Breaking the carbon bind: How digitalization and energy transformation reshape carbon dependency based on wavelet and machine learning approaches

Yu Y., Jian X., Won D., Jahanger A.

ENVIRONMENTAL DEVELOPMENT, vol.55, 2025 (SCI-Expanded, Scopus) identifier identifier

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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.