Business, Economics and Management

Abstract: This paper reviews key deep-sea mining issues. Explained are the potential benefits for International Seabed Authority members and how it can substantially increase the global supply of critical minerals. Deep-sea mining though is controversial because of its uncertain effects on deep-sea ecology with severe negative effects being possible. As a countermeasure it is argued that the existing tax and royalty system operated by the ISA could be used to manage pollution levels should these turn out to be serious which at present is not clearly known.

Abstract: The present study examined how volatility in exchange rate shapes banking-sector financial stability across the G7 and six high-income European countries, consisting of 13 developed economies. The study analyses the time period from 2000–2023. To measure volatility, present study employed GARCH(1,1) conditional variance of monthly real effective exchange rates. Whereas stability is measured through two supporting indicators: the bank Z-score (solvency) and the non-performing loan (NPL) ratio (credit quality). Our analysis combines Fully Modified OLS and two-step System GMM for analysing long-run and dynamic effects. To assess distributional heterogeneity, the Method of Moments Quantile Regression (MMQR) is employed, while Dumitrescu–Hurlin tests are used for examining causality. The results showcase that volatility in exchange rate significantly reduces bank solvency and elevates credit risk. These effects are highly uneven: the adverse impact are faced by most fragile banking systems, those in the lower quantiles of the Z-score distribution and the upper quantiles of the NPL distribution. Causality runs unidirectionally, moving from volatility to instability. Institutional quality, which is proxied by the rule of law and regulatory quality, is seen to significantly decrease the credit-risk channel but not the solvency channel. Our findings provide implications for developed-economies in support of targeted, fragility-sensitive macro-prudential policy.

Abstract: Saudi highland Arabica coffee has been cultivated for centuries on mountain terraces across Jazan, Asir, and Al-Baha. It commands premium prices (SAR 100–200 per kilogram) in one of the world’s fastest-growing coffee markets, while its UNESCO recognition as an Intangible Cultural Heritage of Humanity further strengthens its market position. Yet, despite these favourable conditions, many smallholder farmers remain unable to cover their production costs. Drawing on a stratified survey of 347 farms, a purposive cost-structure dataset of 19 farms, and qualitative field observations, this study examines the structural constraints that keep the sector in a low-viability equilibrium. The analysis shows that labour and water account for 72% of variable production costs, median yield is only 0.25 kg per productive tree compared with a biological potential of 4.0 kg, and 42% of farmers report no commercial sales, relying instead on household consumption and social gifting. Government subsidies keep about 40% of farms profitable, but the employment-based eligibility criterion excludes many younger growers needed for the sector’s long-term sustainability. The study proposes a practical reform package centred on rural infrastructure, cooperative extension and shared labour, performance-based subsidies, and contract farming to transform highland coffee into a viable specialty commodity aligned with Saudi Vision 2030.

Abstract: Voluntary certification policies — which let firms self-nominate for innovative or privileged status — increasingly channel fiscal, financial, and regulatory support, yet evaluating them is deceptively hard: certified firms may outperform because certification works, or simply because firms apply when already rising. We show that voluntary certification can certify winners precisely because rational firms time entry to moments of transitory expansion: the measured premium largely reflects when firms participate, not what participation does. We formalise this as anticipatory take-up: because the benefits are most valuable while a firm scales, forward-looking firms register at the crest of a growth run-up. The mechanism yields three predictions — a cross-sectional premium, a premium that largely predates certification, and entry driven by recent growth, not profitability. We test them in the Italian innovative-SME regime, linking panel data for roughly 2,900 certified and 1,200 non-certified SMEs to each firm's registration date and sector, and treating performance as a six-dimensional vector. Certified firms grow far faster than balanced peers yet are financially less solid, especially when smaller and capital-intensive. Once registration timing is exploited through event-study and staggered difference-in-differences designs, the premium proves largely selection: about 81 per cent of the revenue advantage predates registration, and the residual merely continues a pre-existing trend. A hazard model confirms it — entry rises with recent growth and smaller size, not profitability. The paper reframes the evaluation of voluntary certification from average effects to observable dynamic selection: such schemes mark firms already on distinctive trajectories rather than create them. The lesson generalises — robustness to omitted variables is not robustness to selection on trends, and cross-sectional evaluations mislead unless they exploit the timing of take-up.

Abstract: In this article, the relevance of the known economic benefits of marriage and cohabitation is reconfirmed. Based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) of the period 2015 to 2022 and 16 European countries, the severity of financial distress is explained by relational, health, financial, as well as general characteristics. The study reveals that singles are less likely to report greater easiness to make financial ends meet. Furthermore, income and employment appear to enable overcoming financial distress, while liabilities and reduced health facilitate financial distress. From all countries, Swedes and Danes report greater easiness to make ends meet. Econometrically, the study relies on ordered and non-ordered, panel and non-panel logistic estimators, which implement, if required, the Mundlak correction and/or calibrated longitudinal weights. Overall, the study illustrates that partnership relieves financial distress.

Abstract: Artificial intelligence is increasingly expected to raise productivity by automating tasks, augmenting human work, reducing information-processing costs and supporting new forms of economic organisation. Yet productivity gains do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand. This conceptual review develops the notion of the Distributional Absorption Threshold of AI-Induced Productivity, defined as the point beyond which productivity gains associated with AI are no longer accompanied by proportionate increases in broadly distributed real purchasing power and household consumption. The review argues that the macroeconomic significance of AI-induced productivity depends not only on technological efficiency, but also on the distributive transmission of productivity gains through labour income, disposable income, prices, investment, public expenditure, transfers and external demand. The framework distinguishes between a favourable transmission path, in which AI-induced productivity strengthens purchasing power and effective demand, and a critical transmission path, in which productivity gains are weakly transmitted to households and may generate absorption tension. The review formulates conceptual propositions and proposes possible indicators for future empirical research, including the productivity-real labour income gap and an absorption tension indicator. Its contribution is theoretical: it reframes the AI productivity debate beyond automation anxiety by linking technological change, income distribution and effective demand in a single analytical framework.

Abstract: This paper discusses the progress of research in the field of "Artificial Intelligence and Macroeconomics/Econometrics" from 1975 to October 14, 2025, using a bibliometric analysis approach. The main research method of this paper is bibliometric analysis using VOSviewer software, which examines the characteristics of published articles such as co-authors' collaborations, geographical distribution, keywords, and reviews of scientific productions of different institutions and journals. By reviewing 256 articles in the Scopus database from 1975 to October 14, 2025, the findings showed that the growth of research in this field has accelerated since the 2010s. The 2023-2024 season has peaked. The United States and China have been the largest producers of scientific papers in this field. The results were grouped into five main research clusters: (1) AI-based economic decision-making; (2) forecasting econometrics and spatial econometrics; (3) innovation and sustainability; (4) cost effectiveness and sectoral risks; and (5) business intelligence and organisational data. Co-citation analysis shows that the intellectual foundation of this field is based on econometrics, statistics, and artificial intelligence. Policy implications suggest that increased interdisciplinary collaboration and open data infrastructure can accelerate the integration of AI into macroeconomic policymaking.

Abstract: This study investigates the varying implications of digital infrastructure on sustainable economic growth in five major emerging countries (Egypt, India, Kenya, Saudi Arabia and Sudan) during the period 2014-2025. We use panel data, System GMM estimation and Random Forest regression and classification to study the impact of internet penetration, mobile broadband subscriptions and fixed broadband subscriptions on sustainable GDP per capita growth. Our results indicate the statistical and economic impact of digital infrastructure on economic growth. The biggest benefits are from internet penetration, then mobile broadband. Fixed broadband is not statistically significant in the full model, which reflects the low access in these countries. Random forest regression (R2=0.538, RMSE=2.31) and classification (F1-score=0.714, AUC-ROC=0.792) models can accurately predict 75% of high and low growth regimes in out-of-sample validation. Internet penetration drives growth in middle income countries (Egypt, India) while mobile broadband drives growth in low income countries (Sudan, Kenya). Average education >6 years doubles the benefits of digital infrastructure growth on human capital. The sustainability implications of these results are relevant for the SDG 4 (quality education), SDG 9 (infrastructure and innovation), and SDG 10 (reduced inequality) programs. There is a digital divide as the MENA countries have 87% internet penetration compared to 44% in Sub-Saharan Africa. We recommend the expansion of mobile broadband in low-income countries and investments in internet infrastructure and human capital in middle-income countries to maximize sustainable growth.

Abstract: Mortality in smallholder cattle and buffalo systems is driven by climate, and the climate signal is not uniform. Heat and cold stress, flooding, and climate-sensitive disease emergence act through different physiological and ecological pathways, yet livestock-loss models usually compress them into a single elevated-mortality state. This paper builds a climate-state-dependent mortality model for the Thai national herd, separating an endemic baseline from a temperature-extreme regime and a moisture- and disease-driven regime. A 100,000-iteration Monte Carlo, calibrated to the 2024 national herd and a 2017 primary farmer survey updated to 2026 prices, generates the annual loss distribution and decomposes it by climate driver. The average year is governed by endemic mortality, which accounts for about 81 percent of expected loss but none of the extreme tail. The tail belongs entirely to the two climate regimes: the moisture- and disease-driven regime carries roughly 69 percent of losses beyond the 95th percentile and the temperature regime about 31 percent. The driver of the typical year is therefore not the driver of the catastrophe. A second result concerns the ecological footprint of insurance. Using a reduced-form behavioral layer, mortality-payout design is shown to suppress adaptive destocking and lift stocking pressure 10 to 16 percent above a sustainable land-carrying-capacity benchmark, so that an instrument promoted for climate adaptation can degrade the rangeland it is meant to protect. The findings argue for regime-specific risk financing, for pairing insurance with heat-adaptation and animal-health investment, and for treating the carrying-capacity externality as a design parameter rather than a side effect. The paper closes with a research agenda for climate-state livestock-loss modeling, intended to give the field a transparent and reproducible starting point.

Abstract: The energy transition requires substantial investments in renewable energy sources and in policy tools that effectively guide market participants' decisions. We examine whether the European Emissions Trading System has effectively driven renewable energy deployment in the power sector. Combining panel econometric analysis of installed renewable capacity across European Member States, a cross-country survey of 127 firms in six EU ETS countries, and an hourly, market-based estimation of inframarginal rents across a panel of eight Member States, we find that public support mechanisms consistently drive renewable-capacity expansion. The EU Allowance price does not exhibit a significant direct effect on renewable deployment after controlling for structural and market variables. In electricity markets, the ETS primarily operates through mechanisms that shape wholesale electricity prices and carbon-cost pass-through, generating persistent inframarginal revenues for low-carbon technologies through marginal price formation. A comparison between regulatory CO₂ factors used for indirect ETS cost compensation and observed market-based pass-through indicators reveals materially divergent magnitudes across Member States, with regulatory-to-observed ratios ranging from 1.2x in Italy to 46.2x in Sweden. The evidence suggests that, in European electricity markets, the ETS currently operates more directly as a wholesale-price transmission and revenue-redistribution mechanism than as an autonomous driver of renewable investment deployment.

Abstract: This paper researches how economic downturns and currency movements affect the quality of bank loans in Central, Eastern, and Southeastern Europe. The analyzed dataset includes annual data for 14 national banking systems covering 2008–2023. We estimate a bias-corrected dynamic fixed-effects model and check robustness with Driscoll-Kraay and cluster-robust standard errors. Further, we extend the analysis by specifying profitability-based thresholds and conducting forward-looking scenario simulations. Credit risk is very persistent. Following an initial deterioration, the quality of bank loans recovers slowly, and our estimated bias-corrected autoregressive coefficient of 0.944 implies a half-life of 12.0 years. Among the macro-financial drivers, lower real GDP per capita growth and exchange rate depreciation predict higher non-performing loan (NPL) ratios. The exchange-rate coefficient is significant only with selected inference approaches. After adjusting the depreciation series for breaks due to the euro adoption, the exchange-rate result remains, suggesting it is not solely a measurement artifact. The threshold test indicates that linearity is not supported at the 5 percent level and that a profitability cutoff is estimated at a 1.80 percent return on equity. However, the low regime only has 32 observations, so regime-specific precision is limited. No significant interaction between depreciation and institutional quality is found between the 14 countries. A severe, combined adverse situation increases the NPL ratio from 6.54 to 12.32 percent over 5 years. These findings may interest policymakers designing macro-prudential frameworks in emerging Europe, central banks conducting stress tests, and researchers studying the international transmission of financial shocks under institutional quality conditions.

Abstract: The Economics of Science examines resource allocation (funding, personnel, infrastructure) and management models for research. A key challenge is assessing research quality and effectiveness when expert review is costly and research is increasingly large-scale and interdisciplinary. Bibliometric analysis of large abstract database records can help address this by identifying research structures and priorities. This study focuses on the Economics of Science with two objectives: identifying key research areas through publications in OpenAlex and using the Mt-KaHyPar program to partition a hypergraph of keywords for analysis of the subject matter within these blocks. The study utilized bibliometric records from the OpenAlex abstract database, containing 474 million scholarly works. A query returned exact matches for specific keywords related to the economics of research, covering the period from 2023 to 2026. In total, 5,556 records were identified, with 5,275 being unique; out of these, 5,250 included data in the “keywords” and “concepts” fields, which were used for analysis. This study illustrates the effectiveness of keywords from OpenAlex in categorizing bibliometric records into subject areas. By utilizing the Mt-KaHyPar program to partition a hypergraph formed from these keywords, balanced blocks were created, revealing themes in the “Economics of Science.” Four key themes emerged: 1) “The Institutional and Interdisciplinary Foundations of Knowledge Production”; 2) “Ethics, Knowledge Governance, and Corporate-Academic Dynamics”; 3) “Pharmaceutical Economics, Bio-Industrial Innovation, Drug Development, and Agro-Food Technologies”; 4) “Digitalization of Science and Computational Methods.” Notably, the “Ethics” theme significantly overlaps with the core theme based on co-occurring keywords. The topic “Ethics, Knowledge Governance, and Corporate-Academic Dynamics” is identified as highly relevant for further research in the field of the Economics of Science.

Abstract: Contemporary ESG (Environmental, Social, and Governance) regulation creates costs and risks for businesses, which are associated with the stringency of requirements. This article demonstrates that the key source of these problems is the fragmentation of legal regulation, the inconsistency of reporting standards, and the methodological heterogeneity of ESG indices. Based on a comparative legal analysis of eight jurisdictions (the US, EU, China, India, Brazil, Russia, South Africa, and Kazakhstan), three models of ESG regulation are identified: prescriptive, market-oriented, and state-centralized. It is shown that extraterritorial pressure (CBAM, CSDDD) and internal regulatory conflicts (for example, in the US) greatly increase compliance costs, especially for developing economies. High divergence in corporate ESG ratings (up to 50–60%) has been empirically confirmed, making global indices of limited applicability for regulatory purposes. In response to these identified issues, a country-specific ESG index, integrated into a closed-loop feedback management system, has been proposed. A two-tier methodology has been developed: calculating a company index (taking into account regulatory burden, extraterritorial pressure, and adaptability) and aggregating it into a country index based on macrostatistics with the possibility of transitioning to Big Data aggregation, including the use of digital SaaS platforms for managing production and environmental data. The results can be used by national regulators to improve the comparability of ESG data and differentiate government support measures.

Abstract: Although economic complexity (ECI) is closely linked to structural transformation, its implications for inclusive green growth (IGG) remain underexplored, particularly in sub-Saharan Africa (SSA). Notably, there is a knowledge gap on how progress in ECI affects IGG. Second, there is a policy gap concerning how progress in energy equity conditions the impact of ECI on IGG. We address these gaps by employing cross-country data from 35 SSA countries for the period 2010-2020. Findings based on Lewbel’s (2012) two-stage least squares and Driscoll–Kraay’s (1998) robust standard errors estimators reveal that ECI does not promote IGG. Particularly, we find that although ECI promotes economic growth, it comes at the expense of income equity and environmental sustainability. The contingency analysis also demonstrates that while improving energy equity amplifies (mitigates) the growth effect (inequality downside) of ECI, it exacerbates the environmental cost. These findings underscore the need for policymakers to design complementary and compensatory policy mechanisms that ensure SSA’s drive toward economic complexity translates into greener and more inclusive growth.

Abstract: Nigeria operated multiple exchange rate regimes which have created significant distortions in the economy resulting to low standard of living, inflation, and loss of investors' confidence. To address these challenges, the Nigerian government officially implemented exchange rate unification to close the gap between official and parallel market rates to reduce poverty level in the economy. The purpose of this study is to examine the effect of exchange rate unification on poverty in Nigeria which is yet to be investigated in the literature. The Autoregressive Distributed Lag model (ARDL) was used as a method of analysis whilst poverty is measured using multidimensional poverty. Data on Multidimensional Poverty, Exchange Rate Unification (ERU) (ratio between official and parallel rate), inflation, economic growth, unemployment, and government social expenditure were extracted from World Bank’s data base and Central Bank of Nigeria statistical bulletin. The study found that exchange rate unification is very significant in explaining long run poverty dynamics than conventional macro economic variables in Nigeria. The study further reveals that exchange rate unification exerts significant reduction in multidimensional poverty, contradicting the perception that unification harms the poor through short-run inflationary pressures, thereby establishing exchange rate unification as an effective policy instrument for inclusive development.

Abstract: This study analyzes the organizational and environmental determinants that predict the intention to adopt biogas–solar microgrids within a circular bioeconomy framework. A quantitative, applied, cross-sectional, and exploratory design was used with 71 valid responses from actors linked to productive, agro-industrial, livestock, energy, and waste-management sectors. The questionnaire measured perceived benefits, barriers, institutional conditions, financial feasibility, environmental value, organizational capabilities, and adoption intention. Psychometric reliability was assessed using Cronbach’s alpha and McDonald’s omega, and predictive modeling compared supervised classification, regression, and unsupervised segmentation techniques. ExtraTrees achieved the best classification performance, with a test ROC-AUC of 0.889, while RandomForestRegressor showed the best regression performance. Organizational capabilities and environmental criteria emerged as the most influential predictors, and K-Means identified two readiness profiles. The findings suggest that adoption intention depends on a systemic configuration of organizational maturity, environmental legitimacy, financial feasibility, and institutional support.

Abstract: Bridging the renewable energy funding gap in African countries remains a major challenge, as domestic resources are often insufficient to support capital-intensive investments. In this context, donor financing, particularly grants and concessional loans, is vital for supporting the energy transition. This paper examines the effect of public debt on donor financing for renewable energy in twenty-eight African countries over the period 2000–2023. Using Driscoll–Kraay standard errors, Panel-Corrected Standard Errors (PCSE), and Feasible Generalized Least Squares (FGLS) techniques, we identify a significant nonlinear relationship, indicating an inverted U-shaped effect of public debt on donor financing. The results also show a negative effect of total debt service on donor financing support, while the role of institutional quality appears to be moderately important. These findings underline the importance of maintaining sustainable debt levels and effective debt management to attract donor financing and support the energy transition.

Abstract: This study examines the role of agricultural and tourism entrepreneurship in reducing rural poverty through community empowerment within a place-based development framework. Using data from 400 respondents in Jambi Province, Indonesia, and employing Partial Least Squares Structural Equation Modeling (PLS-SEM) combined with the Analytical Hierarchy Process (AHP), the results reveal that agricultural entrepreneurship (β = 0.482, p < 0.001) and tourism entrepreneurship (β = 0.361, p < 0.001) significantly enhance community empowerment. In turn, community empowerment has a strong negative effect on poverty (β = -0.533, p < 0.001). The mediation analysis confirms that empowerment fully mediates the relationship between entrepreneurship and poverty reduction. Furthermore, AHP results indicate that community empowerment is the highest policy priority (44.2%), followed by agricultural entrepreneurship (33.8%) and tourism entrepreneurship (22.0%). This study contributes to the literature by integrating dual-sector entrepreneurship and identifying empowerment as a key mechanism in sustainable and inclusive rural development.

Abstract: This paper investigates the causal relationship between artificial intelligence (AI) investment, smart city governance infrastructure, and urban total factor productivity (TFP) across ten leading digital economies over the period 2010--2026. Drawing on a novel panel dataset that integrates ICT capital expenditure, digital infrastructure indices, Global Innovation Index scores, and the United Nations E-Government Development Index, we estimate dynamic System Generalized Method of Moments (GMM) models combined with Spatial Durbin specifications and machine-learning-based regime clustering. Our results indicate a statistically and economically significant positive association between AI investment and urban TFP: a ten percent increase in AI investment (as a share of GDP) is associated with approximately 1.5 percent higher TFP, conditional on digital infrastructure endowment and innovation capacity. We further document an inverted-U (EKC-type) relationship between AI intensity and employment polarization, suggesting that economies surpassing a threshold AI investment level of approximately 5.2 percent of GDP begin to experience convergence in skill demand. Spatial spillover effects are quantitatively important, with indirect TFP effects accounting for roughly one-third of total impacts. These findings are robust across alternative specifications, sub-period analyses, and a jackknife leave-one-out procedure. Our study contributes to the emerging literature on AI-driven urban transformation by providing causal panel evidence and a tractable theoretical framework, and offers policy implications for economies at different stages of digital transition.

Abstract: This paper provides the first systematic, cross-country empirical comparison of the Recovery and Resilience Facility (RRF) and Cohesion Policy funds (CPF) in the domain of renewable energy deployment. Covering 14 EU Member States, the analysis combines quantitative cross-country evidence on financing volumes, technology mixes, implementation speed, and reported capacity achievements. The findings show that the RRF represents a major amplification of EU renewable energy financing, with planned allocations exceeding Cohesion Policy expenditure by a factor of five to ten. At the same time, claims of superior performance-based delivery require qualification: green transition financial progress lags the general RRF disbursement rate, milestone fulfilment for renewable energy falls short of planned indicative rates in most countries, and reported operational capacity figures raise plausibility concerns. The analysis reveals no meaningful correlation between milestone and target fulfilment and progress with renewable energy Country-Specific Recommendations, suggesting that administrative compliance with milestones does not immediately translate into structural reform outcomes. These findings carry direct implications for the design of the post-2027 EU financial framework, particularly regarding the stabilisation of performance indicators, the introduction of attribution protocols for reform-linked achievements, and the preservation of complementarity between performance-based and non-performance-based approaches.