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Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector

机译：基于STIRPAT模型的水足迹影响因素分析：来自北京农业的证据

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Beijing suffers from a severe water shortage. To find the key factors that impact the agricultural water footprint ( WF ) within Beijing to relieve the pressure on water resources, this study quantifies the agricultural WF within Beijing from 1980 to 2012 and examines the factors of population, urbanization level, GDP per capita, Engel coefficient, and total rural power using an extended stochastic impact by regression on population, affluence and technology (STIRPAT) model. Ridge regression is employed to fit the extended STIRPAT model. The empirical results reveal that the Engel coefficient, which is defined as the total amount of food expenses accounted for the proportion of total personal consumption expenditures, has the largest positive impact on the increase in the agricultural WF , followed by urbanization. In contrast, total rural power, population, and GDP per capita can decrease the agricultural WF . Finally, policy recommendations from technological development, agriculture plantation structure adjustment, and virtual water imports are provided to cope with water shortages.
机译：北京严重缺水。为了找到影响北京缓解农业水资源压力的关键因素，本研究对1980年至2012年北京的农业WF进行了量化，并研究了人口，城市化水平，人均GDP，恩格尔系数，以及通过对人口，富裕程度和技术（STIRPAT）模型进行回归分析得出的扩展随机影响力的农村总发电量。采用Ridge回归来拟合扩展的STIRPAT模型。实证结果表明，恩格尔系数（定义为食品支出总额占个人消费支出总额的比重）对农业自负增长的影响最大，其次是城市化。相比之下，农村的总力量，人口和人均GDP会降低农业的WF。最后，提供了技术开发，农业种植结构调整和虚拟水进口的政策建议，以应对水资源短缺。

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