Search Results (4,071)

Rural entrepreneurship is increasingly recognised as a critical driver of economic resilience and regional development, particularly in developing regions such as the Eastern Cape, South Africa. This exploratory study examines how entrepreneurial activities stimulate local economic growth, job creation, innovation, and community empowerment in Mnquma Local Municipality, where persistent unemployment and poverty remain significant challenges. Adopting an exploratory research design underpinned by a positivist philosophy, this study synthesises existing literature on the relationships among employment generation, economic contribution, innovation promotion, economic diversification, community engagement, and local supply chain development. Data were collected from 349 respondents drawn from a population of 3750 formally registered rural entrepreneurs. The data were analysed using descriptive statistics, reliability and validity tests, correlation analysis, and Partial Least Squares Structural Equation Modeling (PLS-SEM) in SPSS and SmartPLS, to examine the relationships among employment generation, economic contribution, innovation promotion, economic diversification, community engagement, supply chain development, and economic resilience. The findings suggest that strong rural entrepreneurship enhances local adaptability and supports sustainable economic performance in the face of external shocks. The study emphasises the importance of inclusive, evidence-based policies that promote rural enterprise development through improved financial mechanisms, infrastructure investment, and strengthened stakeholder networks; thereby providing localised insights to inform policy and sustainable development in South Africa. Full article

This paper estimates the short-run relationship between real output growth and unemployment in Albania over 1992–2023 and asks how sensitive that relationship is to the way crisis episodes are dated. Albania offers a demanding test: a transition economy with high informality, extensive self-employment and sustained emigration, whose sample contains three major shocks in three decades. We estimate first-difference specifications in which the change in the unemployment rate is regressed on output growth, with a lagged dependent variable capturing persistence and separate indicators for the three crisis episodes. Because conventional standard errors are unreliable at this sample size, we report alternative estimators alongside bootstrap inference and a full set of residual and stability diagnostics. Our main practical result concerns crisis dating. Crisis dummies are conventionally dated to the year a crisis began internationally, but for a small open economy whose transmission is indirect, the year in which a shock reaches domestic activity need not be that year. Albania did not contract in 2008 or 2009. We date the financial-crisis indicator from an external chronology of transmission—credit growth, non-performing loans, exports, investment, remittances and the fiscal stance—assembled from contemporaneous institutional sources and fixed before estimation, which places the arrival of the crisis in the Albanian economy in 2009 and 2010. The estimated crisis coefficient is highly sensitive to that choice: a 2008-dated indicator produces the best-fitting specification in this paper on every information criterion together with a large negative coefficient; the conventional 2008–2009 window straddles the turning point and returns an estimate indistinguishable from zero; the 2009–2010 window returns a positive one. These coefficients are individually imprecise, and their intervals overlap, so we do not claim that the data identify any one dating as correct, and we do not treat the sign of an estimate as evidence for or against a window. Our claim is narrower: the substantive conclusion a reader would draw about the crisis is determined by a specification choice that the data cannot settle, so the window has to be fixed ex ante on transmission evidence and its consequences reported. We demonstrate this for Albania and do not assert that published work on other countries is affected; we specify the test that would settle it. The dating choice leaves the estimated Okun coefficient itself almost unchanged. That coefficient is negative in every specification, estimator, subsample and phase definition we report, with a normal-times impact estimate of approximately −0.21, smaller in absolute value than the range reported for advanced economies. Its statistical significance is sensitive to the mode of inference: under the null-imposed bootstrap we regard as most appropriate at this sample size, the coefficient is not significant at conventional levels in the specification that carries that estimate. The evidence for the sign of the relationship is considerably stronger than the evidence for its magnitude, and comparisons of level with advanced-economy estimates should be read against that. The three crisis coefficients are large but imprecisely estimated, and restrictions of equality between them are not rejected, so we report them as descriptive magnitudes and do not rank the episodes. We validate the modeled unemployment series against the national Labour Force Survey over the years in which both exist and re-estimate the model on the national series; the crisis coefficients and the crisis-dating pattern are essentially unchanged. We do not detect a structural break at the 1997 pyramid-scheme collapse once dynamics and crisis indicators are jointly modeled, and we do not detect cyclical asymmetry, though the confidence intervals are wide enough that both are failures to detect rather than evidence of stability or symmetry. Full article

Against the backdrop of the coordinated advancement of China’s “dual-carbon” goals and the national “Digital China” strategy, whether green financial policy can promote corporate digital transformation through resource reallocation has become an important question for high-quality development. Drawing on the resource-based view and financing-constraint theory, this study uses micro-level data on Chinese listed firms to evaluate how green credit policy affects corporate digital technology investment and its structural composition. Treating the 2012 Green Credit Guidelines as a common policy shock with differential exposure across environmentally sensitive industries, the baseline estimates show that green credit policy significantly increases firms’ overall digital technology investment, significantly reduces technical-personnel intensity, and significantly increases digital-asset intensity. This asymmetric pattern indicates that green credit policy affects not only the overall level of digital technology investment but also its internal structure. A standardized composition measure, COMP = z(DAI) − z(TI), further confirms a significant shift toward digital assets relative to technical personnel. The baseline findings are robust to a range of alternative specifications, inference procedures, and identification checks. Financing-cost and financing-constraint tests provide evidence consistent with an asset-substitution channel, while the mechanism analysis is interpreted cautiously rather than as strict causal mediation. Heterogeneity analysis and the green-innovation extension are supplemented with formal coefficient-difference tests. Overall, the findings show that green credit policy promotes digital technology investment while inducing a structurally asymmetric reallocation from technical personnel toward digital assets. Full article

Battery energy storage systems (BESSs) are increasingly coupled with photovoltaic (PV) generation, yet their deployment is governed by economics rather than by technical feasibility. This review synthesises 122 indexed Q1/Q2 studies (2016–2026) on the financial payback and return on investment of PV-coupled storage, extending the analysis to vehicle-to-grid (V2G) integration. Records retrieved from Scopus, Web of Science, IEEE Xplore, and the MDPI portal were screened to peer-reviewed journals, assigned to ten thematic clusters, appraised against a ten-criterion reporting-transparency rubric, and combined by narrative synthesis. Reported payback periods range from a few years to beyond the asset’s service life, and the levelized cost of storage spans roughly 170–350 USD/MWh. In the reviewed corpus, the retail-to-export price spread, the stacking of self-consumption, arbitrage, grid-service revenues, and degradation-aware operation each move returns in a consistent direction, whereas which of them binds hardest is a property of the case rather than a ranking the evidence supports. V2G is almost always analysed in isolation from stationary storage; an illustrative harmonised comparison indicates that it substitutes for stationary capacity rather than adding to it. The review maps the combined PV+BESS+V2G revenue stack and identifies an integrated, degradation-corrected, policy-sensitive economic model as the principal research gap. Full article

Background and Objectives: Artificial intelligence (AI) tools promise efficiency gains in oncology, yet adoption depends on economic factors that remain under-characterized in Eastern European health systems. We quantified AI economic literacy, cost-effectiveness perceptions, reimbursement readiness, and return-on-investment (ROI) confidence among Romanian oncology professionals; we described candidate economic adoption profiles and examined whether sector was associated with the strength of the indirect association between literacy and willingness to invest via ROI confidence. Materials and Methods: A multicenter cross-sectional survey (N = 108) was conducted between September 2025 and April 2026 at “Victor Babes” University of Medicine and Pharmacy Timisoara and affiliated oncology services. Participants completed a 25-item AI Economic Literacy Index (AIELI; 0–25) plus 1–5 scales for ROI confidence, willingness to invest, perceived financial barriers, cost-effectiveness perception, and adoption intention. Analyses used Spearman correlations, multivariable logistic regression, k-means clustering, and covariate-adjusted moderated mediation with 5000 bootstrap resamples. Results: Mean age was 41.3 ± 10.7 years; 58.3% were female. AIELI was moderate (13.7 ± 4.6/25). Familiarity favored cost-effectiveness analysis (59.3%) over AI-specific reimbursement codes (16.7%). High willingness to invest occurred in 48.1% and was independently associated with higher AIELI (aOR 1.78 per +1 SD; 95% CI 1.17–2.71), higher ROI confidence (aOR 1.93; 1.24–3.02), lower perceived financial barriers (aOR 0.58; 0.37–0.91), and prior AI training (aOR 2.34; 1.13–4.86). Three exploratory profiles were identified: Cost-Conscious Adopters (n = 43), Reimbursement-Cautious (n = 37), and Budget-Constrained Skeptics (n = 28). Moderated-mediation models were consistent with a sector-conditional indirect association, largest in private clinics (β = 0.193; 95% CI 0.087–0.318) and weakest in public hospitals (β = 0.072; 0.014–0.158). Conclusions: In this exploratory cross-sectional sample, AI economic literacy and ROI confidence were associated with willingness to invest in oncology AI, interpreted as stated support for investment rather than an enacted procurement decision, since many respondents lacked formal budgetary authority. Because the design cannot establish temporal ordering, whether sector-tailored capability-building and reimbursement clarity would increase adoption remains a hypothesis for prospective evaluation. Full article

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

In the arid regions of southeastern Tunisia, international migration—often driven by declining agricultural yields due to climate change—can be associated with strengthening the resilience of agro-systems through the valorization of migratory assets, particularly remittances invested in adaptation practices that support agricultural sustainability. However, this relationship may become neutral or even negative when migrants do not engage in this process. This study aims to analyze the relationship between migration, remittances, climate change adaptation practices, and agricultural production in the coastal oases of southeastern Tunisia. The methodology relies on a literature review, a field survey of 212 households, and statistical and econometric analyses. The comparative analysis reveals differences between migrant and non-migrant households in financial resources, with agricultural sources significant at the 5% level and non-agricultural sources at the 10% level, as well as in adaptation practices, particularly soil fertilization and conservation (1%) and drip irrigation (5%). By contrast, the first econometric regression shows a positive association between migration and adoption of adaptation practices. The second regression, which analyzes the Cobb–Douglas function, demonstrates that production factors (K and L), adoption of adaptation practices, and migration are associated with a positive relationship with agricultural output. The migration coefficient (MIG) attains statistical significance only at the 10% threshold, since the 95% confidence interval [−0.02; 0.60] includes zero. At this level of significance, migration appears to be associated with the performance and sustainability of the agricultural system. After applying the Kennedy correction, the coefficient indicates that migrant households produce on average 31.9% more than non-migrant households. Financial flows, as a fundamental component of migration, are not statistically significant in the model. The study highlights both gaps and opportunities for adaptation through migration. Policies should further mobilize migrants to participate in climate change adaptation processes, particularly in vulnerable regions, by relying on reforms in agricultural investment, awareness-raising, extension services, and cooperative governance of land management. Full article

With the increasing power of institutional investors, in order to compete for more investment agency business, the competition among institutional investors has become increasingly fierce. This paper studies a non-zero-sum game between two competing institutional investors who adopt mean–variance preferences and account for model uncertainty in order to derive robust optimal portfolios. The ambiguity-averse institutional investors can invest in a financial market with one risk-free bond and one individual stock. The objective of each institutional investor is to maximize the mean–variance utility of his terminal wealth relative to that of his competitor under the worst-case scenario of the alternative measures. By means of stochastic dynamic programming, we obtain closed-form expressions for the robust Nash equilibrium strategies and prove a verification theorem. Numerical simulations are finally presented to examine how model parameters influence the equilibrium strategies and to extract associated economic interpretations. Full article

In semi-arid Ghana, girls bear primary responsibility for household water collection in communities without on-premises access, a burden intensified by climate change associated with rising temperatures and frequent droughts. Yet little is known about how this responsibility shapes their embodied health and well-being. Guided by a political ecology of health framework, this qualitative study examined how water insecurity shapes girls’ physical, psychosocial, and neurological health in two communities, Wechiau and Kandue, in Ghana’s Upper West Region. We conducted in-depth interviews with nineteen purposively selected girls aged 10 to 19 and analyzed the transcripts thematically. Four interconnected themes emerged: structural and political determinants of water access, environmental and ecological conditions, gendered social relations, and embodied health consequences. Financial barriers, governance failures, and infrastructure deficits force girls into long queues and repeated trips, a burden that falls disproportionately on girls under cultural norms exempting boys from water duties. This gendered allocation of labour produces gendered health harm, whereby girls sustain musculoskeletal pain and injury carrying heavy loads over hazardous terrain, while chronic time loss disrupts sleep, schooling, and psychological well-being. Girls with pre-existing conditions such as epilepsy face the most acute risk, as water fetching directly exacerbates their vulnerability to seizures. These findings show that addressing this crisis requires more than infrastructure investment. Coordinated public–private investment in water access must be paired with social protection for vulnerable households, gender-responsive water policy, and rainwater-harvesting strategies that confront the structural and gendered inequities placing this burden on girls. Full article

Digital transformation (DT) is a strategic priority for healthcare systems seeking to improve the quality of care, patient safety, and operational efficiency. In Saudi Arabia, DT is central to Vision 2030; however, evidence regarding its effects on healthcare quality and safety remains dispersed across technologies and healthcare settings. This narrative review aimed to examine the impact of digital transformation on healthcare quality and patient safety in Saudi Arabia and to identify the principal implementation facilitators, barriers, and policy implications. A structured narrative literature search was conducted for publications from January 2010 to March 2026 using PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, the Saudi Digital Library, and relevant Saudi governmental and policy documents. In total, 64 peer-reviewed studies, reviews, reports, and policy documents addressing digital health initiatives and outcomes related to quality, safety, efficiency, or patient experience were included. Evidence was synthesized thematically, focusing on electronic health records, digital prescribing, online appointment systems, clinical and pharmacy automation, claims and financial analytics, telemedicine, governance, and workforce factors. The reviewed evidence indicates that Electronic Health Record (EHRs), computerized provider order entry, e-prescribing, barcode-supported medication systems, telemedicine, digital appointment platforms, and automated workflows can improve access to care, documentation, care coordination, medication safety, workflow efficiency, and patient experience. However, the benefits of DT are constrained by fragmented infrastructure, limited interoperability, workforce resistance, insufficient digital literacy, funding limitations, and unclear or fragmented governance and regulatory arrangements. In conclusion, digital transformation has substantial potential to enhance healthcare quality and patient safety in Saudi Arabia. Achieving these benefits requires context-sensitive implementation, interoperable systems, strong governance, sustained investment in infrastructure and workforce capacity, and effective leadership and change-management strategies aligned with Vision 2030. Full article

The sustainability of marine ecological conservation and restoration, a key issue in the implementation of Sustainable Development Goal 14 (SDG 14), which aims at reducing marine pollution, protecting and restoring marine environments, promoting the sustainable use of marine resources, and strengthening scientific monitoring requirements, depends not only on policy design and financial investment, but more critically on effective coordination among policy implementation, resource management, and performance feedback. This paper analyzes the institutional evolution of marine ecological conservation and restoration audit in China and the main issues and their causes based on the text analysis and qualitative interpretation of the 302 marine ecological issues in the audit reports publicly released by Chinese audit authorities from 2008 to 2024. Audits found that the problems were predominantly in three areas: policy accountability and strategic implementation; project approval and implementation management; and fiscal management and fund coordination. The emergence of these is strongly linked to the fragmentation of department responsibilities, mismatch between project management cycles and ecological restoration cycles, and inadequate coordination between fund allocation and ecological performance. Although some problems were repeatedly reported across different years, their observed temporal and spatial distribution was influenced by the scope of audit coverage and the extent of public disclosure. On this basis, this paper proposes to improve audit oversight in the three areas of policy accountability coordination, project life-cycle management, and the performance of fiscal funds. This is to promote the implementation of SDG 14 requirements along the chain of “objective–resources–projects–monitoring–rectification”, serve as a reference for improving the Chinese supervision and accountability mechanisms for marine ecological conservation and restoration, and provide practical experience from China on the implementation of SDG 14 at the levels of national policy implementation and public accountability. Full article

Research increasingly examines initial access to higher education (HE) in sub-Saharan Africa, yet less is known about the persistence of enrolled students in fragile contexts where costs are high and outcomes are uncertain. This study provides the first large-scale quantitative analysis of perceived HE worth in Guinea-Bissau, a resource-scarce Lusophone state. Drawing on Human Capital Investment (HCI), Behavioural Economics (BE), and Afrocentric perspectives on resilience and hope, we analysed survey data from 2255 students across six HE institutions (HEIs). A binary indicator of whether HE was worth the cost was used to assess ongoing valuation, and the data were analysed using sequential block-entry logistic regression after multiple imputation. Overall, 30.1% of students did not affirm that HE was worth the cost, indicating that enrolment does not guarantee sustained valuation. While cost-benefit reasoning remains important, perceptions were strongly shaped by campus climate and forward-looking expectations. Perceived advantages of HE, even without completion, outweighed the value of credentials alone, suggesting a pragmatic evaluation. We suggest anticipatory resilience as a lens for understanding students’ sustained valuations of HE amid uncertainty, grounded in anticipated benefits, institutional experience, and relational resources. Policy should prioritise campus climate, reduce financial strain, and reinforce the intrinsic value of HE. Full article

Digital governance has increasingly emerged as a critical institutional mechanism for strengthening public administration, enhancing accountability, improving service delivery, and advancing inclusive development across Africa. Governments across the continent have expanded investments in e-governance systems, digital public infrastructure, artificial intelligence (AI)-enabled administrative systems, interoperable service platforms, and data-driven governance reforms as part of broader modernization and Sustainable Development Goal (SDG) agendas. Despite these developments, digital governance outcomes remain uneven and are frequently constrained by fragmented governance architectures, weak institutional coordination, administrative capacity deficits, regulatory limitations, and persistent socio-economic inequalities. To address these challenges, this study conducts a qualitative Systematic Literature Review (SLR) guided by PRISMA 2020 reporting standards to examine how digital technologies interact with governance systems, institutional structures, administrative capability, and socio-technical inequalities across African public sectors. The review synthesized 42 included studies published between 2015 and 2025 using structured Boolean search strategies, predefined inclusion and exclusion criteria, abductive thematic synthesis, and framework-oriented analytical procedures. The findings identify three interconnected governance constraints shaping digital transformation outcomes across African public sectors: fragmented and centralized governance arrangements; institutional and administrative capability deficits; and persistent digital inequalities and exclusionary governance systems. Although digital governance reforms demonstrate important potential for improving transparency, interoperability, citizen participation, financial inclusion, and administrative efficiency, sustainable transformation outcomes depend heavily on institutional coordination, adaptive governance systems, digital inclusion, regulatory effectiveness, and accountable AI governance arrangements. Building on the synthesized evidence, the study develops a decentralized AI-enabled digital public governance framework linking decentralization, interoperability, institutional coordination, digital inclusion, adaptive governance, and ethical AI governance to sustainable public-sector transformation outcomes. The study contributes theoretically by reconceptualizing digital governance as an institutionally embedded governance transformation process rather than merely a technological modernization agenda. The findings further contribute to sustainability debates by demonstrating how inclusive and decentralized digital governance systems can strengthen institutional resilience, public-sector innovation, and sustainable development outcomes across diverse African governance contexts. Full article

The value of money is shaped by economic, political, and social dynamics, prompting individuals to rely on different saving and investment instruments to preserve and grow their wealth over time. Focusing on Romania as a representative emerging-economy context, this study documents a survey-based dataset capturing individuals’ self-assessed financial literacy, saving habits, investment preferences, and perceptions regarding the safety and benefits of different investment instruments. Primary data were collected between November 2024 and February 2025 from 875 respondents across 41 Romanian counties and Bucharest, using a computer-assisted web interviewing (CAWI) questionnaire. They were analyzed using chi-square goodness-of-fit tests to examine whether responses regarding self-assessed financial literacy, risk perception, and investment behavior departed from expected distributions. The findings show that, although respondents most frequently rated their financial literacy as medium to high, they allocate a limited share of savings to investment, favor cryptocurrency and real estate, and prioritize safety over returns. By openly documenting the survey design, sample structure, and analytical procedure, this paper aims to support the reuse, replication, and cross-country comparison of behavioral financial datasets, while offering practical insights for policymakers and financial institutions designing evidence-based interventions in emerging markets. Full article

The global energy transition is reshaping the relationship between traditional fossil-fuel and renewable energy sectors, making the transmission of financial shocks across brown and green energy equity markets increasingly important. This paper examines connectedness between brown and green energy equity markets using a unified quantile–frequency framework that captures both market-state dependence and investment-horizon heterogeneity, and it further investigates how innovations in transition and physical climate risk are associated with changes in connectedness. Using global energy equity ETFs representing carbon-intensive and renewable energy sectors, we obtain three main findings. First, connectedness is substantially stronger in both lower- and upper-tail market states than around the median, indicating pronounced state dependence in risk transmission. Second, the frequency decomposition reveals that short- and long-horizon connectedness contribute similarly on average, but their relative importance varies markedly across market states: long-horizon connectedness is relatively more important in the lower-tail state, whereas short-horizon connectedness is relatively more important around the median. Third, transition and physical climate risks exhibit distinct frequency-specific associations with connectedness. Physical risk innovations are negatively associated with changes in total connectedness, whereas transition risk innovations are positively associated with changes in short-horizon connectedness; the latter association is also stronger at the short than at the long horizon, although this cross-horizon difference is only marginally significant. These findings show that risk transmission across brown and green energy equity markets is jointly state- and horizon-dependent and that the association between climate risk and connectedness differs across climate risk dimensions and investment horizons. The results have implications for portfolio diversification, risk management, renewable energy investment, and the assessment of financial risks associated with the global energy transition. Full article