Under Vietnam’s State land ownership regime, the Government holds supreme authority over compulsory land acquisition. The results show that many improvements in land acquisition policies have been made, but poor implementation measures largely cannot prevent or even mitigate the adverse impacts on displaced persons. In particular, ineffective compensation measures and a lack of production land and livelihood alternatives accelerate the resistance of communities displaced as a result of hydropower development. The close alliance between the local government and the investor, which is considered as an “interest group”, is the main factor that leads to the ignorance of benefits of displaced people within the compulsory land acquisition process

This study investigated the impacts of the COVID-19 pandemic on livelihoods of households with migration workers, who returned home to the central coastal region during the peak disease outbreak in Vietnam. Five hundred and twenty-nine households with returned migration workers aged eighteen and above in the coastal areas of Quảng Bình, Quảng Trị, and Thừa Thiên Huế provinces participated in this study. Results showed that the livelihoods of all studied households were highly vulnerable due to impacts of the COVID-19 pandemic, with almost 90% at moderate or high risk according to the vulnerability index. All livelihood assets were negatively affected, and financial, psychological, and social assets were the most affected, with Common Vulnerability Score System scores of 3.65, 3.39, and 3.17, respectively. Male, younger workers, or those with a lower education level and fewer social networks were found to be more vulnerable than others. This study suggests that young laborers could aim to attain a higher level of education and/or practical skills to be able to obtain stable employment with benefits such as social insurance if they desire to out-migrate. Further, social programs which allow for migration workers at the destination to meet each other may have positive impacts on their vulnerability.

No abstract

The widespread development of hydropower dams has led to involuntary displacement, which has become a significant global issue. In Vietnam, around 70,000 households were displaced in 2020, causing uncertainty and social disruption. The aim of this study is to analyze the effects of resettlement on the livelihood and food security of displaced households, explore the underlying challenges and causes of these effects, and recommend policy implications for sustainable livelihood development and poverty alleviation. This study conducted a decade-long sociological examination of three displaced communities in Thua Thien Hue province, Vietnam. Our research reveals that resettled households are unable to regain their former standard of living due to the loss of cultivated land and restricted access to public property, which exacerbates food insecurity. Unemployment, illiteracy, and low income further perpetuate poverty. These findings highlight the deficiencies in current policies and planning approaches and call for implementing socially responsible resettlement processes guided by principles of equity. Addressing the inequalities arising from displacement and enabling affected communities to participate in growth is economically justified and morally imperative.

Wetlands are highly productive ecosystems with the capability of carbon sequestration, providing an effective solution for climate change. Recent advancements in remote sensing have improved the accuracy in the mapping of wetland types, but there remain challenges in accurate and automatic wetland mapping, with additional requirements for complex input data for a number of wetland types in natural habitats. Here, we propose a remote sensing approach using the Google Earth Engine (GEE) to automate the extraction of water bodies and mapping of growing lotus, a wetland type with high economic and cultural values in central Vietnam. Sentinel-1 was used for water extraction with the K-Means clustering, whilst Sentinel-2 was combined with the machine learning smile Random Forest (sRF) and smile Gradient Tree Boosting (sGTB) models to map areas with growing lotus. The water map was derived from S-1 images with high confidence (F1 = 0.97 and Kappa coefficient = 0.94). sGTB outperformed the sRF model to deliver a growth map with a high accuracy (overall accuracy = 0.95, Kappa coefficient = 0.92, Precision = 0.93, and F1 = 0.93). The total lotus area was estimated at 145 ha and was distributed in the low land of the study site. Our proposed framework is a simple and reliable mapping technique, has a scalable potential with the GEE, and is capable of extension to other wetland types for large-scale mapping worldwide.