Li Meng, Tong Yuan, Zhu Junming, Xu Shuntian
School of Environmental Science and Engineering, Shanghai Jiaotong University, Shanghai, China.
School of Public Policy and Management, Tsinghua University, Tsinghua, China.
Sci Data. 2024 Dec 5;11(1):1327. doi: 10.1038/s41597-024-04204-0.
Water is crucial for achieving the UN Sustainable Development Goals, particularly SDG 6. As a major source of water use and pollution, industrial sector requires improved water management based on more systematic and refined analysis. Such analysis, however, is compromised by the accuracy, granularity, and coverage of industrial water data. Here we present an open dataset of China's industrial water use, compiled from 1,480,265 plant-level reports. This high-resolution multi-scale dataset offers unparalleled details, supporting multi-scale analysis at the province, city, and county levels, and across 2-digit, 3-digit, and 4-digit industrial classifications. It provides comprehensive information on water use, recycling, pollution, and wastewater processing. Such data enables further macro- and micro-level analysis, including multi-regional input-output analysis, structural decomposition analysis, statistical analysis, machine learning, as well as many other advanced analytical methods. This dataset can equip researchers and policymakers with a valuable tool to advance sustainable water management, fostering alignment with global sustainability goals.
水对于实现联合国可持续发展目标至关重要，尤其是可持续发展目标6。作为用水和污染的主要来源，工业部门需要基于更系统、更精细的分析来改进水资源管理。然而，这种分析受到工业用水数据的准确性、粒度和覆盖范围的影响。在此，我们展示了一个中国工业用水的开放数据集，该数据集由1480265份工厂层面的报告汇编而成。这个高分辨率的多尺度数据集提供了无与伦比的详细信息，支持省、市、县各级以及两位数、三位数和四位数工业分类的多尺度分析。它提供了关于用水、循环利用、污染和废水处理的全面信息。这些数据能够进行进一步的宏观和微观层面分析，包括多区域投入产出分析、结构分解分析、统计分析、机器学习以及许多其他先进的分析方法。该数据集可为研究人员和政策制定者提供一个推进可持续水资源管理的宝贵工具，促进与全球可持续发展目标的一致性。
