilience, and accountability in the technological domain. This, in turn, creates a basis for empirical testing of the propositions and for advancing the measurement agenda of the T pillar.

5.3. Implications for Policy, Firms, and Stakeholders

The proposed framework T → CTR → CDR suggests a practical implication: under Tech4.0 conditions, the governability, accountability, and resilience of socio-technical systems may increasingly condition whether environmental, social, and economic goals remain feasible over time. In this sense, the argument developed here is not merely about adding another ESG topic, but about clarifying whether accountability architectures should pay more explicit attention to technological systems and dependencies.

For regulators and public governance. The framework provides a way to treat the technological axis as a policy object without reducing it to sectoral compliance or a set of loosely connected “digital topics.” If T is understood as a macro-level condition of long-term Technosphere governability (Section 4.2), policy may need to articulate more explicitly baseline expectations for corporate operationalization through CTR. Practically, this implies a shift from high-level principles to verifiable requirements across four domains: (i) incident-disclosure standards and a comparable taxonomy of incidents; (ii) minimum requirements for managing critical dependencies and digital supply chains; (iii) conditions for independent verification/assurance of high-risk algorithmic systems and critical infrastructure; (iv) procedural guarantees for contestability and clear accountability in automated decisions. In Tech4.0 governance-gap terms, this can be interpreted as a gradual strengthening of control architectures that explicitly accounts for nonlinearity and cascading effects—so that institutional governability does not lag the scalability and interdependence of technologies.

For firms and boards of directors. CTR may be positioned as a component of strategic sustainability and risk governance rather than merely as an extension of ESG policies. The key managerial implication is the institutionalization of CTR governance: board-level and executive accountability, lifecycle processes for digital systems, and the integration of CTR metrics into ERM, investment decisions, and internal control architectures. Importantly, CTR targets dynamic, nonlinear risk: stress testing, scenario analysis of cascades, management of dependency concentration (platforms, providers, components), and workforce-transition planning as a condition of maintaining governability during automation. In this sense, CTR maturity may become part of the firm’s dynamic capabilities to preserve critical functions and maintain a correctable business model under technological shocks.

For investors, stakeholders, and engagement practices. CTR offers a language and structure to assess technological risks as a distinct sustainability axis that complements E–S–Ec. Embedding CTR indicators in disclosures may improve comparability and may reduce information asymmetry around “latent” techno-risks that are weakly captured by conventional ESG metrics—for example, the auditability of algorithmic decisions, dependency-management maturity, incident-disclosure practices, and the resource-and-energy “shadow” of digitalization. This supports more substantive engagement: instead of generic calls for “ethical AI,” stakeholders can pose verifiable requests about governability and resilience—ranging from contestability procedures and meaningful human oversight to independent assurance and normalized incident and compute-load metrics. At the capital-market level, such a shift may support more accurate pricing of technological risk and a more realistic treatment of trade-offs between the speed of digitalization and the limits of governability.

5.4. Limitations

As with any conceptual study, this work has limitations that should be considered when interpreting its conclusions and when developing the framework empirically. First, the proposed definition of T and the architecture T → CTR → CDR are theory- and model-based. Their purpose is to clarify the analytical focus of technological sustainability, identify its main system properties, and sketch a preliminary framework for discussing governability and resilience in more structured terms, while opening the discussion toward later empirical inquiry. The manuscript does not empirically validate the propositions (P1–P10), nor does it assess the statistical robustness of the hypothesized links between CTR capability maturity, exposures, incident profiles, and the dynamics of functional recovery. The set of propositions is not exhaustive. For each proposition, the paper provides only minimal measurement cues (illustrative proxies and disclosure artifacts). The contribution is to outline the logic (exposures → capabilities → outcomes) and delineate a research space for subsequent operationalization (indicators and data sources).

Second, the literature strategy described in Section 2 combines mapping of large publication corpora (Scopus) with the selection of conceptually relevant “anchor” sources. This design improves transparency and helps justify the conceptual synthesis, but it remains sensitive to selection effects. Emphases may tilt toward domains where observability and normative frameworks are more articulated (e.g., AI/data/cyber governance), while sectoral and regional variation in CTR practices may be unevenly represented. In addition, the mapping exercise is used as a diagnostic and navigational tool for field fragmentation rather than as an evidentiary procedure.

Third, the manuscript draws primarily on the most institutionally developed examples of regulation and disclosure (notably, the European context). This strengthens the governance-gap argument, but it may limit the transferability of some practical implications to jurisdictions with different regulatory regimes, capital-market structures, and infrastructures of oversight. Likewise, the generality of the proposed CTR capabilities and the applicability of the measurement agenda are likely to vary across sectors and connectivity regimes. Differences in the criticality of digital functions, platform dependence, supply-chain maturity, and computational intensity can reshape exposures, accountability artifacts, and observable outcomes.

Finally, the unit of analysis is predominantly corporate. While T is defined as a system-level property of the Technosphere, operationalization and measurement focus on corporate responsibility and accountability circuits. Macro-level Technosphere dynamics—cross-sector cascades, transboundary effects, institutional competition across regulatory regimes, the political economy of infrastructure, and asymmetries of platform power—are treated mainly as boundary conditions for CTR. Their formalization and modeling remain tasks for subsequent interdisciplinary research.

6. Conclusions

This conceptual paper shows that historically dominant sustainability frameworks and ESG reporting regimes—developed around the E–S–Ec triad—were designed to manage industrial externalities and relatively stable domains of measurement. In the Tech4.0 era, however, a qualitatively different impact-and-risk landscape emerges. Technological contours (AI systems, data, platform infrastructures, and interdependencies across digital supply chains) form a highly connected socio-technical environment in which causal chains become distributed, observability and auditability are uneven, and local failures can propagate into cross-sector cascades. This shapes the Tech4.0 governance gap (Section 3.4), manifested both as a deficit of conceptual clarity regarding the technological dimension of sustainability and as institutional lag—governance regimes, metrics, and accountability mechanisms failing to keep pace with rising digital connectedness and the partial autonomization of technological systems.

To respond to this analytical problem, the paper argues that Technological Sustainability (T) may merit explicit recognition as a distinct sustainability dimension/pillar. T does not replace E–S–Ec; rather, it complements them by specifying the technological conditions under which sustainability objectives remain achievable in a technologically mediated environment. In this formulation, T concentrates on two core properties of the Technosphere: governability (including observability, verifiability/auditability, and correctability) and resilience (the capacity to preserve and restore critical functions under disturbance).

The conceptualization of T is clarified through explicit analytical limiting conditions: trajectories of technological development become analytically unacceptable when they drift toward (i) governability loss, (ii) system fragility and cascading failures, (iii) erosion of institutions and legitimacy, or (iv) incompatibility with biophysical limits via the growth of the material–energy footprint of digitalization and the accumulation of technological externalities. In this sense, T provides a framework that integrates the technological axis into sustainability both in an “instrumental” mode (technology as a means to achieve E–S–Ec goals) and in a “constraint” mode (technology as a source of systemic risks and limits).

To connect the system-level perspective of T with the corporate level of governance and verifiability, the paper discusses the architecture T → CTR → CDR as a possible operational pathway, where CDR is treated as a digital sub-layer within the broader CTR domain. Importantly, CTR is defined not as the mere presence of formal policies, but as a set of reproducible capabilities that sustain the governability and resilience of a firm’s technological contours. These capabilities are structured into four clusters, each linked to exploratory propositions: A-capability (algorithmic governability, transparency, and auditability), B-capability (cyber-resilience, and the governability of data and dependencies), C-capability (institutional contours of trust and accountability), and D-capability (biophysical compatibility and the management of technology-induced external effects). Together, these clusters shift the discussion of “technological sustainability” from fragmented topical domains (ethics, privacy, cybersecurity, AI governance) toward an integrated accountability-and-governability structure that is comparable in rigor to ESG logic while remaining oriented to the Technosphere’s systemic properties.

Extending this conceptual contribution, the paper articulates a measurement agenda intended to move the field from declarations to measurability by distinguishing (i) exposures and structural risk drivers, (ii) control capabilities and governance artifacts, and (iii) observable outcomes. This separation provides a basis for shaping disclosure and assurance expectations, reducing information asymmetry between firms and stakeholders, and enabling more disciplined discussions of trade-offs between the speed of digital transformation and the limits of governability and resilience.

The study’s limitations follow the nature of conceptual work. The proposed architecture requires subsequent empirical validation; the availability and comparability of indicators of technological governability and resilience remain uneven; and the rapid evolution of Tech4.0 practices may increase the risk of “metric lag.” These issues and the conditions for interpretation are discussed in Limitations (Section 5.4).

Overall, the paper argues for the analytical value of making the technological dimension explicit alongside E–S–Ec and links this system-level perspective to possible corporate accountability mechanisms through CTR and CDR. A practical implication is that sustainability governance in the Tech4.0 era may require a more symmetric development of technological capability contours and accountability regimes. Without such development, ESG-oriented sustainability may remain partly “above” the Technosphere—unable to capture governability, cascades, and the biophysical limits of digital growth.

7. Future Research Agenda

Although the T → CTR → CDR framework offers a conceptually plausible pathway, its empirical development would require research designs that separate structural exposure to technorisk (connectedness, criticality of digital contours, platform dependence) from CTR capabilities as managerial capacity, and that can observe effects over time. Accordingly, the main priority for future research is to build analytical configurations that are sensitive to Tech4.0 nonlinearity and cascading dynamics and that can identify causal links between the maturity of capability clusters A–D, incident characteristics, and functional resilience. Below, we first outline key empirical research directions and then specify methodological approaches for effect identification and the operationalization of CTR indicators at scale.

First, event studies around technological incidents—major cyberattacks, failures of critical digital infrastructure, and salient “AI incidents”—are a promising avenue. Such designs can examine market reactions, trust dynamics, and subsequent adjustments in relevant non-financial indicators. The central question is whether damage and recovery speed differ with the maturity of capability clusters A–D and with the quality of incident disclosure.

Second, panel studies are needed to test associations between CTR practices and the stability of non-financial metrics over time. For example, researchers can examine how dependency management maturity, audit/assurance practices, and disclosure discipline relate to incident frequency and severity, volatility in relevant indicators, and the resilience of critical functions under disturbance.

Third, multi-sector pilots of CTR matrices (capability profiles and indicators) would help assess organizational change and implementation pathways. A key focus is how firms adapt lifecycle governance for digital systems, stress testing and exercises, digital supply-chain management, and how stakeholder behavior and engagement practices respond. In addition, cross-country comparisons across regimes with various levels of regulatory maturity are important. These can test the proposition that external pressure—stricter disclosure rules, clearer liability expectations, and stronger independent verification—strengthens the relationship between CTR capabilities and observable outcomes.

Given endogeneity concerns—organizations with more mature practices may differ systematically in unobserved ways—future work should prioritize quasi-experimental designs: (i) difference-in-differences around changes in the regulatory environment and disclosure regimes; (ii) natural experiments linked to exogenous shocks (large-scale cyber incidents, abrupt regulatory shifts, disruptions in digital-component supply chains); (iii) mixed-methods strategies that combine case studies (to identify mechanisms) with quantitative panels (to estimate effects and test the moderators specified in the propositions). Particular attention is required for extracting CTR measures from corporate disclosures. Content analysis and NLP methods can provide scalable tools for constructing comparable indicators where access to internal data is limited.

At the theoretical level, CTR can be developed as an integration node across several research streams. First, resilience theory and functional robustness in complex systems, including the translation of resilience concepts from socio-ecological to socio-technical contexts. Second, institutional theory, focusing on how technologies reshape rules, legitimacy, and accountability mechanisms. Third, stakeholder theory, examining how risks and responsibilities are redistributed across actor networks and value chains. Finally, the political economy of technology, addressing how infrastructural dependencies, knowledge asymmetries, and the concentration of platform power shape the macro-level governability trajectory of the Technosphere.

Taken together, these directions frame CTR not as a practical checklist of practices, but as a research program in which technological sustainability is understood as the dynamic compatibility of technological evolution with the long-term viability of social and natural systems.