A comparative study on LinkedIn and Sina Weibo users’ perceptions of the carbon-neutral city

Abstract

A carbon-neutral city is one of the most critical topics in carbon neutrality. To study the general public and professionals’ focus, we analysed the posts on Weibo and LinkedIn through Pycharm, Navicat Premium, KHCoder, and Tableau. This study included 1908 microposts (14,668 sentences) on Weibo and 533 posts (3733 sentences) on LinkedIn. On Weibo, the most influential users were governments and organisations; for example, Baotou Daily, Beijing Ecological Environment, 922 Green Travel, Baotou Evening News, and Baoding Evening News. On LinkedIn, the most influential person was the co-director of Carbon Neutral Cities Alliance. The most popular topics on LinkedIn included “city,” “carbon,” “climate,” “neutral,” “energy,” “emission,” “sustainability,” “sustainable,” “neutrality” and the keywords of “world”; while “carbon,” “city,” “energy,” “development,” “new,” “green,” “promote,” “neutrality,” “construction” and “industry” are more prevalent in Weibo. Both LinkedIn and Weibo users focus on “energy” and related issues. LinkedIn users mentioned “climate” and “sustainability” most in their posts, but Weibo users concerned about green development in the construction industry.

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“…In addition, some states have moved toward a clean energy industry and a zero-carbon grid by setting clean energy goals (e.g., Nevada and Maine) or signing executive orders or taking regulatory action (e.g., Minnesota and New Jersey) [8]. In short, the goal of a zero-carbon society and policy promotion has become a trend [9].…”

Change Management’s Points of Connection with Project Management and Psychology

“…Relevant data were cleaned and put into a proper input file format for content analyses with the text mining software, KH Coder 3 (Higuchi, 2016(Higuchi, , 2017(Higuchi, , 2022. KH Coder 3 was also used with similar purposes in numerous other studies (Mori et al, 2022;Popescu & Zaharia, 2019;Zeng et al, 2022) as the website itself (Higuchi, 2022) shows.…”

“…Content analysis and specifically text mining as a specific methodology for discovering knowledge from a large amount qualitative textual data, for which Ramya et al (2017) presented different techniques and framework, have a wide range of successful applications in various fields of science including engineering (e.g., text-mining patents for biomedical knowledge (Rodriguez-Esteban & Bundschus, 2016), social media users' perceptions of the carbon-neutral city (Zeng et al, 2022)), health care (e.g., pre-impressions of COVID-19 vaccination among medical stuff by Mori et al (2022), management (e.g., new product idea identification (Christensen et al, 2017), and politics (Charalampakis et al, 2016). By now, using such techniques is also an accepted way of carrying out bibliometric analyses, too, see for instance, Popescu and Zaharia's (2019) work since the limitations of "traditional" content analysis (i.e., it "involves subjective human interpretation, as a research team must either formulate a classification scheme and apply it manually or train coders generally based on deep learning" (Barbierato et al, 2022, p. 214)) can be overcome by lexical analysis, which helps "reduce subjective interpretation" (Lai & To, 2015, p. 140) due to the subjective definitions of categories and/or differences between coders.…”