Analysis of the Causes and Impacts of the"Power Outage"Incident:A Big Data-driven Perspective on Public Comments
From September to November 2021,"power outage"incidents occurred in more than 20 provinces and cities across China."Power outage"refers to the government's emergency measures to control part of the electricity demand based on an approved power outage schedule without prior notification to residents.Different from the previous"power outage"that mainly occurred at the end of the year to achieve energy-saving and emission reduction goals,this incident happened at the end of the third quarter and the beginning of the fourth quarter.It took place in the first year of the"14th Five-Year Plan"and was not in the period of assessing energy consumption control.Additionally,there were no extreme weather conditions or natural disasters.Thus,the incident triggered extensive online discussion among official media,experts,scholars,and the general public.On digital platforms such as Weibo and Byte headlines,they analyzed the causes and impacts of the incident.In the realm of energy management,"power outage"has a significant impact on industrial production,daily life,and economic operations.Without understanding the causes and impacts,it is impossible to develop effective measures to prevent similar incidents from happening again.Therefore,it is essential to analyze the causes and impacts of the"power outage"incident and provide policy implications to deal with incidents.Dealing with incidents like"power outages",harnessing the collective wisdom of the public to provide support for government decision-making is an important scientific problem in the field of energy management.Collective Intelligence Theory argues that collective intelligence can be formed by aggregating the knowledge,experience,and insights of numerous individuals,which surpasses individual wisdom.This can be achieved through a"crowdsourcing"approach,which leverages online platforms to organize and coordinate the participation of a large number of individuals,enabling knowledge sharing and the harnessing of collective wisdom.Based on Collective Intelligence Theory,the paper proposes a big data research framework of"Integrating multi-source data from multi-perspectives → Identifying useful information through the classification of comments→Mining causal relationships of incidents using text analysis."A total of 21387 pieces of texts about discussions and comments from experts,scholars,and the general public on the incident are collected from social media platforms and journals through web crawling.Big data analysis methods such as social network analysis,BERTopic modeling,and sentiment analysis are employed.Based on keywords and topic words,the paper systematically analyzes the causes and impacts of"power outage"incident.Overall,the paper explores the reasons behind the"power outage"incident and its consequences,as well as negative comment themes of public opinion,which provides support for guiding public opinion.Based on our proposed big data analysis farmwork,this study has the following three main findings:First,the comments on the"power outage"incident mainly focuses on the impacts of the outage,public opinion trends,reasons for the outage,and mitigation measures.Second,a four-level attribution system that explains the causes of the"power outage"incident is identified.The first-level external factors include politics,economy,and society,while the first-level internal factors include the incident subject,incident information,and incident analysis.We find that the gap between the continuously increasing electricity demand and the reduced electricity supply caused by the tight coal supply and insufficient renewable energy generation is the main factor.Third,the incident has multidimensional impacts on politics,economy,society,enterprises,and residents,which further triggers negative public emotions.We also find that the reasons for negative emotions are related to the local government's"one-size-fits-all"implementation approach and misleading information such as"big chess theory","low-level red",and"high-level black".The research makes contributions via two ways:First,using Collective Intelligence Theory and"crowdsourcing"mod-el as our theoretical foundation,we explore the internal and external causal framework and multidimensional impacts of the"power outage"incident.Thus,the paper goes beyond the limitations of existing research that focuses only on external factors,provides a more comprehensive research perspective for studying incidents.It expands the applications of Collective Intelligence Theory and enriches the engineering management practices for handling incidents.Second,we construct a data-driven research framework for incidents,which integrates multiple data sources,classifies useful information from diverse perspectives,and applies text analysis to uncover causal relationships.The framework is not confined to a specific theoretical perspective of qualitative analysis or a strict paradigm of quantitative empirical research.By effectively integrating diverse perspectives and mining public attitudes and demands towards the incidents,it enables the development of better response measures and policies,and enhances emergency management capabilities.
power outagepublic commentsBERTopic modelingcauses analysis
万方数据
