Search Results (1,035)

This article examines how artificial intelligence (AI), conceptualized as a general-purpose technology (GPT), shapes economic growth and structural transformation through a structured literature review covering the period from 2015 to 2025. The study adopts a structured, mechanism-oriented synthesis approach grounded in transparent search, screening, and thematic classification procedures rather than formal meta-analytic protocols. It develops an integrative innovation ecosystem framework that links three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics. The findings indicate that AI adoption does not generate uniform or automatic growth effects. Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts. In most cases, outcomes depend less on the technology itself and more on complementary conditions—human capital formation, digital and data infrastructure, institutional coordination, and governance capacity—that enable effective diffusion. Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes. Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development. The study contributes by distinguishing foundational theoretical perspectives from the contemporary 2015–2025 evidence base, clarifying the relationship between task transformation and structural transformation, and emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes. Full article

This study investigates the complex and nonlinear interaction between the dual circulation paradigm and high-quality full employment. Moving beyond the limitations of conventional static partial equilibrium frameworks, the analysis conceptualizes this relationship as a system of three interrelated feedback loops. Drawing on system dynamics (SD) theory, a set of nonlinear differential equations is developed, with model parameters calibrated using macroeconomic data from 2010 to 2025. The simulation results yield three main findings. First, international trade, cross-border investment, and technological exchange jointly form a core reinforcing feedback loop that underpins the mutually beneficial interaction between domestic and international circulations. Second, the integrated development of education, technology, and human capital emerges as a critical state variable for overcoming the persistent trade-off between employment quantity and quality. Third, the interplay between horizontal market expansion and vertical technological advancement constitutes a dual driving mechanism that facilitates the system’s transition toward a higher-level equilibrium, with multi-factor interactions generating pronounced nonlinear multiplier effects. Overall, the study provides a quantitative basis for designing adaptive and targeted employment policies within the dual circulation framework. Full article

This study examines how two forms of financial literacy—objective financial literacy (OFL; demonstrated knowledge of interest rates, inflation, and diversification) and perceived financial literacy (PFL; self-assessed confidence in financial matters)—relate to financial wellbeing through distinct capability pathways, and whether self-regulation conditions these links. We use three nationally representative cross-sections from Portugal (2015, 2020, 2023; N = 3648), a European setting marked by declining objective literacy and constrained market participation. Guided by capability theory, we propose a dual-lane model in which OFL operates through behavioural capability (BC; enacted saving, investing, and planning behaviours) to shape objective financial wellbeing (OFW; resilience, assets, and saving), while PFL operates through perceived capability (PC; financial self-efficacy and perceived control) to shape subjective financial wellbeing (SFW; perceived security, satisfaction, and freedom from financial stress). We also test whether non-impulsive, future-oriented behaviour (NIB) strengthens the associations along the objective lane. Structural equation models provide partial support for the dual-lane model, revealing three asymmetries with implications for European policy: (1) the link between behavioural capability and objective financial wellbeing weakens in 2023, suggesting that macroeconomic conditions can undercut even prudent financial behaviour; (2) perceived financial literacy directly predicts subjective financial wellbeing, but perceived capability does not mediate this association, indicating that financial confidence shapes wellbeing independently of self-efficacy; and (3) non-impulsive, future-oriented behaviour amplifies the association between objective literacy and objective wellbeing in 2015 and 2023 but not in 2020, showing that the benefits of self-regulation are context-dependent. The findings inform financial education and policy across Europe by distinguishing intervention levers for objective versus subjective outcomes and identifying conditions under which behavioural interventions are most effective. Full article

The study investigates the relationship between political signal quality, uncertainty measures, and sovereign CDS connectedness among major oil-exporting countries using a time-varying parameter vector autoregressive (TVP-VAR) approach along with regression and quantile-based techniques. The findings indicate a moderate degree of sovereign connectedness, suggesting the presence of cross-country spillover effects in sovereign risk markets. The results further show that Qindex is negatively associated with sovereign connectedness both in the case of normal market conditions and mild stress levels. In contrast, conventional uncertainty indicators appear to exert relatively weaker effects across model specifications. Overall, the findings suggest that the informational quality of political communication may play a role in shaping sovereign spillover dynamics alongside broader macroeconomic and financial conditions. Full article

Autoregressive models with exogenous variables (ARX) constitute a fundamental class of dynamic regression models used extensively for time series analysis across a wide range of applications. A pervasive limitation of the existing Bayesian analyses of ARX models is their near-exclusive reliance on the Gaussian error assumption, which is routinely violated in empirical applications exhibiting heavy-tailed innovations, distributional outliers, or excess kurtosis. To address this deficiency, we develop a rigorous Bayesian estimation framework for these models whose errors are drawn from the scale-mixtures of normal (SMN) family, which is a rich, symmetric, heavy-tailed class of distributions. Exploiting the hierarchical stochastic representation of the SMN family through observation-specific latent scale-mixing variables, the ARX model is embedded in an augmented data structure that restores Gaussian conditional structure. Under three distinct prior formulations—namely, normal-gamma, Zellner’s g-prior, and Jeffreys’ prior—we derive closed-form full conditional posterior distributions for the ARX coefficient vector and the error scale parameter, which follow multivariate normal and inverse-gamma distributions, respectively. In addition, for the SMN-specific shape parameters, we derive the full conditional posteriors for each distribution in the family, and some of them are non-standard distributions handled by embedding Metropolis-Hastings steps within the Gibbs sampler. The resulting hybrid MCMC algorithm is validated through a comprehensive simulation study spanning three ARX model configurations and all three SMN special cases. A real macroeconomic application to US consumer price inflation demonstrates the practical utility of the framework, confirming heavy-tailed residuals and yielding precise, well-calibrated posterior estimates. Full article

E-commerce has reshaped short-term financial management by altering transaction speed, payment structures, and supply chain coordination. This study examines how large publicly listed e-commerce firms, viewed as complex digital business systems, adjusted their working capital policies during and after the COVID-19 shock. The sample is based on the 100 largest e-commerce companies worldwide by market capitalization, as reported by CompaniesMarketCap (February 2026), and is reduced to 76 firms from 23 countries due to data availability, yielding 802 firm-year observations. Firm-level data are obtained from LSEG Datastream, while macroeconomic variables are sourced from the World Bank. The analysis distinguishes between two dimensions of working capital: flow-based operational adjustment, measured by the cash conversion cycle (CCC), and stock-based balance-sheet adjustment, captured by net working capital relative to total assets (WC/TA). Fixed-effects models with firm-clustered standard errors are employed. The results indicate a substantial contraction of the CCC during the pandemic, followed by partial persistence of that contraction rather than a return to pre-pandemic norms. In contrast, WC/TA remains broadly stable during the crisis but declines in the post-pandemic period, suggesting a delayed balance-sheet adjustment. Business-model heterogeneity is not statistically significant, which may reflect a common system-level response across e-commerce firm types. Leverage and supply-chain pressures are associated with working capital intensity (WC/TA), while inflation shapes operate cycle duration (CCC). The findings are consistent with a two-stage adaptive response to systemic disruption. Full article

This paper assesses how the adoption of the COBIT framework is associated with the financial stability of commercial banks in an emerging economy—Jordan. As banks rely increasingly on digital technology, the management of technological risk has become central to their soundness, raising the question of how IT governance is associated with bank-level risk. Using a panel of 12 listed Jordanian commercial banks over 2014–2023, we estimate the relationship between COBIT adoption and stability, measured by the natural logarithm of the Z-score, employing a random-effects panel model. We construct two original, text-based measures of COBIT engagement from banks’ annual reports: a disclosure-frequency count (COBITF) and a binary adoption indicator (COBITD). The results show that COBIT engagement is positively associated with bank stability, whereby a one-unit rise in disclosure frequency is associated with an increase in the Z-score of roughly 2.2%, and the association is robust to the inclusion of bank-specific and macroeconomic controls and to a two-stage least-squares (2SLS) treatment of endogeneity for COBITF. The findings are presented as conditional associations with a plausible governance channel. The study contributes replicable, longitudinal measures of IT-governance engagement for data-scarce emerging markets and offers empirical evidence that engagement with a specific IT-governance framework is positively associated with bank stability. Full article

This study set out to investigate the interactions between business cycles, financial cycles and monetary policy in South Africa. Explicitly, the study aims to examine the role of financial factors in business cycle models and the possibility of a unified macroeconomic framework in South Africa. Further, the study assesses the effects of demand shocks, supply shocks, interest rate shocks, and financial shocks on macroeconomic fluctuations. The study applied an analytical approach integrating the Generalised Method of Moments and System Generalised Method of Moments with a Structural New Keynesian Dynamic Stochastic General Equilibrium framework. Accordingly, it was concluded that the financial cycle plays a significant role in business cycle models and is a main driver of macroeconomic fluctuations in South Africa. Further, a unified macroeconomic framework for monetary policy analysis that links the financial system to the real economy in South Africa possibly exists. This study contributes to the South African Reserve Bank’s efforts by deepening understanding of the interactions between the financial system and the real economy and their implications for monetary policy in South Africa. By comparing the standard Taylor rule with a finance-augmented Taylor rule in a DSGE framework, the study helps answer the question of whether financial stability should be adopted as a second objective of monetary policy. Full article

Water scarcity is increasingly recognized as a macroeconomic challenge, yet its implications for sovereign financing conditions remain insufficiently understood. This study examines whether water scarcity is associated with sovereign borrowing costs and whether this relationship strengthens once hydrological pressure exceeds a critical threshold. Using an international panel of 105 countries over the period 2000–2024, the analysis combines second-generation panel diagnostics with nonlinear threshold estimation to examine long-run relationships and regime-dependent effects. The results indicate that water scarcity is positively associated with sovereign risk premiums, but the relationship is distinctly nonlinear. A critical threshold is identified at 61.37% water stress, beyond which the estimated association becomes substantially larger, with the coefficient increasing from 0.005 below the threshold to 0.024 above it. This pattern suggests that severe hydrological pressure is more strongly associated with higher sovereign borrowing costs than moderate water stress. The analysis further suggests that financial development, renewable energy deployment, and stronger institutional quality are associated with a weaker relationship between water scarcity and sovereign risk premiums, highlighting the potential importance of domestic resilience capacity. These findings remain broadly robust across alternative sovereign risk measures, alternative water scarcity proxies, dynamic specifications, and smooth-transition nonlinear models. This study contributes to the emerging literature on environmental macro-financial linkages by providing evidence that water scarcity may be increasingly relevant for sovereign financing conditions, particularly in economies facing severe and persistent hydrological stress. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

The assessment of humanity’s ecological footprint has become increasingly critical in contemporary discourse due to growing environmental challenges. This study examines the economic evaluation of the ecological footprint with a particular focus on forest ecosystem services and food productivity. Using harmonized secondary data from FAOSTAT, EUROSTAT, the World Bank, and IPBES, the analysis covers selected developed and emerging economies, including the European Union, the United States, China, Brazil, and other representative countries. This study investigates the macroeconomic implications of natural capital degradation by applying a panel data econometric model to European Union countries over the period 2010–2023. Moving beyond descriptive approaches, the research formulates and tests three hypotheses linking biodiversity, environmental pressure, and green transition variables to economic performance. Using harmonized data from Eurostat and Statista, the study employs a fixed-effects regression framework to estimate the impact of biodiversity indicators, greenhouse gas emissions, renewable energy share, and environmental protection expenditures on GDP per capita. The results demonstrate that biodiversity preservation and resource efficiency are positively associated with economic performance, while environmental degradation—proxied by greenhouse gas emissions—exerts a statistically significant negative effect. Additionally, the findings confirm that investments in renewable energy and environmental protection contribute to long-term economic stability. By providing a transparent data structure, explicit variable operationalization, and reproducible econometric specification, the study offers an original empirical contribution to ecological economics and addresses the limitations of prior literature that relied primarily on descriptive synthesis. Full article

This paper examines the drivers of house price cycles across EU countries between 2005 and 2024 and measures their synchronicity. We used panel data methods—fixed effects, dynamic panel models (Arellano–Bond GMM), and a pooled VAR framework—to capture static and dynamic relationships between house price growth and key macroeconomic variables. The results show that the dynamics of house prices are highly persistent. GDP growth has a clear positive effect, while higher unemployment and interest rates push prices down. Migration flows, however, are not statistically significant at the EU aggregate level. Property taxation shows a positive coefficient, which probably reflects structural and institutional differences rather than a direct dampening effect on prices. Dynamic analysis suggests that macroeconomic shocks have persistent and economically meaningful impacts on house price growth. Hierarchical cluster analysis revealed three distinct groups of countries, meaning that house price cycles are only partially synchronized across the EU. Unlike previous studies that typically examine individual determinants or synchronization separately, this study integrates panel econometric methods, dynamic VAR analysis, and hierarchical clustering within a unified framework to jointly assess macroeconomic drivers, dynamic interactions, and structural heterogeneity of house price cycles across EU countries. In general, common macroeconomic drivers and structural heterogeneity coexist—this is important for the stability of the housing market and sustainable development. Full article

Although the European Union positions the bioeconomy at the core of its sustainability transition and the European Green Deal, the cross-country distribution of bioeconomy value-added associated with mission-oriented public R&D support remains highly uneven. This paper investigates how national researcher capacity (as a proxy of absorptive capacity) shapes the macroeconomic effectiveness of bioeconomy-oriented public R&D support, and how societal climate-oriented environmental concern acts as a direct structural driver of bioeconomy value-added. Using a panel dataset of 27 EU Member States from 2008 to 2020, the study constructs an original bioeconomy-specific measure of government budget appropriations for R&D (GBARD) and estimates two-way fixed-effects models with Driscoll–Kraay standard errors to account for cross-sectional dependence. The findings reveal a clear capacity-dependent conditional moderation effect: public R&D support is significantly associated with higher bioeconomy value-added only when a critical mass of researcher capacity is present. Sectoral disaggregation demonstrates that business enterprise researcher capacity acts as the primary transmission channel linking public funds to the market, whereas higher-education capacity shows no statistically significant short-to-medium-term moderating effect, consistent with the academic research commercialisation time lags documented in the literature. Additionally, societal climate-oriented environmental concern is positively associated with bioeconomy value-added in the baseline models, consistent with its role as a demand-side factor fostering receptive conditions for bio-based transitions. The study concludes that increasing mission-oriented R&D funding alone is likely insufficient; to successfully cross the “valley of death,” public R&D should be accompanied by complementary policies that build private-sector absorptive capacity and cultivate green market demand. Full article

This study evaluates the demand dynamics of the 20 leading strategic destinations in the global tourism market by modeling the interactions between traditional macroeconomic determinants and climate-linked environmental sustainability indicators. The primary objective is to assess the predictive capacity of physical and structural environmental factors—including water stress, air pollution, renewable energy adoption, and sanitation infrastructure—relative to established economic metrics like GDP per capita. Employing non-parametric predictive frameworks on a panel dataset of 400 observations (2000–2019), the empirical analysis suggests that tree-based ensemble models, notably Extra Trees (90.54%) and CatBoost (84.75%), yield higher predictive accuracy than conventional multiple linear regression (73.97%). Interpretations derived from cooperative game theory via SHAP analysis suggest that environmental determinants may serve as important predictive drivers of tourism demand. Specifically, variables such as water stress (28.20%), renewable energy share (27.12%), and sanitation infrastructure carry substantial predictive weight, whereas the benchmark macroeconomic indicator (2.30%) exerts a relatively marginal influence within the model architecture. These findings imply that environmental sustainability metrics may capture international tourism demand variations more effectively than traditional economic variables. The results suggest that acute environmental vulnerabilities may be associated with reduced tourism inflows, potentially reflecting limitations in destination sustainability thresholds. Broadly, the evidence is consistent with the notion that contemporary global tourism demand may be increasingly interdependent with ecological resilience and low-carbon transition policies. It is important to note that the findings reported here reflect predictive associations derived from machine learning models and should not be interpreted as evidence of causal relationships. Full article