Unraveling the Impact of Climate Change on Food Security in Malaysia: Insights from Vector Error Correction Modeling

This study examines the influence of climate variables on paddy production in Malaysia, focusing on historical data from 1980 to 2016. The employed methodology incorporates Multiple Linear Regression (MLR) to identify the critical predictors, Johansen cointegration tests to explore the long-term relationships, and Vector Error Correction Models (VECMs) alongside Granger causality tests to analyze the dynamic interactions among variables. The performed analysis reveals consistent patterns in mean rainy days and rainfall amounts, indicating a relatively stable climate. In contrast, mean 24-hour temperatures show an upward trend, while mean 24-hour relative humidity exhibits a decline. The findings identify the mean rainfall amount and 24-hour relative humidity as significant predictors of the paddy production. The advanced analytical techniques confirm two long-term cointegrating relationships among the variables. Granger causality tests reveal a bidirectional relationship between the mean rainfall amount and paddy production, suggesting mutual predictability. Conversely, the mean 24-hour relative humidity exhibited a unidirectional relationship, predicting paddy production but not vice versa. These findings underscore the critical role of climate variables, particularly rainfall and humidity, in shaping the paddy cultivation outcomes in Malaysia.