Purpose: The purpose of this paper is to estimate a recent trend in climate change and its impact on livelihood of community living in Nam Dinh province, Vietnam. Further, it aims to increase the government attention for adaptation measures by providing awareness of climate change and its negative impacts on livelihood. Design/methodology/approach: For study purpose, cross-sectional and secondary data sets were used. The community perceptions about climate change were recorded by face-to-face interviews of 500 respondents from Nam Dinh province, Vietnam in April 2015 by using a well-structured questionnaire, whereas secondary data were collected from the statistical yearbook of General Statistics Office of Vietnam. To accomplish the study objectives, Cobb–Douglas production function and Likert scale were used to estimate the community perceptions of climate change and impact of climate change on livelihood, respectively. Findings: Results depict that climate change negatively impacted on the productivity of rice and livestock. Particularly, frequently occurring of droughts, floods and salinity intrusion negatively impacted on rice productivity, while livestock productivity is decreased by frequent occurring of flood storms in study area. Originality/value: The study results suggest a government support is essential to achieve sustainable livelihoods for coastal communities living in the Red River Delta, particularly some adaptation measures in the context of climate change are required in study area.

The precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and nondeterministic behavior of data series with respect to time, the foremost challenge is to develop and identify the practical method to handle the above stated complex issues. As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. Taking into consideration the theory of fuzzy sets, FTS, fuzzy rules, triangular membership functions, distance measures, and modified weighted average method, a robust and effective fuzzy rules-based methodology was developed for the prediction of time-series data regarding crop production and share prices. Conventional statistical forecasting methods such as Holt’s linear trend, Holt’s exponential trend, and Holt’s damped exponential trend models were also applied on time-series data for comparison. To identify the primacy of modeling and forecasting, the techniques of root mean squared error (RMSE) and mean absolute error (MAE) were used as a criterion. The numerical values of RMSE and MAE such as 106.51 and 74.8897 clearly demonstrated that the proposed fuzzy rules-based method is robust for forecasting of production and market share prices in the environment of uncertainty.