Exploring the New Energy Vehicle Industry’s Progress Path under the Carbon Peaking and Carbon Neutrality Goals: Evidence from Online Q&A Community’s Emotional Analysis
Hao Xu 1,2
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School of Economics & Management, Nanjing Institute of Technology, Nanjing, China
School of Information Management, Nanjing University, Nanjing, China
School of Electrical Engineering, Nanjing Institute of Technology, Nanjing, China
School of Business, Nanjing Xiao Zhuang University, Nanjing 211171, China
Fuzhou University Library, Fuzhou, Fujian, China
Submission date: 2023-10-07
Final revision date: 2023-12-10
Acceptance date: 2023-12-16
Online publication date: 2024-05-20
Publication date: 2024-06-07
Corresponding author
Rong Wang   

School of Business, Nanjing Xiao Zhuang University, Nanjing 211171, China
Pol. J. Environ. Stud. 2024;33(4):4807-4823
The automotive industry’s low-carbon transformation is crucial to a nation’s ability to fulfill its “Carbon Peaking and Carbon Neutrality” (CPDN) commitment. However, China’s automotive industry still has a number of issues that have sparked debate. Based on the fact that people use social Q&A platforms to get information, solve problems, and aid in decision-making, negative answers in massive amounts of information typically have a higher degree of information perception and are easier to spread. This work constructed the algorithm of emotion calculation and classification, negative network construction for social Q&A platforms, and carried out empirical research with Zhihu. The 175 questions and 5220 corresponding answers for new energy automobiles were organized as a database to search the development path for the new energy automobile industry. The new energy vehicle industry’s development path primarily entails: resolving the issue of charging difficulty and popularizing charging heaps; attending to the battery safety issue and the head brand of new energy vehicles concentrating notably on quality control. The empirical findings also demonstrate that algorithms developed can more effectively complete the task of sentiment analysis, aid users in making decisions, and contribute to realizing the CPDN goal.
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