Introduction

With the globalization of the economy, the problem of carbon transfer caused by inter-regional trade has become increasingly prominent (Kim et al., 2019; Wang et al., 2022a). The pollution paradise hypothesis holds that developed regions transfer carbon emissions generated by high-polluting and high-emission industries to less developed or less environmentally regulated regions (Li et al., 2022; Liu et al., 2022). This has resulted in a series of undesirable outcomes including global carbon imbalances (Xu et al., 2022a), heightened environmental pollution in developing countries (Essandoh et al., 2020), and carbon market failure (Xu et al., 2022b). Some countries and regions have already paid more attention to the environmental externalities of regional carbon transfer (Lu et al., 2020; Zhang et al., 2020), such as the implementation of carbon tax and subsidy policies in Sweden (Runst and Thonipara, 2020) and the carbon border adjustment in the EU (Tagliapietra and Wolff, 2021). However, there is still a lack of literature on operationalizing carbon quotas at the local level and domestic inter-regional carbon transfers, especially in developing countries.

As the world’s largest emitter of carbon, China has announced at the United Nations General Assembly that its CO2 emissions would peak by 2030 and strive for carbon neutrality by 2060. During the 13th Five-Year Plan period, numerous provinces in China established mandatory emission reduction targets (Shao et al., 2018). In the 14th Five-Year Plan, China explicitly proposed to reduce CO2 emissions by 18%Footnote 1, and implemented carbon quota allocation policies in many provinces (see Table S1). The central government has also included carbon emissions reduction in the annual performance evaluation index of local governments, which effectively reduces regional carbon emissions through green investment and carbon trading (Chen and Lin, 2021; Qin et al., 2023). However, the geographical carbon emissions reduction responsibility allocation system ignores the carbon transfer problem caused by inter-regional trade. Under the pressure of high-intensity carbon reduction, local governments may accomplish local carbon reduction targets through carbon transfer (Monica and Neha, 2021). This strategy has the potential to convert less developed or poorly regulated environmental regions into pollution havens, thereby significantly undermining the inter-regional carbon reduction structure and accelerating carbon market failure (Li et al., 2022; Liu et al., 2022). According to statistics, the carbon emissions driven by out-of-province consumption demand account for ~57% of the province’s overall carbon emissions in China, while the share of out-of-province carbon emissions in developed coastal provinces is up to 80% (Feng et al., 2013; Duan et al., 2018). Thus, it is essential to compensate the inter-regional responsibility allocation system from the demand side. Without policy attention to carbon transfer, developed regions will further achieve local carbon reduction targets through carbon transfer, while less developed regions will be relegated to pollution havens. It will greatly hinder the achievement of global long-term carbon reduction goals (Li et al., 2022; Yuan et al., 2022).

The rapid growth of the digital economy has received widespread attention for its impact on all aspects of society, including the potential impact on carbon emissions and transfer. On the one hand, the digital economy can improve energy efficiency and promote industrial innovation (Pan et al., 2022; Ma et al., 2022), reduce production costs and affect regional industrial structure (Zhang et al., 2022), and stimulate industry transfer (Li and Wang, 2022; Wang et al., 2022b), subsequently impacting inter-regional carbon transfer. On the other hand, the digital economy breaks down trade barriers among regions and facilitates market integration (Niu et al., 2023), while also changing the traditional consumption patterns of households and enterprises. Households and enterprises can obtain high-quality goods from other places at lower costs (Li et al., 2019a; Niu et al., 2023), thereby facilitating carbon transfer among regions. Furthermore, under the pressure of high-intensity carbon emissions reduction targets and environmental regulation, Chinese local governments have the motivation to reduce carbon emissions through carbon transfer (Wu and Cao, 2021; Pu and Fu, 2018). The digital economy offers an effective means for local governments to transfer carbon emissions. Regions with advanced digital economies can more easily transfer carbon emissions reduction pressure to regions with poorer digital economies. Existing studies have generally explored the carbon transfer caused by trade among regions (Peters et al., 2011; Essandoh et al., 2020), and investigated the effects of the digital economy on carbon emissions from production (Wang et al., 2022b) and consumption (Li et al., 2019b; Xue et al., 2022). However, as far as we know, there is a scarcity of studies examining the effect of the digital economy on carbon transfer among cities.

This study verifies the existence of the pollution haven paradise hypothesis among domestic cities using the life cycle assessment method and investigates the effect and specific mechanisms of the digital economy on inter-city carbon transfer with the two-way fixed effects model. The potential research contributions are as follows: First, existing studies mainly discuss the influencing factors of carbon transfer from the perspectives of carbon tax (Chen, 2022), economic structure (Duan et al., 2018), climate policies (Grubb et al., 2022) and green finance (Tong et al., 2022). In this paper, the impact and mechanism of the digital economy on inter-regional carbon transfer are investigated for the first time, and the inconsistent conclusions of the digital economy on carbon emission in the literature are explained from the perspective of carbon transfer. Second, the existing literature is generally based on macro or meso data to explore international (Essandoh et al., 2020; Wang et al., 2022a) and domestic provincial carbon transfer (Yang et al., 2020; Yuan et al., 2022), there is scant evidence of carbon transfer among cities and at the micro level. In this study, comprehensive household consumption data combined with city-level multi-region input-output tables, are used to measure inter-city carbon transfer in China and demonstrate the pollution paradise hypothesis among cities. Third, this paper investigates the effects of the digital economy on carbon transfer, grounded in the principle of consumption responsibility allocation. The empirical evidence provided in this paper will help the government to formulate carbon emission policies and promote balanced development between regional economic and environmental development.

The remainder of this paper is structured as follows: “Literature review and research hypothesis” reviews the relevant literatures on the digital economy and carbon transfer, and subsequently presents the research hypothesis. “Data and methods” describes the data sources, the variables and the identification strategy of the empirical model. “Empirical results” shows the empirical results. “Conclusions and policy implications” presents the related conclusions and policy implications.

Literature review and research hypothesis

Measurement and research status of carbon emissions and transfer

Existing research on measuring regional carbon emissions mainly includes the production-based accounting method and the consumption-based accounting method. The production-based accounting method measures carbon emissions generated by domestic regional production, which does not consider where the commodity is utilized (Shan et al., 2018; Moran et al., 2018). Chen et al. (2021) employed satellite nighttime light data to measure regional carbon emissions, while Yu et al. (2020) utilized regional energy consumption based on the IPCC method to measure regional carbon emissions. It has been widely applied in the Kyoto Protocol and the United Nations Framework Convention on Climate Change (Mi et al., 2016). However, this method ignores carbon leakage among regions, disrupting the equitable allocation of carbon emissions responsibilities (Meng et al., 2018; Yang et al., 2020). Consequently, the consumption-based accounting method offers a more equitable and rational approach, which can address the allocation of carbon responsibility for both production and consumption dimensions by tracing the entire process of product production, transportation, and purchase (Duan et al., 2018). It has been increasingly employed in policy research (Yuan et al., 2022; Long et al., 2021).

The input-output table method is the principal consumption-based accounting method to measure regional carbon emissions and carbon transfer. In previous studies, single-region input-output tables are often employed to measure regional consumption-side carbon emissions (Long et al., 2021; Li et al., 2019b). However, due to the limitations in identifying inter-regional differences in production efficiency and technology, potential biases may arise in the results (Meng et al., 2018). Consequently, multi-region input-output tables, which incorporate more extensive information, have been widely used to examine regional consumption-side carbon emissions (Long et al., 2021) and carbon transfer among regions (Wu and Cao, 2021; Yuan et al., 2022).

In the process of exploring carbon transfer, numerous studies have explored inter-regional carbon transfer at the global (Zhang et al., 2020; Wang et al., 2022a) and regional levels (Chen et al., 2019a; Yuan et al., 2022). These studies generally found that developed regions tend to transfer carbon emissions generated by low-efficiency, high-polluting sectors to less developed or environmentally lenient regions through international trade and industrial relocation (Chen et al., 2019a; Yuan et al., 2022), corroborating the existence of the pollution haven hypothesis (Monica and Neha, 2021). The economic structure, income, consumption patterns, capacity to benefit, and differences in fuel mix among regions are essential causes of inter-regional carbon transfer (Duan et al., 2018; Shan et al., 2018). In contrast, carbon trading mechanisms, carbon quotas and carbon taxes can have a significant dampening effect on inter-regional carbon transfer (Tong et al., 2022; Moosavian et al., 2021). However, due to the absence of multi-region input-output tables at the city level, only a few literature employing single-region input-output tables have discussed carbon transfer in specific cities, such as cities within Brussels (Athanassiadis et al., 2018), China’s Heilongjiang province (Chen et al., 2019a), 13 cities of China (Mi et al., 2016), and 52 major cities in Japan (Long et al., 2021). These studies do not address inter-city carbon linkages on a national scale and the production heterogeneity among cities, which may lead to under- or overestimation of carbon emissions (Chen et al., 2019b).

Moreover, the life cycle assessment method is frequently employed as a complement to the consumption-based accounting methods and is extensively employed to measure indirect carbon emissions at the micro-household level (Yuan et al., 2019; Mi et al., 2019). This method typically requires careful combination and cross-mapping of consumption inventories and input-output tables to ensure accurate estimations of indirect emissions. Nevertheless, as available micro-household data generally contain fewer consumption categories than the number of input-output table sectors, there may be some bias in the estimations (Long et al., 2021).

Accordingly, this paper combines the life cycle assessment method with the inter-city multi-region input-output table to measure inter-city carbon transfer from the micro-household perspective, addressing the gap in inter-city carbon transfer. In addition, this paper matches the multi-region input-output table with monthly household consumption data of 218 commodities based on the authoritative consumption database in China, which further improves the accuracy of household carbon emissions and carbon transfer.

The impact of the digital economy on carbon emissions

The existing literature primarily examines the effects of the digital economy on carbon emissions from the production side and the consumption side. In terms of carbon emissions on the production side, scholars have generally found that the digital economy can contribute to reducing carbon emissions by improving the energy usage structure of industries (Li and Wang, 2022; Zhang et al., 2022), enhancing technological innovation (Pan et al., 2022; Ma et al., 2022), and optimizing the efficient distribution of energy (Kloppenburg and Boekelo, 2019; Chen and Lin, 2021). However, the impact of the digital economy on carbon emissions is also subject to the Jevons paradox (Fich et al., 2022). Although the digital economy can improve energy use efficiency, it concurrently stimulates a rise in industrial energy demand (Lange et al., 2020), which in turn promotes an increase in carbon emissions. In addition, some scholars found heterogeneous effects of the digital economy on carbon emissions across different industries (Wang et al., 2022b) and regions (Usman et al., 2021; Zhang et al., 2022). For instance, Wang et al. (2022b) found that digital economy has a carbon-reducing effect on digital industries and a carbon-increasing effect on industries that use digital technologies indirectly. Usman et al. (2021) found that the digital economy has a decarbonization effect only for countries with less pollution.

There is little literature on the effects of digital economy on consumer-side carbon emissions and no consensus has been reached. Li et al. (2019a) found that the digital economy can promote consumption expansion and consumption upgrading through payment convenience and wage premiums, which in turn increases household carbon emissions. However, some scholars found that the digital economy can promote green consumption by raising residents’ environmental awareness, ultimately leading to a reduction in household carbon emissions (Li et al., 2019b; Meng et al., 2023).

As far as we know, no existing literature directly examines the impact of the digital economy on inter-city carbon transfer. Inter-regional carbon transfer is of great significance in the equitable allocation of carbon reduction responsibilities and the realization of global environmental sustainability. Especially in the context of the information age, the digital economy, as an essential production factor, is likely to have an impact on inter-regional carbon transfer. Therefore, exploring the effects and underlying mechanisms of the digital economy on inter-regional carbon transfer holds great significance.

Research hypothesis

In contemporary society, the accelerated growth of the digital economy influences numerous domains worldwide, including inter-regional carbon transfer. Drawing upon existing research and theoretical frameworks, the following hypotheses are proposed in this paper:

Hypothesis 1: Digital economy facilitates inter-regional carbon transfer.

As the prevalence and adoption of digital technology advance, the dissemination of information and resource allocation have been significantly improved. Enterprises can more easily expand their production operations in other regions with digital platforms and virtual services. This results in the carbon footprints of goods generated in the production process involving more regions. As a consequence, this paper proposes that the digital economy may aggravate the inter-regional carbon transfer.

Hypothesis 2: Digital economy exerts a greater effect on the carbon emissions transfer from developed cities, and mainly promotes the transfer of carbon emissions from secondary industries in developed cities to undeveloped cities.

According to the pollution haven hypothesis, inter-regional differences in environmental regulations, production costs, and resource endowments may lead carbon-intensive industries in developed regions to transfer to weaker environmental regulations or less developed regions. The digital economy allows enterprises in developed cities to more easily transfer carbon emissions to other cities by facilitating cross-regional business expansion and reducing information transfer costs. Meanwhile, carbon-intensive industries are mainly concentrated in secondary industries, such as petrochemicals, iron and steel, and cement. Therefore, this paper proposes that the digital economy exerts a greater effect on the carbon emissions transfer from developed cities, and mainly promotes the transfer of carbon emissions from secondary industries in developed cities to undeveloped cities.

Hypothesis 3: Mandatory environmental regulations exacerbate the positive effects of the digital economy on carbon transfer.

To address global climate change and environmental challenges, countries around the world have implemented a series of environmental regulatory measures. For instance, China has set mandatory carbon emission reduction targets for each province and integrated these objectives into the performance assessment systems of local governments. However, under the pressure of stringent carbon reduction targets and performance assessments, local governments may be inclined to use the digital economy to transfer the pressures of carbon emissions reduction pressures to regions with more lenient environmental regulations, thereby achieving their carbon reduction targets. Consequently, this paper proposes that the mandatory environmental regulations may exacerbate the facilitative effects of the digital economy in carbon transfer.

Hypothesis 4: The digital economy primarily facilitates inter-regional carbon transfer by promoting market integration and relocation of carbon-intensive industries. The digital economy breaks inter-regional trade barriers and promotes the integration of domestic markets. In this case, enterprises and residents can obtain raw materials and commodities from other regions more rapidly and economically. The strengthening of inter-regional trade inevitably leads to an increase in inter-regional carbon transfer. Thus, the digital economy may facilitate inter-regional carbon transfer by promoting domestic market integration. Moreover, there exist significant disparities in regional factor endowments and economic development in China. Digital economy, through cross-border e-commerce and information dissemination, facilitates the global distribution of production processes for enterprises, leading to the transfer of carbon-intensive industries to regions with lower production costs. Consequently, this paper proposes that the digital economy may promote inter-regional carbon transfer by facilitating the relocation of carbon-intensive industries.

Data and methods

Data

The household consumption data are obtained from the Urban Household Survey of the National Bureau of Statistics of China for 2012 and 2015. The Urban Household Survey provides detailed information on the basic characteristics of urban households and their members, as well as the income and consumption expenditure of these households. It covers 57 prefecture-level cities in Liaoning, Shanghai, Guangdong, and Sichuan provinces, representing the different geographical locations and different levels of economic development in northeastern, eastern, southern, and southwestern China. A notable strength of this database is that it comprehensively records the monthly consumption expenditure for more than 200 goods and services for ~20,000 urban households per year. As one of the most detailed and authoritative consumption databases in China, the Urban Household Survey has been widely used to examine household energy demand (Zhou and Teng, 2013), social inequality (Tan et al., 2014), and household consumption behavior (Yu et al., 2019).

The city-level multi-region input-output tables used to measure inter-city carbon transfers are derived from the China Emissions Accounts and Datasets for the years 2012 and 2015 Footnote 2. The China Emissions Accounts and Datasets provide a multi-regional input-output table for China, including detailed information on emissions and economic activities at the city level. The dataset includes 313 administrative units in China, accounting for over 95% of the population and more than 97% of its GDP. It has been widely used to explore environmental issues resulting from inter-regional economic relationships (Zhu et al., 2023) and the evolution of industrial structures (Han et al., 2020). The multi-region input-output table comprises 42 socioeconomic sectors and 5 final consumption categories, consistent with the national-scale input-output table published by the National Bureau of Statistics of China (Zheng et al., 2021).

To estimate carbon emissions, this study employs Socio Economic Accounts and Environment Accounts from the World Input-Output Database Footnote 3. It provides comprehensive records of total output, value-added, energy consumption, and carbon emissions for various sectors across countries, providing a valuable resource for exploring trade globalization (Liu and Wang, 2022) and sustainable development (Yang et al., 2022).

Constructions of the variables

Carbon transfer

The life cycle assessment method has been widely used to measure household carbon emissions (Li et al., 2019b; Long et al., 2021). Referring to Miller and Blair (1985), the relationship between household final consumption demand and the output of corresponding sectors can be represented as follows:

$$X = \left( {1 - A} \right)^{ - 1}Y$$
(1)

where X = (x1, x2, … x42)T denotes the 42-sector total output vector; I is the unit matrix; A denotes the direct consumption coefficient matrix, (I-A)−1 denotes the Leontief inverse matrix (Yuan et al., 2019; Long et al., 2021), Y = (y1, y2, … y42)T denotes the 42-sector final consumption demand vector of households.

The sectoral direct carbon emissions intensity vector is denoted as D = (d1, d2, … d42), where the direct carbon emissions intensity dk of sector k is determined by the rate of annual carbon emissions ek to the annual output of corresponding sector xk.

$$d_k = \frac{{e_k}}{{x_k}}$$
(2)

Then the household carbon footprint E can be expressed as the product of the increase in output X of the corresponding sector guided by household consumption demand and the carbon emissions intensity D of the corresponding sector (Bin and Dowlatabadi, 2005).

$$E = D\left( {I - A} \right)^{ - 1}Y$$
(3)

In this study, We harmonize the household consumption data from the Urban Household Survey, multi-region input-output tables and energy intensity data from the World Input-Output Database, the household carbon emissions are calculated by the above formula. Furthermore, the carbon footprints are divided into local and non-local carbon emissions according to whether the carbon emissions occur in the city where the household is located. To minimize the impact of outliers, we truncate the household’s carbon emissions by 1% for 20,427 households. The detailed methodologies for data matching and calculation can be found in supplementary information.

Digital economy

Principal component analysis has been extensively employed for the measurement of multidimensional composite metrics (Lever et al., 2017; Louf et al., 2023). In this paper, we construct a comprehensive evaluation framework for the digital economy based on existing literature on digital economy measurement (Li and Wang, 2022; Zhang et al., 2022), including 5 dimensions and 16 indicators. These dimensions include digital industry, digital user, digital environment, digital platform, and digital innovation. The detailed indicators and data sources are listed in Table 1. According to the principle of eigenvalue >1 (Lever et al., 2017), the method of principal component analysis is used to standardize the above indicators and then downscale the dimensions to obtain a comprehensive index of the digital economy for cities.

Table 1 Measure variables of the digital economic index.
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We further examined the validity of digital economy indicators. The Kaiser-Meyer-Olkin test and Bartlett’s sphericity test are effective methods for comparing simple and partial correlation coefficients between variables and are frequently employed to determine the suitability of variables for principal component analysis (Li et al., 2021; Zeng et al., 2021). In general, if the Kaiser-Meyer-Olkin value is >0.7 and Bartlett’s sphericity test with a p-value not greater than 0.01, it indicates that these indicators are appropriate for factor analysis (Kaiser and Rice, 1974). The test results show that the digital economy indicators in this study are appropriate for principal component analysis (Kaiser-Meyer-Olkin value = 0.867 > 0.7, p = 0.000 < 0.01). Moreover, the factor analysis results of the principal component analysis revealed that the common factors account for 73.62% of the total variance, indicating that the index has strong construct validity for the digital economy.

Control variables

To more accurately identify the impact of the digital economy on inter-regional carbon transfer, individual, household, and city-level characteristics that may affect household carbon emissions are controlled in our empirical model. Specifically, the individual characteristics include age, gender, marital status, and education level of the head of household. Some literature shows that the age of the head of household has a positive effect on household emissions (Zhou and Teng, 2013). Meanwhile, the gender of the household head, marital status, and education level also have an impact on household carbon emissions (Li et al., 2019b; Zhou and Teng, 2013). For example, female, highly educated, and married households tend to have a higher consumption desire and demand, which may lead to an increase in household carbon emissions.

Household characteristics such as household size, proportion of elderly, proportion of children, and total household income may also affect household consumption behavior and carbon emissions. To be specific, household income and household size are closely related to household consumption and thus have a positive effect on household carbon emissions (Zhu et al., 2023; Zhou and Teng, 2013). Moreover, household structure also has an impact on household carbon emissions (Li et al., 2019b). For example, products consumed by children and the elderly (e.g., imported health products, medical devices, etc.) tend to involve more expenses and longer product chains, and thus a higher proportion of elderly or children may lead to higher carbon emissions. In this paper, household structure is measured by two indicators: the proportion of children under 16 and the proportion of elderly over 60.

Referring to existing literature, some city characteristics such as GDP per capita (Usman et al., 2021), urbanization rate (Li and Wang, 2022), industrial structure (Zhang et al., 2022), financial development (Pan et al., 2022), and openness (Xue et al., 2022) are included in our model as control variables. In particular, the financial development level is represented by the year-end ratio of total loans from financial institutions to the city’s GDP, the industrial structure is expressed as the proportion of the tertiary industry to the city’s GDP, and the degree of city openness is quantified as the ratio of import and export trade to the city’s GDP. The specific variable descriptions, sources, expectations, and citations are shown in Table 2.

Table 2 Variable Description.
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Descriptive statistic

The descriptive statistics of variables are reported in Table 3. The average annual carbon emissions of each household are 5493.8 kg. About 54% (3014.48 kg) of household carbon emissions come from other cities, which shows that inter-city carbon transfer in China is widespread and serious. The considerable standard deviation of the digital economy indicates that there are significant differences in the digital economy among Chinese cities.

Table 3 Descriptive statistic results.
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Figure 1 shows the distribution of household carbon footprints by city development level, revealing that household carbon emissions in first-tier cities (6842 kg) are higher than those in second- (6158 kg) and third-tier cities (4756 kg). However, ~58% of these emissions are derived from other cities, and this proportion is notably higher than observed in second- (50%) and third-tier cities (50%). Compared with second and third-tier cities, first-tier cities exhibit a greater degree of carbon transfer.

Fig. 1: Carbon emissions of urban household.
figure 1

Note: The classification of cities at different levels of development refers to the “2016 China City Commercial Attraction Ranking” published by the New First Line City Institute. For easy understanding, we combine the new first-tier cities and second-tier cities into second-tier cities, and the third-tier cities and below are uniformly classified as third-tier cities. Source: Authors.

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Figure 2 explores the sources of non-local carbon emissions based on the target cities and industries for carbon transfer. The carbon emissions transferred to third-tier cities account for ~67% of non-local carbon emissions, while the carbon emissions from the secondary industry account for ~86% of non-local carbon emissions. Figure 2 suggests that underdeveloped third-tier cities in China primarily bear the burden of inter-city carbon transfer, most of which is carried out through carbon-intensive secondary industries. The above conclusions indicate the existence of the pollution haven hypothesis among Chinese cities.

Fig. 2: Sources of non-local carbon emissions.
figure 2

Note: The secondary industry includes extractive industries, manufacturing, water, electricity, steam, hot water, gas, and construction. Source: Authors.

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Identification strategy

This study employs a two-way fixed effects model to explore the impact of the digital economy on household carbon emissions. This model minimizes the endogeneity issues arising from omitted variables to the greatest extent possible (Zhang et al., 2022; Li and Wang, 2022). The basic estimation model is as follows:

$$E_{ict} = \beta _0 + \beta _1Dig_{ct} + \beta _2X_{ict} + \delta _t + \mu _c + \varepsilon _{ict}$$
(4)

where Eict denotes the carbon footprints of household i, including logarithm of total household carbon emissions, the logarithm of local household carbon emissions, and the logarithm of non-local household carbon emissions. Digct denotes the digital economic level of city c where households are located in year t. Xict denotes the control variables. μc and δt respectively control the city and year fixed effects, and εict denotes the random disturbance term.

Empirical results

Benchmark regression results

Table 4 reports the benchmark regression results of the digital economy on household carbon emissions. The result in column (1) reveals that the relationship between the digital economy and household carbon emissions is positive and statistically significant (β = 0.298, p = 0.000). With every unit added to the digital economy, household carbon emissions increase by 29.8% compared to the average (5494 kg). In column (3), the regression coefficient of the digital economy’s impact on non-local household carbon emissions is also positive and statistically significant (β = 0.578, p = 0.000). This result implies that the digital economy intensifies inter-city carbon transfer, and the coefficient indicates that for every unit added to the digital economy, non-local household carbon emissions increase by 57.8% relative to their mean (3014 kg). Consequently, while the digital economy overall increases carbon emissions, it also facilitates the transfer of emissions to other cities, thus reducing local emissions. Hypothesis 1 is validated, and the above results provide a plausible explanation for the inconsistent conclusions in existing literature regarding the impact of the digital economy on carbon emissions.

Table 4 Benchmark regression results.
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The estimation results of other control variables on household carbon emissions are consistent with expectations. However, GDP per capita (β = −1.002, p = 0.000), financial development (β = −0.396, p = 0.000), openness (β = −0.212, p = 0.000) and industrial structure (β = −0.012, p = 0.000) are negatively correlated with household carbon emissions, which may result from the carbon-reducing effect of urban agglomeration (Han et al., 2018).

Heterogeneity analysis

City development level

The level of city development has an impact on the production behavior of enterprises and the consumption habits of residents. Consequently, the effect of the digital economy on carbon transfer may exhibit heterogeneity across different development levels of cities where households are located. Thus, we construct city development-level dummy variables based on city development levels and interact them with the digital economy for regression analysis. Table 5 reports the results of the digital economy’s effect on household carbon emissions in cities with varying development levels. The results reveal that the digital economy primarily increases household carbon emissions in second-tier cities (β = 0.251, p = 0.000). The increase in household carbon emissions is less noticeable for first- (β = 0.116, p = 0.257), which may be attributed to the agglomeration effect of carbon reduction in first-tier cities (Holian and Kahn, 2015).

Table 5 Heterogeneity of city development levels.
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Columns (2) in Table 5 show that the digital economy significantly reduces local carbon emissions in first-tier cities (β = −0.400, p = 0.001) and third-tier cities (β = −0.15, p = 0.098), while notably increasing non-local carbon emissions across all city tiers. The estimated results suggest digital economy facilitates carbon transfer across all city tiers. From the difference of coefficients, the digital economy’s impact on both local and non-local carbon emissions is stronger in first-tier cities than in second- and third-tier cities, indicating that the digital economy primarily fosters the transfer of carbon emissions from first-tier cities to other cities. Hypothesis 2 is validated.

Non-local carbon emissions sources

To verify whether the effect of the digital economy on carbon transfer may differ across various destination cities, we explore the heterogeneous effects of the digital economy on carbon transfer by categorizing destination cities. We further conduct a detailed analysis of the carbon-intensive secondary industry. Columns (1)–(3) in Table 6 report the effect of the digital economy on carbon transfer in cities at different tiers, revealing a significant promotional effect on carbon emissions transfer to all level cities. The coefficients suggest that, compared to first (β = 0.217, p = 0.003) and second-tier cities (β = 0.257, p = 0.001), the digital economy more prominently contributes to carbon emissions transfer to less developed third-tier cities (β = 0.525, p = 0.000).

Table 6 Heterogeneity of non-local carbon emissions sources.
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Columns (4)–(6) in Table 6 report the effects of the digital economy on carbon transfer in the carbon-intensive secondary industry across different city tiers. The findings indicate that the digital economy significantly promotes the transfer of carbon emissions from carbon-intensive secondary industries to other cities. Examining the coefficients, the digital economy primarily fosters the transfer of carbon emissions from carbon-intensive secondary industries to third-tier cities (β = 0.470, p = 0.000), as compared to first- (β = 0.132, p = 0.074) and second-tier cities (β = 0.281, p = 0.000). These results substantiate Hypothesis 2.

Environmental regulations

We presume that mandatory environmental regulations may prompt local governments to leverage the digital economy for carbon transfer to achieve their carbon reduction goals. To test this hypothesis, referring to Domazlicky and Weber (2004), a composite index of various pollutant emissions (wastewater, sulfur dioxide, and particulate matter) is used as a proxy for the intensity of mandatory environmental regulation in cities. Considering that environmental regulations may inherently produce emissions reduction effects by lowering carbon emissions intensity, it becomes difficult to identify the increase in non-local carbon emissions caused by carbon transfer in absolute terms. Therefore, we also adopt the proportion of non-local carbon emissions in total carbon emissions as a measure of carbon transfer, Table 7 shows the estimated results of the interaction between the digital economy and environmental regulation.

Table 7 Heterogeneity of environmental regulations.
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Table 7 demonstrates that the effect of mandatory environmental regulations on both non-local carbon emissions (β = 0.224, p = 0.000) and the proportion of non-local carbon emissions (β = 0.065, p = 0.000) is significantly positive. Local governments facing high-intensity mandatory environmental regulations may transfer carbon emissions to other cities to achieve their carbon reduction targets. The interaction term’s coefficient for non-local carbon emissions is positive but not statistically significant (β = 0.085, p = 0.281), which may be due to the confounding effect of carbon reduction caused by environmental regulations. Further investigation of the interaction term’s effect on the proportion of non-local carbon emissions reveals a significantly positive coefficient (β = 0.116, p = 0.000), confirming that mandatory environmental regulations amplify the digital economy’s role in promoting carbon transfer. These results support Hypothesis 3.

Robustness test

The carbon emissions at the household level are unlikely to significantly affect the digital economy at the city level, so the issue of reverse causality in this study may not exist. However, considering the potential endogeneity problems arising from measurement errors and omitted variables, we employ instrument variables, alternative variables, and additional control variables to assess the robustness of our findings.

Instrumental variable

We employ the interaction term between the number of post offices in cities in 1984 and the share of online retail sales in total retail sales in the previous year as an instrument variable for the digital economy in cities. Before the digital economy era, postal services were the primary means of communication. Consequently, the number of post offices in a city can, to some extent, reflect the city’s level of information development. The distribution of post offices can also influence subsequent internet access, technology dissemination, and internet usage habits in different regions, thereby affecting the popularity and evolution of the Internet. It satisfies the correlation requirement of the instrumental variable.

Furthermore, with the rapid evolution of digital technologies including the internet and big data, the historical influence of the number of post offices on production and consumption behaviors is diminishing. The number of post offices in 1984 is unlikely to affect current household carbon emissions and inter-regional carbon transfer, meeting the instrument variable’s excludability requirement. However, the historical number of post offices cannot capture time-varying trends. Therefore, drawing on Nunn and Qian (2014), an interaction term between the number of post offices in cities in 1984 and the share of online retail sales in total retail sales in the previous year is used as an instrument variable for the regional digital economy. The regression results are presented in Table 8.

Table 8 The results of the instrumental variable.
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Column (1) in Table 8 reports the first-stage regression result, indicating a significant positive correlation between the instrument variable and the digital economy (β = 0.006, p = 0.000), which is consistent with the expectation. The F-statistic value of 14,838.6 suggests that there is no weak instrument variable issue. Columns (2)–(7) in Table 8 report the two-stage least squares (2SLS) estimation results after introducing the instrument variable. Columns (2)–(4) results show the digital economy significantly increases household total carbon emissions (β = 0.283, p = 0.000) and non-local carbon emissions (β = 0.643, p = 0.000), while reducing local carbon emissions (β = −0.158, p = 0.057). Columns (4)–(6) reveal that compared to first- (β = 0.258, p = 0.008) and second-tier cities (β = 0.152, p = 0.128), the digital economy still exhibits a more pronounced effect on promoting carbon transfer to third-tier cities (β = 0.587, p = 0.000). The conclusions drawn in the previous analysis remain robust.

Substitution of dependent variable

The generation of household carbon emissions is fundamentally rooted in household energy consumption, implying that energy consumption transfer among cities can reflect the transfer of environmental pressures to some extent. In this study, we measure household energy consumption based on sectoral energy intensity data from the World Input-Output Database, and examine the effect of the digital economy on environmental pressure transfer by substituting household energy consumption for household carbon emissions. The regression results are reported in Table 9.

Table 9 The results of energy consumption.
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The results in columns (1) and (3) in Table 9 indicate that the digital economy significantly increases household total energy consumption (β = 0.415, p = 0.000) and non-local energy consumption (β = 0.546, p = 0.000). Columns (4) to (6) reveal that, compared to first (β = 0.310, p = 0.000) and second-tier cities (β = 0.184, p = 0.018), the digital economy has more pronounced effect on promoting non-local energy consumption from third-tier cities (β = 0.558, p = 0.000). These suggest that the previous empirical results are robust.

Substitution of the independent variable

The digital economy used in this paper may have some potential measurement errors. Therefore, the financial inclusiveness index measured by Peking University is used as a robustness analysis to substitute for the digital economy. The regression results are reported in Table 10. The results in Columns (2) and (3) indicate that the financial inclusion index significantly increases non-local household carbon emissions (β = 0.85, p = 0.000) and reduces local household carbon emissions β = −1.11, p = 0.000). The conclusion that the digital economy promotes the transfer of household carbon emissions to other cities remains robust. The results in columns (4)–(6) demonstrate that the digital economy exhibits the most prominent effect on promoting carbon emissions from the second (β = 1.00, p = 0.000) and third-tier cities (β = 0.658, p = 0.001). These results support the conclusion that the digital economy drives the transfer of carbon emissions pressures to less developed cities.

Table 10 The results of the inclusive finance index.
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Add control variable

Since the 12th Five-Year Plan, China has implemented a series of carbon emission reduction policies to mitigate emissions such as the three batches of low-carbon city pilots since 2010, the carbon trading emission rights pilot since 2013, and the change of environmental protection fees to taxes since 2018, etc. The combined effects of these policies may affect carbon emissions and carbon transfer. During the sample period, the primary carbon reduction policies implemented in China include the carbon emissions trading pilot policy and the low-carbon city pilot policy. We incorporate these pilot policies as the control variables to eliminate potential endogeneity impacts. The regression results are presented in Table S2. Columns (1)–(3) in Table S2 demonstrate that after controlling for China’s carbon reduction policies, the digital economy significantly increases household total carbon emissions (β = 0.293, p = 0.000) and non-local carbon emissions (β = 0.575, p = 0.000). Columns (4)–(6) reveal that compared to first (β = 0.210, p = 0.004) and second-tier cities (β = 0.255, p = 0.001), the digital economy still has the most pronounced effect on promoting non-local carbon emissions from third-tier cities (β = 0.521, p = 0.000). These results confirm the robustness of the previous empirical results.

Mechanism analysis

To determine the causal relationship between the digital economy and carbon transfer among cities, this paper investigates the mechanisms of market integration and industrial transfer.

Market integration

We presume that the digital economy may promote inter-city carbon transfer by facilitating domestic market integration. To test this hypothesis, we calculate the ratio of non-local household consumption to total household consumption and use it as a proxy variable for household-level market integration. Furthermore, referring to Parsley and Wei (2001), the market segmentation index constructed based on the consumer price index of Chinese cities is used as a proxy variable for market integration at the city level. The regression results are reported in Table 11.

Table 11 Mechanisms of market integration and industrial transfer.
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Column (1) in Table 11 demonstrates that the digital economy significantly increases the proportion of non-local consumption expenditure (β = 0.262, p = 0.000), and the result in Column (2) reveals digital economy significantly breaks down inter-regional market segmentation among cities (β = −0.021, p = 0.004). These findings provide evidence that the digital economy facilitates inter-city carbon transfer through the promotion of market integration, thereby confirming Hypothesis 4.

Industrial transfer

We presume that the digital economy may promote inter-city carbon transfer by facilitating the shift of carbon-intensive industries. To test this hypothesis, we draw from Dou and Han (2019) and use the relative output and employment rates of the secondary industry to identify the transfer of carbon-intensive industries. The regression results are reported in columns (3) and (4) of Table 11.

The regression results show that there is a significant negative relationship between the relative output value (β = −0.178, p = 0.018) and the relative employment rate (β = −0.059, p = 0.001) of the local secondary industry. This preliminarily confirms that the digital economy facilitates inter-city carbon transfer by promoting the shift of local carbon-intensive industries to the outside, thus validating Hypothesis 4.

Conclusions and policy implications

Conclusions

This paper measures the inter-city carbon transfer in China based on city-level multi-region input-output tables and micro-household consumption data, and further explores the effects and mechanisms of the digital economy on inter-city carbon transfer. Our research reveals that carbon transfer is widespread among Chinese cities, with third-tier cities primarily bearing the burden of high-intensity carbon transfer pressures, which supports the pollution haven hypothesis. Second, the digital economy exacerbates the inter-regional carbon transfer by promoting market integration and facilitating industrial relocation. Compared to first and second-tier cities, the digital economy primarily promotes the transfer of high-intensity carbon emissions to third-tier cities. Third, the more developed the local city, the stronger the promoting effect of the digital economy on carbon transfer. Mandatory environmental regulations incentivize governments to use the digital economy for carbon transfer to achieve local carbon emission reduction targets. These conclusions still hold after robustness tests using instrumental variables, replacing measurement variables, and excluding relevant policies.

We compared our findings with other literature. Feng et al. (2013) found that ~57% of carbon emissions in Chinese provinces originate from other provinces, while Hasegawa et al. (2015) estimated that carbon leakage among 47 prefectures in Japan accounts for 51.7% of total emissions. These results are similar to our calculated outcome of 54%, lending credibility to our findings.

The impact of the digital economy on carbon transfer is thought-provoking. As found in this paper, the digital economy promotes the development of an integrated domestic market, which makes the division of labor more specialized across regions. In addition, the regional industrial structure may change due to a more specialized division of labor, increasing the share of the Secondary sector of the economy in the industrial developed regions. Regions with developed digital economies may also attract more foreign direct investment for further development due to their technological advantages. Although this process increases inter-regional carbon transfer, this will certainly promote economic growth to a certain extent (Pan et al., 2022). Meanwhile, the carbon-reducing effect of the scale of regional specialization will also promote China to achieve carbon neutrality (Han et al., 2018). However, without the right supporting environmental regulation policies, it is easy to lead the less developed or carbon-intensive industrial regions to become pollution havens, thereby undermining the long-term economic and environmental development of the regions.

With the largest carbon emitter and a major digital economy developer in the world, investigating the effects of China’s digital economy on inter-regional carbon transfer holds international significance for striking a balance between environmental and economic benefits. It provides valuable insights for countries around the world, especially developing countries, to better understand and address carbon transfer issues in the process of digital transformation. Consequently, we propose the following policy recommendations.

Policy implications

Improving inter-regional synergistic carbon reduction systems

The aforementioned analysis highlights the inadequacy of the central government in setting carbon emissions reduction targets solely for local governments. High-intensity mandatory environmental regulations could potentially exacerbate inter-regional carbon transfer, rendering it crucial to enhance the regional cooperative carbon reduction system. In response, local governments should proactively measure and publicly report inter-regional carbon transfer data to establish a foundation for jointly taking responsibility for inter-regional carbon emissions. Building upon this foundation, the establishment of an inter-regional carbon quota trading market can mitigate the negative environmental externalities caused by carbon transfer. Furthermore, drawing upon the development trajectory of China’s urban agglomeration, such as the Yangtze River Delta urban agglomeration, a “pairing” approach can be adopted for regions with frequent carbon transfer. This strategy facilitates overall carbon emissions reduction through the optimization of inter-regional industrial structures, thereby preventing third-tier cities from becoming pollution havens for more developed cities.

Accelerating the fair progression of the digital economy

The advancement of the digital economy ought to be linked to the current dual-carbon goal development vision, emphasizing the low-carbon environmental effect of the digital economy and strengthening regulation. The disparities in the digital economy across regions and industries significantly affect the coordination of carbon reduction efforts among regions. Therefore, digital economy development strategies should be tailored to favor undeveloped regions and high-carbon industries, ensuring equitable growth of the digital economy in second and third-tier cities and across various industries. By doing so, the economic benefits and low-carbon effects brought by the digital economy to second and third-tier cities can be fully realized.

Improving regional carbon reduction efficiency

China’s carbon emissions reduction policies cannot be handled in a “one-size-fits-all” manner but rather be tailored to the development levels of cities. Contrasting with first-tier cities that primarily focus on high-value-added and low-carbon industries, third-tier cities mainly bear the carbon emissions generated by carbon-intensive industries. The government should augment support for green production in third-tier cities, such as providing guidance on green technologies, offering green finance loans to enterprises, and granting subsidies for green production. These measures not only facilitate the green transformation of third-tier cities, but also enable them to share the carbon emissions burden of first-tier cities sustainably.

There are inevitable limitations to the above study. Although this paper has made every effort to improve the accuracy of the data, it has assumed the carbon intensity of the same industry is consistent across cities. The carbon intensity of undeveloped cities may be higher due to technological backwardness, and thus the carbon transfer to these areas may be underestimated.