Business, Economics and Management

Abstract: Public financial support can be rapidly disbursed yet remain unevaluable when fiscal, beneficiary, exposure, and outcome records are not linked. This study audits Mongolia’s livestock-sector support using budget-execution and livestock-loss data for 2010–2024, along with aggregated records for 289 classified White Gold concessional-loan applications as of 20 June 2025. The analysis distinguishes implementation, delivery conversion, concentration, spatial traceability, and the evidentiary requirements for causal effectiveness. Support execution rose from MNT 10.9 billion in 2010 to MNT 316.1 billion in 2024; the mean execution rate was 87.55% and the minimum 45.62%. Livestock mortality was episodic, with 10.2 million losses in 2010 and 9.3 million in 2024, but aggregate fiscal data cannot separate prevention, compensation, or anticipatory financing. Of the classified applications, 145 were approved and 133 disbursed; MNT 411.4 billion was recorded as disbursed, equal to 90.8% of the approved value and 47.2% of the requested value. Disbursement was concentrated in working capital (81.4%), cashmere-related purposes (80.9%), Ulaanbaatar-registered firms (95.0%), and five banks (94.7%; HHI = 0.246). The program is measurable for implementation and concentration, but not for final regional incidence or causal effectiveness. Evaluation-ready policy requires province-level exposure, supplier and beneficiary tracing, and post-finance outcomes.

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: 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: The Gulf Cooperation Council (GCC) states have to find a way to balance their reliance on hydrocarbon resources and their ambitious aims on moving to renewable energy (for instance, the Saudi Vision 2030.) Despite the policies in place, the implementation of renewable energy technologies in the region is relatively low indicating that more needs to be understood about the reasons behind the use of renewable energy. The present research will analyze the macroeconomic determinants of renewable energy consumption in the 6 GCC states for the periods 2000-2025 using a combination of panel econometric techniques and machine learning. Panel data were constructed from data from the World Bank and the International Energy Agency. The econometric results show that R&D expenditure and trade openness are the two most important determinants of renewable energy consumption, which jointly explain about 63% of the variation (adjusted R² = 0.629). The random forest model is consistent with these findings, with R&D expenditure as the most important predictor followed by trade openness. No statistically meaningful relationship was found between GDP, foreign direct investment, inflation and renewable energy consumption in the final models due to the structural characteristics of GCC economies. The random forest model also had a low RMSE value of 0.032 and an out-of-sample R-squared of 0.425, indicating satisfactory predictive performance for an initial model. Sub-period analysis also indicates that the relationships between R&D expenditure and trade openness became stronger after 2015, which coincides with the introduction of national energy transition strategies in the GCC. The study implies that innovation in terms of technology as well as the open nature of economy could promote energy transition in the region whereby high amount of investments in research and development are required as well as deeper international trade linkages might be supporting the acceptance of green energy approach.

Abstract: Criminal extortion and protection fees have emerged as significant threats to the sustainability of township businesses in South Africa, undermining entrepreneurship, formalisation, investment, and local economic development. While FinTech has transformed financial inclusion and digital transactions globally, its potential to disrupt cash-based extortion economies and strengthen the resilience of township enterprises remains largely unexplored. This paper investigates how FinTech innovations, including blockchain-enabled payment systems, mobile money, digital payment technologies, and complementary community-based digital platforms, can enhance economic resilience by reducing vulnerabilities associated with cash-based transactions and extortion. Drawing on the diffusion of innovation theory, this qualitative systematic literature review examined the opportunities, challenges, and institutional conditions influencing the adoption of these technologies within township economies. The findings suggest that FinTech innovations have the potential to reduce cash dependency, enhance transaction transparency, strengthen financial inclusion, improve collective reporting mechanisms, and build community resilience against extortion networks. However, their effectiveness depends on complementary investments in digital literacy, affordable digital infrastructure, institutional trust, enabling regulatory frameworks, and multi-stakeholder collaboration. The paper contributes to digital governance and FinTech scholarship by reframing technology adoption not only as a financial inclusion strategy but also as a governance mechanism that can enhance transparency, accountability, collective reporting, and economic security within vulnerable local economies. It further demonstrates how FinTech-enabled digital financial ecosystems can support the long-term sustainability of township economies through enhanced business resilience, secure digital transactions, and inclusive local economic development.

Abstract: (1) Background: Generative and, increasingly, agentic artificial intelligence (AI) are reshaping how firms create, deliver, and capture value. Yet business model innovation (BMI) research largely assumes that humans design business models and digital technologies support their decisions. The capacity of AI systems to generate value-bearing artifacts and undertake goal-directed action challenges this assumption. (2) Approach: Integrating research on business models, dynamic capabilities, and AI in management, this concept paper develops a framework that distinguishes three logics of AI-enabled BMI: a supporting logic (AI as instrument), a generative logic (AI as co-creator), and an agentic logic (AI as economic actor). It explains how these logics vary in the locus of agency and the tempo of innovation. (3) Contribution: The paper advances six propositions showing how generative and agentic AI redistribute agency in value creation, delivery, and capture, while shifting BMI from episodic redesign toward continuous and potentially autonomous adaptation. (4) Conclusions: It develops a research agenda spanning value architectures, human–AI configurations, governance and legitimacy, organizational capabilities, and measurement, thereby providing a theoretically grounded and empirically tractable foundation for research on AI-enabled BMI.

Abstract: Sustainable development of the agro-industrial complex (AIC) requires an integrated assessment of investment activity, production performance, financial returns, and resource provision at the district level. This study evaluates the production and economic performance of 13 districts of the North Kazakhstan Region using a composite scoring approach based on seven groups of indicators: investment activity, gross agricultural output, crop yields, livestock productivity, crop-production profitability, livestock-production profitability, and capital intensity. The information base includes official district-level statistics, materials of the regional agricultural authorities, data on fixed production assets for 2021–2023, and national land-use reporting. The results reveal substantial territorial differentiation. Kyzylzhar and Taiynsha districts form the high-performance group, Esil District is classified above average, whereas Ualikhanov District occupies the lowest position in the aggregate ranking. The analysis also identifies a scale-efficiency mismatch: large agricultural territories do not necessarily demonstrate high profitability or productivity, while several medium-sized districts achieve stronger returns on land and production resources. The findings provide a comparative basis for territorially differentiated agricultural policy, targeted investment support, technological modernization, and the development of processing infrastructure in the North Kazakhstan Region.

Abstract: Artificial intelligence is rapidly transforming education globally, yet the economic consequences of AI-enabled education remain insufficiently understood. Existing research has largely examined AI in relation to learning outcomes and instructional efficiency, with limited attention to its implications for human capital formation, productivity, and inclusive economic growth. This study develops and empirically examines a framework linking AI-enabled education with economic outcomes through the formation of human capital and the development of labour-market-relevant skills. Using OECD country-level panel data for 38 countries over the period 2015-2025 (N = 350 country-year observations), we construct a composite AI-Enabled Education Index (AIEI) using Principal Component Analysis. The index incorporates indicators of digital educational infrastructure, AI skills, educational technology adoption, teacher digital competence, and AI-related educational policies. We employ two-way fixed effects estimation with country and year fixed effects, mediation analysis, and robustness tests including system GMM and instrumental variables. The AIEI demonstrates a significant positive association with human capital formation (β = 0.28, p < 0.001). Human capital mediates the relationship between AI-enabled education and labour productivity, with the indirect effect accounting for 45.5% of the total effect (Sobel Z = 4.82, p < 0.001). AI-enabled education is also positively associated with inclusive economic growth (β = 0.28, p < 0.001). Heterogeneity analysis reveals that effects are larger in countries with higher digital infrastructure (difference = 0.20, p < 0.001) and stronger institutional quality (difference = 0.20, p < 0.001). AI-enabled education contributes to human capital formation, productivity, and inclusive economic growth, with human capital serving as a key mediating mechanism. Investments in digital infrastructure, teacher development, and institutional quality amplify these benefits. The findings provide evidence for policy interventions that leverage AI to enhance educational and economic outcomes.

Abstract: People with disabilities experience attitudinal, communication, environmental, and digital barriers when using financial services. This descriptive implementation case study examined a co-designed, three-day disability-inclusion training intervention delivered to 25 staff in a commercial bank in Bangladesh. Study-specific questionnaires assessed self-rated familiarity with disability types and inclusive-banking rules or policies, confidence identifying and addressing barriers, confidence supporting customers with disabilities, and frequency of considering disability inclusion before and immediately after training. Participant-level pre/post records could not be reliably linked, so group-level descriptive comparisons were used. The proportions selecting the two highest response categories were 4/25 (16%) before and 21/25 (84%) immediately after training for familiarity; 12/25 (48%) and 23/25 (92%) for confidence identifying and addressing barriers; and 8/25 (32%) and 20/25 (80%) for confidence supporting customers. The group mean for frequency of considering disability inclusion was 2.5 before and 4.4 immediately after training on a five-point scale. At approximately 9-10 weeks, participant-level follow-up data were available for 20 participants; 15/20 (75%) selected very or extremely confident when asked about initiating interactions with people with disabilities, while self-reported use of inclusive language and avoidance of assumptions varied. The follow-up questionnaire did not repeat the immediate measures and therefore could not assess sustained effects. Reported organizational responses included disability-inclusive wording in a proposed recruitment policy, video-call sign-language support and Braille materials reported as available, and digital accessibility work underway. Findings suggest disability-led training can be implemented in financial institutions, but the uncontrolled design, study-specific self-report measures, unpaired analysis, short follow-up, and lack of independent verification preclude causal conclusions.

Abstract: Contemporary adults report declining social excitement, weakening conversational motivation and a sense of emotional flattening within peer groups, even with the sheer amount of experiential currency available around. This paper argues that this paradox arises not from individual habituation alone, but from what this paper proposes as Collective Hedonic Adaptation (CHA). Experiential homogenization (EH) across social groups systematically depletes the Interpersonal Information Utility (IIU) of shared narratives. This paper introduces the Avocado Paradox framework, integrating Hedonic Adaptation Prevention (HAP) theory, Optimal Distinctiveness Theory (ODT) and Socioemotional Selectivity Theory (SST) into a unified conceptual model linking Experiential Saturation (ES) to declining Conversational Novelty (CN), Social Engagement Quality (SEQ), Perceived Life Excitement (PLE) and Subjective Well-Being (SWB). Six propositions are advanced including Algorithmic Curation as a historically novel accelerant of narrative capital collapse. Implications for consumer experience design, brand differentiation strategy, and well-being research are discussed.

Abstract: Information Technology (IT), Information Systems (IS) and Computer Assisted Audit Techniques (CAAT) tools are common in auditing innovative environment. For instance, CAAT and associated auditing tools potentially enhance quality and enables auditors to achieve efficiency. However, audit firms still struggle to reduce resistance in the use of CAAT and other IT audit tools toward the plain compliance of the related standards. This study performs a comprehensive literature survey and analysis of frameworks of the main actors of the usage of IT audit tools. It toes the ISA 620 standard and the resistance theory. It further performs an in-dept analysis on the IT auditing tools in respect to application to auditing procedures. Findings show that the research concerning CAAT and IT auditing tools is increasing. So also, the networks of authors are going from the more traditional outlook spanning other frontiers. Findings also show that the application of IT tools generate more jobs and insights for the financial auditor while it spurs efficiency. We show that the adoption of the IT audit tools is strategically faced with conflicting priorities of the financial auditor inasmuch as the use of specialist is presently dominating the assurance services and it is hard to share from the acquired cake. The onus of application of innovative tools such as AI whose impact must be shared between the audit firm, and the client is yet under debate. We show that audit firms encourage compliance of ISA 620 where audit partners’ IT background strengthens the effective use of IT audit tools. We identified data from papers, frameworks and models that discusses the use of IT audit tools, in auditing. These constructed data were then categorised by technique, engagement phase, and attributes to enhance their effective usage. The analysis categorises into, IT auditing tools in lieu of dragged efficiency, ISA 620 adoption framework (620AF) resistance, overcoming regulatory and methodology hurdles, and identifying gaps for future research, and classifying topics mostly encountered in the literature. Overall, we contribute to the IT audit literature and practice by showing how engagement partners can be complaint with relevant standards and be less resistant and diffuse IT audit tools. The implications of this study to academia are apart from the traditional authors of IT/IS auditing and tools, other networks of authors need to be incentivised. Policy makers and assurance gatekeepers need to tighten their approaches to enhance compliance of ISA standards in the use of auditing tools in the auditing procedures.

Abstract: To promote the sustainable development of New Energy Vehicles（NEVs）, multilevel governments are formulating an increasing number of policy mixes. A policy mix combines different policy tools from various governance levels, where interactions may occur. Using data from China’s NEV industry, this study creatively divides policies into strategic, supply-side, demand-side and environment-side policies, then further divides the policies into central policies and local policies. Then we investigates how multi-type policies at both the central and local levels affect innovation output and further explores their synergistic effects. The findings show that at the single-level government level, the demand-side subsidy phase-out policies have an inverted U-shaped impact on the innovation output of NEV. And the other multi-type policies exert a positive influence on NEV innovation output. However, when all policies are implemented simultaneously, the involvement of supply-side policy and demand-side policy at the local government level have a policy superposition effect, the effects of local environmental-side policy is crowded out. Synergy is present both within same-type policies and across multi-type policies in multilevel governments. The multilevel governments should pay attention to the coordination and optimization of different types and levels of policies in the NEV industry.

Abstract: Based on Information Processing Theory (IPT), this study examines the impact of suppliers’ digital transformation (SDT) on focal firms’ green innovation (FGI) through supply chain information processing. From an information processing perspective, it identifies three boundary conditions: geographical distance, environmental regulation intensity, and analyst attention, and further analyzes how they moderate the relationship between SDT and FGI. A “focal firm–year–supplier” dataset of Chinese listed firms from 2008 to 2022 was established through data matching. A series of robustness checks and endogeneity treatments were conducted to ensure the reliability of the findings. The results indicate that SDT can significantly enhance FGI by facilitating information transmission and integration across the supply chain. Moreover, this positive effect becomes more pronounced as geographic distance decreases, environmental regulation intensity increases, and analyst attention rises; these findings provide indirect evidence supporting the information-processing in supply chain. By systematically analyzing how SDT promotes innovation performance beyond organizational boundaries through information transmission and integration, this paper reveals a novel type of interaction between suppliers and focal firms, thereby contributing to the literature on the determinants of green innovation and information spillovers.

Abstract: Many economic time series are compositions: educational attainment shares, waste treatment routes, energy mixes, sectoral employment. Forecasters usually transform such series to log-ratio coordinates and apply generic univariate or multivariate methods, imposing no economic restriction on how the parts move. Evolutionary game theory offers an obvious candidate restriction, since replicator dynamics makes the growth rate of a share proportional to its relative payoff advantage. We take that restriction seriously as a forecasting device. A discrete-time replicator-mutator map with linear payoffs and a common congestion term is estimated under three pooling regimes, country-specific, fully pooled, and shrunk between the two, and raced against five log-ratio benchmarks in an expanding-window rolling-origin design on three Eurostat panels. The central finding is that the pooling regime governs accuracy more strongly than the choice between structural and statistical models. Moving from country-specific to pooled parameters reduces the mean absolute scaled error by 27.8% on educational attainment and by 15.3% to 19.8% on municipal waste routes, whereas the best structural specification differs from the best benchmark by only 1.3% on educational attainment. Structural specifications enter the model confidence set on the attainment panel but not on the waste panels, where no-change forecasts dominate. Win rates diverge sharply from mean losses, which suggests combination rather than selection.

Abstract: This study examines entrepreneurs’ awareness and preventive activities regarding the environment and employees as key elements of the Quality 5.0 concept. Quantitative research was conducted among manufacturing companies in Greater Poland that had not implemented or certified a quality management system. Data were collected using CATI interviews based on an original questionnaire assessing the importance and implementation of 24 preventive management factors, with detailed analysis of 10 factors. A quota-random sample of 380 enterprises, stratified by company size, was used. The findings indicate that maintaining good relationships with the environment, ensuring occupational safety and ergonomics, careful supplier selection and continuous evaluation, and improving employees’ qualifications and skills are perceived as the most important preventive management factors. Environmental relations and workplace safety also achieved the highest implementation levels, suggesting their strongest contribution to a preventive management approach. In contrast, participatory management received relatively low importance and implementation ratings, highlighting the need for further investigation. The results emphasize the role of preventive management practices in supporting the Quality 5.0 concept and provide practical guidance for organizations seeking to strengthen sustainable, employee-oriented, and proactive management approaches.

Abstract: The quantification of structural resilience and sub-percentile tail risk represents a major challenge across both corporate financial engineering and modern industrial logistics. Traditional aggregation architectures—principally linear risk matrices and parametric extreme-value copulas—harbor critical, systemic blind spots. This paper highlights a numerical limitation of the Gumbel extreme-value copula in deep-tail regions (F≥0.999). Analytical results indicate that the logarithmic structure of the tail generator produces progressively higher sensitivity near the distribution boundary, yielding an empirical condition number greater than 1,220 at the regulatory 99.9% Value-at-Risk (VaR) threshold. This numerical conditioning issue increases sensitivity to sample noise and data scarcity, resulting in a 36.5% underestimation of systemic tail damage. The proposed model formalizes risk scenarios by mapping multi-node threats as normalized directional unit vectors within a compact 3D vector space. Interactions are then calculated algebraically using geometric invariants—the Dot Product for root-cause convergence and the Cross Product Norm for dynamic, second-order risk resonance—effectively contracting high-dimensional combinations into a stable framework. Rather than treating risks as frame-dependent scalar probabilities, this generalized High-Dimensional Geometric Invariant Operational Risk Framework replaces legacy structures with domain-agnostic invariants capturing dynamic risk resonance and multi-trigger cascades. Empirical results across rugged operational environments, acute data scarcity (Ntrain = 100), and high-dimensional scaling (50 risk factors) demonstrate that the proposed model outperforms standard alternatives by a factor of approximately 13 in out-of-sample predictive accuracy (MSE = 0.08193) while maintaining absolute parametric stability. Furthermore, a Taylor-series tensor contraction successfully collapses 1,275 second-order interactions into just 2 free parameters. This framework bypasses iterative Maximum Likelihood Estimation (MLE) bottlenecks, unlocking real-time, low-latency Monte Carlo stress testing for systemic banking compliance and global supply chain risk governance.

Abstract: Sustainable investing has moved environmental, social, and governance (ESG) criteria toward the center of cross-border capital decisions, yet the country-level evidence on whether these criteria attract foreign direct investment (FDI) rests almost entirely on static models and rarely accounts for the quality of a country’s accounting and reporting environment. This study estimates a dynamic model of FDI for 265 economies observed from 2006 to 2020, combining the three ESG pillars with four accounting and tax variables: mandatory adoption of International Financial Reporting Standards (IFRS), the strength of auditing standards, the extent of business disclosure, and the corporate tax burden. A fixed-effects estimator and a two-step system generalized method of moments (GMM) estimator address persistence and the reverse causality between FDI and national conditions, and a panel smooth transition regression tests whether the relationship is nonlinear. Accounting transparency attracts FDI: audit quality and disclosure carry large positive and significant coefficients under fixed effects and across income groups, while the aggregate governance index enters negatively. The corporate tax burden deters FDI with no evidence of an optimal-tax turning point. The relationship is nonlinear in governance rather than income: a panel smooth transition regression locates a governance threshold near 0.97 on the standardized scale, above which the negative association between IFRS and FDI disappears and the effects of disclosure and sustainability reporting strengthen. The results reframe the ESG–FDI question around the reporting and assurance environment through which investors read a country’s ESG credentials, and around the governance quality that makes that environment credible.

Abstract: This research considers the acceptance of Artificial Intelligence (AI), technological and organizational readiness, willingness to change, and concerns related to AI adoption among employees of local government or-ganizations in Greece. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), extended featuring Organizational Readiness for Change theory and AI-specific individual and organizational determinants, the study puts forward an integrated conceptual framework for examining employees' behavioral intention to adopt AI. The research adopted a quantitative research methodology using a structured online questionnaire dis-tributed through Google Forms to municipalities and public organizations across Greece, yielding 239 valid responses from local government employees. The outcome of the primary research indicates that there is a positive attitude towards AI and a moderately strong intention to use AI-based applications in the workplace, with participants recognizing AI's potential to improve ef-ficiency, service quality, and decision making. Nonetheless, still the technological and organizational readiness remains at a transitional stage, with considerable challenges in digital skills, data governance, strategic planning, and infrastructure. Employees demon-strate preparedness to engage with the organizational changes AI adoption requires, however, concerns continue about job transformation, new skill requirements, and the wider impact of AI on public sector employment.

Abstract: Given the growing global concern about sustainability, green energy has emerged as a key pathway to protecting the environment and ensuring economic stability. The study examined the mediating role of green procurement policy in the relationship between green energy projects and environmental value for Independent Power Producers (IPPs) in Ghana. The study was explanatory and quantitative in nature and underpinned by Stakeholder Theory and Resource-Based View. The study included 153 respondents selected from various IPPs and data collected from them using structured questionnaires. Structural Equation Modelling (SEM) with AMOS was used to analyse data. Results showed that green finance and technology adoption have a significant impact on environmental value, while green procurement policy has a strong positive influence on it. Moreover, green procurement policy was found to partially mediate the relationship between green finance and technology adoption and green value. The study finds that implementation of green procurement systems facilitates the utilization of financial resources and technology to support environmental sustainability in the Ghanaian energy sector. It highlights the urgent need for policies and industry leaders to implement sustainable financing, technology innovation and green procurement as a way of paving the way for Ghana's green energy transition.