Impact of extreme weather in production economics: Extracting evidence from user-generated content
The last decade has witnessed an increase in the number of extreme weather events globally. In addition, the economic output around the world is at all-time high in terms of production and profitability. However, global warming and extreme weather are modifying the natural ecosystem and the human so...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/24757 |
| Acceso en línea: | https://hdl.handle.net/10115/24757 |
| Access Level: | acceso abierto |
| Palabra clave: | Extreme weather Production economics User-generated content Data-driven method |
| Sumario: | The last decade has witnessed an increase in the number of extreme weather events globally. In addition, the economic output around the world is at all-time high in terms of production and profitability. However, global warming and extreme weather are modifying the natural ecosystem and the human social system, leading to the appearance of extreme climate events that have an adverse impact on the world economy. To address this challenge, the present study identifies the main impacts of extreme weather on production economics based on the analysis of user-generated content (UGC) on the social network Twitter. Methodologically, a sentiment analysis with machine learning is developed and applied to analyze a sample of 1.4 m tweets; in addition, computing experiments to calculate the accuracy with Support Vector Classifier, Multinomial Naïve Bayes, Logistic Regression, and Random Forest Classifier are conducted. Second, a topic modeling known as latent Dirichlet allocation is applied to divide sentiment-classified tweets into topics. To complement these approaches, we also use the technique of textual analysis. These approaches are used under the framework of computer-aided test analysis system and natural language processing. The results are discussed and linked to appraisal theory. A total of 7 topics are identified, including positive (Sustainable energies and Green Entrepreneurs), neutral (Climate economy, Producer’s productivity and Stock market), and negative (Economy and policy and Climate emergence). Finally, the present study discusses how the recent trend of an increase in extreme weather conditions has significantly impacted international markets, leading companies to adapt their business models and production systems accordingly. The results show that the climate economy and policy, producers’ productivity, and the stock market are all heavily influenced by extreme weather and can have significant effects on the global economy. |
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