Business, Economics and Management

Abstract: Machine learning models are increasingly used in credit risk, financial statement fraud detection, financial distress prediction, and related financial classification tasks with substantial consequences. Yet model comparison still often privileges statistical discrimination even though superior predictive performance does not necessarily imply superior financial decisions. This limitation is acute in settings involving rare events, where class imbalance, probability miscalibration, temporal distribution shift, asymmetric error costs, operational capacity constraints, and governance requirements interact. This conceptual and methodological paper develops an integrated framework for decision validity without introducing a new dataset or estimating additional models. The synthesis draws on two complementary empirical streams: evaluating deep learning for financial statement fraud under severe class imbalance and temporally consistent evaluation of credit risk models that accounts for asymmetric costs under distributional shift. These streams integrate established research on precision and recall, probabilistic calibration, concept drift, classification with unequal costs, profitability-based credit scoring, explainable artificial intelligence, and model governance. The proposed framework consists of five sequential gates: validity for rare event detection, probability validity, temporal validity, decision-utility validity, and governance validity. The gates are intentionally not compensatory: strong performance at a later stage should not automatically offset a fundamental failure at an earlier stage. The paper also maps credit default and financial statement fraud, a minimum reporting standard, and a research agenda for dynamic thresholds, temporal calibration, explanation stability, and utility under capacity constraints. The central conclusion is that model superiority in financial machine learning is conditional rather than absolute and should be asserted only relative to an explicit deployment and decision environment.

Abstract: Almost everything known about equity rounds in young firms comes from the records of professional investors, so the evidence is selected on the presence of an intermediary. We read rounds instead from the share-premium reserve, a compulsory, disclosed and restricted line of the Italian statutory balance sheet, for 3,225 certified innovative SMEs over 2016–2025: 2,991 priced capital increases on 1,330 firms. Firms about to raise grew twice as fast in the preceding year and held identical cash, yet six in ten closed it at a loss; profitability falls and intangible assets rise only after the round. Seven panel estimators place the association across firms and not within them; a taxonomy estimated blind to the event finds the mechanism in all five financial configurations, with incidence differing eightfold; six flexible learners fail to improve on the linear specification. Selection operates on growth, not on profitability, a distinction intermediated samples cannot see.

Abstract: Retail media refers to retailer-operated online and offline media environments in which brands place advertisements and retailers use first-party transaction data to measure and attribute advertising outcomes. By 2026, spending in North America will rise by 17.8% year-on-year to $71.09 billion, and by 2030, it is expected to reach $142.07 billion and account for 23.9% of the total U.S. digital ad spend. In-store digital media is one of the fastest-growing types of this. The attribution rules for this format are defined independently by each network; attribution windows vary substantially, from seven days after a view to same-day click attribution, and performance metrics range from total sales to gross merchandise value (GMV).The network cannot determine whether the same consumer has been identified by another network, and according to industry surveys, the performance data of the same advertising campaign across different networks may differ by as much as 15 per cent. Seventy-five per cent of the advertisers are unaware of how to determine an additional effect, and only 15 per cent believe their organisation can measure such changes. As the number of networks run by brands has increased from six to eleven, the total self-reported incremental effects across networks have been consistently higher than the actual incremental effects at the brand level; in-store digital media is understated under the last-click attribution model because it lacks a click signal. This study develops a computational calibration and budget-reallocation framework that preserves the original attribution rules of individual retail media networks while mapping their self-reported effects onto a unified incremental scale. Geographic holdout experiments are used as attribution-independent anchors, and network-specific conversion relationships are estimated through a hierarchical Bayesian model implemented by parallel NUTS sampling. Cross-network repeated exposure is encoded in a sparse user–network–time matrix using anonymized identifiers, duplicated increments are corrected through vectorized contribution reallocation, and monotonic cubic-spline response curves are fitted for constrained budget optimization at 1% budget increments. The computational dataset contains nine retail media networks, 42 brands, and 317 advertising campaigns; all cross-platform records are organized in 24-hour aggregation windows, with a maximum association window of seven days.The in-store calibration anchor contains 23 cities, 133 stores, and 23,901 ad-slot weeks, with the spatial-recognition error radius controlled within 20 m to provide a high-precision offline reference for computational calibrationComputational evaluation shows that the mean absolute cross-network discrepancy decreases from 14.8% to 3.2% after Bayesian calibration. Duplicate reach accounts for 27.4% of total reach, including 21.3% from users reached by exactly two networks and 6.1% from users reached by three or more networks. For in-store digital media, the normalized incremental effect increases from 0.583 under last-click attribution to 0.90 after Bayesian calibration and 0.96 after overlap correction, while the median unit-budget return rank improves from 6 to 3. Under a fixed total budget, the constrained reallocation algorithm predicts a 12.6% increase in brand-level incremental response, including 7.9 percentage points associated with reallocating budget toward complementary in-store reach and 4.7 percentage points associated with redistribution among online networks. The resulting workflow provides a reusable computer-side procedure for cross-platform data standardization, Bayesian metric calibration, sparse-matrix overlap correction, marginal-response estimation, and constrained budget optimization, thereby improving the reproducibility of cross-network incremental-effect measurement and allocation analysis.

Abstract: This study investigates the centralized CBI concept in CBI Value co-creation. Centralized CBIs are formed by multi-corporations to facilitate venturing, innovation and co-creation. The study expands on earlier study in CBI value co-creation via the development of a synergetic approach blending cognitive processes and value co-creation. A conceptual framework and scenarios are developed for the central CBI based on thematic analysis on cases of CBIs grounded in data and cognitive generation application to different organizational combinations. It is posited that Parent Companies(PCs) and other organizations (CBIs, UBIs) form centralized CBIs in a Value Co-Creation Ecological System which contains several Meta-Cognitive Processes (Collaboration, Business Restructuring, Communication, Ecological Advantage Function & Adaptation, Opportunity Perception with Strategic Adaptability. Predictive analysis with ML models are also conducted for the proof of concept using the scenarios. An algorithm and an app (Centralized CBI) are thereafter developed to ease the assessment of Centralized CBI formation. This study further expands the CBI theme across multi-industry collaboration with inter-sectorial co-creation efforts using different scenarios, the MMSST (Mixed Methods Strong Structuration Theory) and Cognitive Generation as the core conceptual theories.

Abstract: The sports industry is undergoing a major transformation driven by technological progress, especially in artificial intelligence. Leveraging these capabilities, AI can serve as a strategic asset to enhance leadership capacity, cultivate a stronger organizational culture, and drive improved organizational performance. This review examines the dynamic relationship among artificial intelligence, leadership capabilities, and organizational culture within sports organizations. This paper explores the implications of classical leadership theories alongside recent AI literature, highlighting potential applications for leaders in decision-making, strategic planning, communication, talent management, and organizational transformation. AI systems can bolster leadership competencies such as visioning, achievement orientation, empowerment, networking, and communication by leveraging data-driven insights and predictive analysis. Furthermore, digitalization and the adoption of AI are transforming organizational culture, which encompasses shared values, beliefs, and behavioral norms. The review highlights the critical role of leaders in fostering adaptive cultures that champion innovation, creativity, collaboration, and the adoption of technology. The findings indicate that successfully integrating AI into sports organizations requires leadership capable of merging technological innovation with a human-centric management style. The paper suggests that leveraging AI to develop leadership skills yields positive outcomes for sports organizations' cultures and fosters sustainable competitive advantages in today's sports landscape.

Abstract: Based on bilateral trade panel data from the fifteen member states of the Regional Comprehensive Economic Partnership (RCEP) from 2002 to 2024, this paper employs an extended gravity model to systematically examine the impact effects and transmission mechanisms of exporting countries' environmental regulation depth on bilateral trade scale. The results indicate that the deepening of environmental regulations significantly suppresses bilateral export scale, a conclusion that remains robust after conducting multiple robustness checks. Mechanism tests reveal that environmental regulation deepening transmits to the export side through three channels: first, it compels high-carbon industries to reduce emissions, leading to a significant decline in per capita CO₂ emissions; second, compliance expenditures crowd out R&D resources in the short term, reducing green patent output and indicating that the innovation offset effect of the Porter Hypothesis is subject to a lag; third, it drives the elimination of backward production capacity and substitution by clean energy, reducing per capita energy consumption. Moderation effect analysis shows that countries with higher financial development levels experience greater short-term export declines under environmental regulation shocks, reflecting the accelerated capacity rationalization effect of financial systems. Heterogeneity analysis reveals pronounced structural differences: exporting countries with lower institutional quality and lower urbanization levels are more severely suppressed by environmental regulations, whereas transparent institutional environments and factor agglomeration levels effectively buffer the compliance shock. This paper supplements the empirical evidence on the trade effects of environmental regulations from the supply-side perspective of exporting countries, providing an experiential basis for coordinating green transition with stable foreign trade development.

Abstract: Italy grants registered innovative start-ups fee exemptions, investor tax relief and guaranteed credit, and removes that status sixty months after incorporation. This paper asks whether certification changes firms’ markups. Markups are estimated from a translog production function on 76,466 firm-years of certified start-ups, certified innovative SMEs and ordinary SMEs over 2015–2024. Certified firms price about 12 per cent below ordinary SMEs, but within firms the gap shrinks to 2 per cent and vanishes once persistence is modelled: registration selects firms rather than changing their pricing. Exploiting the statutory expiry, and a 2020 extension that shifted it by one year for adjacent cohorts, the loss of status leaves markups unchanged on average (2SLS −0.005, first-stage F = 750). An unsupervised partition shows this null combines falls of five to six per cent in capital- and materials-intensive firms with none elsewhere; regression learners and double machine learning confirm the central estimates.

Abstract: Purpose: This review explores how artificial intelligence (AI) and AI-enabled digital transformation (AI-DT) may reshape the executive role of the Chief Financial Officer (CFO). It treats the CFO as a member of the top management team and as a strategic, operational, and governance actor rather than only as the leader of the finance function. Design/methodology: An integrative review synthesizes 22 publications identified through targeted searches, review through the CFO lens by one of the authors, publisher and DOI verification, and backward and forward citation searching. One author conducted the identification, initial screening, extraction, and coding; the second author independently audited every inclusion, classification, extraction, and claim–source decision. All differences were resolved by consensus. The final hierarchy contains two direct role-transformation studies, one direct executive-relationship study, five CFO antecedent-and-outcome studies, and fourteen contextual finance-function studies. Findings: The strongest direct evidence does not support simply replacing traditional finance tasks with AI-related strategic tasks. Instead, the reviewed publications suggest a cumulative rebalancing across seven dimensions: (1) responsibilities and mandate, (2) decision rights and authority, (3) strategic influence, (4) executive relationships, (5) competencies and capabilities, (6) professional identity, and (7) governance and accountability. Evidence is strongest for digitalization, analytics, automation, and multi-technology transformation; it is more limited for AI-specific research and very limited for generative or agentic AI. Four provisional configurations—AI Value Strategist, Transformation Catalyst, Digital Steward and Governor, and Efficiency-focused Finance Operator—are presented as emerging but testable propositions rather than validated job types. Originality/value: The review clearly distinguishes between direct insights into the CFO’s role and those that originate from the broader finance and organizational context or are related but less robust. It specifies which technologies are involved and develops a multilevel research agenda covering AI investment, AI-related value creation, human–AI work allocation, and responsible corporate governance.

Abstract: Enterprise AI applications can create value by reducing the labor required to deliver a business service.The people who use an application, the work it performs, and the units on an invoice need not coincide.This paper distinguishes these elements and shows how they relate. It estimates savings from theshare of professional work an application can support, the time it saves after review and rework, andthe proportion of released capacity that translates into lower labor cost. Those savings bound the pricefrom above. What can be charged within that bound is set by the customer’s best alternative ratherthan by the size of the savings, and a corollary states the range of value shares a vendor can feasiblypursue. The billing unit is then a separate choice, governed by what both parties can verify and bythe incentives each unit creates. An illustrative human resources (HR) and payroll case shows thecalculation. The framework explains when per-worker subscriptions are defensible, when transactionor outcome charges are more suitable, and why a value estimate alone cannot establish the profit-maximizing price. It argues that the quantity on which the value depends most, the conversion ofreleased time into lower cost, is chosen by the buyer and cannot be verified by either party, which iswhy applications of this kind are sold by subscription rather than by result. It provides a practicalmethod for analysis and teaching without assuming a universal rate of AI productivity improvement.

Abstract: Copper is recognized as a strategic mineral resource supporting the energy transition and digital development. Based on copper trade data from 2015 to 2024 for 146 countries, the copper industry chain is divided into four functional layers: the raw material layer, the refining and processing layer, the recycling layer, and the equipment layer, for which directed weighted trade networks are constructed. In particular, the equipment layer is used as a proxy for investment in copper-processing capacity and manufacturing capability. Complex network methods, community detection, and dynamic targeted-node attack simulations are employed to examine the structural evolution and resilience of the global copper industry chain trade network. The following results are obtained: (1) Significant hierarchical heterogeneity is exhibited in the global copper industry chain trade network. The refining and processing layer is the densest, while the equipment layer increasingly converges toward it, and both show a trend toward concentration in core countries in the later period. (2) China’s core position is systematically strengthened across all layers. Raw material exports are dominated by Chile and Peru; in 2024, Chile is replaced by the Democratic Republic of the Congo as the largest trade corridor to China in the refining and processing layer; in the recycling layer, a shift from dominance by traditional developed economies toward a multi-node collaborative East Asia–Southeast Asia pattern is observed; the equipment layer is shifted from a pattern led by the United States and Germany to one in which China is made the dominant hub of both supply and demand. (3) Marked reorganization is observed in the community structure. In the raw material layer, Chile is transferred to the China-centered community. In the refining and processing layer, the China-centered community is expanded toward the Asia-Pacific and Africa. In the recycling layer, the trans-Pacific community is gradually integrated. In the equipment layer, three coexisting blocs are formed: an Asia-Pacific community, a Europe–Africa transcontinental community, and Americas communities characterized by North–South differentiation. (4) Network resilience is ranked in the following order: the refining and processing layer > the equipment layer > the recycling layer > the raw material layer. In 2024, resilience is markedly weakened in the refining and processing and recycling layers; some recovery is observed in the raw material layer; the equipment layer remains relatively stable overall.

Abstract: The perishable agro-product supply chain in Bangladesh faces numerous barriers that contribute to food spoilage, economic losses, and reduced supply chain efficiency. Identifying and understanding the interrelationships among these barriers is essential for developing a resilient and sustainable supply chain. This study aims to identify, prioritize, and analyze the critical barriers affecting the perishable agro-product supply chain in Bangladesh. A three-phase methodology integrating the Delphi method, Best-Worst Method (BWM), and Interpretive Structural Modeling (ISM) was employed. Initially, barriers were identified through an extensive literature review and expert consultation. Subsequently, BWM was applied to rank the barriers based on their relative importance, while ISM was utilized to examine their hierarchical relationships and driving-dependence structure. The findings revealed 24 significant barriers affecting the supply chain. BWM results indicated that lack of government support for adopting green supply chain management (GSCM), product freshness depletion, informal payments to local influential groups and law-enforcement agencies, lack of support and guidance from regulatory authorities, and climate change are the most critical barriers. ISM analysis further demonstrated that lack of government support, lack of support and guidance from regulatory authorities, and climate change are the key driving barriers that significantly influence other supply chain obstacles. A nine-level hierarchical structure was developed to illustrate the interrelationships among the identified barriers. The proposed framework provides valuable insights for policymakers, practitioners, and supply chain managers to formulate effective risk mitigation strategies, enhance supply chain resilience, reduce food wastage, and promote sustainable agro-product supply chain management in Bangladesh.

Abstract: The voluntary carbon market (VCM) certifies and trades greenhouse gas mitigation and removal outcomes generated outside compliance markets, yet concerns about additionality, measurement, reporting and verification (MRV), double counting and credit quality continue to constrain market credibility. Stringent integrity requirements may simultaneously increase the cost and expertise burden borne by new climate-tech firms and carbon mitigation project developers. This study examines how institutional requirements for environmental integrity are connected to the market entry and value creation of these actors. A SciVal-based research landscape analysis is combined with a qualitative analysis of legal, policy and market-operation documents, and the European Union, Singapore and the Republic of Korea are compared functionally as cases with different institutional contexts and stages of development. The comparison shows that integrity requirements simultaneously constitute market-access conditions and entry requirements, that entry burdens are addressed not by relaxing quality standards but by entry-enabling conditions such as standardization, technical and advisory support, burden mitigation, collective participation and early-stage finance, and that market participation is converted into economic value only where revenue realization, price valuation, revenue predictability and commercial scaling operate. On this basis, an Integrity–Entry–Value (IEV) structure is proposed as a conditional rather than linear account of VCM formation, offering conditional policy implications for late-forming markets.

Abstract: This study examines accounting connectivity between FY2024 sustainability statements prepared under the European Sustainability Reporting Standards (ESRS) and corresponding IFRS financial statements. Using the full eligible population of 22 Portuguese companies listed on the Euronext Lisbon regulated market, it develops and applies a 26-item Accounting Connectivity Index (ACI) across four dimensions: Referential, Quantitative, Methodological, and Accounting Consequence Connectivity. The analysis combines structured content analysis with qualitative assessment of sustainability-to-accounting pathways. ACI scores range from 17.1 to 94.8, with a population mean of 57.1. Quantitative Connectivity is the strongest and most widespread dimension, largely reflecting traceable relationships between EU Taxonomy metrics and financial-statement amounts. Accounting Consequence Connectivity, by contrast, is concentrated in seven companies. The results show that quantitative traceability does not necessarily imply substantive accounting connectivity and support a distinction between disclosure connectivity and integration into accounting assumptions, estimates, measurement, and financial-statement outcomes. Connectivity pathways also differ across business models. The findings remain stable under alternative population definitions, item specifications, and weighting approaches. The study contributes a replicable, sector-neutral framework for evaluating the nature and depth of connectivity between sustainability and financial reporting.

Abstract: The absence of a monetary value anchor independent of social belief is an unresolved foundational problem of monetary theory. This paper adopts the stance of a conditional research program: if one accepts the normative premise that civilizational survival is a lexicographically prior meta-constraint, the monetary value anchor can migrate from scarcity to negentropic creation. The premise itself is left to historical adjudication; no claim of necessity is made. It should be stated in advance that this paper provides a complete and testable research program: H1 constitutes executed component-level correlational evidence, whereas H2–H5 are fully designed but not yet executed tests, whose empirical realization belongs to subsequent research. Building on the Cheng value equation W=√(E×T), the paper proceeds in two steps. First, it constructs the negentropic theory of value: value is the net increase of systemic order, argued through conceptual definition, stratified connotation, explicit boundary-setting, and a normative foundation that answers—via a lexicographic hierarchy of goals—the challenge of why order-maximization must be the meta-objective; five counter-arguments (subjective value theory, labor theory of value, state credit theory, algorithmic scarcity, and nihilism) are addressed systematically. Second, it derives civilization utility theory: civilization combats entropy through continuous negentropic creation, with the mastery of cosmic truth as the ultimate directional goal. The asymptotic dominance of the cognitive term in the civilization utility function is established as a conditional proposition, supported by an operational counterfactual accounting path for individual scientific truths (e.g., relativity → satellite timing → precision agriculture). On this dual foundation, the paper constructs the negentropic value equation Wτ=√(Eτ·Tτ·Iτ), proves the First Negentropic Theorem (existence, uniqueness, and a welfare-loss bound), a three-period overlapping-generations model, a general equilibrium with endogenous α-coefficients (including scenario analysis under violated assumptions), mechanism defenses (endogenous measurement cost, a capture game, and wealth-mitigated quadratic voting), a four-layer CRN blueprint positioned as a forward-looking research program rather than an immediately deployable engineering scheme, and a compressed macrostability framework explicitly delegated to future research. Cross-country panel evidence shows that R&D intensity alone explains 79% of the variance in innovation-output density, rendering GDP per capita insignificant. We declare throughout: the value equation is a posited model rather than a physical theorem; the truth-mastery construct is a heuristic device rather than a directly measurable variable; all claims stand open to falsification.

Abstract: Research Question/Issue: This study examines the differential effects of board diversity on environmental, social, and governance (ESG scores) and financial performance across different types of companies. We also assess the impact of the 2021 NASDAQ rule relating to board diversity on ESG scores and financial performance. Research Findings/Results: We find the percentage of non-white board members to have a positive impact on financial performance, while the percentage of female board members has a positive effect on Tobin’s Q, but a negative effect on social ratings. Moreover, we find the NASDAQ rule requiring boards to have at least one minority member to have a negative impact on governance ratings. Theoretical/Academic Implications: This paper explores various theories that explain how board diversity impacts firm performance. These theories include agency, resource dependency, human and social capital, stakeholder, upper echelons, social identity, and critical mass theories. Practical Implications: Our results suggest that firms with greater board diversity may have a greater potential for above average financial returns. Moreover, institutional lenders may view these firms to have lower reputational risk. Relatedly, the positive effect of outside directors on environmental scores may attract financial managers who want to invest in environmentally conscious firms.

Abstract: As the electric two-wheeler (E2W) sector rapidly proliferates in India, achieving genuine sustainability is strongly dependent upon upstream supply chain dynamics. Drawing on sustainable supply chain and partnership theories, this research investigates how strategic vendor partnerships (SVP) drive economic, environmental, and social sustainability within this emerging market. We deconstruct the SVP framework into three fundamental practices: outsourcing, vendor-managed inventory (VMI), and vendor selection tied to long-term alliances (VSLTA). For an evaluation of how these practices impact critical sustainability metrics like emission control, waste reduction, resource optimization, cost efficiency, and delivery reliability, we surveyed procurement and supply chain experts from major Indian E2W manufacturers and their tier-1 suppliers. The gathered data was empirically evaluated utilizing exploratory factor analysis and multiple regression. The analysis reveals that sustainable performance is significantly and positively enhanced by all three facets of SVP. Notably, establishing enduring relationships through VSLTA, proved to be the most impactful catalyst for sustainability, followed closely by VMI integration and outsourcing strategies. Finally, this research advances the academic discourse on sustainable e-mobility operations in developing economies and equips industry managers with evidence-based strategies to build supply chains that are both highly competitive and ecologically responsible.

Abstract: UNESCO Global Geoparks are recognized as instruments for territorial development. However, limited research explains how emerging geoparks can transform distinctive territorial assets into sustainable and inclusive tourism destinations. This study examines the Kütralkura UNESCO Global Geopark in Chile and asks how its tourism potential can be activated by addressing governance gaps, territorial capacity constraints, and destination visibility. Adopting a pragmatic approach, we analyzed an open-ended survey of 32 tourism ecosystem actors across the Geopark. Thematic analysis identified four interrelated dimensions shaping tourism development. First, sustainable territorial competitiveness depends on strategically leveraging geo-natural capital, biocultural heritage, and territorial identity as tourism assets. Second, strategic destination governance requires multilevel coordination and sustained vertical institutional commitment. Third, territorial tourism capacity highlights the need to balance tourism development and visitor use with environmental conservation and local capabilities. Fourth, destination activation and visibility involve negotiating local and international branding while strengthening residents’ geoheritage literacy. The findings demonstrate that tourism potential in emerging geoparks depends not only on resource endowment but also on governance, territorial capacity, and destination activation. The study contributes practical insights for geopark management and proposes landscape management as an integrated approach to sustainable tourism development in emerging economies.

Abstract: Initial Coin Offerings (ICOs) have become an important blockchain-based fundraising mechanism; however, ICO white papers vary considerably in structure, content, and disclosure quality, limiting transparency, comparability, and informed investor decision-making. Because white papers often serve as the primary source of project information during fundraising, the absence of standardized disclosure practices represents a significant challenge. Existing literature has examined ICOs from perspectives including tokenomics, governance, disclosure quality, investor behaviour, and fundraising performance, yet little attention has been devoted to developing a harmonized disclosure framework for ICO white papers. To address this gap, this study proposes a harmonized ICO white paper framework developed through a multi-stage research process. A corpus of the ten highest-funded pure ICO white papers was analysed using content analysis, and the findings were compared with academic literature and regulatory guidance to identify both commonly disclosed and externally recommended disclosure elements. The study resulted in a harmonized framework comprising nineteen disclosure sections organized into five major components that integrate empirical industry practice with best-practice recommendations. The contribution of this paper is the development of an evidence-based harmonized ICO white paper framework that provides a standardized disclosure structure to enhance transparency, comparability, and informational completeness within blockchain fundraising ecosystems.

Abstract: This article examines the diversity of industrial relations systems in the European Union. Although member states operate within shared regulatory frameworks and EU-level coordination, historical trajectories, institutional arrangements and socioeconomic conditions have produced distinct national models. Drawing on academic literature, EU policy documents, Eurofound reports and country-specific institutional analyses, the study compares the Nordic, Continental, Anglo-Saxon, Southern and Eastern hybrid models in terms of bargaining structures, coordination mechanisms and the role of social partners. The comparison reveals cohesive tripartite cooperation in Nordic countries, corporatist stability and codetermination in Continental systems, decentralised and market-driven regulation in Anglo-Saxon contexts, fragmented implementation and greater conflictuality in Southern Europe, and institutional instability in Eastern hybrid systems shaped by post-socialist legacies and EU-driven reforms. Digitalisation, demographic pressures and platform labour are placing all models under strain and require more adaptive forms of governance. The article concludes that national diversity remains substantial, while the EU encourages selective functional convergence in areas where common principles interact with domestic institutional capacity.

Abstract: We examine whether improvements in firm profitability are associated with a lower carbon-intensity response to revenue growth. Using 26,866 firm-year observations for Chinese listed companies from 2012 to 2022, we estimate firm and year fixed-effects models in which the carbon-intensity response is measured as the change in carbon intensity relative to the change in revenue. The baseline association between marginal profit and the carbon-intensity response is negative. Importantly, the result remains negative in a denominator-free specification that relates the change in carbon intensity directly to the change in net profit, indicating that the finding is not solely driven by the shared change-in-revenue denominator. The association is weaker above an estimated marginal-profit threshold and is attenuated among firms with higher market value. The results are consistent with an organizational-slack channel.