Provincial water availability footprint evaluation and transfer analysis of China’s grain products: A life cycle perspective
Yijie Zhai, Yueyang Bai, Xiaoxu Shen, Tianzuo Zhang, Yuke Jia, Ke Ren, Xinying Zhou, Ziyue Cheng, Jinglan Hong
Abstract
Water scarcity has been a critical constraint limiting China’s food security and sustainable development. Previous research mostly focused on water consumption and neglected the water quality constraints on water usability. This study conducts a life cycle assessment-based water availability footprint analysis and footprint transfer evaluation of typical agricultural products (i.e., grains) in China during 2019–2020 from the perspective of consumption–pollution integration. A summary of the normalized water availability footprint result of China’s annual grain production in 2019 was 1.28 based on the Chinese regional benchmark. The resource, health, and ecosystem damages accounted for 42.61%, 36.52%, and 20.87 % of the total normalized results. Direct irrigation water consumption played a significant role in footprint result (nearly 90 %). Pollution caused by mercury emissions contributed 9.15 % to the footprint result. The high footprint results were concentrated in the main grain-production regions (e.g., Shandong and Henan) and arid region (i.e., Xinjiang). Furthermore, the water availability footprint transfer of China’s interprovincial grain trade was 0.32 during 2019–2020. Heilongjiang and Inner Mongolia provinces were the largest footprint exporters, whereas Shandong and Guangdong provinces were the largest importers. Meanwhile, a negative footprint benefit was generated at the national level. An increase of 3.88 × 10-2 in footprint result was noted, and this was mainly ascribed to the grain exportation of Xinjiang Province. Conversely, Heilongjiang was the largest footprint benefit exporting province. It is necessary to simultaneously evaluate the adverse water availability impacts caused by combined water consumption and pollutants. It is important to scientifically recognize the key influencing factors based on the consistent normalization benchmark.