Introduction

Total carbon emissions refer to the total amount of carbon dioxide emissions generated by the creation of GDP in a country or region in a natural year, while carbon emissions intensity refers to the amount of carbon dioxide emissions per unit of GDP, and controlling the total amount of carbon emissions and carbon emissions intensity can effectively improve carbon emissions performance. The “dual-carbon” strategy is essential for a sustainable future. It’s crucial for China’s goal of a beautiful, green, and high-quality development. The strategy demands a methodical transition. We shift from energy consumption control to managing carbon emissions—both their total volume and intensity. This transition aims for a reduction in both, working in synergy. This shift seeks balance. It integrates energy security with economic growth and climate change mitigation. The adoption of clean energy and clean production methods is key. They are vital for controlling greenhouse gas emissions, especially carbon dioxide. Within the “dual-carbon” framework, several critical issues demand immediate academic scrutiny in the realm of “dual-control”: Identifying the principal determinants of carbon emissions’ total volume and intensity, discerning variations in the impact of these determinants on both dimensions of emissions, and elucidating the primary mechanisms through which these impacts are transmitted.

The development of the digital economy and technological progress are acknowledged as pivotal influences on the total amount and intensity of carbon emissions. Over recent years, scholarly consensus has emerged that the nexus between the digital economy and carbon emissions is intricate and multifaceted, possessing both positive and negative impacts on emissions, with the direction and magnitude of these effects being indeterminate. Drawing on urban panel data, Xu et al.1 have determined that the digital economy significantly diminishes regional carbon emissions. Similarly, Yang et al.2, utilizing provincial panel data, have demonstrated that the advancement of the digital economy can achieve regional carbon emission reductions. Conversely, Wang et al.3 have identified that the energy consumption associated with the construction of digital infrastructure and the industrialization process within the digital economy can lead to an increase in carbon emissions. Furthermore, some researchers propose that the relationship between the digital economy and carbon emissions follows a non-linear, “inverted U” pattern. Fei et al.4, in their analysis of the digital economy index across 30 Chinese provinces from 2011 to 2019, have concluded the presence of a significant inverted U-shaped correlation between the digital economy’s development level and the aggregate carbon emissions. Cheng et al.5 have also found that the dynamic impact of China’s digital economy on carbon emission reduction mirrors this inverted U-shaped relationship. Additionally, there is evidence of significant spatial effects and regional disparities in the interplay between the digital economy and carbon emissions. Miao et al.6, through spatial econometric regression on panel data from prefecture-level cities, have revealed that the spatial direct impact of the digital economy on carbon emissions surpasses its spatial indirect impact. Zhou et al.7, in their empirical study using city panel data, discovered that the development of the digital economy not only has a local inverted U-shaped effect on reducing emissions but also a U-shaped spatial spillover effect conducive to emission reduction.

Technological progress is also a key factor driving carbon emission reduction and high-quality economic development, and Brännlund et al.8 point out that technological progress can reduce carbon emissions by improving the efficiency of fossil energy use. Yang et al.9 regressed panel data of 30 provinces from 2005 to 2015 and found that the main causal factor leading to carbon emission reduction was technological progress, but it also pointed out that the impact of technological progress on carbon emissions has a certain rebound effect, i.e., it is can lead to an increase in carbon emissions in some aspects. Some scholars have also examined the heterogeneous effects of different types of technological progress on carbon emission reduction. Shao et al.10 using provincial panel data, found through empirical tests that green technological progress shows a strong technological dividend effect, which can simultaneously advance carbon emission reduction in the region as well as spatially related regions. Recent studies have delved into the impact of heterogeneity in technological progress on the differences in carbon emissions among Chinese cities. In particular, the distinction between production-oriented and green-oriented technological progress reveals their different roles in carbon emission patterns. On the one hand, the heterogeneity of green-type technological progress is identified as a key driver that exacerbates the inter-city carbon emission gap, a finding empirically supported in the study of Chen and Yao11. On the other hand, the heterogeneity of production-based technological progress shows the opposite effect and helps to narrow the carbon emission gap between cities, which is further corroborated by the study of Wang et al.12, although they also point out that the effect of green-based technological progress is not significant in this regard. Further, the diversity of technological innovations has also received attention. Liu et al.13 categorized technological innovation into three types: technology introduction, imitation innovation and independent innovation, and found that the effect of technological progress in promoting carbon emission reduction becomes more and more significant with the enhancement of the capacity of independent innovation and imitation innovation. This indicates that the type and capability of technological innovation have an important impact on the realization of carbon emission reduction.

Existing literature mainly focuses on the two major perspectives of “digital economy” and “technological progress” to study the main factors affecting the total amount and intensity of carbon emissions, and empirically examines the mechanism, direction and magnitude of the impact of these factors on carbon emission performance. However, the existing studies mainly examine this issue from the one-dimensional perspective of “digital economy” or “technological progress”, and lack a systematic and comprehensive analytical framework that integrates the two perspectives, and do not examine “digital economy” and “technological progress” at the same time. It also fails to examine the impact and spatial spillover effects of “digital economy” and “technological progress”, the two core explanatory variables that determine China’s green, low-carbon and high-quality development, on regional carbon emission performance. In this paper, by constructing a benchmark regression model and spatial neighborhood weight matrix, spatial distance weight matrix and spatial economic weight matrix, we systematically examine the impacts and spatial spillover effects of the two core explanatory variables, digital economy and technological progress, on regional total carbon emissions and carbon emission intensity, and further analyze and test the mechanism by which digital economy and technological progress affect carbon emission performance. The research in this paper provides empirical evidence on how the Chinese government achieves green, low-carbon and high-quality development, and is of some significance in promoting the modernization of the governance system and governance capacity, and helping to achieve the goals of “dual control” and “dual carbon”.

Although existing literature has made some progress in studying the relationship between digital economy, technological progress, and carbon emissions, there are still shortcomings. Firstly, most studies focus on a single perspective, either exploring the digital economy separately or technological progress separately, lacking a systematic analysis that combines the two. Secondly, existing research often overlooks the spatial correlation and heterogeneity of carbon emissions between regions, as well as how these factors affect carbon emissions through complex economic structures and social behaviors. In addition, the exploration of the dynamic and nonlinear characteristics of the impact of digital economy and technological progress on carbon emissions is not sufficient, which limits the in-depth understanding of carbon emission governance strategies.

The main contributions of this study include: firstly, constructing an analytical framework that integrates the digital economy and technological progress, providing a new perspective for understanding the underlying mechanisms of their impact on carbon emissions; Secondly, by introducing spatial econometric models, the spatial correlation and heterogeneity of carbon emissions between regions were revealed, providing new empirical support for the formulation of regional emission reduction strategies; Thirdly, the findings of this study will help policy makers better understand the role of digital economy and technological progress in achieving the “dual carbon” goals, and thus formulate more effective environmental and industrial policies.

Mechanism analysis and research hypotheses

Digital economy and total carbon emission and intensity

According to Chen et al.14, the digital economy is an economic activity that takes digital information as its key resource, uses the Internet platform as its main information carrier, is driven by digital technology innovation, and takes a series of new modes and forms of business as its form of expression. From the connotations and characteristics of the digital economy, digital technology is different from traditional production technology, which is rich in non-consumption and environment-friendly features, which can advance the use of high-tech and reduce the negative impact on the environment, so as to reduce the total amount of carbon emissions15. However, the digital economy is currently in a phase of swift expansion, and the energy and power demands associated with the development of digital infrastructure and the industrialization of digital sectors pose notable challenges. This aspect of the digital economy can lead to an increase in carbon emissions16,17,18. Recent studies, such as Zhou et al.7, suggest a U-shaped relationship between digitalization and low-carbon economy efficiency, indicating that after a certain threshold, further digitalization could lead to a decrease in carbon emissions and intensity. This aspect of the digital economy can lead to an increase in carbon emissions, as the growth in demand for ICT products and the associated expansion of digital supply chains can drive up carbon emissions, particularly through the significant energy consumption in the production of these technologies19,20. For digital technologies to be energy-efficient, they must not only curtail the total volume of carbon emissions quantitatively but also significantly diminish the carbon emission intensity qualitatively. Accordingly, this research proposes the following hypothesis:

H1: The digital economy has a direct impact on total carbon emissions and intensity but the direction of this impact is uncertain.

The open sharing, integration, and innovation of digital factors, coupled with their free mobility, confer certain spatial spillover effects. These digital elements influence not just their region of origin but also exert an indirect spatial impact. This can stimulate the development of the digital economy in neighboring regions21. Given this, the digital economy’s influence extends to the total carbon emissions. Consequently, its spatial spillover effects can lead to a similar spillover in carbon emissions’ total volume across regions. Shi Dan et al.22 assert that data within the digital economy is a vital new production factor. Unlike other production elements, it exhibits characteristics of increasing marginal returns. This can significantly boost economic development levels. Subsequently, this has a spatial spillover effect on carbon emission intensity in other regions23. Accordingly, this research proposes the following hypothesis:

H2: The digital economy has a spatial spillover effect on the total amount and intensity of carbon emissions in other regions.

Cai et al.24 believe that digital technology in the digital economy is characterized by permeability, substitutability, and synergy. The digital economy is divided into digital industrialization and industrial digitization. The scale, scope, and long tail effects of the digital economy can effectively promote the incubation and formation of new industries and formats. Existing literature mainly conducts research from the perspectives of industrial structure rationalization25, industrial structure advancement26, and digital industry coordinated development27, suggesting that the digital economy can reduce carbon emissions by promoting industrial structure transformation and upgrading. The level of industrial digitization is an indicator reflecting the scope of application of digital technology. Tian et al.28 believe that digital technology improves the production and management efficiency of real enterprises, alleviates information asymmetry, and realizes the transformation and upgrading of industrial structure through the linkage effect and paradigm effect between enterprises. Another way by which the digital economy can advance the optimization and upgrading of industrial structure is the penetration and integration of digital technology and traditional industries, and the upgrading of industrial structure through virtual agglomeration and the synergistic effect. The transformation and upgrading of industrial structure can drive the rational allocation of production factors, further optimize the energy structure, enhance the efficiency of energy use, and at the same time improve the efficiency of production, which will lead to a significant reduction in the total amount and intensity of carbon emissions. The mechanism of action is shown in Fig. 1. Accordingly, this research proposes the following hypothesis:

H3: The digital economy can advance industrial restructuring and then affect the total amount and intensity of carbon emissions.

Fig. 1
figure 1

Mechanism of the digital economy on carbon emission reduction.

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Technological progress and the total amount and intensity of carbon emissions

Tang et al.29 view technological progress as an outcome of innovation or the introduction of new technology. Through technological innovation or introduction, not only can we optimize the allocation of production factors and improve energy utilization efficiency, but we can also promote the advancement of industrial structure, thereby achieving carbon reduction30. Technological innovation or introduction can refine the allocation of production factors and boost energy use efficiency, leading to carbon emission reductions. Yet, some scholars caution about a potential rebound effect from technological progress. They argue that as technology expands production, it can increase energy consumption, potentially negating the intended carbon emission reductions. Based on these perspectives, this research proposes the following hypothesis:

H4: Technological progress has a direct effect on the total amount and intensity of carbon emissions but the direction is uncertain.

Chen et al.31 assert the existence of an interprovincial spatial spillover effect from technological progress. This effect occurs because the technological prowess of a region’s enterprises can influence other regions through the movement of scientific and technological expertise. Expertise typically flows between neighboring provinces or is drawn to economically prosperous areas, creating a siphon effect. This dynamic can elevate the technological capabilities of both neighboring and economically advanced regions, contributing to a reduction in carbon emissions. Moreover, the mobility of scientific and technological expertise can exert competitive pressure on enterprises in less technologically advanced regions. This pressure can catalyze transformation and technological upgrading, fostering a spatial spillover effect that facilitates carbon emission reduction. Accordingly, this research proposes the following hypothesis:

H5: Technological progress causes spatial spillover on the total amount and intensity of carbon emissions.

Hoffman’s theorem posits that technological progress is pivotal for transforming the structure of factor endowments. This transformation has multifaceted impacts:

  1. 1.

    It enhances production efficiency and technological innovation, fostering green innovation and elevating the level of green practices, thereby reducing carbon emissions.

  2. 2.

    It facilitates the innovation and optimization of technological factors, harmonizing resource allocation within regions. This leads to the phasing out of highly polluting, backward industries and accelerating the greening process, further cutting carbon emissions.

Moreover, Liu et al.32 suggest that technological progress can also foster knowledge spillover and amplify economies of scale, propelling green total factor productivity. The knowledge spillover from technology-driven enterprises in a region inspires other businesses to adopt improved production management practices, increasing efficiency and reducing resource wastage, which in turn leads to positive emission reduction effects.

Additionally, regional technological progress can promote industrial clustering, enhancing scale effects and enabling collective emission reduction and management. This process elevates the regional greening level, reducing both the total carbon emissions and their intensity, as illustrated in the mechanism depicted in Fig. 2. Accordingly, this research proposes the following hypothesis:

H6: Technological progress improves the level of greening and consequentially affects the total amount and intensity of carbon emissions.

Fig. 2
figure 2

Mechanism of technological progress on carbon reduction.

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Research Design and Data sources

Model construction

This research tries to verify the possible impact of digital economy and technological progress on carbon emissions, and takes the two indicators of total carbon emissions and intensity as the explanatory variables. The regression model is set as follows:

$$\:{lnCE}_{it}={a}_{0}+{a}_{1}{lnDig}_{it}+{a}_{2}{lnTec}_{it}+{a}_{3}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(1)
$$\:{lnCI}_{it}={\beta\:}_{0}+{\beta\:}_{1}ln{Dig}_{it}+{\beta\:}_{2}{lnTec}_{it}+{\beta\:}_{3}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(2)

where subscripts i and t represent regions and years, respectively; \(\:{CE}_{it}\) denotes the total carbon emissions of region \(\:i\) in year \(\:t\); \(\:{CI}_{it}\) denotes the carbon emissions intensity of the region \(\:i\) in year \(\:t\); \(\:Di{g}_{it}\) denotes the level of the digital economy of the region \(\:i\) in year \(\:t\); \(\:Te{c}_{it}\) denotes the technological progress of the region \(\:i\) in year \(\:t\); \(\:control{s}_{it}\) denotes a series of control variables at the regional level; \(\:{a}_{0}\) is the intercept term of the total carbon emissions; \(\:{\beta\:}_{0}\) is the intercept term of the carbon emissions intensity; \(\:{d}_{i}\) is the individual fixed effect; \(\:{\gamma\:}_{t}\) is the time-fixed effect; and \(\:{\varepsilon\:}_{it}\) is a randomized perturbation term.

Existing research results show that there is a strong spatial correlation between digital economy, technological progress and total carbon emissions. In order to verify the spatial effect of the current digital economy and technological progress on the total amount and intensity of carbon emissions, this research constructs the spatial neighbor weight matrix, spatial distance weight matrix, and spatial economic weight matrix, respectively. The specific matrix forms are as follows:

1. Spatial adjacency weight matrix. Based on the spatial adjacency of region locations, \(\:{W}_{1}\) takes the value of 1 when region \(\:i\) and region \(\:j\) are not adjacent, and 0 when region \(\:i\) and region \(\:j\) are adjacent.

$$\:{W}_{1}=\left\{\begin{array}{l}0, \:\: if\:Region\:i\:is\:not\:adjacent\:to\:Region\:j\\\:1,\:\:if\:Region\:i\:is\:adjacent\:to\:Region\:j\end{array}\right.$$
(3)

2. Spatial distance weighting matrix. Based on the distance between regional units, the closer the distance, the stronger the spatial effect; the farther the distance, the weaker the spatial effect.

$$\:{W}_{2}=\left\{\begin{array}{l}0(i=j)\\\:1/{d}_{ij}\qquad (i\ne\:j)\:\:\end{array}\right.$$
(4)

3. Economic distance weighting matrix. Based on the inverse of the difference in GDP per capita between regions multiplied by the matrix of the inverse of distance, taking into account both economic development and geographical distance.

$$\:{W}_{3}=\left\{\begin{array}{l}0(i=j)\\\:1/\left|perGD{P}_{i}\right.-\left.perGD{P}_{j}\right|{d}_{ij}\:\:\:\:\:\:\:(i\ne\:j)\end{array}\right.$$
(5)

In this research, the SDM spatial Durbin model with two-way fixed effects is selected for regression based on the test results, and in order to verify the spatial relationship between the current digital economy and technological progress on the total amount and intensity of carbon emissions, the primary terms of the digital economy and technological progress are used to import the spatial econometric model with reference to Eqs. (1) and (2), and the model specific expressions are as follows:

$$\:{CE}_{it}={\gamma\:}_{0}+\rho\:W{CE}_{it}+{\gamma\:}_{1}Di{g}_{it}+{\gamma\:}_{2}Te{c}_{it}+{\gamma\:}_{3}control{s}_{it}+{\theta\:}_{0}WDi{g}_{it}+{\theta\:}_{1}WTe{c}_{it}+{\theta\:}_{2}Wcontrol{s}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{jt}$$
(6)
$$\:{CI}_{it}={\chi\:}_{0}+\delta\:W{CI}_{it}+{\chi\:}_{1}Di{g}_{it}+{\chi\:}_{2}Te{c}_{it}+{\chi\:}_{3}control{s}_{it}+{\varphi\:}_{0}WDi{g}_{it}+{\varphi\:}_{1}WTe{c}_{it}+{\varphi\:}_{2}Wcontrol{s}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{jt}$$
(7)

where \(\:\rho\:\) denotes the autoregressive coefficient of total carbon emissions; \(\:W\) denotes the spatial matrix; \(\:{\theta\:}_{0}\) denotes the spatial interaction term of the digital economy on total carbon emissions; \(\:{\theta\:}_{1}\) denotes the spatial interaction term of technological advances on total carbon emissions; \(\:{\theta\:}_{2}\) denotes the spatial interaction term of control variables on total carbon emissions; \(\:\delta\:\) denotes the autoregressive coefficient of the carbon emissions intensity; \(\:{\varphi\:}_{0}\) denotes the spatial interaction term of the digital economy on the carbon emissions intensity; \(\:{\varphi\:}_{1}\) denotes the spatial interaction term of technological advances on carbon emissions intensity; \(\:{\varphi\:}_{2}\) denotes the spatial interaction term of control variables on carbon emissions intensity; and \(\:{\varepsilon\:}_{jt}\) is a random perturbation term.

In order to explore the indirect effect mechanism of digital economy on carbon emissions, the following model is constructed:

$$\:{lnCE}_{it}={a}_{0}+{a}_{1}{lnDig}_{it}+{a}_{2}{lnDig}_{it}\times\:{lnInd}_{it}+{a}_{3}{lnTec}_{it}+{a}_{4}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(8)
$$\:{lnCI}_{it}={\beta\:}_{0}+{\beta\:}_{1}{lnDig}_{it}+{\beta\:}_{2}{lnDig}_{it}\times\:{lnInd}_{it}+{\beta\:}_{3}{lnTec}_{it}+{\beta\:}_{4}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(9)

where \(\:{lnDig}_{it}\times\:{lnInd}_{it}\) is the interaction term between the level of digital economy and the level of industrial structure, and the meanings of the rest of the variables are unchanged.

In order to explore the indirect effect mechanism of technology level on carbon emissions, the following model is constructed:

$$\:{lnCE}_{it}={a}_{0}+{a}_{1}{lnDig}_{it}+{a}_{2}{lnTec}_{it}+{a}_{3}{lnTec}_{it}\times\:{lnGre}_{it}+{a}_{4}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(10)
$$\:{lnCI}_{it}={\beta\:}_{0}+{\beta\:}_{1}{lnDig}_{it}+{\beta\:}_{2}{lnTec}_{it}+{\beta\:}_{3}{lnTec}_{it}\times\:{lnGre}_{it}+{\beta\:}_{4}{lncontrols}_{it}+{d}_{i}+{\gamma\:}_{t}+{\varepsilon\:}_{it}$$
(11)

where \(\:{lnTec}_{it}\times\:{lnGre}_{it}\) denotes the interaction term between the level of technology and the level of greening, and the meanings of the rest of the variables are constant.

Variables and data description

Due to the more serious missing data for all Tibet and confined to the availability of provincial macro data for 2022 and subsequent years, the data used in this paper are macro panel data for 30 provinces, municipalities, and autonomous regions for the period 2013–2021.

1. Explained variables: total carbon emissions and carbon emissions intensity.

Total carbon emissions \(\:Tota{l}_{it}\)is expressed by the total carbon emissions of the region in that year, with specific reference to the estimation of carbon dioxide emissions by province in the work of Wei33, and the specific formula is as follows:

$$\:C{O}_{2}=\sum_{n=1}^{30}{E}_{i}\times\:C{F}_{i}\times\:C{C}_{i}\times\:CO{F}_{i}\times\:\frac{44}{12}$$
(12)

Where \(\:i\) denotes the consumed energy categories of coal, gasoline, kerosene, diesel, fuel oil and natural gas; \(\:{E}_{i}\) denotes the total energy consumption in each province and city; \(\:C{F}_{i}\) denotes the average calorific value of various fuels; \(\:C{C}_{i}\) denotes the carbon content per unit of calorific value; \(\:CO{F}_{i}\) denotes the oxidation rate of energy; and 44/12 denotes the conversion coefficient of carbon dioxide mass. All the above data are taken from China Energy Statistical Yearbook 2014–2021 and the Statistical Yearbook of each province and city in 2022. The specific coefficients can be referred to the calculation method in the work of Wei18.

Carbon emission intensity \(\:{CI}_{it}\) is expressed as the ratio of the total carbon emissions of the region to the total GDP in that year, and the data of the total GDP are taken from the Statistical Yearbook of each province and city in 2014–2022.

Core explanatory variables: level of digital economy and level of technological progress

The level of the digital economy \(\:Di{g}_{it}\)refers to the construction of digital economy indicators as proposed by Zhou et al.34. This framework adopts the indicator of “Digital Technology Adoption (DPA)” and utilizes “Internet users” and “mobile phone users” as secondary indicators for measurement. Additionally, in light of the current state and characteristics of China’s digital economy development, and drawing on the frameworks established by Zhao et al.34 and Pan et al.35, the “fiber optic cable route length” has been selected as another sub-indicator that reflects the degree of “Digital Technology Adoption.” The specific indicators and sources are shown in Table 1. The technical level \(\:Te{c}_{it}\) is measured by total factor productivity, using the DEA Malmquist model and MaxDEA software to measure the total factor productivity level of 30 provinces, cities, and autonomous regions in China. Following Du’s36 methodology for calculating the total factor productivity index, the input indicators encompass capital input, labor input, and energy input. The anticipated output indicator is the regional gross domestic product (GDP). Capital investment is gauged by the capital stock index. Drawing on Zhang’s37 approach to measuring capital investment, the method selects the total fixed capital among provinces and employs the fixed assets investment price index for deflation. Using 2013 as the base year, the physical capital stock from the end of 2013 to 2021 is estimated using the perpetual inventory method. The economic depreciation rate applied is 9.6%, with data sourced from the China Statistical Yearbook 2014–2022 and the respective provincial and city Statistical Yearbooks. Labor input is indicated by the number of employees at year-end, with data obtained from the annual report of China Energy Statistical for the years 2014–2022. Energy input for the region is represented by total energy consumption, with data derived from the annual report of China Energy Statistical for 2014–2021 and the 2022 Statistical annual report of various provinces and cities.

Table 1 Secondary indicators and proxy variables of the digital economy and their sources.
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Mechanism variables: industrial structure and greening level

Industrial structure \(\:In{d}_{it}\) is measured by the ratio of output value of tertiary industry to that of secondary industry, with data from China Statistical Yearbook 2014–2022; greening level \(\:Gr{e}_{it}\) is measured by the number of green patents granted in the region, with data from China Science and Technology Statistical Yearbook 2014–2021 and Statistical Yearbook of each province and city in 2022.

Other control variables

The control variables selected in this research are as follows: ①Energy structure \(\:En{e}_{it}\): measured by regional energy consumption per unit of GDP, with data from China Energy Statistical Yearbook 2014–2021 and the Statistical Yearbook of each province and city in 2022; ②Regional greening level \(\:Fo{r}_{it}\): measured by the regional forest coverage rate, with data from China Statistical Yearbook 2014–2022; ③Population density \(\:Pe{o}_{it}\): The ratio of population size to regional area was used to measure, and the data were all obtained from the Statistical Yearbook of each province and city from 2014 to 2022; ④Economic development level \(\:Ec{o}_{it}\): The per capita regional GDP was used to measure, and the data were all obtained from the China Statistical Yearbook from 2014 to 2022. The results of descriptive statistics are shown in Table 2 below.

Table 2 Descriptive statistics of variables.
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Empirical analysis

Benchmark regression results

Regression of Eqs. (1) and (2), the results are obtained as shown in columns (1) and (3) of Table 3, in order to ensure the accuracy of the regression, this research further controls the fixed effects of the year on the basis of Eqs. (1) and (2), at the same time, in order to solve the problems of cross-section correlation, heteroskedasticity and autocorrelation that may exist in the model, this research chooses the bidirectional fixed-effects model under the standard error of the DK for the regression estimation, and obtains the results as shown in columns (2) and (4) of Table 3.

Table 3 The baseline regression results.
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Table 3, column (2), reveals that the coefficients for the digital economy and technology level on total carbon emissions are positive. This suggests that advancements in the digital economy and technology significantly contribute to the increase in total carbon emissions.In contrast, column (4) of Table 3 shows that the regression coefficients for the natural logarithm of the digital economy development level (lnDig) and the technology level (lnTec) are significantly negative. This indicates that higher levels of digital economy development and technology are associated with a reduction in carbon emission intensity, which supports the development of a green economy. The regression outcomes confirm Hypotheses 1 and 4, which propose a direct influence of the digital economy and technology level on both total carbon emissions and their intensity. Moreover, the empirical findings indicate that while the development of the digital economy positively affects the total carbon emissions, it exerts a significantly negative impact on carbon emission intensity.

This dichotomy may stem from the current rapid growth phase of the digital economy, characterized by high electricity and energy consumption. This leads to an initial increase in carbon emissions. However, the digital economy’s contribution to the growth rate of regional GDP exceeds the growth rate of total carbon emissions, an undesirable output. Consequently, as the digital economy continues to develop, the carbon emission intensity is expected to decline.

Robustness test

Given potential biases in the initial regression outcomes, this study conducts robustness checks by substituting the indicators for the core explanatory variables. The approach aligns with established methodologies:

For the technology level indicator, this research adopts the measure utilized by Dai et al.38, which is the sales revenue from new products of large-scale enterprises. The revised regression results using this indicator are presented in columns (1) and (3) of Table 4. Similarly, for gauging the digital economy’s development level, the study applies the indicators proposed by Zhao et al.32. The outcomes from this alternative measurement are displayed in columns (2) and (4) of Table 4. In order to exclude outliers from interfering with the regression results, the raw data can be cleaned somewhat before performing the econometric analysis. The regression results after performing a 1% shrinkage are shown in columns (3) and (6) of Table 4.

Table 4 The robustness test results.
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As shown in Table 4, replacing the proxy indicators of the level of development of the digital economy and the level of technology and conducting a shrinking-tailed test, the results show that the direction of the coefficient estimates of the robustness test and the benchmark regression converge roughly, and the estimation results all show the robustness of the benchmark regression results.

Endogeneity test

To mitigate the endogeneity concerns associated with panel data, this research employs instrumental variables following the approach of Huang et al.39. The instruments are constructed from the interaction of two historical indicators—fixed-line telephones per 100 people and post offices per million people in 1984—with the national IT service revenues from the prior year. These historical indicators serve as proxies that are unlikely to be correlated with the current error term but are related to current digital development.

Due to the inherent difficulty in capturing these historical indicators in panel data format, the study applies the methodology introduced by Nunn et al.40. This method leverages interaction terms for the endogeneity test, with the findings presented in columns (1) and (3) of Table 5.

Furthermore, to reinforce the robustness of the model, the lag term of the digital economy is also utilized as an instrumental variable. The outcomes of this additional test are delineated in columns (2) and (4) of Table 5.

Table 5 The endogeneity test results.
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As can be seen in Table 5, replacing the measures and using the instrumental variables approach, the direction of the coefficient estimates for the endogeneity test and the baseline regression results converge roughly.

Spatial econometric analysis

The SDM spatial Durbin model regression with two-way fixed effects is performed on Eqs. (6) and (7), and the results are shown in Table 6.

Table 6 The spatial econometric regression results.
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Table 6 illustrates the spatial econometric analysis, revealing that the indirect effects of the digital economy and technology level on both the total amount and intensity of carbon emissions are not significant under the distance matrix. However, when considering the economic matrix, the indirect effect of the digital economy’s development level on carbon emission intensity is significantly positive, indicating a substantial spatial spillover effect. This positive spillover may be attributed to the rapid movement of digital elements and technologies towards economically developed regions due to the siphon effect. The influx of these carbon-intensive digital elements and technologies could lead to a swift increase in the carbon emission intensity of the region, triggering a significant spatial spillover effect. Additionally, under the neighbor matrix, the indirect effect of the technology level on total carbon emissions and intensity is significantly positive, suggesting a spatial spillover effect in neighboring regions. The likely explanation is that the flow of technical personnel is predominantly towards neighboring areas, enhancing the technological level of enterprises in these regions. This improvement subsequently leads to an increase in both total carbon emissions and intensity in the neighboring regions, thus facilitating spatial spillover effects.

The regression outcomes confirm Hypotheses 2 and 5, which propose that the development level of the digital economy and technological progress have spatial spillover effects on the total carbon emissions and their intensity.

Mechanism analysis

This research employs a two-way fixed-effects model, adjusted with DK standard errors, for the regression estimation of Eq. (8) to (11). To delve into the mechanisms by which the development level of the digital economy and the level of technology influence the total carbon emissions and their intensity, cross terms are incorporated: specifically, LnDig*LnInd, which represents the interaction between the digital economy development level and industrial structure, and LnTecLnGre, which captures the interaction between the technology level and the degree of greening.

Following the analytical approach of Ji et al.41, these cross terms are utilized to explore the underlying mechanisms. The regression results, which detail the interplay of these factors, are presented in Table 7.

Table 7 The mechanism test results.
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Hypothesis 3

posits that the development of the digital economy will encourage the reduction of carbon emissions through the promotion of industrial structure upgrading. Analysis of columns (1) and (3) in Table 7 reveals that the regression coefficients for the interaction term LnDig*LnInd, which represents the synergy between the digital economy’s development and industrial structure, are significantly negative. This suggests that as the industrial structure continues to upgrade, the impact of the digital economy’s development on reducing both the total volume and intensity of carbon emissions becomes increasingly pronounced.

Further examination of columns (2) and (4) in Table 7 shows that the interaction term LnTec*LnGre, reflecting the relationship between technological level and the degree of greening, has an insignificant effect on carbon emission intensity but a significantly negative effect on the total carbon emissions. This implies that technological progress has a more substantial role in reducing carbon emissions as the level of greening increases.

The regression results substantiate Hypotheses 3 and 6, confirming that the advancement of the digital economy can facilitate the transformation and upgrading of the industrial structure, thereby promoting carbon emission reduction. Additionally, technological progress, by enhancing the level of greening, contributes to the reduction of carbon emissions.

Conclusions and recommendations

This research, grounded in theoretical analysis and empirical evidence from panel data across 30 provinces and cities from 2013 to 2021, offers insights into the impact of the digital economy and technological progress on carbon emissions.

Research conclusion

Firstly, the dual impact of digital economy and technological progress on carbon emissions

The primary finding of this study is that the development of the digital economy and the improvement of technological levels have not only promoted economic growth, but also significantly increased the total amount of carbon emissions. However, this growth is not entirely unfavorable, as it also reduces the intensity of carbon emissions, which means that the carbon emissions per unit of GDP are decreasing, thereby promoting the green development of the economy. This discovery emphasizes the need to balance the economic benefits brought by the digital economy and technological progress with their environmental impact when formulating relevant policies, in order to ensure the realization of sustainable development.

Secondly, the non-uniformity of spatial spillover effects

The study also revealed the differences in the spatial spillover effects of digital economy and technological progress on carbon emissions under different spatial weight matrices. Under the spatial distance matrix, the impact of these factors on the total carbon emissions and intensity is not significant, which may indicate that geographical distance to some extent limits the environmental effects of digital economy and technological progress. However, under the economic distance and spatial adjacency matrix, the positive spillover effect of the digital economy on carbon emission intensity is significant, and the positive spillover effect of technological progress on total carbon emissions and intensity is equally significant. These results indicate that economic connections and geographical proximity play a crucial role in the spatial transmission of carbon emissions, providing a new perspective for collaborative emission reduction between regions.

Thirdly, the driving role of industrial structure upgrading and the improvement of green level

The digital economy helps reduce carbon emissions by promoting the optimization and upgrading of industrial structure. This indicates that the application of digital technology can drive traditional industries towards more efficient and environmentally friendly development. Meanwhile, technological progress can also help reduce carbon emissions by improving the level of greenization, such as promoting the use of clean energy and enhancing energy efficiency. These findings provide policy makers with a clear direction that by supporting the digital economy and technological progress, the green transformation of industrial structure can be effectively promoted, and carbon emissions can be reduced.

Policy suggestion

Macro level strategy

Firstly, comprehensively promote the research and application of low-carbon technologies

The central and local governments at all levels should fully consider the bidirectional impact of digital economy and technological progress on the total and intensity of carbon emissions. This means that while pursuing economic benefits, effective environmental regulatory measures must be taken to control and reduce carbon emissions. Policy formulation should encourage the research and application of low-carbon technologies, as well as the construction of carbon emission trading markets, to promote carbon reduction and the development of green economy.

Secondly, establish a regional carbon emission cooperation mechanism

Given the spatial spillover effects of digital economy and technological level on carbon emissions, each region should consider its potential impact on surrounding areas when formulating development strategies. This requires strengthening cooperation between regions, jointly planning and implementing collaborative emission reduction measures to achieve a balance of carbon emissions within and outside the region. For example, by establishing a regional carbon emission cooperation mechanism, sharing low-carbon technologies and best practices, and jointly addressing the challenges of climate change.

Thirdly, continuously optimize the industrial structure and green level

In order to achieve the coordinated development of the goals of “digital power”, “technological revitalization” and “dual carbon”, the government should continue to promote the optimization and upgrading of industrial structure, as well as the improvement of green level. This includes supporting green technology innovation, promoting the use of clean energy, and improving energy efficiency. At the same time, policies should encourage enterprises to adopt environmentally friendly materials and processes, reduce carbon emissions in the production process, and promote economic development towards a greener and more sustainable direction.

Micro level measures

Firstly, accelerate the digital transformation of enterprises

Enterprises should actively respond to policy calls and accelerate the pace of digital transformation, especially in the manufacturing and energy sectors. By establishing digital transformation demonstration projects, enterprises can not only improve production efficiency and market response speed, but also achieve optimized resource allocation and energy conservation. The combination of digital transformation and green transformation will bring new growth points and competitive advantages to enterprises.

Secondly, accelerate the digital technology transformation of traditional industries

Enterprises should utilize digital technology to transform traditional industries, and achieve automation and intelligence of production processes by introducing technologies such as intelligent manufacturing, cloud computing, and big data analysis. This can not only improve production efficiency and reduce energy consumption, but also promote the development of new industries, new formats, and new models, bringing innovation momentum and market opportunities to enterprises.

Thirdly, achieve innovation driven development of green and low-carbon technologies

Enterprises should regard technological innovation as the core driving force for green transformation, and accelerate breakthroughs in green and low-carbon technologies by increasing research and development investment. Enterprises should aim at the forefront of technology with strategic thinking, improve the green level of products and services through technological innovation, meet market demands for environmental protection and sustainability, and achieve long-term sustainable development of the enterprise.