Under China’s rural revitalization strategy, rural tourism has become an important means of activating rural resources, increasing farmers’ income, and promoting agricultural transformation. However, converting tourism development into sustainable agricultural benefits remains a challenge in underdeveloped agricultural regions with weak industrial foundations. Taking Guangnan District in Wenshan Prefecture, Yunnan Province, as the research area, this study examines the effect of rural tourism on agricultural economic revitalization and the mediating role of industrial integration. Annual data from 2005 to 2025 are examined using Bayesian methods. The findings show that rural tourism has a significant positive long-term effect on agricultural economic revitalization. Industrial integration plays a partial mediating role, indicating that rural tourism promotes agricultural development both directly and indirectly through agriculture–tourism integration. The marginal contribution of rural tourism declines as tourism expands, while its economic benefits are mainly realized through long-term accumulation. Guangnan District should therefore shift from tourism scale expansion to quality improvement, strengthen the links between tourism and local agricultural production, processing, and marketing, and improve transportation, cold-chain logistics, and digital infrastructure. These measures can promote deeper agriculture–tourism integration and sustainable rural economic development.

This study examines the dynamic and asymmetric effects of digital economy development on firm performance in China using annual time-series data spanning 2002 to 2024. Internet penetration rate is adopted as the primary proxy for digital economy development, while the aggregate Return on Assets (ROA) of Chinese A-share listed companies serves as the measure of firm performance. Employing the Autoregressive Distributed Lag (ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL) frameworks, this study investigates both the long-run equilibrium relationship and short-run dynamic adjustment between these variables. The ARDL bounds test confirms the existence of a stable cointegrating relationship at the 1% significance level. The estimated long-run coefficient of digital economy development is 1.821, indicating that a 1% increase in internet penetration rate is associated with an approximate 0.018 percentage-point increase in aggregate firm ROA. The short-run coefficient (8.232) substantially exceeds the long-run estimate, demonstrating that the immediate impact of digital economy shocks is considerably more pronounced than the equilibrium effect. The error correction term coefficient (−0.800) is statistically significant, implying that approximately 80% of any short-run deviation is corrected within one year. The NARDL exploratory analysis does not confirm statistically significant asymmetric effects, reflecting the trajectory of uninterrupted digital expansion in China during the sample period. Robustness checks using an alternative proxy confirm the stability of the core findings. These results offer both theoretical insights and practical implications for digital infrastructure policy and corporate digital transformation strategies in emerging economies.

This study aims to estimate the effect of disability on household income in Thailand using different econometric approaches and to examine heterogeneity across the income distribution. We analyze data from Thailand’s 2021 Socioeconomic Survey covering 46,775 households, of which 4255 (9.1%) report having at least one disabled member. Employing three complementary methods—ordinary least squares regression, propensity score matching, and quantile regression—we find that households with disabled members experience significant income penalties. The OLS estimate with full controls shows a 10.0% income penalty, while propensity score matching yields 17.4%, suggesting that standard regression underestimates the true effect. Quantile regression reveals striking heterogeneity: the disability effect ranges from 4.8% at the 10th percentile to 30.5% at the 90th percentile. This pattern suggests that among Thailand’s poorest households, both disabled and non-disabled families face universal constraints; leaving minimal scope for disability is strongly associated with reduced household income, while at higher income levels disability creates barriers to advancement. Decomposition analysis indicates disability affects income through both reduced hourly wages (8.2% lower) and fewer work hours (12.4% reduction). These findings reveal that Thailand’s disability allowance of 800 baht per month—representing only 29% of the poverty line—is grossly inadequate, covering merely 17% of the observed income gap. The results highlight urgent needs for allowance increases with inflation indexation, differentiated support across the income distribution, improved employment quota enforcement, and streamlined registration to address the 46% unregistered rate. Policymakers should prioritize raising the monthly allowance to a level commensurate with the national poverty line, implementing tiered benefit structures based on disability severity, and strengthening employment quota enforcement mechanisms to reduce disability-related income inequality.

Environmental, Social, and Governance (ESG) disclosure has become increasingly important in capital markets, yet its profitability implications for investment banks remain underexplored, particularly in comparative settings. This study examines the dynamic and asymmetric relationship between ESG disclosure and financial performance using an unbalanced panel of 24 investment banking institutions, comprising 12 Chinese and 12 foreign banks, over 2015–2025. ESG disclosure is measured by a report-based ESG Disclosure Index (EDI), while profitability is represented by Return on Assets (ROA). The retained panel contains 252 institution-year observations, with 228 observations used in the dynamic estimations. Fisher-ADF and Fisher-PP panel unit-root tests are first applied to assess stationarity. The analysis then employs Dynamic Fixed Effects (DFE) panel ARDL(1,1) and NARDL(1,1) models, together with Wald tests and robustness checks. The ARDL results show a significant positive model-implied long-run EDI effect of 0.0269 (p < 0.001). The contemporaneous EDI coefficient is positive but insignificant (β = 0.0091, p = 0.118), whereas the one-year-lagged coefficient is positive and significant (β = 0.0126, p = 0.002), indicating a delayed short-run association. The NARDL results reveal significant long-run asymmetry: cumulative disclosure improvements have a positive effect of 0.0219 (p = 0.013), while cumulative deterioration has a larger negative effect of -0.0320 (p < 0.001). The long-run symmetry Wald test rejects equality (χ² = 10.903, p < 0.001). However, the cross-group Wald test finds no significant difference between Chinese and foreign long-run EDI effects (χ² = 0.164, p = 0.686). These findings indicate that ESG disclosure is financially relevant mainly through delayed and long-run channels and that preventing deterioration in disclosure quality may be especially important for investment banks. Keywords: ESG disclosure, Investment banking, ESG Disclosure Index (EDI), ARDL-NARDL, Financial performance

This study investigates tourism demand behaviour in Thailand during and after the pandemic by applying a set of advanced quantitative modelling techniques designed to address decision-making under uncertainty. Specifically, the analysis integrates quantum walk distribution analysis, Bayesian inference, and Bayesian logistic regression to model how destination attractiveness and gastronomy-related attributes influence domestic tourism demand within a sustainability-oriented framework. The empirical analysis is based on 526 online survey responses collected during the pandemic recovery phase, capturing heterogeneous perceptions and mixed information environments. Model comparison and validation using the Deviance Information Criterion (DIC) indicate that specifications incorporating both destination-based characteristics and gastronomy-related perceptions provide superior explanatory and predictive performance. The results demonstrate that latent attitudinal factors, particularly those associated with sustainable practices, culturally embedded food experiences, and perceived responsibility in destination management, play a statistically significant role in shaping revisiting intentions and demand resilience. Beyond its empirical findings, the study contributes methodologically by illustrating how probabilistic and uncertainty-sensitive models can be combined to improve the measurement and interpretation of complex social behaviours. The proposed framework offers a transferable approach for analysing tourism demand and other social phenomena characterised by perceptual ambiguity, supporting more rigorous indicator construction and empirical inference in applied social science research.

The creative economy, a global driver of economic development, plays a significant role in Thailand. Defined by UNCTAD as the marriage of creativity, culture, and technology, it emphasizes intellectual property and cultural goods (UNCTAD in World investment report 2010: investing in a low carbon economy. United Nations, 2010). This economic model not only promotes innovation, job creation, and cultural diversity (Hesmondhalgh in The cultural industries (2nd ed.). SAGE Publications Ltd. 2002) but also contributes significantly to Thailand’s GDP. While Gross Domestic Product (GDP) serves as a key economic indicator guiding policymakers and economists, forecasting the creative economy’s impact on Thailand’s GDP may have limitations that make it challenging to rely solely on historical data. More adaptable forecasting methods are needed to effectively analyse and predict its growth and impact. This study uses the grey model’s ability to handle small and incomplete data sets and capture the dynamic interactions between various factors, which makes it particularly well-suited for forecasting the GDP of the creative economy in Thailand. The results reveal that a diversification of revenue streams underscore the expanding demand within music, performing arts, visual arts, movies, and broadcasting sectors over the specified timeframe. In addition, the potential of these sectors in Thailand’s economy signals opportunities for further development and investment in the Thai food, traditional medicine, and cultural tourism industries. However, the dynamic nature of these industries and the growth rate of the creative economy are driven by technological advancements, changing consumer preferences, and market dynamics. For the overall performance of the creative economy Total creative industries (TCI), which are affected by the other variables, can be seen to increase for 10-year periods. Therefore, the gross domestic product (GDP) of the creative economy in Thailand is indeed crucial for policymakers, investors, and stakeholders since the creative economy has the potential to affect the growth rate of the overall economy.

This study examines the intentions of foreign workers living in Okayama, Japan, to stay long-term in Japan. Utilizing a Bayesian multinomial logistic regression model, this research provides a novel analytical approach that captures parameter uncertainty and accommodates the categorical nature of migrants’ settlement intentions using primary data collected via a questionnaire survey from January to March 2024. The findings reveal that residence status, previous experience of living in Japan, and graduation from a Japanese education institution significantly influence long-term settlement intentions. In addition, respondents aged 26–35 intend to stay longer than those of other ages, and those from less developed countries, such as Myanmar and Vietnam, intend to stay longer than those from China. Conversely, highly educated migrants express lower settlement intentions, suggesting a potential loss of skilled foreign labor in Japan. Notably, migrants in the Technical Intern Training Program are more likely to stay longer than those with other residence statuses, such as Highly Skilled Professional. In contrast, workers with higher education levels tend to have less intention to stay long-term, indicating a high probability of Japan losing educated foreign labor in the future. These findings contribute to understanding the dynamics of migrant workers in Japan, which is crucial for creating policies for foreign workers that can attract and support long-term settlement. These findings have important implications for policy, particularly in enhancing community integration, reducing workplace discrimination, and designing residence pathways that support long-term retention.

Sustainable development aims to balance current and future needs across economic, social, and environmental dimensions. This study explores the relationships among tourist numbers (Tr), sustainability (SDG), and globalization, divided into economic (GOE), social (GOS), and political (GOC) dimensions, with COVID-19 (CO) included as a short-term factor. Using annual panel data from ASEAN countries (2001–2022), panel unit root, cointegration, and autoregressive distributed lag (Panel ARDL) models were applied to analyses short- and long-term relationships. The findings reveal that sustainability (SDG) and the political globalization dimension (GOC) significantly influence tourist numbers (Tr) in the long term. COVID-19 (CO) affected tourism in the short term. The results underscore the positive impact of sustainable development on tourism and its integration with globalization’s economic and social dimensions. ASEAN countries should prioritize sustainable tourism policies and strengthen international political cooperation to enhance tourism’s long-term benefits.

The banking sector in Myanmar has substantial employee turnover. However, there is still a lack of research on the specific rates and efficacy of the current retention techniques. This study aims to investigate the human capital investment situations of the private banks of Myanmar from employees’ perspectives and examines employee retention intention. This study was survey research where a sample of 410 employees from three private banks in Yangon, Myanmar were collected and analyzed by Cox Proportional Hazard Model to examine the relationships between human capital investment activities and employee retention intention. The research revealed that work-life balance and salary have significant impacts on employee retention intention. However, the relationships between employee retention intention and educational support, training and health benefits are not statistically significant in the current uncertain economic situation. Moreover, private banks’ investment in health benefits of the employees is the lowest from the perspective of employees, and the average year that employees are willing to stay more with their current organizations is 3.5 years. Furthermore, the sector employees are working in and employees’ years of service influence on employee retention intention.

This study examines the dynamic relationship between foreign direct investment (FDI) and key macroeconomic indicators in India using quarterly data from 2006 to 2023. A Vector Autoregression (VAR) model was employed to forecast trends in FDI, while a Structural VAR (SVAR) model was used to identify causal effects of FDI shocks. The VAR (3) model, selected using the Akaike Information Criterion, provided 12-quarter forecasts of FDI inflows. The SVAR model, identified through a recursive short-run restriction, revealed that a positive FDI shock leads to a persistent increase in employment and inflation, but induces negative or unstable responses in GDP and the services sector. Exchange rate and trade openness respond positively in the short term but lack medium-term sustainability. These results indicate that while FDI supports labor market and trade performance, it does not automatically foster long-term economic growth without complementary domestic reforms. The key contribution of this study lies in providing empirical evidence on the heterogeneous and time-dependent effects of FDI shocks on India's macroeconomy, particularly highlighting the disconnect between FDI inflows and long-term growth without structural reforms. Policy recommendations include directing FDI toward labor-intensive sectors, managing inflation through coordinated macroeconomic policies, and strengthening trade infrastructure. The findings can support formulating evidence-based FDI policies to enhance macroeconomic stability and long-term growth, while also offering a foundation for future research to explore sector-specific and non-linear dynamics of investment impacts.

This study examines the impact of foreign aid on economic growth and poverty in Cambodia, Lao PDR, Myanmar, and Vietnam (CLMV). Results indicate a significant positive relationship between Official Development Assistance and Gross Domestic Product, while the impact on poverty is less consistent. The analysis highlights the importance of institutional quality, governance, and infrastructure, particularly access to electricity, in maximizing the benefits of foreign aid. Bayesian Panel Vector Autoregressive (BPVAR) model is employed for this study and the results underscore the potential of improved governance and effective monetary policies for sustaining economic growth and poverty reduction.

This study seeks to determine the existence of a possible nonlinear effect of economic freedom on economic growth in Sub-Saharan Africa by comparatively fitting fixed-effects (FE) and random-effects (RE) models within the framework of panel kink modeling. Using the F-test, the study found that our panel data significantly exhibited the existence of FE. Also, the Breusch-Pagan LM test confirmed the presence of RE in the data. Based on this, we conducted the Hausman test with a specification that took into account the fact that covariance matrices obtained from both models are derived from the same estimated error variance of the efficient estimator to discriminate between both models. The test results strongly favored the FE model. Employing the bootstrap algorithm proposed by Li et al. (2022), economic freedom was found to have a phenomenal kink effect, with a kink value of 59

This study examines how monetary and fiscal policies affect economic growth in China under global economic uncertainty. We estimate a Markov Switching Regression (MSR) model using quarterly data from 1996: Q1 to 2024: Q4. We also apply Bayesian Model Averaging (BMA) to choose the relevant control variables. During expansions, higher policy rates, government revenue, moderate inflation, FDI inflows, and export growth support growth. Government expenditure can crowd out private investment. During recessions, higher policy rates reduce growth. Government expenditure has limited impact, but revenue collection remains growth-supportive. Global uncertainty steadily reduces growth. Government expenditure shows negative effects, which indicates possible crowding out. The findings support that monetary and fiscal policies coordination may sustain long-term growth in China and strengthen the resilience amid global uncertainty. The Impulse response functions (IRFs) from Bayesian Vector Autoregression (BVAR) confirm the persistence and dynamics of policy shocks under global uncertainty. This study adds to the empirical literature on the role of macroeconomic policies in shaping economic growth in the case of China.

This study investigates the rice price relations between export and domestic retail markets in Myanmar in response to the recent increases in rice prices. Given Myanmar’s position as one of the prime rice producers in the world, understanding these rice prices nexus is significant for effective rice markets management. Authors use a bivariate vector autoregressive with exogenous variables (VARX) model with using monthly time series data from January 2014 to July 2022. The results reveal that there is a significant bidirectional Granger causality between the changes in export and domestic rice prices in Myanmar during the studied periods. According to the findings of the results, the rice price fluctuations in export and domestic markets influence one another. It suggests the necessity of implementing policies that can ensure stable rice prices especially for domestic markets to maintain food security for the long term. In this case, the government and relevant agencies are authoritative to moderate opposing effects on local farmers and consumers.

In recent decades, there has been an increase in global tourism demand. However, in developing countries like Myanmar has not seen an increase in international tourism demand for recent years. Tourism sector is vital in Myanmar’s socioeconomic landscape; therefore, this research addresses this gap by employing Bayesian Structural Time Series (BSTS) model to predict future monthly foreign tourist arrivals to Myanmar for January 2024 to December 2028 under ongoing Military Coup D’état. The study used both Local Level and Semi-Local Linear Trend models with using bsts R package for forecasting. The findings reveal that Local Level model forecasts a mean of 24,023 tourist arrivals per month from January 2024 to December 2028, with a range from 21,978 to 27,193. And, Semi-Local Linear Trend model predicts a higher mean of 42,596 arrivals, but with a wider range from 23,410 to 60,637 if Military Coup D’état continues. These forecasts are beneficial for local authorities, industry players, and other tourism stakeholders in Myanmar, aiding in strategic planning and decision-making processes.

The United Nations has promoted and supported the UNCTAD Creative Economy Programme since 2004 to help countries around the world understand how to promote economic development through creativity in industries. This research article aims to determine whether the creative economy will be the major engine to accelerate Thailand’s economic development in the coming decade or not, and what the major creative economy sectors are that must be prioritized or initiated and focused on. The data implemented in this research cover 2011–2018, which consist of creative economy sector income, the IO table, and the SAM table. The methodology utilized in this research was the ML model, the GREY model for predicting the growth rate of income from the major creative economy sectors contribute to Thailand’s economy between 2019–2025, and the CGE model. The study’s empirical findings show that the significant sectoral creative economy consists of fashion, advertising, Thai food, and cultural tourism, which need to be given more stimulus. Furthermore, the economies of Chiang Mai, and Thailand as a whole, would eventually be high-income economies if creative economy sectors were to be promoted and continuously supported by efficient policies. the economic growth of Thailand and Chiang Mai would eventually become high income whenever these economies allow creative economy sectors to be promoted or supported by efficient policies continuously.

Quantum computers have the potential to outperform classical computers in certain computational tasks. This research study attempts to utilize the quantum mechanics theory applied in the renewable energy stock market. The main quantitative analysis that has been utilized in this research article is a survival function based on Hamiltonian Monte Carlo simulation (HMC) to assess the risk of renewable energy stock investing. The daily data stock price during the period 2020–2023 of eight renewable energy stocks, such as Iberdrola (IBDRY), NextEra Energy (NEE), Vestas Wind (VWDRY), JinkoSolar (JKS), Canadian Solar (CSIQ), Daqo New Energy (DQ), Algonquin Power (AQN), and Clearway Energy (CWEN) is included to predict aversion to risk and make profit from those stocks in the world’s renewable energy stock market. The result from the survival function based on HMC suggests that the investor can hold all-renewable energy stocks in the portfolio for only half a year to one year to make a profit or avoid the risky investment, especially the stocks CSIQ and CWEN, which have performed quite well for investors to make a profit for their investment. If this information has already been confirmed appropriately, then it can be pointed out that the renewable energy stock market has more potential for investors to invest in the stock exchanges to drive sustainable energy development in the future.

Trade opening has become an important asset for economic development in many countries and an important engine of economic globalization (Wang, Shan-Li, et al, 2020). With continuous foster on the regional economic cooperation, countries with close proximity further promote the mobility and development of infrastructures and policies. Nevertheless, testing a trade integration model of bilateral trade is not sufficiently well estimated with the Bayesian approach to provide practical evidence of trade integration. Moreover, in identifying the factors determining trade integration, testing using the Bayesian gravity equation is essential. After performing a series of simulation experiments, a relationship between bilateral trade volume and simulated trade determinants was predicted for the trade model. The results of the estimated coefficients on GDP in Thailand and GDP of Yunnan province, China are positively significant predictors of the trade growth. The distance between the countries has a negatively significant estimation that implies barriers in trade. The model predicts trade integration, especially towards the trade on route R3A. The Bayesian approach of the gravity model gives robust estimates for determining the impact factor for the bilateral trade, including the fact that the elasticities of total trade volume with respect to distance, population, and the exchange rate of Thailand are negative while the GDP per capita are positively significant. Further, economic size, GDP per capita, and exchange rate of the destination, and population and area of Yunnan province are positively predicted by the model. The estimated parameters are directly the elasticities, in which increases in GDP is consistent with the higher trade volumes. Further, evidence of the gravity equation is used for understanding trade potential, and after some integrations, the estimation is applied for the real trade. The measures of bilateral trade resistance or costs associated with the trade flow has influenced the expanding of the bilateral trade in the model in the GMS economies. Finally, trade integration can be implemented with evidence and estimates of the gravity model. The Bayesian experiment for the estimation of the impacts of the trade integration on route R3A predicts an increase of GDP, population, exchange rate, and GDP per capita as predominant predictors in the Bayesian gravity model. Thus, the results revealed that economic size, bilateral distance, and GDP per capita has affected the plausible trade agreements for trade integration on route R3A.