## 1 Introduction  
The rapid development of the world economy is increasing the pressure on energy supply and discovering methods for cleaner industrial production has become crucial. Sustainable development requires balancing economic growth, energy consumption, and the resulting environmental impacts [1]. The aggregate intensity of energy consumption, defined as the energy consumed per unit of gross domestic product (GDP), has been recommended as a tool for evaluating the aggregate performance of energy utilization and climate change mitigation [2]. In 2018, the G20 countries accounted for 86% of the global economic economy [3] and 82% of total primary energy consumption [4]. Thus, the G20 countries not only serve as the backbone of the global economy, but their energy efficiencies also determine global energy consumption. In a joint effort to address the challenges of global climate change and sustainable development, the G20 countries, excluding the United States, signed an agreement in 2019 to ensure the full implementation of the Paris Agreement. Since the G20 includesboth developing countries with high energy intensity and developed countries with low energy intensity, comparing the energy intensity of the G20 countries and analyzing the influencing factors can help us identify the key industries in high-energy-intensity countries that could contribute to improving energy intensity governance. Studies have mainly explored the influencing factors of energy intensity at the national level, and the research methods employed primarily include index decomposition analysis (IDA) and structural decomposition analysis (SDA). In IDA, the decomposition results can be expressed in additive and multiplicative forms. Generally, the additive form is applied to the absolute change in the total index, while the multiplicative form is applied to the relative change [5; 6; 7]. A commonly used form of IDA is logarithmic mean Divisia index (LMDI) [8; 9; 10; 11; 12; 13]. [PERSON] [14] proposes eight LMDI models and their decomposition methods, as well as their advantages and disadvantages. Since the LMDI method can only reflect direct effects, [PERSON] and [PERSON] [15] combine the multiplicative Dietzenbacher and Los (D&L) [16] method with a multiplicative LMDI to study the factors that influenced China's carbon emission intensity changes from 2007 to 2012 at the national level, final demand category, and sectoral level, providing a satisfactory solution to the industry segmentation effect problem. [PERSON] and [PERSON] [17] combine IDA with production-theoretical decomposition analysis to identify and quantify the dynamics of energy intensity changes in Chinese provinces from 2005 to 2010. In [PERSON] et al. [18], the extended LMDI method is used to decompose the changes in China's total industrial energy intensity into macro and technical factors using data from 36 industrial sectors in China from 2003 to 2015. Compared with IDA, the combined input-output (I-O) model used for an I-O SDA can quantify not only the direct economic and technological links between national industries, but also the indirect links. I-O SDA separates the factors that affect the economic, energy, and environmental systems and quantifies the contribution of the factors to the energy intensity. The authors of [6; 19; 20] analyze the differences between IDA and SDA. [PERSON] et al. [20] highlight that, for the intensity indicator, multiplicative decomposition is generally preferred to additive decomposition due to the ease of result presentation and greater suitability for tracking purposes. [PERSON] et al. [21] uses I-O SDA to investigate the contributions of changes in the energy mix, sectoral energy efficiency, production structure, final demand structure, and final demand category composition to China's energy intensity fluctuations during 1997-2007. Based on the original additive SDA method, [PERSON] and [PERSON] [7] explore multiplicative SDA, which has been widely used since their seminal work.