{"title":"Breaking the carbon bind: How digitalization and energy transformation reshape carbon dependency based on wavelet and machine learning approaches","authors":"Yang Yu , Xin Jian , DooHwan Won , Atif Jahanger","doi":"10.1016/j.envdev.2025.101226","DOIUrl":null,"url":null,"abstract":"<div><div>As global efforts to combat climate change intensify, digitalization has emerged as a crucial driver in reducing carbon dependency, with energy transformation also playing a significant role. Within this purview, this paper delves into the interplay among digitalization, energy transformation, and carbon dependency, utilizing Chinese country-level data spanning from 2005 to 2021. Recognizing potential variations in emission reduction policies over time, we employ the wavelet spectrum, wavelet local multiple correlation, wavelet coherence and machine learning methods for a comprehensive exploration. The outcomes of the wavelet spectrum analysis offer a visual depiction of the variable dynamics over time, furnishing substantial underpinning for discerning their intricate behaviors. Simultaneously, the findings from the wavelet local multiple correlation and wavelet coherence analyzes underscore disparities in the impacts of digitalization and energy transformation on carbon dependency across different temporal intervals and frequencies. Specifically, digitalization intensifies carbon dependency in the short to medium term (below 8 band), while both digitalization and energy transformation significantly reduce carbon dependency in the long term (above 16 band), demonstrating a dynamic correlation among these variables. Furthermore, the results derived from the machine learning tests demonstrate that the influence of digitalization and energy transformation on carbon dependency reveal time-varying effects, digitalization exacerbates carbon dependency within the threshold range of −0.5 to 0.8, whereas energy transformation effectively reduces carbon dependency beyond the threshold of 0.3. This research investigates the complex interrelations among digitalization, energy transformation, and carbon dependency, providing essential experiences and lessons that are applicable to green and sustainable development efforts worldwide.</div></div>","PeriodicalId":54269,"journal":{"name":"Environmental Development","volume":"55 ","pages":"Article 101226"},"PeriodicalIF":4.7000,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Development","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211464525000922","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
As global efforts to combat climate change intensify, digitalization has emerged as a crucial driver in reducing carbon dependency, with energy transformation also playing a significant role. Within this purview, this paper delves into the interplay among digitalization, energy transformation, and carbon dependency, utilizing Chinese country-level data spanning from 2005 to 2021. Recognizing potential variations in emission reduction policies over time, we employ the wavelet spectrum, wavelet local multiple correlation, wavelet coherence and machine learning methods for a comprehensive exploration. The outcomes of the wavelet spectrum analysis offer a visual depiction of the variable dynamics over time, furnishing substantial underpinning for discerning their intricate behaviors. Simultaneously, the findings from the wavelet local multiple correlation and wavelet coherence analyzes underscore disparities in the impacts of digitalization and energy transformation on carbon dependency across different temporal intervals and frequencies. Specifically, digitalization intensifies carbon dependency in the short to medium term (below 8 band), while both digitalization and energy transformation significantly reduce carbon dependency in the long term (above 16 band), demonstrating a dynamic correlation among these variables. Furthermore, the results derived from the machine learning tests demonstrate that the influence of digitalization and energy transformation on carbon dependency reveal time-varying effects, digitalization exacerbates carbon dependency within the threshold range of −0.5 to 0.8, whereas energy transformation effectively reduces carbon dependency beyond the threshold of 0.3. This research investigates the complex interrelations among digitalization, energy transformation, and carbon dependency, providing essential experiences and lessons that are applicable to green and sustainable development efforts worldwide.
期刊介绍:
Environmental Development provides a future oriented, pro-active, authoritative source of information and learning for researchers, postgraduate students, policymakers, and managers, and bridges the gap between fundamental research and the application in management and policy practices. It stimulates the exchange and coupling of traditional scientific knowledge on the environment, with the experiential knowledge among decision makers and other stakeholders and also connects natural sciences and social and behavioral sciences. Environmental Development includes and promotes scientific work from the non-western world, and also strengthens the collaboration between the developed and developing world. Further it links environmental research to broader issues of economic and social-cultural developments, and is intended to shorten the delays between research and publication, while ensuring thorough peer review. Environmental Development also creates a forum for transnational communication, discussion and global action.
Environmental Development is open to a broad range of disciplines and authors. The journal welcomes, in particular, contributions from a younger generation of researchers, and papers expanding the frontiers of environmental sciences, pointing at new directions and innovative answers.
All submissions to Environmental Development are reviewed using the general criteria of quality, originality, precision, importance of topic and insights, clarity of exposition, which are in keeping with the journal''s aims and scope.