Descriptive statistics and correlation matrix.

The erratic weather puts farming households of Bangladesh at high production risk with significant consequences on food production, income, and livelihood. This study attempts to find the effect of various climate change indicators on agriculture in Bangladesh over the period 1980–2014. The study used the ARDL bounds testing approach to assess the long-run associations and the Granger causality test to determine the causal relationships between the regressors and dependent variables. The outcomes revealed that the first lag of agricultural value-added, second lag of carbon emissions, and average rainfall have a positive impact while the first lag of carbon has negative and significant impacts on agricultural production in the long run; in the short run-past realizations of carbon emission have a negative and significant impact on agricultural value-added. Additionally, the results show a unidirectional causality from carbon emission to agricultural output, agricultural output to average rainfall, and agricultural output to energy consumption. The study fills the gap in the climate change literature by applying the ARDL method to establish the nexus between climate change and agricultural output in Bangladesh.变幻莫测的气候令孟加拉国农户面临极高的生产风险，对粮食生产、收入及生计造成显著影响。本研究旨在探究1980年至2014年间，各类气候变化指标对孟加拉国农业的影响。本研究采用自回归分布滞后边界检验（ARDL bounds testing）方法评估变量间的长期关联，并运用格兰杰因果检验确定解释变量与被解释变量间的因果关系。研究结果显示，长期来看，农业增加值滞后一期、碳排放滞后二期以及平均降雨量对农业生产具有正向影响，而碳排放滞后一期则对农业生产产生显著负向影响；短期内，过往碳排放水平对农业增加值具有显著负向影响。此外，研究结果还表明存在三类单向因果关系：碳排放至农业产出、农业产出至平均降雨量，以及农业产出至能源消费。本研究通过运用ARDL方法构建孟加拉国气候变化与农业产出间的关联机制，填补了气候变化研究领域的相关空白。