Impact of Climate Change on Paddy Productivity in Malaysia's Granary Areas: A Markov Chain Monte Carlo Analysis

Objective - This study simulates paddy productivity across Malaysia's granary areas over a 10-year period, with a focus on the non-linear effects of climate change, particularly rainfall and temperature variability. This study examines how each granary area evolves and reaches its optimal point as climate variability risks increase over time. Methodology/Technique - Using a Markov Chain Monte Carlo (MCMC) approach, the analysis estimates the impact of these climate factors on paddy yields. The findings reveal that rainfall has a positive effect on productivity in areas with low rainfall, such as IADA BLS, IADA PP, and MADA, while excessive rainfall has a detrimental, non-linear impact across all regions. Temperature variability has mixed effects, enhancing productivity in IADA PP and IADA KETARA but negatively affecting areas such as IADA MADA and IADA SEM. Findings - A key finding from the simulation is that each granary area reaches its optimal productivity at different times. IADA PP is projected to achieve the highest yield (6.47 tonnes/hectare) in the 10th year, whereas IADA KER is expected to reach the lowest maximum productivity (5.45 tonnes/hectare) in the 5th year. Notably, IADA BLS and IADA KER achieve peak productivity within just 5 years, faster than other regions. Novelty - IADA KEM exhibits the largest improvement, with a 58.7% increase in productivity over a 10-year period, despite its vulnerability to climate variability. These findings highlight the diverse impacts of climate change on paddy yields and the need for region-specific adaptive strategies. Type of Paper: Empirical JEL Classification: Q51, Q54. Keywords: Climate change, Granary areas, Markov Chain Monte Carlo, Paddy Productivity Reference to this paper should be referred to as follows: Abdullah, M.Z.; Applanaidu, S.D.; Kalimuthu, K. (2025). Impact of Climate Change on Paddy Productivity in Malaysia's Granary Areas: A Markov Chain Monte Carlo Analysis, Acc. Fin. Review, 9(4), 01 – 12 https://doi.org/10.35609/afr.2025.9.4(1)