As one of the world’s fastest-growing economies, Vietnam is tackling environmental pollution, particularly plastic waste. This study contributes to the literature on environmental culture and practical solutions by better understanding households’ behaviours and motivations for (i) sorting waste, (ii) contributing to the environmental fund and (iii) relocating. The questionnaire-based interview method was used to randomly collect information from 730 households in 25 provinces in Vietnam during February 2022. Bayesian regression models, coupled with the mindsponge mechanism, were applied to analyse the data. The results showed that people’s strategies and responses to plastic waste pollution vary: 38.63% of respondents were sorting waste at home, 74.25% of households agreed to contribute to the environmental fund, and 23.56% had a plan to relocate for a better living place. The households’ strategies and intentions were driven by several structural and contextual factors such as age of household head, income, care about the environment, and the perceived effects of polluted waste. More importantly, communication was a robust variable in sorting waste decisions, which suggested that better communication would help increase people’s awareness and real actions in reducing plastic waste and ultimately improving the environment. These findings will benefit the ongoing green economy, circular economy, and green growth transition toward more sustainable development, particularly in developing and fast-population-growing countries.

Vietnam’s forests have experienced a notable transformation over the past 20 years from net deforestation to reforestation and expanding forests. Continued reforestation that aims to achieve further economic and environmental benefits remains a national priority and strategy. We explore the current status of plantation forests and highlight possible means to facilitate their expansion in the uplands of Vietnam. We employ mixed method triangulation to empirically explore plantation forests and their economic role in household livelihood, to quantify trade-offs between plantation forests and shifting cultivation, and to assess the constraints on plantation forest expansion in Nghe An province, north-central Vietnam. Results show that forest in the study area expanded by 406,000 ha (71.1%) between 1990 and 2016. Plantation forests increased by nearly 500% (from 32,000 ha to 190,000 ha), while natural forests expanded by 48.1% (from 538,000 ha to 797,000 ha). Plantation forests contributed an average of 35.1 percent of total household income in wealthier households and 27.9 percent of income in poor households. Switching from shifting cultivation to plantation forests would increase total household income and average carbon stock but decrease food provision. Total Economic Value would be higher for plantation forest scenarios if increased carbon stocks in plantations can be monetized. This carbon income might drive conversion of shifting cultivation to plantation forests. Constraints on further expansion of plantation forest are low external cooperation, education, market stability, and agroforestry extension services. Our empirical results inform national plantation forest development, sustainable upland livelihood development, and climate change mitigation programs to ultimately facilitate forest transition and improve the resilience and sustainability of socio-ecological systems.

Climate change continues to pose a constant threat to nature and human beings, and thus demands adaptability and flexibility in forestry and domestic land use management. In this context, the Reducing Emission from Deforestation and Forest Degradation (REDD+) project was introduced to harness forests to curb greenhouse gas emissions in an effort to combat climate change. As of 2020, REDD+ was implemented in 65 countries across the world, including Vietnam. While the missions of afforestation and forest protection cannot be accomplished without the aid of local residents, low participation from stakeholders, particularly poor farmers, is an obstacle in climate mitigation projects. To contribute to improving REDD+ with a useful Vietnam reference, this study uses a random sample approach coupled with a face-to-face interview method to survey 215 households in Chau Thai, one of 206 poor communes in Nghe An province, to learn about (1) residents’ perception towards the importance of forests, (2) how forests contribute as a source of livelihood, (3) potential for household engagement in REDD+. The research findings show that the important role of forests is well acknowledged, the majority of forestland was allocated to plantation forests, and the seeds are carefully selected to satisfy market demand. In addition, forests are recorded to make major contributions to residents’ income and roughly four-fifths of households revealed their willingness to get involved in the REDD+ project with a monthly subsidy of 500–2000 kVND (22.3–89.2 USD). The study provides valuable information about forest-based rural livelihood and policy options to facilitate REDD+ participation among farmers. This, in turn, helps devise more appropriate policies for climate change mitigation and sustainable rural mountainous development nationwide and beyond.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollution and climate change. To be precise, CVML is an innovative hybrid machine learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective categories based on input attributes. Taking a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness to pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that are available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly in environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

Salinity intrusion forecasting is essential and challenging for hydrometeorology, especially in climate change. Employing machine learning (ML) algorithms and conventional forecasting techniques are gaining popularity and providing high performance. This study presents a method to optimize a machine learning model based on the Long Short-Term Memory (LSTM) algorithm for multistep-ahead salinity forecasting (up to 7 days) at Dai Ngai station, Soc Trang province. The optimization method based on the Bayesian algorithm for hyperparameters optimization and input predictors optimization has been highly effective for predicting salinity with a lead time of 1 day to 7 days. Specifically, the forecast results evaluated by the R2 and RMSE indexes both give satisfactory results on the test data set (with lead time from 1 day to 7 days, R2 ranges from 0.9 to 0.54, and RMSE ranges from 0.27 to 0.53). This study is a premise for improving machine learning models for short-term and long-term salinity intrusion prediction in the Mekong delta and Vietnam.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.