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Shipping is a major source of air pollution, causing severe impacts on the environment and human health, greatly contributing to the creation of greenhouse gases and influencing climate change. The research was investigated to provide a better insight into the emission inventories in the Red River in Hanoi (Vietnam) that is often heavily occupied as the primary route for inner-city waterway traffic.

The total emissions of seven different pollutants (PM₁₀, PM_2.5, SO_x, CO, CO₂, NO_x and HC) were estimated using the SPD-GIZ emission calculation model.

The results show that CO₂ has the most significant contribution to the gas volume emitted: 103.21 tons/day. Remarkably, bulk carriers are the largest emission vehicle, accounting for more than 97% of total emissions, due to their superior number and large capacity.

The result to have a roadmap for making efforts to fulfil its commitment so that it could achieve its net-zero climate target by 2050 in Vietnam as committed at COP26.

In this research, the number of vehicles and types of vessels travelling on the Red River flowing within Hanoi territory and other activity data are reported. The tally data will be used to estimate emissions of seven different pollutants (PM₁₀, PM_2.5, SO_x, CO, CO₂, NO_x and HC) using a method combining both top-down and bottom-up approaches.

This study aims to evaluate the impact of climate change on some specific areas of agricultural production in Quang Nam Province, including assessing the possibility of losing agricultural land owing to sea level rise; assessing the impact on rice productivity; and, assessing the impact on crop water demand.

This study used the method of collecting and processing statistics data; method of analysis, comparison and evaluation; method of geographic information system; method of using mathematical model; and method of professional solution, to assess the impacts of climate change.

Evaluation results in Quang Nam Province show that, by the end of the 21st century, winter–spring rice productivity may decrease by 33%, while summer–autumn rice productivity may decrease by 49%. Under representative concentration pathway (RCP) 4.5 scenario, water demand increases by 31.1% compared to the baseline period, of which the winter–spring crop increases by 28.4%, and the summer–autumn crop increased by 34.3%. Under RCP 8.5 scenario, water demand increases by 54.1% compared to the baseline period, of which the winter–spring crop increases by 46.7%, and the summer–autumn crop increased by 63.1%. The area of agricultural land likely to be inundated by sea level rise at 50 cm is 418.32 ha, and at 80 cm, it is 637.07 ha.

To propose adaptation solution to avoid the impacts of climate change on agriculture, it is necessary to consider about the impact on losing land for agriculture, the impact on rice productivity, assess the impact on crop water demand and other. The result of this assessment is useful for policymakers for forming the agriculture development plan.

This paper aims to offer an empirical study of the impact of institutional quality on the banking system risk and credit risk.

Applying cross-sectional dependent tests and stationary tests to check the property of our sample, the panel corrected standard errors model is recruited as the main estimator, while feasible generalized least squares, pool ordinary least squares (OLS), robust pool OLS and other estimators are used as a robustness check for an unbalanced panel data for 56 economies divided into three subsamples between 2002 and 2015.

The empirical results show several significant contributions. First, an improvement in institutional quality is an important factor to reduce the banking system risk. This effect of the institutions is less important in well-capitalized, highly profitable and in high-economic growth countries. This effect is also stronger in highly liquid banking systems. Notably, a better institutional quality helps to reduce the banking system risk in the highly concentrated banking system. Second, institutional quality has a significant negative relationship with the banking credit risk, especially in highly concentrated banking systems and in high-growth countries. This influence is weaker in highly liquid and well-capitalized banking systems. Finally, better institutions reduce the positive effect of trade openness, but it induces a higher credit risk for the banking system from the trade openness. Notably, a better institutional quality enhances the negative effect of foreign direct investment (FDI) inflow on both banking system risk and credit risk. These findings are documented for a global sample and three subsamples: low and lower-middle-income economies, upper-middle-income economies and high-income economies.

This study provides some recommendations, for policymakers, on the roles of institutions in the banking system and financial stability.

The purpose of this paper is to research the impact of digitalization on employees' future competencies and the conditional role of human resource development (HRD) in the relationship between independent and dependent variables.

Empirical research covered 1209 enterprises from all of Slovakia, Poland and the Czech Republic. The research was conducted from 2019 to 2021. Using structural equation modeling (SEM), a theoretical model was tested and verified.

Confirmatory factor analysis has shown a good fit for the tested model. The purpose and character of our data showed a good alignment with the SEM partial least squares method, as the goal is to predict a construct. The model showed that employee-oriented digitalization positively affected the employees' future competencies, with no impact of customer-oriented digitalization treated as a control variable. Also, the moderating role of HRD has not been shown to be significant for the “digitalization – competencies” relationship.

Previous studies on the development of personnel competencies treated these competencies as antecedents of digital transformation and examined the formal role of HRD in building the competencies. The novelty of this study lies in exploring the pattern of interactions among the impact of an environment built by innovative technologies and HRD on the competencies of the future. Also, the research embedded in the environment of Poland, the Czech Republic and Slovakia has contributed to the complex understanding of the transition to digitalization, as this region has often been omitted in the field of human resource management (HRM) research focused on exploring digital transformation.

The authors investigate the impact of standards compliance on the participation in the global value chain and labour value-added of Vietnam’s small and medium-sized enterprises (SMEs).

The authors use a three-period panel dataset of SMEs combined with Vietnam’s Provincial Competitiveness Index. The authors also use multiple econometric models; and with each model, the authors include all independent variables that are available from the study's data and that are suggested by the literature.

The authors find that standards compliance by Vietnam’s SMEs improved their participation in the global value chain via subcontracts with FDI multinational firms. The authors also find that standards compliance improved the value-added of labour in Vietnam’s SMEs, which is robust to the choice of econometric models.

The study's results suggest that better outcomes for firms and society will be possible if standards are recognised and respected.

This paper complements scant literature on the impact of standards compliance on global value chain participation via subcontracting work and labour value-added, especially in developing countries.

The study estimates the willingness to pay for organic oranges and identifies its influencing factors among consumers in the Mekong Delta, Vietnam.

The study used survey data from 413 households in the Mekong Delta from March 2022 to July 2022. The choice experiment (CE) and contingent valuation method (CVM) were employed to analyze consumers’ willingness to pay (WTP). STATA 17 software was used to analyze research data in the logit model and mixed logit model.

The research results from the CVM approach show that a number of demographic characteristics have a direct impact on WTP, such as education, educational attainment, family size, the presence of children and the elderly in the household, food safety and environmental awareness. The CE model shows product attributes that influence consumers’ WTP, such as country of origin, traceability, quality grade, organic certification, ecolabel and organic content. Both approaches show that price is the main barrier to organic orange consumption.

The study surveyed four large cities in four provinces representing the Mekong Delta region.

Our study helps administrators have a deeper insight into consumer preferences and behavior, specifically the factors that affect consumers' WTP, an important indicator of demand for the success of manufacturers and marketers in developing as well as improving marketing strategies. Knowledge of a product’s WTP on behalf of (potential) customers plays an important role in many areas of marketing management, such as pricing decisions or new product development.

Furthermore, this understanding will inform policymakers about the future of agricultural markets in Vietnam and help them better prepare for the making of sustainable agricultural policies. Develop organic agriculture to both protect human health, protect the living environment and protect the soil from degradation, ensuring sustainable agricultural production. This is also one of the measures to help people stay away from diseases to limit the social burden.

The study confirms that both CVM and CE models can be used to estimate WTP. However, CVM fits the overall WTP estimate, while CE is more appropriate when estimating WTP for individual scenarios through combining attributes with different levels.

Across societies, gendered climate response decisions remain top-down and have limited progress because the influenced risk dynamics and their interrelations are not adequately understood. This study aims to address this gap by proposing an interdisciplinary innovative method, called women climate vulnerability (WCV) index, for measuring and comparing a diverse range of risks that threaten to undermine the adaptive capacity and resilience of rural women.

This paper builds on the literature to identify 12 risk categories across physical, economic and political sectors that affect rural women. These categories and attendant 51 risk indicators form the WCV index. A case study in Ben Tre Province (Vietnam) was used to demonstrate the application of the WCV methodology to rural contexts. The authors combined empirical, survey and secondary data from different sources to form data on the indicators. Structured expert judgment was used to address data gaps. Empirical and expert data were combined using a few weighting steps and a comprehensive coding system was developed to ensure objective evaluation.

The WCV assessment results reveal a reasonably worrisome picture of women’s vulnerability in Ben Tre as top highest-likelihood and deepest-impact risks predominate in physical and economic risk sectors. Stability, human security and governance categories have lowest scores, demonstrating a fairly politically favourable condition in the province. The medium risk scores captured in land and infrastructure categories reveal promising determinants of the adaptation of women in this rural province. The results demonstrate the usefulness of the WCV index in collecting bottom-up data, evaluating a wide variety of risks that rural women face and pinpointing priority areas that need to be addressed.

The WCV is systematic, customisable and localised. It combines field research and empirical data through structured expert judgment, thus enables researchers to fill data gaps and to do evidence-based assessment about diverse risk vulnerabilities. By doing so, the WCV index gives critical insights into the challenges that rural women face. This enables local governments to better understand cross-sectoral risks, pinpoint priority areas of action and timely channel funding and policy resources to support women where they need it most.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.