The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.

The Clean Development Mechanism (CDM) is one of the flexible instruments of the Kyoto Protocol designed to combat climate change so as to bring advantages to developing countries and developed countries alike. Indeed, CDM projects have a two-fold objective: to offset greenhouse gas emissions and to contribute to sustainable development in the host country. However in many cases, the latter objective appears to be marginalized. This is at least partly due to the difficulties surrounding the definition and the measurement of sustainability, in particular in a developing country context. To assess CDM projects' contribution to sustainable development in the host country, scholars and practitioners need adapted indicator sets. A set of indicators were developed by way of an iterative Delphi approach amongst selected Vietnamese experts. The Delphi approach allowed a systematic collection of the experts' judgements on the sustainability indicators through a set of sequentially applied questionnaires, interspersed with feedback from earlier responses. This exercise resulted in the selection of a set of 36 indicators, which emphasise economic efficiency, public health and pollution issues. The exercise yielded a locally supported and context-specific set of sustainability indicators that will allow Vietnamese decision-makers to enhance the sustainability of the approved CDM projects.In the future this set should be continually improved through real-life application and further participation from local stakeholders. This study is a first step in a long-term process towards developing an adapted toolkit for sustainability assessment of CDM projects in Vietnam.Keywords Sustainable development Á Clean development mechanism Á Indicators Á Delphi Á Vietnam Readers should send their comments on this paper to BhaskarNath@aol.com within 3 months of publication of this issue.

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Background Sustainable development (SD) is a common concept. Knowledge and attitudes are essential in the SD process. This study assesses the knowledge, attitudes, and practices (KAP) of local people about SD. Aim To study the factors that influence the understanding of the concept, contents, and indicators of different aspects affecting the health and environmental issues. Methods A cross-sectional study was carried out from June to July 2007 among 546 households in the Quang Tri province. Data were gathered on basis of socio-demographic variables, namely age, gender, education, occupation, income, and region. Chi square tests and multivariate analysis were performed on the obtained data. The data were cleaned and analysed using SPSS 15.0 for windows. Results Occupation is related to knowledge, attitude, or practice. Income is related to knowledge or practice. Gender related to only attitude. Lastly, region is related to attitude or practice. The proportion of wrong understanding about SD is 2.0 times (95% CI: 1.3; 3.1, p \ 0.001) higher than that of the people who have good understanding about it. The rate of willingness to do any related SD programmes of the people who understanding is 2.1 times (95% CI: 1.4;Readers should send their comments on this paper to: BhaskarNath@aol.com within 3 months of publication of this issue.3.2, p \ 0.001) higher than that of the people who have bad one. Conclusions This study shows that knowledge on sustainability of the local communities is low. Occupation and income influence understanding of SD more than region, age, gender, and education. Most of the local people who do not understand SD in general, do not want to participate or act in SD programmes.

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