In September 2020, the Chinese government announced a major strategic goal to strive for peak carbon emissions by 2030 and achieve carbon neutrality by 2060 ^[1][2][1,2]. China’s 14th Five-Year Plan proposes to support localities and key industries and enterprises with the conditions to achieve a peak in carbon emissions ^[3][4][3,4]. The development gap caused by different levels of technology [5], industrial structures [6], and resource endowments [7] have led to different degrees of progress in emission reduction efforts in various industries. In order to achieve the goals of carbon peaking and carbon neutrality on schedule, it is crucial to understand the results of carbon reduction efforts across different industries.

Currently, a new round of Industrial Revolution, represented by the digital economy, is sweeping across the globe at an unprecedented pace, with a very wide radiation range and depth of impact ^[8][9][8,9]. The Digital Carbon Neutrality White Paper points out that digital technology promotes the transformation of key industries towards digitization and greenization, empowers carbon emission reduction, and accelerates the digitalization of various sectors through information and communication technology. The potential for carbon emission reduction in the digital economy is enormous ^[10][11][10,11]. Therefore, it is particularly important to explore the effects of digital transformation on carbon emissions across industries in China, under the background of the goals of carbon peaking and carbon neutrality.

Research on industry carbon emissions often focuses on a specific industry, and input–output analysis (IOA) is a suitable research method for carbon emissions calculation. However, there is limited research on carbon emissions and their effects across all industries, with most focusing on a specific industry, such as the power industry ^[12][13][16,17], industrial sector ^[14][18], heating and power industry ^[15][19], steel industry ^[16][20], and transportation industry ^[17][21]. Currently, three main methods exist for calculating CO₂ emissions, including life cycle assessment (LCA) ^[18][19][22,23], intergovernmental Panel on Climate Change (IPCC), and input–output analysis (IOA) ^{[20][21][22][23][24]}[24,25,26,27,28]. LCA and IPCC have high data requirements, and the accuracy of the results is difficult to guarantee. In comparison, IOA is more operable, and it can calculate direct and indirect emissions for each industry.

There is relatively few analyses that uses input–output models to study the relationship between digital transformation and carbon emissions, particularly from a supply-side perspective. Existing studies on the factors affecting carbon emissions often use econometric models ^{[25][26][27][28]}[29,30,31,32], which cover population ^[29][30][33,34], economics ^[31][35], industry ^[32][36], space ^[33][37], residential consumption ^[34][38], and energy consumption ^[35][39]. However, research on the relationship between digital transformation and carbon emissions using an input–output model is relatively scarce ^{[36][37][38][39]}[40,41,42,43], particularly from a supply-side perspective ^{[40][41][42][43]}[44,45,46,47].

Regarding the impact of digital transformation on carbon emissions, there are three viewpoints currently extant in research. The first holds that digital transformation is helpful in reducing carbon emissions. Scholars claim that digital transformation can promote carbon reduction through means such as improving productivity ^[44][48], changing management and sales approaches ^[45][49], promoting industrial transformation ^[46][50], and accelerating human capital accumulation ^[47][51]. Gelenbe and Caseau ^[48][52] found that digital transformation can reduce energy consumption in industries such as transportation, construction, online learning, and healthcare. The second viewpoint is that digital transformation will exacerbate carbon emissions ^[49][50][53,54]. First, the widespread use of digital products directly increases carbon emissions ^[51][55]. Second, digital transformation increases energy consumption through accelerating product updates ^[52][56] and transportation, and increasing distribution demands ^[53][57]. The third viewpoint is that the relationship between digital transformation and carbon emissions follows a U-shaped pattern ^[54][55][56][58,59,60]. On the one hand, digital transformation will continuously increase CO₂ emissions because of factors such as digital device production ^[57][61], increases in energy consumption ^[58][62], and electronic waste recycling ^[59][63]. On the other hand, digital transformation can decrease carbon emissions by developing more intelligent cities ^[60][64], transportation systems ^[61][65], smart grids ^[62][66], and energy-efficient devices ^[63][67]. The opposing effects produce a U-shaped relationship between digital transformation and carbon emissions.

SDA is a decomposition method used for researching the driving factors of carbon emissions through input–output analysis. The commonly used carbon emission decomposition methods include structural decomposition analysis (SDA) and index decomposition analysis (IDA). In general, the advantage of IDA lies in the flexibility of selecting indicators, making it widely used in constructing comprehensive economic energy efficiency indices ^[64][65][68,69]. The uniqueness of SDA lies in its usability for different IO models, like the traditional Leontief I-O model, the semi-closed I-O model ^[66][70], the Ghosh I-O model ^[67][68][71,72], and various multiregional I-O models. In recent years, the SDA decomposition method has been widely applied to decomposing the driving factors of carbon emissions in different countries, such as Italy ^[69][73], China ^[70][74], Belt and Road Initiative countries ^[71][75], G20 countries ^[72][76], the UK ^[73][77], South Korea ^[74][78], and the EU ^[75][79]. For the driving factors of carbon emissions, most studies have analyzed the impact of structure and technology changes on energy use from the demand side. Yuan and Zhao ^[76][80] decomposed emission changes into emission intensity, technology, and demand effects. Wei et al. ^[77][81] decomposed emission changes into technology, sectoral links, economic structure, and economic scale. Xu et al. ^[78][82] believed that emission changes were caused by import and export effects, energy structure and intensity effects, technology effects, transfer effects, and investment effects. Yu et al. ^[79][83] decomposed carbon emissions from the perspectives of input structure, energy intensity, structural effects, and final demand effects.

In summary, in terms of research scope, few researchers have studied the relationship between digital transformation and carbon emissions in all industries in China. In terms of research methods, input–output models are used less frequently than econometric models, even though input–output models have been proven to be a more suitable research method. In terms of research perspectives, there are few studies that have explored the relationship between digital transformation and carbon emissions from the supply-side perspective, as opposed to the demand side. This research used the Ghosh input–output model to study the induced effects of digital transformation on carbon emissions from 97 industries from 1997 to 2018.