Water Policy Evaluation Based on the Multi-Source Data-Driven Text Mining: A Case Study of the Strictest Water Resource Management Policy in China

Abstract

The strictest water resources management (SWRM) policy is a critical policy to address China’s severe water shortage and pollution problems, and aims to promote sustainable water development and water governance. Based on data mining from multiple sources, including policy text from the strictest water resource management policy from 2011 to 2021, the reports of major media websites, and the Baidu Index, this study used the ROST-CM6 text-analysis tool to analyze the policy content, public opinion, and public perception of the strictest water resources management policy quantitatively and visually. The results found that the policy text and public-opinion are given high attention to the water resources assessment, water control management, and water resources protection, but the policy text focuses on the macro level, and pays more attention to national development and long-term planning. The public opinion belongs to the micro level and is more economic, and there is a certain degree of media bias. With notable regional disparities, the general public’s opinion of the harshest water resource management policy has been rising every year. This research adds to the global body of knowledge on water governance, and serves as a guide for Chinese and other governments looking to improve their water resource management strategies.

Evaluation of fiscal policy with text mining under "dual carbon" target in China

“…Text mining, despite its many advantages, still faces challenges in handling the complexity, ambiguity, and metaphorical nature of natural language [ 31 ]. Cheng Zhe et al (2022) evaluated water policies using text mining methods based on multi-source data, finding that policy texts tend to focus on the macro level and are geared towards national development and long-term planning, while public opinion feedback tends to concentrate on micro-level and economic aspects, revealing varying degrees of media bias [ 32 ]. In the field of new energy vehicle policy, Liu Qin et al (2023) pointed out issues such as insufficient policy consistency, declining policy balance, and an expansion of the negative policy convexity index [ 33 ].…”