Business, Economics and Management

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.

Abstract: The study applies a multiphase, multimethod research approach based on the participatory methodology. It integrates perspectives of professionals in the travel industry and academic experts with the aim to develop an integrated conceptual model of the AI and IoT influence on work, skills development, and job attractiveness in the industry. The research provides a comprehensive understanding of the ways in which digital technologies indirectly shape employment through changes in work processes and development of transferable digital and socio-emotional skills. The paper aimed to donate to redefining the perception of work in tourism and hospitality, emphasizing the sector not only as a career choice but also as a platform for the acquisition of skills relevant in other industries as well. The outcomes revealed that the employees’ aspirations to enter or stay in the industry are not directly influenced by AI and IoT technologies; rather, their effects are mediated through changes in work processes and, more importantly, through the development of skills. The study contributes theoretically by evolving and analytically confirming an incorporated theoretical model that connects technology implementation, work transformation, skills development, and employment outcomes. Practically, the results underscore the importance of human-centered implementation strategies, emphasizing training, communication, and employee inclusion to maximize the benefits of digital technologies.

Abstract: Given the "dual-carbon" goals of China, research has been carried out on the impact of the digital economy on carbon emission intensity. Based on the panel data of the 288 cities in China from 2013 to 2022, a two-way fixed-effects model is employed in this paper to study how the digital economy affects carbon emission intensity at the level of cities and urban agglomerations. The results show that the development of the digital economy reduces the intensity of urban carbon emissions, and there is cross-regional spatial spillover at the level of urban agglomerations. According to the results of the mechanism test, there are two paths for "industry-technology" transmission: At the city level, the digital economy can reduce pollution by improving the structure and upgrades of the industrial system; At the level of urban agglomeration, it can strengthen green-technology innovation capabilities. According to the analysis of heterogeneity, polycentric agglomeration, optimisation-and-upgrading type agglomeration and coastal areas have relatively good carbon reduction effects. Based on the above, personalised regional policies will be formulated to promote the development of the digital economy in line with carbon reduction objectives.

Abstract: Digital traceability has become a critical capability in international trade, yet existing research has not fully explained how institutional, technological, and coordination-related conditions combine to produce successful outcomes. This study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) to 24 trade-corridor cases to identify the configurational drivers of Digital Traceability Success (DTS). The findings show that Digital Trade Readiness (DTR), Market Strictness (MKT), Digital Infrastructure (DIF), and Cross-border Coordination (COO) are necessary conditions for DTS, whereas Blockchain-enabled Traceability (BCT) is not. The sufficiency analysis identifies one dominant pathway DTR * PRK * MKT * DIF * COO with perfect consistency and substantial coverage. These findings demonstrate that digital traceability success is not driven by blockchain adoption alone, but by the joint alignment of institutional readiness, regulatory pressure, infrastructure, risk exposure, and inter-organizational coordination. The study makes two main contributions. Scientifically, it advances the literature on digital trade and supply-chain traceability by offering a configurational explanation grounded in conjunctural causation and causal asymmetry. Practically, it suggests that policymakers and firms should prioritize system-wide readiness, interoperable digital infrastructure, and cross-border governance rather than relying narrowly on blockchain solutions.

Abstract: Special Economic Zones (SEZs) are widely promoted as catalysts for industrialization and export growth in developing countries, yet their capacity to generate sustainable and inclusive regional development remains debated, particularly in sub-Saharan Africa. This study investigates the impact of the Nkok SEZ in Gabon on the forestry sector—a novel case study—by analyzing the resulting economic and spatial disparities between the SEZ (homogeneous space) and its periphery (heterogeneous space). Combining robust econometric methods (Bias-Corrected Fixed Effects, OLS) and principal component analysis (PCA) on time-series data (2014–2022), we show that while the SEZ has significantly boosted export revenues (84%–97% growth) and industrial production through agglomeration and scale economies, these benefits remain largely concentrated. The periphery experiences weaker growth, reinforcing center-periphery dependencies and extractive specialization. Export revenues from the homogeneous space exhibit strong autoregressive effects (77%–94%) but limited macroeconomic diffusion (6%–25%), whereas the heterogeneous space shows lower autoregressive growth but a stronger historical influence on national aggregates, highlighting a structural polarization trap. To address these persistent imbalances, this paper introduces the SEMD model (Segmentation, Evaluation, and Multi-level Disparities Management). This operational framework proposes a six-fold territorial typology (from SEZs to informal circuits), hybrid quantitative-qualitative indicators, and proactive rebalancing mechanisms (vertical and horizontal channels) to institutionalize the diffusion of growth. The SEMD model offers a strategic tool for policymakers in the Global South to reconcile industrial performance with territorial cohesion, moving beyond the mere diagnosis of inequalities toward adaptive, real-time management of polarized development dynamics.

Abstract: This article develops a coherent set of outcome indicators for assessing progress in rural development in South Africa by directly linking policy objectives to measurable de-velopment outcomes. Drawing on a multidimensional conceptual framework encom-passing economic, social and environmental dimensions of rural development, the ar-ticle employs a document‑based research design analysing strategic plans of the De-partment of Rural Development and Land Reform, now known as the Department of Agriculture, Land Reform and Rural Development, for the period 2009–2025. Rural development objectives are first translated into ideal output indicators and subse-quently screened using internationally recognised indicator‑selection criteria to iden-tify outcome‑focused indicators aligned with policy intent. The analysis yields ten core indicators that capture key rural development outcomes, including employment, household income, housing quality, education, health, waste management, and sus-tainable natural resource use. These indicators strengthen the alignment between rural development policy objectives and measurable outcomes and provide a practical and replicable framework for monitoring rural development progress at both national and municipal levels. The proposed indicator set contributes to evidence‑based planning, monitoring, and accountability in efforts to promote a more inclusive and sustainable rural economy.

Abstract: In the context of SDG 7 and SDG 13 of the 2030 sustainable development agenda, a new performance indicator started to gain momentum in scientific research: the renewable energy productivity. Understanding the drivers and the challenges of green energy productivity could help add on to the classical focus of renewable energy research on infrastructure, technical and economic feasibility, environmental and social impacts, by considering more the performance indicators in this field. Only very few studies explored the influencing factors of the renewable energy productivity. Thus, this research aims to reveal the impact of social, economic, energy, and environmental variables on the green energy productivity. The methodological approach involves bibliometric analyses of the literature on green energy productivity (GEP), and panel data regression models involving 16 independent variables. The main findings indicate positive effects of green taxes, female participation in the workforce, and highly educated people on GEP, pointing out the importance of green taxation, education, and gender equality in sustainable development. On the other side, negative relationships of green energy productivity with economic growth, traditional energy variables, and air pollution were found for the European Union’s member states over 2007 and 2023. The results suggest that the analyzed European countries based their economic growth on traditional resources, with less importance provided to the renewable resources and green technologies, as the share of renewable resources of GDP was also negatively correlated. While private financial resources increases the green energy productivity, questions about research and development investments, urbanization, and diversity index are still debatable.

Abstract: This study analyzes the relationship between trade openness, FDI, and economic growth in Ho Chi Minh City within the framework of an extended Solow model, using annual time series data from 2000 to 2024, and provides empirical evidence at the municipal level for Vietnam's leading economic and integration center. The trade open-ness is separated into the ratio of exports and imports to GRDP to reflect the different impacts of economic integra-tion. The ARDL and ECM model are applied in order to instantaneously analyze both short-term and long-term ef-fects. The results show that the variables have mixed integration orders. Capital is the factor with the most positive and stable impact in the long term. Meanwhile, relative exports and FDI have a positive impact in the short term but a negative one in the long term, implying that the benefits of integration depend on the quality of international trade, the ability to absorb technology, and domestic linkages. The negative and statistically significant error correc-tion coefficient indicates the existence of an adjustment mechanism toward equilibrium. The robustness check with the COVID-19 dummy variable approves the stability of the main results. The study points out that the necessity of changing from extensive quantitative integration to enhance the quality of growth and the efficiency of resource allocation.

Abstract: Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. Using 2012–2023 provincial panel data from China, this study measures AER via the entropy method and identifies policy effects using a multi-timepoint difference-in-differences (DID) model. We find that policy-based insurance for the three major staple crops significantly strengthens AER, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major grain-producing areas, and regions with higher natural risks. Our findings confirm that the staple crop insurance policy effectively boosts agricultural resilience, suggesting that differentiated implementation can support more sustainable and targeted agricultural risk governance.

Abstract: Egan et al. (2026) estimate that interchange fees transfer approximately $30 billionper year from cash and debit card users to credit card users, assuming merchants setuniform prices. We extend their sufficient-statistics framework to incorporate merchantsurcharging and show that it attenuates the pooled cross-subsidy by $1–2 billion (3–7%). The correct aggregation uses transfer-weighted sector shares, not expenditureshares; the naive alternative overstates the correction fivefold. Using transaction-leveldata from the Diary of Consumer Payment Choice (2022–2024), we document thatsurcharging has nearly doubled since 2021 and is concentrated in sectors where smallbusinesses face high interchange costs. At the transaction level, credit card purchasesby consumers with household income below $25,000 are surcharged at twice the rate of1those above $150,000 (p = 0.038, respondent-clustered standard errors with merchant-category fixed effects). However, this gradient is fragile: it does not survive aggregationto the respondent level, is present in 2024 but not in 2022, and is largely absorbed bycontrolling for rewards card status. Surcharging widens inequality in the net benefitsof card use primarily through card segmentation—non-rewards cardholders face highersurcharge rates—rather than through an independent income channel.

Abstract: This study examines the determinants of informality in ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, and Singapore) using Partial Least Squares Structural Equation Modeling (PLS-SEM) on panel data from 2015–2022. Five hypotheses tested the effects of institutional quality, social protection, labor market policy, economic growth, and technological advancement (as mediator). Results show that institutional quality significantly reduces informality (β = –0.378; p = 0.015), while social protection, labor market policy, and economic growth exert positive and significant effects, reflecting policy design–implementation gaps and growth patterns that fail to generate formal employment. Technological advancement does not mediate the growth–informality relationship (β = 0.011; p = 0.335). The model explains 88.8% of the variance in informality (adjusted R² = 0.888). Policy implications highlight the need for stronger institutions, inclusive social protection, adaptive labor regulations, and digitalization integrated with e‑governance to foster formalization.

Abstract: The essence of free trade zones lies in addressing development challenges through institutional opening-up and innovation-driven growth. Sustainable development constitutes the fundamental goal of free trade zone construction, opening-up and innovation serve as the core driving forces for their development, and a sound business environment acts as a critical guarantee for their efficient operation. Therefore, based on the panel data of 22 free trade zones in China from 2013 to 2022, this paper adopts Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) to conduct a comprehensive evaluation of their sustainable development levels from six dimensions: environmental optimization, economic development, opening-up, radiation-driven capacity, business environment, and scientific and technological innovation. The results indicate that, first, the overall comprehensive scores of free trade zones in sustainable development show an upward trend with obvious regional divergence in growth rates. Coastal free trade zones maintain robust growth momentum, inland ones achieve steady progress, and border free trade zones witness modest growth. Second, the comprehensive scores of the 22 free trade zones in 2022 present a gradient distribution, reflecting prominent regional development imbalance. On this basis, targeted policy recommendations are put forward in this paper.

Abstract: This paper investigates whether financial markets exhibit deterministic chaotic dynamics and whether such dynamics can improve early detection of systemic crises. Using a global dataset covering major equity indices and volatility indicators over the period 1990–2025, we apply a comprehensive nonlinear framework combining maximum Lyapunov exponents, correlation dimension, sample entropy, recurrence quantification analysis (RQA), and nonlinear vector autoregressions. The results provide robust evidence that financial markets are characterized by positive Lyapunov exponents, indicating sensitive dependence on initial conditions and the presence of deterministic chaos. Importantly, chaos intensifies systematically prior to major crises, including the Dot-com bubble, the Global Financial Crisis, and the COVID-19 market crash. We construct a composite Early-Warning Index (EWI) based on nonlinear indicators, which significantly outperforms traditional benchmarks such as Value-at-Risk and volatility-based models in predicting crisis events. The findings suggest that financial instability is largely endogenous, emerging from nonlinear amplification mechanisms rather than purely exogenous shocks. By integrating chaos theory into financial econometrics, this study provides a novel framework for understanding market dynamics and offers practical tools for systemic risk monitoring. The results have important implications for macroprudential policy and the design of forward-looking early-warning systems.

Abstract: This paper examines the dynamic relationship between innovation, total factor productivity (TFP), and economic growth in Ghana using annual data for the period 1965–2021. Although Ghana has recorded relatively strong economic growth, concerns remain regarding the sustainability of this performance in the absence of consistent productivity improvements. The study combines growth accounting techniques with time-series econometric methods, including the autoregressive distributed lag–unrestricted error correction model (ARDL–UECM), vector error correction modelling (VECM), Granger causality tests, and two-stage least squares estimation. The results provide robust evidence of a stable long-run equilibrium relationship among innovation, productivity, and output. Innovation exerts a positive and statistically significant effect on economic growth, primarily through productivity-enhancing channels, while TFP emerges as the dominant long-run driver of growth. Short-run dynamics reveal feedback effects between innovation, productivity, and economic growth. However, growth accounting results indicate substantial volatility in TFP growth, suggesting that Ghana’s expansion has been driven largely by factor accumulation rather than sustained efficiency gains. The findings offer policy-relevant insights for productivity-centred growth strategies in Sub-Saharan Africa.

Abstract: This paper examines whether the rise of remote work following the COVID-19 pandemic has generated a structural transformation in urban spatial organization across major metropolitan areas in advanced economies. While much of the existing literature treats COVID-19 as a temporary shock, this study argues that it has induced a persistent reconfiguration of cities toward more polycentric and decentralized spatial structures.Using a multi-source dataset combining Google mobility reports, NASA/VIIRS night-time light satellite data, OECD and national labor force surveys, and urban economic indicators, the study constructs a novel Urban Polycentricity Index (UPI) to measure spatial dispersion of economic activity. The empirical analysis covers New York, London, Paris, Berlin, and Munich over the period 2019–2025.The methodology integrates structural break tests, difference-in-differences estimation, and spatial equilibrium modeling to identify both the timing and magnitude of post-COVID spatial shifts. Results indicate a significant structural break around 2020–2021, followed by a sustained increase in remote work adoption and urban polycentricity. Satellite and mobility data confirm a systematic redistribution of economic activity from central business districts toward suburban and peripheral zones.Findings show that remote work is a statistically significant driver of urban decentralization, associated with flatter density gradients, reduced commuting intensity, and higher polycentricity. Counterfactual simulations further confirm that, without remote work expansion, cities would have remained substantially more monocentric. Overall, the study demonstrates that COVID-19 has permanently altered urban spatial equilibrium, positioning remote work as a key structural force reshaping metropolitan form.