Cancer mortality rates in the Baltic States (Estonia, Latvia, and Lithuania) exceeds the European Union (EU) average, in part due to limited access to radiation therapy (RT). We updated RT capacity and utilization to inform regional planning. Patient/material and methods: We conducted a census of all 11 RT centres (2016-2023) via a standardized questionnaire, cross-validated with national registries and international databases. We compared technology availability, workforce, and utilization with EU countries in relation to the present cancer burden and projections to 2050. This multicentre observational study adhered to STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) guidelines. Only 35-42% of cancer patients received RT, below the 50% recommendation. Linear accelerator availability ranged from 3.8 to 5.1 per million inhabitants, figures that are almost half those seen in EU countries with higher Gross Domestic Product (GDP) per capita. While the use of intensity modulated RT, volumetric modulated arc therapy and stereotactic RT increased, staffing levels has remained static in recent years. Mortality-to-incidence ratio correlated negatively with GDP (r = -0.7) and RT capacity (r = -0.7). Despite technological progress in the Baltic States, major gaps persist in RT access and workforce levels. Baltic States still underperform compared to EU countries with higher GDP per capita in terms of equipment availability, workforce capacity, and overall cancer outcomes. Future-oriented strategic investments, based on regional collaboration and shared infrastructure are urgently needed, including the development of a regional particle therapy centre, to ensure equitable access to state-of-the art advanced cancer care across the Baltic States.

This study examines the relationship between liberal democracy and climate performance. In the econometric analysis conducted using the panel ARDL model, the long-term effects of liberal democracy, climate change performance, urban population, and per capita gross domestic product (GDP) on carbon emissions (CO 2 ) are analyzed based on OECD member countries. We find that an increase in the level of liberal democracy increases carbon emissions in the long term, while improved climate performance significantly reduces emissions. It means that liberal democracy not only generates a “demand for climate mitigation” but also a “demand for climate-insensitive economic growth and urbanization.” If the institutional capacity for climate mitigation cannot balance these demands, an increase in carbon emissions will be inevitable. In this context, we argue that relationship between liberal democracy and climate change performance is not linear, but “conditional”. Liberal democracies can only succeed in climate mitigation when a certain institutional capacity threshold is exceeded. This capacity can be increased with the following concrete policy recommendations for OECD countries: To base carbon reduction targets on permanent/legislative rather than temporary/political grounds, to strengthen emission monitoring and reporting mechanisms, and to encourage investments in renewable energy. To expand investments in electric public transport and low-carbon transportation systems, and develop energy-efficient building standards. To focus not only on the rate of growth but also on the carbon intensity and energy structure of that growth.

Abstract This study decomposes aggregate labor productivity growth in Turkey from 1999 to 2023 using a chain-linked gross domestic product (GDP) series with an exactly additive decomposition method. Traditionally, this growth has been decomposed into two components: productivity growth within sectors and labor reallocation across sectors. Using the chain-linked GDP series introduces a third component: changes in relative sectoral prices. Although these relative price changes cancel out at the aggregate level, they influence sectoral contributions to overall labor productivity by altering each sector’s weight in total output. Incorporating them, therefore, provides a more comprehensive view of sectoral dynamics by capturing their contributions to aggregate productivity growth. On average, the contribution of structural change slightly exceeds that of the within component. However, both the magnitude and composition of contributions vary considerably across sub-periods. During crisis years, structural change contributed positively while the within-sector component was negative. In contrast, during non-crisis periods, aggregate labor productivity growth declined because the structural-change component weakened persistently and nearly vanished after 2018, despite a positive though limited within-sector component. At the sector level, construction, finance and real estate, community, personal, and government services, and transport and communication largely account for the slowdown, while manufacturing’s contribution stayed steady; its composition shifted away from within productivity across periods.

The healthcare sector in India is experiencing rapidly increasing demand while operating under limited medical resources. With national healthcare expenditure at approximately 3% of Gross Domestic Product (GDP), the system faces shortages in hospital beds, medical professionals, and essential equipment. The widespread adoption of Internet of Things (IoT) devices for health and lifestyle monitoring has transformed modern healthcare by enabling continuous, real-time data collection. Additionally, sixth-generation (6G) cellular networks support IoT-based healthcare services through enhanced ultra-reliable low-latency communication (eURLLC), ensuring timely and reliable transmission of critical health data. This research proposes an intelligent and scalable framework to optimize healthcare resource management using advanced machine learning techniques. The system utilizes real-time data from IoT-enabled devices within a 6G environment to perform dual-task prediction: classification of resource utilization levels and regression-based estimation of efficiency scores. A Classification and Regression Tree (CART) approach is applied to enhance structured decision-making and model interpretability. The framework integrates multiple machine learning models, including Random Forest (RF) and Support Vector Machine (SVM) combined as the Hybrid Kernel Forest Network (HFKN), along with Gradient Boosting (GB), Logistic Regression (LR), and KNearest Neighbors (KNN) forming the TriVector Intelligent Fusion Model (TVIFM). Extreme Gradient Boosting (XGBoost) is proposed as the primary model due to its strong boosting capability and generalization performance. Implemented using a Flask-based architecture, the system supports data upload, training, evaluation, real-time prediction, and visualization of metrics such as accuracy, precision, recall, F1-score, and R², improving resource allocation and operational efficiency.

Introduction Obesity is a significant global public health concern, and there has been an increase in the prevalence of obesity globally over the past three decades. A region that has been particularly affected by the increasing trends of obesity over time is the Indian subcontinent region. The study was conducted (1) to assess the trends in the prevalence of obesity in adults, children, and adolescents in India and compare it with the median global prevalence over the past three decades and (2) to correlate the changes in economic, globalization, urbanization, and physical activity parametersand the prevalence of obesity in Indian adults, children, and adolescents over the same period. Methods This is a retrospective, longitudinal, ecological data-based (national- and global-level measures) study of the prevalence of obesity in India over the period from 1990 to 2022. The main outcome was obesity in adults, 10-19-year-olds, and 5-9-year-old children in India. We also compared this to the median global prevalence over the same period. We correlated the prevalence of obesity with gross domestic product (GDP) per capita-purchasing power parity (PPP), globalization score, economic globalization score, social globalization score, the proportion of urbanization, and insufficient physical activity. We used linear regression models to estimate the change in prevalence and Pearson's correlation coefficients for correlation. Results The prevalence of age-adjusted obesity in the adult population was 0.79% in 1990, and it had increased to 7.27% in 2022. The prevalence of obesity in the 10-19-year-olds was 0.082% in 1990, and it had increased to 2.72% in that age group, and it had changed from 0.20% in 1990 to 4.96% in the 5-9-year-olds. The percentage increase in the prevalence of obesity was highest in 10-19-year-old boys and lowest in adult women from 1990 to 2022. The change in the prevalence for the Indian adult male population was 0.145 (95% confidence interval {CI}: 0.132, 0.157; p<0.001), and it was 0.254 (95% CI: 0.236, 0.271; p<0.001) for women. The change in prevalence for both groups (male and female population) was 0.083 (95% CI: 0.075, 0.091; p<0.001) for 10-19-year-olds, and it was 0.153 (95% CI: 0.141, 0.165; p<0.001) for the 5-9-year-olds. The change in the ratio of global prevalence to Indian prevalence was steepest in 1990-1994 for adultsand in the 1995-1999 period for the 10-19-year-old and 5-9-year-old age groups. There was a strong and significant correlation between economic, globalization, urbanization, and physical activity parameters. Conclusions With the changes in socio-economic and urbanization patterns in India, obesity has become an important public health concern. Additionally, as the prevalence of obesity increases, associated conditions such as non-communicable diseases, including their complications, may also show an increase and may be an additional burden on the healthcare system. Thus, there is a need to implement targeted national programs to address issues related to obesity in adults. Children and adolescents have shown a sharp increase in the prevalence of obesity in the past three decades in India. Thus, there should be a greater emphasis on the prevention and management of obesity in these groups in the public health programs in India.

Purpose This study examines the primary drivers of India’s merchandise trade with 48 African countries from 2000 to 2023. It examines how economic size, governance effectiveness, regulatory quality and corruption shape bilateral trade flows within a rapidly evolving South–South economic partnership. By analysing both formal and informal institutional factors, this paper aims to clarify their relative influence on trade and provide evidence-based insights for policies that can strengthen and sustain India–Africa economic cooperation. Design/methodology/approach Structural gravity model estimated using Poisson pseudo-maximum likelihood, enabling robust treatment of heteroskedasticity and zero-trade observations. Findings Gross domestic product (GDP) of India and its African partners is the strongest predictor of bilateral trade. Governance effectiveness and regulatory quality have significant positive effects, underscoring the importance of institutional capacity. The analysis also identifies a short-term “greasing the wheels” effect of corruption, where higher corruption levels accompany increased trade, revealing a more complex institutional landscape than conventional views suggest. Model-based projections indicate continued growth in India–Africa trade. Originality/value This paper presents one of the few systematic assessments of India–Africa trade, using a modern structural gravity framework that incorporates institutional quality. By identifying a nuanced corruption effect alongside positive governance impacts, it challenges standard assumptions and highlights the interaction of formal and informal institutions in South–South trade. The results offer clear policy guidance for enhancing governance and regulatory environments to deepen India–Africa economic engagement.

This article analyzes the main features of the social insurance system in the countries of the European Union, its structure, financing mechanisms, and coordination rules at the European level. Although each member state has the right to independently establish its national social insurance system, common coordination rules have been introduced to ensure the free movement of European Union citizens and the protection of their social rights. The article compares the share of social protection expenditures in gross domestic product (GDP), key policy directions, and various social models. The results of the study indicate that the social insurance system in European Union countries serves as an effective mechanism for protecting the population from social risks; however, due to population aging and increasing financial pressures, it requires continuous reforms

This study examines the effect of Ease of Doing Business on economic growth, as measured by Gross Domestic Product (GDP), in Southeast Asian countries. The research problem is the persistent differences in the level of ease experienced among Southeast Asian countries and how this impacts economic performance. This study uses secondary data from the World Bank's GDP and Ease of Doing Business indicators based on the Distance to Frontier (DTF) Score from Doing Business. The indicators include things like starting a business, handling building permits, obtaining electricity, registering property, obtaining credit, paying taxes, trading abroad, and resolving bankruptcy. This study uses a Fixed Effect Model to conduct panel data regression analysis on 9 countries in the Southeast Asian region from 2014 to 2020. The results show that all Ease of Doing Business variables have a significant impact on GDP simultaneously. Bankruptcy Resolution has a negative and significant impact on GDP; partially, Building Permit Management, Electricity Procurement, and Cross-Border Trade have a positive and significant impact on GDP. Meanwhile, starting a business, obtaining credit, paying taxes, and registering property did not have a significant impact. This study found that ease of doing business in certain sectors plays a significant role in increasing economic growth. Therefore, the government recommends improving the effectiveness of regulations, infrastructure, and trade to encourage sustainable economic growth.

The objective of this study is to examine the roles of SME, non-SME, and agricultural financing in inclusive economic growth and concomitant headcount poverty reduction in Bangladesh. ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin) tests for non-stationarity of time series variables, the ARDL (Autoregressive Distributed Lag) procedure for cointegration and the associated VECMs (Vector Error- Correction Models) are implemented for convergence toward long-term equilibrium, speed of adjustment toward it, long-run casual effects and short-run feedback effects. Annual data from 1976 through 2023 are employed. Data sources include the Bangladesh Bureau of Statistics and the Bangladesh Planning Commission. Evidences support financing of SMEs to positively influence inclusive real GDP (Gross Domestic Product) growth that in turn help poverty reduction. However, the reduction in poverty through inclusive economic growth alone is relatively subdued than direct SME financing. Agricultural financing has relatively more pronounced effects on poverty reduction. The converging speed of adjustment towards the long-run equilibrium, however, seems tepid. The short-run feedback effects appear reinforcingly positive. The policymakers in Bangladesh should stay focused on due financing to SMEs and agriculture for promoting inclusive economic growth and hence, mitigating poverty. To note, enhancing inclusive economic growth alone cannot sufficiently dent poverty. Other supportive measures and micro-business lending should as well be given a priority.

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 108 CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance.

This paper analyses the relationship between population change and economic development in Nepal from 1961 to 2021. It verifies that demographic transitions, such as population growth, fertility, and mortality evolution, are an intrinsic characteristic of global development and significantly influence the long-term social and economic trajectory of a nation. The main goals of this research are to analyse Nepal's trends in demographic change, to comment on its trends in economic growth, and to assess the effects of the major demographic indicators— like the working-age population, participation in the Labour force, and dependency ratio—on its economic growth. The research tries to bridge the gap in literature by including analysis for countries with recent data. The analysis utilizes a quantitative approach grounded in secondary data from various international and national sources. A multiple regression model, with Gross Domestic Product (GDP) as the dependent variable, was utilized in an analysis of key demographic independent variables like population growth rate, Labour force participation rate, and net migration. The regression results show that the model in total is extremely explanatory with an R² of 0.997 and is significant. This leads to the rejection of the null hypothesis, suggesting an extremely high correlation between economic growth and demographic variables. The indicators reveal positive contributions of the increase in Labour force and population to GDP, while youth population and net migration have negative effects, suggesting high youth dependency and high out-migration may deter economic development. It is concluded that while Nepal's demographic transition offers scope for speeding up economy, the same is threatened in the absence of policy action on education, employment, and social protection.

Leishmaniasis, a parasitic disease caused by Leishmania spp., is a major public health threat. The synergistic effects of environmental and socioeconomic factors on the global distribution of leishmaniasis are unknown. Applying epidemiological data on cutaneous leishmaniasis (CL) from the Global Burden of Disease 2021 database, we used spatial autocorrelation and standard deviation ellipses to explore the spatiotemporal clustering and migration patterns of CL. Four remote sensing-retrieved environmental factors and five socioeconomic factors were selected for analysis. Spearman's correlation coefficient was used to screen for factors correlated with the prevalence of and disability-adjusted life years (DALYs) due to CL. Ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) were used to assess the impact of the influencing factors on the prevalence of and DALYs due to CL. From 1990 to 2008, the global prevalence of and DALYs due to CL exhibited significant positive spatial autocorrelation (Z > 1.96, P < 0.05). Prevalence and DALYs both had one cold spot, located in northern Africa, and two hot spots, located in Central America and Central Asia. Temperature, infant mortality rate (IMR) and humidity were significantly positively correlated with the prevalence of and DALYs due to CL, whereas gross domestic product (GDP) and surface solar radiation (SSR) were significantly negatively correlated with the latter. The GTWR model demonstrated the best regression performance, with adjusted R2 values for prevalence reaching 0.841, 0.984, 0.839 and 0.972, and those for DALYs reaching 0.816, 0.966, 0.837 and 0.972 in Asia, Europe, the Americas and Africa, respectively. Regression coefficients further quantified the individual contributions of each factor to the prevalence of and DALYs due to CL, which could provide a scientific basis for governments to implement targeted control of CL. To our knowledge, this study is the first to analyze the global spatiotemporal distribution patterns of the prevalence of and DALYs due to CL and quantitatively study the spatiotemporal effects of environmental and socioeconomic factors on CL on a global scale. Environmental (temperature, SSR and humidity) and socioeconomic (GDP and IMR) factors were significantly correlated with the prevalence of and DALYs due to CL. The GTWR model outperformed the GWR and OLS models, further confirming the spatiotemporal effects of influencing factors on CL.

This study compared the oral health status of the German population with that of the populations of European Union (EU) and selected Organisation for Economic Co-operation and Development (OECD) countries. Data from the 6th German Oral Health Study (DMS • 6) were compared with those obtained from the Oral Health Country/Area Profile Project (CAPP) database at Malmö University and from a systematic literature search of the PubMed and Google Scholar databases. Priority was given to nationally representative studies. Regarding dental caries in 12-year-olds, 35- to 44-year-olds, and 65- to 74-year-olds, Germany ranked 2nd of 39, 5th of 21, and 10th of 20 countries, respectively. The proportion of periodontally healthy individuals in Germany was comparatively low. However, Germany showed the lowest prevalence of erosive tooth wear among individuals aged 35 to 44 years. Furthermore, the prevalence of molar incisor hypomineralization (MIH) in Germany was 15.3%, which was consistent with the average international prevalence. Among participants aged 12 years, a significant inverse correlation was observed between gross domestic product (GDP) per capita at purchasing power parity and caries experience. Overall, Germany showed a favorable caries profile compared to EU and OECD countries, but lost the leading position achieved in childhood and adolescence with increasing age. This finding suggests that the preventive gains made in early life were not sustained into adulthood owing to the absence of continuous measures. The relatively high prevalence of root caries in Germany is attributed to an intrinsically higher disease risk and greater tooth retention in adults and seniors. In contrast, the periodontal situation was less favorable, although methodologic differences between studies and the high rate of tooth retention must be considered in this context. Similarly, Germany's leading position regarding erosive tooth wear should be interpreted cautiously because of limited data. MIH prevalence was consistent with the international average, and clarification of its etiology is warranted for targeted prevention. The association between GDP per capita and caries underscores the influence of socioeconomic factors and the importance of long-term population-level preventive strategies. (Quintessence Int 2026;57(Suppl): S142-S151; doi: 10.3290/j.qi.b6955513).

In today's world, research and development activities of countries and cities, urban developments and economic developments have significant impacts on air transportation. The purpose of the study is to examine the effects of research and development (R&amp;D) expenditures, urbanization population rate and gross domestic product (GDP) on air transportation using panel data analysis method. In the study, data of the upper-middle country group (Türkiye, Malaysia, Argentina, China, Bulgaria, South Africa, Colombia, Ecuador) for the period 1995-2019 were used. In this context, to determine the factors influencing air transportation, the number of air passengers was selected as the dependent variable, while the urbanization population rate, R&amp;D expenditures, and GDP were selected as independent variables. The analysis included Delta homogeneity tests and cross-sectional dependence tests. Unit root analysis was conducted to assess the stationarity of the selected variables. Following the unit root tests, Pedroni cointegration, Kao, and Westerlund tests were conducted to determine the existence of a long-term relationship between the variables. After these two tests were applied, the panel ARDL method was used to determine the long-term coefficients between the selected factors. The causality between urbanization, R&amp;D, economic growth and air transportation was examined with the Granger causality test. According to the econometric analysis findings of the research, it was concluded that R&amp;D expenditures, urbanization population rate and GDP increased the number of passengers carried in the long term.

Stagflation remains one of the most serious macroeconomic challenges facing developing economies because it combines inflationary pressure, weak growth, and structural instability within the same economic environment. This study was guided by the Expectations-Augmented Phillips Curve Theory, complemented by the Structuralist Theory of Inflation and the Resource-Dependence Theory, which together explain how inflation, labour-market weakness, supply-side shocks, and commodity dependence interact to constrain economic growth. This study examines the effect of stagflation on Zambia’s economic growth as measured by Gross Domestic Product (GDP). Zambia provides a compelling case for this analysis because inflation, exchange-rate instability, external debt stress, commodity-price volatility, and labour-market weakness have repeatedly interacted to undermine growth in a copper-dependent economy. Using annual time-series data covering the period 1964 to 2024, the study employs a Vector Error Correction Model (VECM) after conducting unit-root, lag-selection, multicollinearity, and Johansen cointegration tests. The findings confirm the existence of a stable long-run relationship between GDP and the principal stagflation-related drivers. Exchange-rate depreciation, labour-market weakness, copper-supply disruptions, and inefficient government spending are found to constrain long-run growth, while external debt and population growth display positive long-run coefficients, reflecting the growth-supporting role of debt-financed investment and demographic demand under certain conditions. The short-run GDP equation reports a negative error-correction coefficient, indicating a slow adjustment process toward long-run equilibrium following macroeconomic shocks. Further dynamic evidence shows that inflation and exchange-rate volatility account for a meaningful share of GDP fluctuations, while copper-price declines and debt-service pressures generate notable output losses. The study concludes that stagflation in Zambia is not merely a temporary cyclical disturbance, but a structural constraint on economic growth and transformation. It therefore recommends export diversification, stronger exchange-rate resilience, more productive public expenditure, prudent debt management, and labour-market strengthening as essential pillars of a coordinated policy response.

This study examines the relationship between transportation expenditure and economic performance across 41 countries over the period 1990–2023, highlighting the global role of transportation investment in economic development. Using descriptive statistics, correlation analysis, and hierarchical linear regression, the research investigates the association between gross domestic product (GDP) and multiple dimensions of transportation expenditure. The results indicate a strong positive relationship between GDP and total transportation expenditure, as well as passenger transportation expenditure, underscoring the close linkage between transportation investment and economic activity. In contrast, a significant negative relationship is observed between GDP and transportation expenditure as a share of GDP, suggesting that higher-income countries invest more in transportation in absolute terms while allocating a smaller proportion of national output to this sector, likely reflecting efficiency gains and economies of scale in mature transportation systems. Hierarchical regression results indicate that transportation expenditure is a statistically significant predictor of GDP, with the baseline model accounting for a substantial share of cross-country variation. The inclusion of relative expenditure measures yields modest additional explanatory power, with the second model providing the most stable specification by effectively mitigating multicollinearity. Overall, the findings emphasize the continued importance of sustained transportation investment, while highlighting differences in investment intensity across stages of economic development. For developing economies, strategic and efficient transportation investment remains critical for supporting long-term economic growth. The study contributes to the literature by offering a comprehensive, cross-national perspective on the scaling of transportation expenditure with economic performance and its implications for transportation policy and planning.

Understanding the dynamic relationships among major macroeconomic variables is essential for evaluating economic stability and informing policy design in developing economies such as Nigeria. This study aimed to investigate the interrelationships among Gross Domestic Product (GDP), inflation, broad money supply (M2), interest rate, exchange rate, and unemployment in Nigeria over the period 2001–2023. Annual data were obtained from the Central Bank of Nigeria, World Bank, International Monetary Fund, and CEIC databases and analyzed using a Vector Error Correction Model (VECM) implemented in Python’s statsmodels framework to capture both short-run adjustments and long-run equilibrium dynamics. The findings reveal the presence of three stable long-run cointegrating relationships among the variables. Inflation was found to respond strongly to changes in GDP, interest rates, and exchange-rate movements, whereas the effects of money supply and unemployment were relatively weaker. The results further indicate that economic growth contributes to modest reductions in unemployment, while persistent inflationary pressures and volatile interest rates tend to worsen labor-market outcomes. Exchange-rate depreciation also emerged as a major source of macroeconomic instability. Diagnostic tests suggest that the estimated model is broadly robust, although mild indications of serial correlation and multicollinearity remain. The study concludes that Nigeria’s macroeconomic environment is shaped by deep structural weaknesses that require stronger policy coordination, improved exchange-rate management, and sustained structural reforms to enhance price stability and employment outcomes. These findings contribute empirical evidence on long-run and short-run macroeconomic interactions in Nigeria and provide policy-relevant insights for strengthening economic management.

The Global Innovation Index (GII) offers a comprehensive assessment of innovation across various countries and regions worldwide, significantly enhancing our understanding of economic and societal development. This study evaluates the performance of the National Innovation System (NIS) in Uganda using GII scores over 10 years. The study examines GII scores from 2016 to 2025, along with its constituent sub-indices and pillars, which collectively provide a comprehensive evaluation of Uganda's national innovation performance. The analysis employed generalised linear and panel-corrected standard error models based on the 2016–2025 GII reports. The findings indicate that innovation positively influences Gross Domestic Product (GDP), local knowledge and technology, and creative outputs. The study identified human capital, research, business sophistication, and creative outputs as having the most significant explanatory power concerning the GII. The results suggest that an effective regulatory framework, institutional support, domestic human capital, Research and Development (R&amp;D), technology, and creative outputs are crucial for fostering a vibrant innovation ecosystem. The findings of this study are pivotal for policymakers, including local and national governments and business authorities. These findings may also catalyse national discussions on strategies and practices aimed at enhancing one or more evaluated innovation pillars.

The agricultural sector ranks third as the largest contributor to Indonesia’s Gross Domestic Product (GDP). Serang Regency is the second-largest rice-producing region in Banten Province. The agricultural sector’s contribution to Serang Regency’s Regional Gross Domestic Product (RGDP) was recorded at 9,47% in 2023, highlighting the sector’s strategic role in the local economy. However, the magnitude of this contribution does not align with the current state of farmer succession, which continues to face serious challenges, marked by a low proportion of young farmers compared to older farmers. This situation could threaten the sustainability of food production as well as future socioeconomic stability. Therefore, this study aims to analyze the factors influencing rice farmer succession in Serang Regency. This study employs a quantitative approach. The study location was selected using a purposive sampling method in Serang Regency, based on its status as the second-largest rice-producing area in Banten Province. The research instrument consists of a closed-ended questionnaire using a 5-point Likert scale. The sample in this study consists of rice farmers in Serang Regency, specifically in Tanara and Cikeusal sub-districts, aged 19–39 years, with a total sample size of 105 respondents. The analysis in this study employs Structural Equation Modeling – Partial Least Squares (SEM-PLS). The results of this study indicate that individual and social factors have a positive effect on farmer succession, while inhibiting factors have a negative effect on farmer succession. Meanwhile, economic factors, motivational factors, and government policies have no effect on farmer succession.

Monitoring water clarity dynamics in Hongze Lake under the influence of the south-to-north water diversion project using an enhanced QAA algorithm.