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.

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 research article intends to confirm that sustainable infrastructure development plays a significant role in driving the economies of Thailand, Cambodia, Laos, Myanmar, and Vietnam (CLMV countries). The panel data were collected from 2010 to 2021 to quantify the technical efficiency of infrastructure development and its impact on the economies of these countries: the GDP, the telecommunications sector, the energy sector, water system resources, and the transportation sector. In addition, the stochastic meta-frontier and the stochastic meta-frontier based on the copula model were employed to quantify the efficiency economies, represented by driving GDP up using fewer input factors (infrastructure input factors are less used). The result of this research study found that the stochastic meta-frontier model-based copula performed well enough to be the appropriate model to evaluate the technical efficiency of sustainable infrastructure development, which will impact the economies of these countries in the future. Additionally, this model suggests that the efficiency trend has decreased. It is implied that these countries lack technological sharing or technological transfer for infrastructure development. If this outcome is believable, it must be a priority to collaborate on sharing and transferring technology for infrastructure development among them. The research suggests that infrastructure development in sectors like telecommunications, energy, water resources, and transportation in Thailand and CLMV countries requires greater collaboration. Specifically, there's a need for technological sharing or transferring to prioritize emerging collaborations for future infrastructure development.

Cryptocurrency is not a newfangled but, it is also intensively mentioned during the Covid-19 pandemic collapses all of the economic activities. As it totally differs from traditional money which is guaranteed and supported its value by gold, digital currencies are considered as uncertain and extreme financial instrument including seemed to be uncovered of assumption of normality. Moreover, the condition of digital appearances is not simple as represented as the purchasing power nature. Traditionally statistical distributions are therefore ineffective. To address with this complicated issue, the applications of the extreme value theory and quantum-wave distribution are employed in this paper. In terms of methodological processes, daily observations were set between 23th November 2018 and 23th November 2020, and the dataset was classified as pre-pandemic of Covid-19 periods and post-pandemic of Covid-19 periods. The value at risk (VaR) and Expected Shortfall (ES) were the main tools for providing the baseline of investment risk foresights, and the empirical result confirmed that the quantum distribution could be the potential option for risk computation and management in the upcoming future of modern financial econometrics.

This research aims to quantify the technical efficiency of economic growth based on export promotion in China's various economic regions using infrastructure construction data from 2001 to 2019. The copula-based meta-stochastic frontier model (CMSFM) is used to process it. According to CMSFM's findings, the average efficiency of overall economic areas is 0.1335. The Eastern region's efficiency is 0.0190, the Central region's efficiency is 0.0353, the Western region's efficiency is 0.0893, and the Northeast region's efficiency is 0.3906. According to the model's findings, the Northeast region has the highest technical efficiency. Furthermore, the outcome of estimation using the CMSFM model indicates that China's telecommunication infrastructure needs to be improved.

Based on real situations that mankind is confronting with the difficult era; insufficiency in food supplies, natural disasters, epidemic, etc. The paper is to econometrically compute portfolio optimization and predict efficiency frontiers for solving the most sensible scenario to suggest a sustainable policy in the three important pillars such as the growth of economic systems, environmental management, and public healthcare. The main observations are annual time-series information between 2000 and 2017 and collected from three countries in ASEAN. Singapore, Thailand, and Malaysia are the target. Methodologically, this research is to apply the quantum mechanism and the wave function for clarifying a real data distribution; true mean, and standard deviation of the data. These outcomes are the initial raw material for the modern portfolio optimization (for short-run policies) and efficient frontier computation (for long-term policies). Empirically, the results show some exclusive issues that can be the help for managing feasible budget allocations fairly and sustainably.

This paper is a contribution seeking an econometric solution for the mathematical problem known as a cooperative game. The theoretical coalition of world major rice exporters includes India, Thailand, and Vietnam. In terms of methodological processes, yearly time-series variables (2008-2018) such as the values of rice production, rice consumption, and rice exporting profits are observed. The causal model is employed to clarify three mixed approaches. The first is the structural dependent analysis based on Bayesian statistics referred to as the 'Bayesian copula'. The empirical results confirm that these three countries have deep structural dependences in the market. In the second method, the trends of observed variables are predicted by the Bayesian structural time-series model. The last section is the 'Shapley value' with coalition scenarios. Optimised results causally prove that rice exporting profits are a double increment when cooperative behaviours continuously exist. Hence, the potential outcomes framework is to finally recognise the Organization of Rice Exporting Countries (OREC).

Suspicious information and chaotic situations caused by the Covid-19 pandemic are the main issues in recent economic predictions. The ASEAN community is a group of member countries that are trying to decentralize restricted systems, especially financial sectors. The huge gap in the investigation in ASEAN economies is obscure information. To deal with this issue, an example of applying a novel tool for exploring invisible factors forcing fluctuations in ASEAN economies is empirically presented in this paper. Cryptocurrencies – the indexes that are intentionally defined as a decentralized market – are chosen. Four major digital currencies – Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Ripple (XRP) – have been collected as a daily time-series sample from January 1, 2018, to December 27, 2020. The main objective is to present the outlook of risk management by using quantum formalism in parts of data transformation, extreme analysis, and risk foresight. The result provides strong evidence that a quantum mechanism not only can be applied in a computational lab, but also can be a predominant alternative for studying ASEAN in terms of big data analyses.

The huge challenge for measuring and forecasting port efficiencies was one of the major concerns in logistics economics. This paper was aimed to deeply study the univariate calculation for the technical efficiency ratio in six major ports in Thailand, Singapore, Malaysia, and the Philippines. The annual time-series data from 2005 to 2018 was observed, including container flows, numbers of vessel arrivals, transshipments, the ranges of quay lengths, and the units of functional terminals. Observed data were categorized to be a panel. Two econometric methods such as Bootstrapping Panel Data Envelopment Analysis (BPDEA) and Bayesian Structural Time-Series Forecasting model (BSTSF) were applied for clarifying and predicting ports’ bias-corrected technical efficiency ratio. The findings were used to recommend a specific policy for the uniqueness of port locational bearings.

This study investigates how information and communication technology (ICT) development affects macroeconomic variables in the Association of Southeast Asian Nations (ASEAN). A dynamic stochastic general equilibrium (DSGE) model, widely used for empirical macroeconomics research, was utilized to compare and analyze the results to determine which variable is most affected by ICT development. As the economy evolves over time, it is affected by unexpected factors such as technology shock. This study utilized yearly data for the period 2008-2015 to compute the steady-state, dynamics, and correlation of technology shock with relevant variables namely: investment (I), consumption (C), labor supply (L_s), capital stock supply (K_s), total factor production (Z), and total production (Y). The impulse response function (IRF) results for 10 periods indicate that, when ICT development was included, investment increased rapidly at first, gradually achieving equilibrium in the seventh period. In contrast, without ICT development, equilibrium was achieved only in the ninth period. Moreover, consumption, labor, and total production initially increased and then reached equilibrium in the eighth, fourth, and ninth periods respectively. However, without ICT development, equilibrium was achieved in the sixth, second, and sixth periods respectively. Capital and total factor production, both with and without ICT development, increased slightly at first and then reached equilibrium within the same period. Thus, the variables affected by ICT development are investment (I), consumption (C), labor supply (L_s), and total production (Y). The most affected variable was investment, followed by labor supply, total production, and consumption.

Nowadays, information and communication technologies are widely used in Thailand. Also, the government has a policy to support economic development through ICT, expecting to help increase the growth of Thai economy. The research purpose is to study the impact of ICT on Thai economic trends. The researchers use ICT data which includes values of communications, computers, information, and other services covering international telecommunications and computer data from 1976 to 2017, to estimate ICT parameter with the Bayesian linear regression approach. All of parameters used in the DSGE model are to predict the effects of ICT on some parts of Thai economic sectors. The estimated result shows that ICT investment can positively contribute to an increase of consumption and future investment. Therefore, ICT investment is beneficial to Thai economic trends in positive ways.

This paper aims to study the relationship between Indian ICT industries and GDP by applying Bayesian inference. Five yearly predominant indexes collected during 2000–2015, including Indian GDP, fixed phone usages, mobile phone distributions, Internet servers, and broadband suppliers, are analyzed by employing the Markov-switching model (MS model) and Bayesian vector autoregressive (BVAR) models. In addition, the Bayesian regression model is used to investigate the ICT multiplier related to Indian economic growth. The empirical results indicate that IT sectors are becoming the major role of Indian economic expansion in the forthcoming future, compared with telecommunication sectors. Moreover, the result of the ICT multiplier confirms that high technological industrial zones should be systematically enhanced continuously, in particular, research and development in cyberspace.

In this paper, we explored supply chain structures and characteristics of the incentive travel industry, which is a special sector of the tourism and hospitality industry. Since the conceptual framework tailor-made for this sector cannot be found, we developed a generic model to outline key players and main operations such as collaborations and relationships between players in the supply chain. Then we verified the model with empirical data collected from the incentive travel industry in Thailand. A focus group discussion was organised to validated and contextualise the proposed framework. Four followed up in-depth interviews with incentive supply chain players in Thailand were conducted. Data were cross-validated using various data sources including private sector, public sector and educational institutes. Findings show that the main players in incentive supply chain are the incentive houses, who plan and design the incentive programme for the corporate considering the return-on-investment (ROI), and destination management companies (DMC), who execute the programme at the destination. Relationships and collaboration between players are vital to the success of incentive program delivery since the incentive travel programmes are considered special events that required tailored made supplies and operations. Finally, potential research arena also discussed with suggested methods.

Aim: The goal of this paper is to quantify the contribution of ports to GDP in three different nations. Singapore’s port, Malaysia’s Bintulu port, and Thailand’s Leamchabang port are the ASEAN ports examined in this paper.Methodology: Cargo throughput (tons) is the output variable, and the four inputs (ship count, vessel handled capacity (DWT: Dead Weight Tonnage), number of employees per terminal, and terminal area) are all described in the published data (m2). The weighted aggregative technique is used to construct inputs and outputs data details.Findings: Progress in port efficiency in Singapore, Malaysia, and Thailand is demonstrated by the findings. Singapore’s port is the most productive of the three and has the least disruption to throughput (TFP).Novelty/Implications: While trade and maritime transport cooperation between Korea and ASEAN has been strengthened, little research has been conducted on analyzing container port efficiency in ASEAN. Since this is the first study of its kind in ASEAN, it can be considered the beginning of port studies in the region. Based on these findings, we can select candidate cities and nations for a port cooperation initiative on a global scale.

This study aims to determine the impact of important components of Thai business cycle during prosperity and depression phases. The BVAR and MS-BVAR models are used to analyze the relationship of each variable. The variables consist of population, GDP, inflation, balance of payments, government cash balance, interest rate, and exchange rate. The data correlated in this study are secondary data during 1979 to 2014 obtained from various sources including World Bank World Development Indicators and the Global Development Finance database, World Resources Institutes (WRI), and Bank of Thailand (BOT). The results of this study indicate that each variable in this model has statistical significant relationship. From the analysis, each variable has different impact on Thai business cycle during prosperity and depression phases.

this research provides brief descriptions of Thailand’s economic structure on the extensive margin of activity by developing a dynamic stochastic general equilibrium model with the endogenous determination of the number of business entries on the market. To this aim, the model intended to point out the role of frictions (i.e. monetary and nonmonetary shocks) toward business fluctuations based on the endogenous firms’ entry and the contribution of financial accelerators. In particular, the framework exhibits the role of the banking system and the wage bill as advanced constraints in explaining the overall changes of the Thai economy between 2000Q1 to 2016Q1. The results of this research demonstrate the role of stabilization policy on a model where firm entry responds to several shocks and uncertainty environment. The outcomes of the model support the empirical finding of Poutineau and Vermandel (2015) on several aspects. The estimated model confirms that the effect of frictions on the model economy influence entrepreneurs ’decisions to enter into the goods market, and thus can be used to duplicate actual world data. There are four other main findings. i) Monetary transmission mechanisms influence on entrepreneurs’ decision which temporarily slowed down investment activities on both existing businesses and new entrants. Besides, the outcome reports the number of entrant are more likely to depend on the bank loans, not the one set by the authorities. ii) The endogenous firm entry implies the countercyclical number of business entry on the goods market toward banks markups and loan spread, hence being in line with the empirical evidence. iii) The end results of the model’s impulse responses, accounting for the frictions, appear to be contradicting the core prediction’s claims since the outcomes are unsound. The end results prove that there is a chance for entrepreneurs to simultaneously invest on both margins of activity. iv) The overall impulse response patterns of the model turns out to be significantly in line with the ones the researcher would have expected using empirical findings, in some cases, appear to be differ from the literature related to these sorts of models. These depend on the contribution of the financial accelerators that amplify the effects of frictions to the model economy. 3 rd International Conference on Management Economics and Social Sciences on 8 th 9 th July 2017, in Pattaya ,Thailand ISBN: 9780998900018 2

This research is to provide a theoretic framework regarding foreign tourists using public land transportation in the north of Thailand. It focuses on the need of foreign tourists to employ roadway and railway transportation as a medium for travelling. Based on literature, two latent variables consisting of economic variables and public land transportation variables are connected; they both would have an impact on the need of foreign tourists visiting the north of Thailand. Survey research was conducted to collect the data from foreign tourists of two multilevel areas: upper/lower areas and region. A total of 400 responses were analyzed by using multilevel structural equation modeling analysis (MSEM). The results indicated that they are a high relationship between the economic variables and public land transportation variables for foreign visitors who wanted to visit whole areas in the north of Thailand, whereas there is low relationship between the economic variables and public land transportation variables for those foreign visitors who wanted to visit between upper north and lower north of Thailand. This research suggests that all involved parties promoting tourism industry in the north of Thailand should focus on both economic factors and public land transportation factors. However, they should be concerned that economic factors are more related to public land transportation factors for tourists who want to travel the whole region, whereas economic factors are less related to public land transportation factors for tourists who want to travel between of sub region (between the upper north and the lower north of Thailand)

This paper applies the multilevel linear regression (MLM) model to investigate factors that affect international tourists' spending per day when travelling in the northern region of Thailand. Four hundred questionnaires were collected by a convenience sampling method in 9 provinces of northern Thailand (Chiang Mai, Chiang Rai, Lamphun, Lampang, Uttaradit, Pitsanulok, Sukhothai, Kampaeng Phet and Nakonsawan) from October, 2015 to December, 2015. The results represent the mixed affected model which contains significant fixed effect explanatory variables (age, intention of revisit, and the attitude to reuse domestic land transportation) and random variance components, including individual income. In addition, the MLM can explain that the overall international tourists' spending per day in the region level (level 1) statistically depends on the individual income in the province level (level 2).