Optimal lag selection criteria of the BVAR model.

The existing conditions of domestic agricultural production and the resulting products will not be able to fruitfully address the increasing food demand due to the limited fertile land and water resources in Saudi Arabia. Moreover, the escalating threat of a hotter climate, the deterioration in precipitation, and harsh droughts in Saudi Arabia have reduced the predictability of water management efficiency and resulted in the exhaustion of water bodies and serious degradation of ecosystems that have directly affected agricultural systems and indirectly, food security. This study also aims to assess the impact of water efficiency on food insecurity in Saudi Arabia. The study applied the Bayesian Vector Autoregressive (BVAR) model for the reference period for the data extended from 2000–2020. Likewise, we used both impulse response functions (IRFs) and forecasting variance error decomposition (FVED) through 1000 Monte Carlo simulations according to the BVAR model to examine both the response of food insecurity to the shocks on water management efficiency used for various purposes and the decomposition of error variance in food insecurity. The results show that food insecurity was not observed throughout this study. The results of the BVAR analysis indicate that in the short run, the coefficients of water use efficiency are significant based on the Food Insecurity Multidimensional Index (FIMI). Also, the BVAR model provides a better forecast with an interdependence on water use efficiency for agricultural purposes and FIMI. Moreover, the results obtained from IRFs have shown a significant effect of water efficiency on FIMI. Water use efficiency for agriculture and industrial purposes reduces food insecurity while increasing water for services use increases food insecurity. Water use efficiency is the key factor affecting food insecurity in the short run. The results reveal that the water use efficiency shocks will decrease food insecurity. The shocks experienced by food insecurity can be predicted as self-shock over a span of ten years. Emphasis is given to the task of water management that may support food security in Saudi Arabia through implementing and enhancing the water use efficiency as an integral part of achieving the SDGs in Saudi Arabia.

沙特阿拉伯可耕地与水资源有限，当前国内农业生产现状及产出已无法有效应对日益增长的粮食需求。此外，愈发严峻的气候变暖威胁、降水状况持续恶化以及频发的严重干旱，降低了水资源管理效率的可预测性，导致水体枯竭与生态系统严重退化，直接冲击农业生产体系，间接危及粮食安全。本研究旨在评估沙特阿拉伯水资源效率对粮食不安全状况的影响。研究选取2000年至2020年的扩展数据集，采用贝叶斯向量自回归（Bayesian Vector Autoregressive, BVAR）模型开展分析。同时，基于BVAR模型，通过1000次蒙特卡洛模拟，运用脉冲响应函数（Impulse Response Functions, IRFs）与预测方差误差分解（Forecasting Variance Error Decomposition, FVED）两种方法，分别考察粮食不安全对多用途水资源管理效率冲击的响应，以及粮食不安全的误差方差分解情况。本次研究全程未观测到粮食不安全状况。BVAR分析结果显示，短期维度下，基于多维粮食不安全指数（Food Insecurity Multidimensional Index, FIMI）的水资源利用效率系数显著。此外，针对农业用水效率与FIMI的相互依存关系，BVAR模型可提供更优的预测效果。脉冲响应函数的分析结果表明，水资源效率对FIMI存在显著影响：农业与工业用水效率可降低粮食不安全程度，而服务业用水占比提升则会加剧粮食不安全状况。短期来看，水资源利用效率是影响粮食不安全状况的关键因素。研究结果显示，水资源利用效率的正向冲击可降低粮食不安全程度。粮食不安全所面临的冲击，在十年周期内可被判定为自我冲击。本研究强调，需重视水资源管理工作，通过落实并提升水资源利用效率，将其作为沙特阿拉伯实现可持续发展目标（Sustainable Development Goals, SDGs）的核心环节，从而支撑该国粮食安全。