{"title":"Research on the impact of enterprise digital transformation based on digital twin technology on renewable energy investment decisions","authors":"Mengying Cao, Wanxiao Song, Yanyan Xu","doi":"10.1186/s42162-024-00447-8","DOIUrl":null,"url":null,"abstract":"<div><p>In the context of global climate change and sustainable development, enterprise digital transformation has become key to improving efficiency and competitiveness. Digital twin technology, as an emerging tool, enables real-time monitoring, prediction, and optimization by creating dynamic virtual models of real-world processes. This paper explores the impact of digital twin-based transformation on renewable energy investment decisions. Through empirical analysis of over 200 companies globally, the study finds that companies using digital twin technology exhibit higher accuracy and efficiency in renewable energy investment decisions. These companies show improved forecasting of energy consumption and investment returns, gaining a competitive edge. On average, these companies experience a 15% ROI increase for their renewable energy investments and enjoy a 20% acceleration in the decision-making process. Furthermore, the study delves into how the adoption of digital twin technology differs across various company sizes and industries, providing actionable insights and guidance for enterprises embarking on their digital transformation journey.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00447-8","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Informatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1186/s42162-024-00447-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0
Abstract
In the context of global climate change and sustainable development, enterprise digital transformation has become key to improving efficiency and competitiveness. Digital twin technology, as an emerging tool, enables real-time monitoring, prediction, and optimization by creating dynamic virtual models of real-world processes. This paper explores the impact of digital twin-based transformation on renewable energy investment decisions. Through empirical analysis of over 200 companies globally, the study finds that companies using digital twin technology exhibit higher accuracy and efficiency in renewable energy investment decisions. These companies show improved forecasting of energy consumption and investment returns, gaining a competitive edge. On average, these companies experience a 15% ROI increase for their renewable energy investments and enjoy a 20% acceleration in the decision-making process. Furthermore, the study delves into how the adoption of digital twin technology differs across various company sizes and industries, providing actionable insights and guidance for enterprises embarking on their digital transformation journey.