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Exploring public opinions on climate change policy in "Big Data Era"—A case study of the European Union Emission Trading System (EU-ETS) based on Twitter

Abstract Public awareness has an important effect on the legislation and implementation of climate change policies. Against the backdrop of the "Big Data Era," social media is an appealing and promising tool for a timely and complete understanding of public perception and attitudes towards climate policies. This paper examines the public's spontaneous attention and awareness about carbon emissions trading (ETS). Tweets related to the EU-ETS, published between 2008 and 2019, were collected for multi-dimensional analysis. Empirical results show several important findings. First, public attention on the EU-ETS has increased significantly since 2011. Second, government officials and industry practitioners have a stronger influence in the discussions than the public and industrial enterprises. Third, topic followers mostly gathered in Belgium (16.65%), the UK (11.6%), and some non-regulated countries like the US and Australia. Fourth, the public mainly focused on the policies and legislation, allowance price, and allocation. The innovation of this study rests in the development of a social media data-based research framework to examine the public's cognition of climate policies, which integrates the advantages of public social media, social network analysis, and text topic analysis. This study provides comprehensive analysis and support for climate policy implementation and public acceptance improvement.
- Beihang University China (People's Republic of)
- Beihua University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).58 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
