中国公众对气候风险的观点表达和政府回应。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Xiaolei Sun, Longfei Li, Yiran Shen, Yingjie Sheng, Dayong Zhang, Qiang Ji
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引用次数: 0

摘要

公众对气候风险的意见表达和相应的政府应对措施是公众与政府应对气候变化动态互动的重要指标。本研究以中国为背景,首次利用词嵌入技术构建了国家、省、市三级气候风险公众观点表达和政府反应的指数。这些指数是根据2011年1月至2023年12月在领袖留言板平台上收集的数据得出的。这些指数不仅有利于研究公众对气候风险的意见表达和政府对气候风险的反应,而且为加强政府与公众之间的气候风险沟通提供了强有力的数据支持。此外,这些指数有助于加强区域气候适应举措和加速气候适应型城市的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Climate risk expressions of public views and government responses in China.

Expressions of public views on climate risk and corresponding government responses serve as crucial indicators of the dynamic interaction between the public and the government in tackling climate change. Focusing on the context of China, this study utilizes word embedding techniques to construct indices of expressions of public views and government responses on climate risk at the national, provincial, and city levels for the first time. These indices are derived from data collected on the Message Board for Leaders platform spanning from January 2011 to December 2023. The developed indices not only facilitate the study of expressions of public views and government responses to climate risk but also provide robust data support for enhancing climate risk communication between the government and the public. Furthermore, these indices are instrumental in bolstering regional climate adaptation initiatives and accelerating the development of climate-resilient cities.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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