Economies

32 pages, 1715 KB

Open AccessReview

Circular Economy in Agriculture—Review of Concepts, Practices, and Policy Implications

by Mirela Tomaš-Simin, Dragana Novaković, Dragan Milić, Tihomir Novaković, Marica Petrović, Vladislav Zekić, Dejan Janković and Bojana Komaromi

Economies 2026, 14(9), 408; https://doi.org/10.3390/economies14090408 - 11 Sep 2026

Abstract

The circular economy (CE) has gained increasing attention as a framework for improving resource efficiency and sustainability in agricultural systems. However, circular agriculture remains an evolving field characterized by conceptual diversity, heterogeneous practices, and different assessment approaches. This review examines its conceptual foundations, [...] Read more.

The circular economy (CE) has gained increasing attention as a framework for improving resource efficiency and sustainability in agricultural systems. However, circular agriculture remains an evolving field characterized by conceptual diversity, heterogeneous practices, and different assessment approaches. This review examines its conceptual foundations, practical applications, and policy implications through combined bibliometric and qualitative thematic analysis. Following a structured literature search and screening process, 462 publications were analyzed using VOSviewer to identify the thematic structure and evolution of the field. The bibliometric analysis reveals a research landscape dominated by nutrient cycling, waste management, resource recovery, and environmental sustainability, while economic, social, governance, and implementation dimensions remain comparatively less developed. The qualitative synthesis shows that nutrient and biomass recycling, integrated farming systems, and resource-efficient and digital technologies can support resource circularity, although their sustainability outcomes are context-dependent. Importantly, greater circularity does not necessarily imply greater sustainability due to potential economic, environmental, technological, and system-level trade-offs. Future research should prioritize multidimensional assessment frameworks, long-term empirical evidence, economic and social performance, and governance conditions for implementation. Circular agriculture should ultimately be evaluated by whether resource recirculation generates measurable and lasting sustainability benefits. Full article

(This article belongs to the Special Issue Integrated Territorial Approaches to Circular Economy: Innovation, Governance, and Local Value Chains)

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16 pages, 277 KB

Open AccessArticle

Bank-Specific and Macroeconomic Determinants of Return on Equity in Cambodian Commercial Banks

by Varabott Ho and Siphat Lim

Economies 2026, 14(9), 407; https://doi.org/10.3390/economies14090407 - 11 Sep 2026

Abstract

This research investigates the impact of bank-specific and macroeconomic variables on return on equity as a measure of bank profitability in Cambodia during the period 2014–2024. Based on panel estimation methods, including dynamic panel modeling, the findings indicate that profitability is driven by [...] Read more.

This research investigates the impact of bank-specific and macroeconomic variables on return on equity as a measure of bank profitability in Cambodia during the period 2014–2024. Based on panel estimation methods, including dynamic panel modeling, the findings indicate that profitability is driven by both bank-specific features and country-level economic conditions. The dynamic panel estimates imply positive and statistically significant lagged return on equity, which means moderate profit persistence. Nevertheless, the coefficient is still less than one, indicating that profitability gradually reverts rather than being permanent. In terms of bank-specific factors, non-performing loans have a negative and statistically significant effect on profitability in all models, supporting the view that decline in asset quality leads to deterioration in bank performance via higher provisioning costs, reduced income generation and lower shareholder returns. The size of banks is always positive and statistically significant, meaning larger banks benefit from economies of scale, better market dominance positions, broadening customer bases and greater income diversification. The impact of capital-related variables is also mixed, indicating that the effects of capital strength on profitability depend on model specification. It is only when controlling for bank-specific effects and dynamic adjustment that the loan-to-deposit ratio becomes significant in pooled and random-effects models. Macroeconomic conditions also affect profitability. GDP growth has a positive and significant effect; inflation has a negative and significant effect. Full article

16 pages, 492 KB

Open AccessArticle

The Impact of Cross-Border E-Commerce Pilot Zone Policy on Urban Export Trade in China

by Jun Luo, Junjie Li and Sijia Wei

Economies 2026, 14(9), 406; https://doi.org/10.3390/economies14090406 - 10 Sep 2026

Abstract

This study estimates the effect of the Cross-Border E-Commerce Pilot Zone (CBECPZ) policy on city-level exports in China. Using panel data for 83 Chinese cities from 2013 to 2024 and a staggered difference-in-differences (DID) design, we find that CBECPZ designation increases city-level exports [...] Read more.

This study estimates the effect of the Cross-Border E-Commerce Pilot Zone (CBECPZ) policy on city-level exports in China. Using panel data for 83 Chinese cities from 2013 to 2024 and a staggered difference-in-differences (DID) design, we find that CBECPZ designation increases city-level exports by approximately 10% to 12%. The estimate remains stable across a variety of robustness checks and after accounting for spatial dependence. The spatial estimates also provide suggestive evidence of competition for mobile resources among designated cities. Full article

(This article belongs to the Section International, Regional, and Transportation Economics)

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21 pages, 1296 KB

Open AccessArticle

When Creative Destruction and Industry Renewal Fail to Emerge: A Longitudinal Study of Lebanon’s Banking Sector

by Samar Abou Ltaif and Alkis Thrassou

Economies 2026, 14(9), 405; https://doi.org/10.3390/economies14090405 - 10 Sep 2026

Abstract

Financial crises are often expected to trigger industry renewal through processes of creative destruction; however, this outcome is not automatic. This study examines why the mechanisms associated with creative destruction failed to emerge in Lebanon’s banking sector during the crisis. Using a qualitative [...] Read more.

Financial crises are often expected to trigger industry renewal through processes of creative destruction; however, this outcome is not automatic. This study examines why the mechanisms associated with creative destruction failed to emerge in Lebanon’s banking sector during the crisis. Using a qualitative longitudinal case study and process-tracing approach, the analysis draws on regulatory documents, financial data, institutional reports, and secondary sources covering the period from 2014 to 2024. The findings show that successive regulatory interventions contained short-term pressures while preventing the restructuring mechanisms associated with industry renewal from emerging. Instead, these responses preserved a weakened banking structure and reinforced three interconnected mechanisms: strategic inertia, capability erosion, and the failure of the restructuring mechanisms associated with creative destruction. Over time, banks lost much of their capacity to perform core financial functions, public trust declined, and households and firms increasingly relied on cash and informal financial channels. The consequences extended beyond the banking sector, weakening financial intermediation, investment, monetary stability, and broader economic recovery. The study contributes to strategic management theory by showing that creative destruction is not an automatic response to crisis but an institutionally mediated process. The Lebanese case demonstrates how weak governance, the absence of credible restructuring, and limited mechanisms for institutional exit and resource reallocation can prevent industry renewal from emerging. It further shows how prolonged reliance on short-term crisis management can erode organizational capabilities and preserve dysfunctional institutions rather than promote adaptation and renewal. Full article

(This article belongs to the Section Macroeconomics, Monetary Economics, and Financial Markets)

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16 pages, 331 KB

Open AccessArticle

Economic Freedom, Financial Development and Inequality Dynamics

by Margaret Rutendo Magwedere

Economies 2026, 14(9), 404; https://doi.org/10.3390/economies14090404 - 10 Sep 2026

Abstract

Internationally, rising income inequality has become a critical policy concern, as social disparities often translate into economic and financial vulnerabilities. While economic freedom is generally associated with market efficiency and growth, its distributional consequences remain contested. This study investigates the interplay between economic [...] Read more.

Internationally, rising income inequality has become a critical policy concern, as social disparities often translate into economic and financial vulnerabilities. While economic freedom is generally associated with market efficiency and growth, its distributional consequences remain contested. This study investigates the interplay between economic freedom, financial development, and income inequality across 26 economies from 2002 to 2024, employing panel data techniques, mainly the system generalised method of moments (GMM). In addition to the core variables, the analysis incorporates inflation, economic development, and education to control broader macroeconomic influences. The results indicate that both economic freedom and financial development exacerbate income inequality, particularly when access to financial resources is skewed toward higher-income groups. These findings contribute to the growing empirical literature on economic freedom and the finance–inequality nexus and underscore the importance of inclusive financial policies in the Global South. Policymakers are urged to design interventions that expand equitable access to financial services, thereby ensuring that economic freedom and financial development foster fair income distribution rather than reinforce existing disparities. Full article

(This article belongs to the Special Issue Institutions, Structural Change, and Inclusive Growth in Developing Economies)

54 pages, 1301 KB

Open AccessArticle

Does Institutional Quality Moderate the Effect of Trade Openness on Renewable Energy Transition? Quantile Evidence from Emerging Economies

by Artikov Beruniy, Sukhrob Kholmatov, Nodir Jumaev, Zokir Mamadiyarov, Yusubov Inomjon, Tairova Masuma Mukhamed-Rizaevna and Dodiyev Fozil Utkurovich

Economies 2026, 14(9), 403; https://doi.org/10.3390/economies14090403 - 9 Sep 2026

Abstract

This study examines whether institutional quality moderates the relationship between trade openness and renewable energy transition across thirteen emerging economies over the period 2007–2024. Using a panel framework, the analysis employs Feasible Generalized Least Squares (FGLS), Driscoll–Kraay standard errors, and Method of Moments [...] Read more.

This study examines whether institutional quality moderates the relationship between trade openness and renewable energy transition across thirteen emerging economies over the period 2007–2024. Using a panel framework, the analysis employs Feasible Generalized Least Squares (FGLS), Driscoll–Kraay standard errors, and Method of Moments Quantile Regression (MMQR) to capture both average and distribution-specific effects. The results show that gross fixed capital formation and institutional quality are among the most consistent positive determinants of renewable energy consumption, whereas urbanization and technological innovation, proxied by high-technology exports, exert predominantly negative effects. The effect of trade openness is heterogeneous across the distribution of renewable energy consumption, shifting from positive at lower quantiles to negative at higher quantiles. Most importantly, the interaction between trade openness and institutional quality is positive and statistically significant, with its effect becoming stronger toward the upper quantiles. This finding suggests that stronger institutions enhance countries’ capacities to translate the benefits of international trade, including technology diffusion, investment, and knowledge spillovers, into renewable energy development. The study therefore contributes to the literature by providing quantile-based evidence that the trade openness–renewable energy relationship is conditional on institutional quality and the stage of renewable energy transition. The findings suggest that trade liberalization and institutional strengthening should be pursued as complementary policy strategies to accelerate renewable energy transition in emerging economies. Full article

(This article belongs to the Special Issue The Economics of Energy Transition: Policy Frameworks and Innovations)

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26 pages, 517 KB

Open AccessArticle

Public Education Expenditure and Economic Growth in Egypt: An ARDL Bounds Testing Approach

by Amr M. Elseraty, Ali A. Kammoun, Mohamed A. M. Sallam, Mousa G. Selmey, Yasser Ghallab, Ahmad Shaheen and Mustafa A. Radwan

Economies 2026, 14(9), 402; https://doi.org/10.3390/economies14090402 - 9 Sep 2026

Abstract

The relationship between public education spending and economic growth remains contested in developing economies, where quality, not just quantity, of investment may matter most. This study examines whether public education expenditure has driven economic growth in Egypt over 1980–2023, addressing the under-studied role [...] Read more.

The relationship between public education spending and economic growth remains contested in developing economies, where quality, not just quantity, of investment may matter most. This study examines whether public education expenditure has driven economic growth in Egypt over 1980–2023, addressing the under-studied role of education quality and testing the robustness of the estimated relationship to structural breaks linked to major reforms. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach together with Zivot–Andrews structural break unit root tests and CUSUM/CUSUMSQ parameter-stability tests, the study models short- and long-run effects of education expenditure on GDP growth, controlling for capital formation, labor participation, trade openness, and foreign investment, and incorporating quality proxies such as student–teacher ratios and completion rates. The bound F-statistic (6.23) exceeds the 5% upper-bound critical value of 3.83 (Narayan in 2005 on small-sample critical values), confirming long-run cointegration, and the error-correction coefficient (−0.835, p < 0.01) indicates rapid adjustment to equilibrium. Physical capital is the strongest driver of long-run growth, while education expenditure shows a weakly negative long-run association (significant at 10%), suggesting allocative inefficiency rather than absent returns; quality proxies (completion rate, student–teacher ratio) show a negative short-run and positive one-period-lagged effect, consistent with a delayed adjustment process. Stability tests confirm the long-run relationship is structurally invariant once short-run dynamics are accounted for. Thus, improving the efficiency and quality of education spending, rather than its volume, appears essential for Egypt to advance Vision 2030, SDG 4, and SDG 8. Full article

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38 pages, 10325 KB

Open AccessArticle

Integrating Fuzzy Logic with Grey Relational Analysis for Enhanced Cryptocurrency Transaction Modelling

by Kongolo Olivier Musampa and Clement Jules Mba

Economies 2026, 14(9), 401; https://doi.org/10.3390/economies14090401 - 9 Sep 2026

Abstract

This study develops an integrated Fuzzy-Grey Relational Analysis (GRA) framework for modelling cryptocurrency transaction volume. Using daily Bitcoin data from July 2010 to March 2025 (n = 5359 observations), we perform GRA with sensitivity analysis (ρ = 0.1–0.9) to identify on-chain and [...] Read more.

This study develops an integrated Fuzzy-Grey Relational Analysis (GRA) framework for modelling cryptocurrency transaction volume. Using daily Bitcoin data from July 2010 to March 2025 (n = 5359 observations), we perform GRA with sensitivity analysis (ρ = 0.1–0.9) to identify on-chain and market factors exhibiting the strongest geometric association with transaction volume. Network activity metrics—active addresses (GRG = 0.920), transaction count (GRG = 0.907), and miner revenue (GRG = 0.840)—are more closely related to volume than price (GRG = 0.821) or market sentiment (GRG = 0.673) across all market regimes. These three factors serve as inputs to a Mamdani-type Fuzzy Inference System with three empirically parameterized rules. In walk-forward validation (145 windows, 30-day horizon, 4350 out-of-sample predictions), the Fuzzy-GRA model reduces RMSE by 32.2% and sMAPE by 13.5% compared to linear regression. Ablation tests confirm that on-chain variables account for 89% of this predictive improvement. The Diebold-Mariano test verifies statistical significance (p < 0.0001), while overfitting diagnostics reveal better model generalization (overfit ratio: 0.081 vs. 0.126). The findings imply that transaction volume exhibits stronger geometric association with network activity metrics than with price or sentiment indicators, with implications for liquidity analysis and risk management. Full article

(This article belongs to the Special Issue Modeling and Forecasting of Financial Markets)

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19 pages, 1343 KB

Open AccessArticle

AI-Driven Collaborative Energy Decision-Making Model for Macroeconomic Decarbonization

by Olena Zhytkevych, Andriy Matviychuk and Natalia Osadcha

Economies 2026, 14(9), 400; https://doi.org/10.3390/economies14090400 - 8 Sep 2026

Abstract

This study addresses the growing complexity and heterogeneity of global decarbonization processes, which limit the effectiveness of traditional linear forecasting and policy approaches, and aims to develop an integrated model for coordinated forecasting and management at the macroeconomic level. The proposed D-CPFR (Decarbonization—Collaborative [...] Read more.

This study addresses the growing complexity and heterogeneity of global decarbonization processes, which limit the effectiveness of traditional linear forecasting and policy approaches, and aims to develop an integrated model for coordinated forecasting and management at the macroeconomic level. The proposed D-CPFR (Decarbonization—Collaborative Planning, Forecasting and Replenishment) framework combines country clustering based on self-organizing maps, nonlinear forecasting using multilayer perceptrons, and scenario-based multi-criteria optimization. The results demonstrate that clustering serves not only as an analytical tool but also as a structural basis for forming network interactions among countries with similar decarbonization characteristics, enabling coordinated decision-making and policy alignment. The model provides a mechanism for integrating forecasting outputs with joint management processes, including information exchange, scenario coordination, and investment planning within and across clusters. The findings confirm that the hybrid approach improves the representation of nonlinear relationships and supports more accurate and differentiated modeling of decarbonization trajectories. The proposed framework can be applied to the development of adaptive climate strategies, enhancement of resource allocation efficiency, and support of sustainable economic development across countries with varying levels of economic and energy development. Full article

(This article belongs to the Topic AI-Driven Information Governance for Sustainable Decision Making and Innovation)

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23 pages, 1362 KB

Open AccessArticle

Forecasting Tax Revenue in Cambodia: A Comparative Study of Traditional Time-Series Models and Machine-Learning Methods

by Tepwinuth Chhim and Siphat Lim

Economies 2026, 14(9), 399; https://doi.org/10.3390/economies14090399 - 7 Sep 2026

Abstract

The study examines the predictive ability of traditional econometrics and machine-learning models for Cambodian tax revenue estimates using monthly LNTAX from January 2009 to March 2026. Repeated out-of-sample forecasts were then produced through a rolling-origin forecasting design, across two test horizons: 12-months and [...] Read more.

The study examines the predictive ability of traditional econometrics and machine-learning models for Cambodian tax revenue estimates using monthly LNTAX from January 2009 to March 2026. Repeated out-of-sample forecasts were then produced through a rolling-origin forecasting design, across two test horizons: 12-months and 24-months. We evaluated different techniques: moving average, exponential smoothing, damped trend, ARIMA, CART, GRNN and KNN. The performance against the moving average benchmark was compared using regression analysis with clustered standard errors, and forecast accuracy measured in terms of mean squared error and symmetric mean absolute percentage error. The results provide evidence that LNTAX is non-stationary in levels, and then stationary at first differencing. The best overall predictive vision is achieved within ARIMA with the smallest errors in most of the evaluations and significantly largest decreases in MSE and sMAPE compared to moving average. Damped-trend exponential smoothing also does quite well, especially for percentage accuracy over the longer horizon. In general, machine-learning models outcomes are mixed: CART shows average performance, whereas KNN in some instances is able to improve sMAPE versus naive-based models but GRNN ranks the weakest. The results indicate that time-series models have a superior forecasting performance than the state-space framework-based univariate tax revenue forecasting model in this context. Hence, it is suggested that ARIMA is used as the main benchmark model and future research should be conducted in hybrid methods, more macroeconomic variables, structural breaks and a policy-oriented forecasting evaluation. Full article

(This article belongs to the Special Issue Taxation Policies and Their Economic Effects)

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26 pages, 4345 KB

Open AccessArticle

Twin Transition and Labor Informality in Asia-Pacific: Threshold Effects and Income-Group Heterogeneity

by Vu Mai Phuong, Nguyen Thi Giang and Le Thi Anh

Economies 2026, 14(9), 398; https://doi.org/10.3390/economies14090398 - 7 Sep 2026

Abstract

Research on the digital and green “twin transition” has concentrated on European, high-income settings, leaving its consequences for labor informality in developing Asia underexplored. This study examines how digital and green transitions, individually and interactively, are associated with labor informality and productivity across [...] Read more.

Research on the digital and green “twin transition” has concentrated on European, high-income settings, leaving its consequences for labor informality in developing Asia underexplored. This study examines how digital and green transitions, individually and interactively, are associated with labor informality and productivity across 17 Asia-Pacific economies from 2005 to 2022, using ILOSTAT and World Bank panel data. Informality is proxied by own-account and contributing family workers as a share of total employment, achieving 93.5 percent sample coverage versus 17 percent for the ILO modelled series; composite digital and green transition indices are constructed using principal-component-based weights, with dynamic panel models complemented by threshold and income-group analyses. At the full-sample level, digital transition shows no significant average association with informality, while the green index shows a positive association attributable to conflation of traditional biomass with modern renewable energy in standard indicators. Threshold analysis identifies a break at 47.5 percent internet penetration, beyond which the digitalization informality relationship changes sign. Separate subgroup estimates suggest a negative DIGI × GREEN interaction among lower-middle-income economies, but an additional pooled income-group interaction model with country and year fixed effects reverses the implied lower-middle-income sign and is statistically imprecise. The evidence should therefore be read as suggestive and specification-sensitive evidence of income-contingent heterogeneity, not as definitive proof of a robust lower-middle-income synergy. The findings imply that twin-transition policies in developing Asia should be sequenced cautiously and evaluated with stronger data rather than applied uniformly across income contexts. Full article

(This article belongs to the Special Issue Regional Economic Development: Policies, Strategies and Prospects)

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26 pages, 333 KB

Open AccessArticle

Determinants of CO₂ Emissions in Hydrocarbon-Dependent Economies: A Multi-Method Panel Analysis of GCC Countries

by Ihsen Abid

Economies 2026, 14(9), 397; https://doi.org/10.3390/economies14090397 - 7 Sep 2026

Abstract

This study investigates the key drivers of CO₂ emissions in Gulf Cooperation Council (GCC) countries, focusing on energy consumption, economic growth, urbanization, trade openness, and institutional quality over the period 1980–2023. The analysis adopts a structured econometric framework in which each technique [...] Read more.

This study investigates the key drivers of CO₂ emissions in Gulf Cooperation Council (GCC) countries, focusing on energy consumption, economic growth, urbanization, trade openness, and institutional quality over the period 1980–2023. The analysis adopts a structured econometric framework in which each technique serves a distinct analytical purpose. Fixed-effects regression with Driscoll–Kraay standard errors is used to estimate contemporaneous relationships while accounting for cross-sectional dependence. LASSO regression is employed as an exploratory variable-selection tool to identify the most relevant predictors of emissions, while Common Correlated Effects Mean Group (CCEMG) and instrumental-variable fixed-effects (IV-FE) estimators are used as robustness checks to account for cross-sectional dependence, slope heterogeneity, and potential endogeneity. The results consistently identify energy consumption as the dominant determinant of CO₂ emissions across all specifications, reflecting the persistent dependence of GCC economies on fossil fuel-intensive energy systems. GDP per capita also exhibits a positive relationship with emissions, suggesting a possible non-linear income–emissions relationship; however, the results do not provide robust support for the conventional EKC hypothesis. Population density is negatively associated with emissions, suggesting potential efficiency gains from urban concentration. In contrast, regulatory quality does not show a statistically robust effect and is excluded from the preferred specification by the LASSO procedure. The impact of trade openness appears model-dependent, indicating that its environmental effects vary according to the balance between scale and technology-transfer effects. Overall, the findings provide policy-relevant insights for balancing economic growth, energy transition, and decarbonization objectives in hydrocarbon-dependent economies. Full article

27 pages, 1088 KB

Open AccessArticle

Has the Environmental Protection Tax Contributed to China’s ‘Dual Carbon’ Targets?

by Xinran Li and Tong Zhang

Economies 2026, 14(9), 396; https://doi.org/10.3390/economies14090396 - 7 Sep 2026

Abstract

Following the formal implementation of the Environmental Protection Tax Law of the People’s Republic of China in 2018, debate has persisted regarding its policy effectiveness, particularly concerning its impact on carbon emissions. In the context of the ‘dual carbon’ targets, accurately evaluating the [...] Read more.

Following the formal implementation of the Environmental Protection Tax Law of the People’s Republic of China in 2018, debate has persisted regarding its policy effectiveness, particularly concerning its impact on carbon emissions. In the context of the ‘dual carbon’ targets, accurately evaluating the emission-reduction effects of the environmental protection tax is of considerable policy significance. This study treats the 2018 transition from pollution discharge fees to the environmental protection tax (hereinafter referred to as the ‘fee-to-tax reform’) as a quasi-natural experiment. Utilising provincial-level panel data from China spanning 2007 to 2022, and employing TWFE, DID, and SDM-DID models, the analysis comprehensively assesses the impact of the fee-to-tax reform on per capita carbon emissions and carbon emissions intensity. The findings indicate that the transition from pollution discharge fees to the environmental protection tax significantly curbed the growth of both carbon emissions and carbon emission intensity; this result remains robust across a series of sensitivity tests. Heterogeneity analysis demonstrates that regions with a higher degree of ‘greening’ in their tax systems experience more pronounced reductions in carbon emissions. Further spatial econometric analysis reveals that while the environmental protection tax effectively curbs local carbon emissions, it may also increase the risk of ‘carbon leakage’ to neighbouring regions. These results provide empirical evidence and policy guidance to refine the design of environmental tax systems and promote coordinated regional carbon emissions reductions. Full article

(This article belongs to the Special Issue Energy Transition, Climate Change, and Macroeconomic Dynamics)

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43 pages, 689 KB

Open AccessArticle

Supply-Chain Disruption, Supplier-Country Health, and the Reallocation of US Import-Value Shares: Monthly Evidence Under Timing-Disciplined Measurement

by Shawn McCarthy and Gita Alaghband

Economies 2026, 14(9), 395; https://doi.org/10.3390/economies14090395 - 6 Sep 2026

Abstract

Supply-chain disruptions since 2018 renewed attention to supplier-country conditions; this paper examines whether deterioration in those conditions precedes changes in US import-value shares when each measure is dated to match the question asked. From 564,983 deduplicated news records (2018–2026) and direct-source IMF, COMTRADE, [...] Read more.

Supply-chain disruptions since 2018 renewed attention to supplier-country conditions; this paper examines whether deterioration in those conditions precedes changes in US import-value shares when each measure is dated to match the question asked. From 564,983 deduplicated news records (2018–2026) and direct-source IMF, COMTRADE, and BIS data for 62 supplier economies, we build a Country Health Score under two datings: reference-dated, assigned to the months the data describe, and availability-lagged, shifted by one-to-three-month frontier-lag proxies. Among 29 dominant commodity–country cells across 14 countries, reference-dated deterioration is followed by within-commodity share erosion over three to twelve months, concentrated after 2022 and surviving Holm and wild-cluster inference at every horizon (largest Holm or wild-cluster

p = 0.014

). Excluding Canada and Mexico, the association is solid at three months and suggestive at six. The association survives a pre-specified mechanical-channel gate (re-estimation without the export-coverage component) and a timing placebo; conflict-coverage spikes are a second, timelier predictor (post hoc). The same regression on the availability-lagged composite is null, and the corresponding coefficients differ formally at three and six months, consistent with timing-misalignment attenuation. Return regressions delimit rather than support the claim: no predictive relationship replicates out of sample. These results document a timing-consistent association, not an identified causal effect. Observations postdating the frozen analysis plan will provide the first fully prospective test. Full article

(This article belongs to the Section International, Regional, and Transportation Economics)

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30 pages, 3315 KB

Open AccessReview

Digital Finance Research Trends, Evolution, and Future Directions: A Bibliometric Analysis

by Gebreamlak Yitbarek Zemo, Zinabu Gebru Weldemichael and Vertesy Laszlo

Economies 2026, 14(9), 394; https://doi.org/10.3390/economies14090394 - 5 Sep 2026

Abstract

Digital finance has emerged as a rapidly evolving field shaped by advances in financial technology (FinTech), blockchain, artificial intelligence, digital banking, and payment innovations. Our analysis applied bibliometric techniques to examine the evolution, trends, intellectual structure, and future directions of research in the [...] Read more.

Digital finance has emerged as a rapidly evolving field shaped by advances in financial technology (FinTech), blockchain, artificial intelligence, digital banking, and payment innovations. Our analysis applied bibliometric techniques to examine the evolution, trends, intellectual structure, and future directions of research in the field. Data were collected from the Web of Science Core Collection using a search strategy designed to capture publications that explicitly address research trends, evolution, and future directions in digital finance. After applying predefined screening criteria, a final dataset of 334 publications published between 2016 and 6 March 2026, was analyzed using Bibliometrix, Biblioshiny, and VOSviewer. The analysis included performance indicators, science mapping, co-authorship networks, co-citation analysis, keyword co-occurrence analysis, thematic mapping, thematic evolution, author productivity analysis using Lotka’s Law as a descriptive reference, and Bradford’s Law. The findings indicate a substantial increase in publications over the study period, with an annual growth rate of 33.35%. Research output is highly concentrated in China and India, which together account for approximately 70.06% of the publications. The intellectual structure of the literature is primarily organized around themes related to FinTech, blockchain, cryptocurrency, innovation, and financial inclusion. At the same time, emerging areas include artificial intelligence, sustainability, ESG, green finance, and decentralized finance. The results also reveal fragmented collaboration networks and significant geographical concentration in research production. This study contributes to the literature by providing an overview of the evolution and thematic development of trend-focused digital finance research and identifies promising directions for future investigation. The findings should be interpreted within the scope of the selected trend-oriented literature and not as a representation of the digital finance literature landscape. Full article

(This article belongs to the Special Issue Emerging Trends in the Digital Economy: Opportunities, Challenges, and Implications for Developing Countries)

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36 pages, 4516 KB

Open AccessArticle

Do Daily Adaptive Machine Learning Stock Rankings Survive Trading Costs? Evidence from Cross-Sectional Technical Signals

by Ferdinantos Kottas

Economies 2026, 14(9), 393; https://doi.org/10.3390/economies14090393 - 5 Sep 2026

Abstract

This study examines whether daily machine learning stock rankings based on technical information contain out-of-sample ordering information and whether that information can be converted into economically implementable returns. Using a dynamically screened Nasdaq source universe from 2021 to 2026, four XGBoost objectives are [...] Read more.

This study examines whether daily machine learning stock rankings based on technical information contain out-of-sample ordering information and whether that information can be converted into economically implementable returns. Using a dynamically screened Nasdaq source universe from 2021 to 2026, four XGBoost objectives are evaluated in a chronological walk-forward design. Test NDCG converges to 0.495–0.504, but permutation analysis places the corresponding random-ranking mean near 0.45, indicating statistically detectable but modest cross-sectional ordering information. Economic performance is substantially weaker. Under the execution convention implied by the next-day open-to-close target, every invested portfolio is bought at the open and liquidated at the close, so round-trip turnover equals two. Pseudo-Huber Top-1, treated as an ex-post concentration diagnostic, produces a 34.9% gross annual geometric return with 89.5% volatility and an 86.5% maximum drawdown; the Newey–West mean-return test is not significant (p = 0.106). At five basis points per trading leg, its zero-cash net CAGR falls to 4.8%; crediting idle capital with the daily risk-free rate raises total-return CAGR to 9.3%, but the excess-return inference is unchanged (p = 0.297). A matched-horizon regression on SPY open-to-close returns yields a statistically insignificant net annualized alpha (p = 0.436). Hansen’s SPA test across the synchronized 12-strategy family gives p = 0.207, and the Deflated Sharpe Ratio probability for Pseudo-Huber Top-1 is 0.462. The result is also highly time- and tail-dependent. The evidence therefore supports a distinction between statistically detectable ranking information and robust implementable abnormal performance rather than a persistent trading anomaly. Full article

(This article belongs to the Special Issue Modeling and Forecasting of Financial Markets)

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41 pages, 358 KB

Open AccessArticle

Policy Implementation and Competitiveness Ranking Trajectories in the GCC: Examining Saudi Arabia’s Vision 2030 Relative to Resource-Similar Peers (2022–2026)

by Fouad Ahmed Atallah

Economies 2026, 14(9), 392; https://doi.org/10.3390/economies14090392 - 5 Sep 2026

Abstract

Natural resource endowments are often treated as major determinants of regional competitiveness, yet institutional and rentier-state scholarship suggests that institutional architecture, reform capacity, and policy implementation may also shape competitiveness trajectories. This study examines Saudi Arabia’s competitiveness trajectory under Vision 2030 in comparison [...] Read more.

Natural resource endowments are often treated as major determinants of regional competitiveness, yet institutional and rentier-state scholarship suggests that institutional architecture, reform capacity, and policy implementation may also shape competitiveness trajectories. This study examines Saudi Arabia’s competitiveness trajectory under Vision 2030 in comparison with Kuwait, the United Arab Emirates, and Qatar. The analysis covers 2022–2026, complemented by historical IMD benchmarks and independently sourced real-economy indicators, particularly economic growth and foreign direct investment (FDI). Saudi Arabia improved from 24th in 2022 to 13th in 2026, with Government Efficiency and Business Efficiency both reaching 4th globally. However, FDI remained at 1.25% of GDP, substantially below the UAE’s 7.90% and the 5.7% Vision 2030 target. Comparative evidence further indicates that neither infrastructure strength nor governance centralization alone accounts for observed competitiveness trajectories. The findings reveal incomplete correspondence between ranking improvement and real-economy transmission, supporting a conditional institutional interpretation in which reform capacity operates alongside regulatory credibility, implementation quality, and broader economic conditions. The study distinguishes reform velocity, institutional effectiveness, real-economy transmission, and institutional durability as analytically distinct dimensions of institutional transformation. Given its small-N descriptive comparative design, the study identifies theory-consistent patterns rather than causal effects of Vision 2030. Full article

(This article belongs to the Special Issue Regional Economic Development: Policies, Strategies and Prospects)

29 pages, 9950 KB

Open AccessArticle

Dynamic Volatility Spillovers Between Global Volatility Indices and the Magnificent Seven: What Drives System-Wide Volatility Connectedness?

by Havva Koç

Economies 2026, 14(9), 391; https://doi.org/10.3390/economies14090391 - 5 Sep 2026

Abstract

This study examines the dynamic connectedness between the daily volatility series of global volatility indices (VIX, OVX, and GVZ) and those of the Magnificent Seven companies using data from 22 May 2012 to 25 June 2026. The analysis employs the time-varying parameter vector [...] Read more.

This study examines the dynamic connectedness between the daily volatility series of global volatility indices (VIX, OVX, and GVZ) and those of the Magnificent Seven companies using data from 22 May 2012 to 25 June 2026. The analysis employs the time-varying parameter vector autoregressive (TVP-VAR) connectedness framework to decompose volatility transmission into within-group and cross-group spillovers. Unlike conventional connectedness analyses that focus primarily on aggregate connectedness measures, this decomposition identifies the dominant source of system-wide volatility connectedness. The results reveal a moderate but highly dynamic transmission structure that intensifies during periods of heightened market stress, particularly during the COVID-19 pandemic. The decomposition further reveals that, although the system incorporates equity-, oil-, and gold-market uncertainty through the VIX, OVX, and GVZ, cross-group spillovers between the global volatility indices and the Magnificent Seven remain comparatively limited. Instead, system-wide connectedness is driven primarily by within-group interactions among the Magnificent Seven. Apple, Meta, and Amazon emerge as net volatility transmitters, whereas Microsoft, Alphabet, and NVIDIA act as net receivers. Pairwise spillovers strengthen during major stress episodes, particularly in the VIX–Apple, VIX–Meta, and Amazon–Meta relationships. The main connectedness patterns remain robust across alternative forecast horizons, lag specifications, rolling-window lengths, and the Diebold –Yilmaz connectedness framework. Overall, the findings highlight the dominant role of within-group spillovers in shaping system-wide connectedness and provide practical insights for portfolio diversification, risk management, and hedging. Full article

(This article belongs to the Collection International Financial Markets and Monetary Policy)

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40 pages, 1051 KB

Open AccessArticle

Mandatory Transaction-Based Reporting and the VAT Compliance Gap: Evidence from EU Member States

by Melissa Nihal Cagle

Economies 2026, 14(9), 390; https://doi.org/10.3390/economies14090390 - 4 Sep 2026

Abstract

The European Union has mandated digital reporting of cross-border VAT transactions from 2030, yet quasi-experimental evidence on the official compliance gap remains limited and does not cover the full range of national transaction-reporting architectures. This paper estimates their effect on that gap in [...] Read more.

The European Union has mandated digital reporting of cross-border VAT transactions from 2030, yet quasi-experimental evidence on the official compliance gap remains limited and does not cover the full range of national transaction-reporting architectures. This paper estimates their effect on that gap in 24 member states over 2000 to 2023, coding twelve mandates from the underlying legal instruments and applying the Callaway and Sant’Anna framework with never-treated comparisons. The baseline staggered estimate indicates a 4.23 percentage point reduction (95% CI [−6.45, −1.83]), remaining between 3.73 and 5.43 points across nineteen specifications including an imputation estimator and a neighbour-excluding comparison, and 2.76 under the most conservative identification check. Point estimates more than triple over five years. Pre-adoption coefficients are individually indistinguishable from zero though jointly significant, and formal sensitivity analysis shows the adoption-year and average post-adoption effects withstand modest though not large parallel-trend violations. The results do not show continuous reporting outperforms periodic reporting, and exploratory analysis detects no capacity moderation. What orders the cohort estimates is the completeness of the obligation, since the three mandates reaching one side of the transaction or part of the taxpayer population produce the three weakest effects. Findings support the fiscal premise of the VAT in the Digital Age reform and counsel patient evaluation of the rollout. Full article

(This article belongs to the Section Macroeconomics, Monetary Economics, and Financial Markets)

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