Spatiotemporal Heterogeneities in the Impact of Chinese Digital Economy Development on Carbon Emissions
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1. Introduction
The three main contributions of this paper are as follows. Firstly, the paper engages in theoretical discussions concerning the pathways through which the rapid evolution of the digital economy influences carbon emissions. These pathways encompass the direct impact of the digital economy on carbon emission intensity, indirect influences through the augmentation of economic agglomeration, and the “snowball effect” of carbon emissions. Secondly, from a spatial perspective, empirical analyses are conducted to examine the spatial spillover effect of digital economy development on carbon emissions, employing the static spatial Durbin model while also verifying the mediating effect of economic agglomeration. Lastly, from a temporal standpoint, the paper investigates the path-dependent characteristics of carbon emissions by utilizing the dynamic spatial Durbin model based on Han–Phillips GMM.
2. Theoretical Analysis and Hypotheses
2.1. The Impact of the Digital Economy on Carbon Emissions
The digital economy not only directly reduces carbon emissions but also indirectly reduces carbon emissions by increasing economic agglomeration.
2.2. Spatial Spillover Effects of the Digital Economy on Carbon Emissions
The digital economy can affect carbon emissions in neighboring regions through spatial spillovers.
2.3. The Snowball Effect of Carbon Emissions
Carbon emission levels are affected by both the spatial spillover effects of the digital economy and the snowball effect of carbon emission levels.
3. Methods
3.1. Model Settings
where represents the carbon emission of the province i during the period t, represents the digital economy level of the province i during the period t, and stands for the control variables. and represent individual and time fixed effects, respectively. represents random disturbance.
where represents the economic agglomeration of the province i during period t and is the square term of . If and are significant, the indirect effect exists. Then, the third step is to test whether the coefficient is significant. If is not significant, the direct effect does not exist, indicating that there is only a mediating effect known as the full mediating effect. If is significant, the direct effect is also significant and is called the partial mediation effect.
where represents the spatial lag coefficient and W represents the spatial weight matrix, which indicates the relationship of each province. Model (4) incorporates the combined impact of the digital economy and carbon emissions on carbon emissions with spatial lagged effects, termed the spatial Durbin model (SDM).
where and represent the elasticity coefficients of the spatial interaction terms for the explanatory variables, the core explanatory variables, and the control variables, respectively. Model (5) can simultaneously test the impact of the digital economy on carbon emissions and examine the time lag effect of carbon emissions.
3.2. Spatial Weighting Matrix Settings
where and represent the GDP per capita for provinces i and j, respectively. represents the geographical distance between provinces i and j.
3.3. Variable Descriptions
3.3.1. Explained Variable
where represents the carbon emission factors for the eight main fossil energy sources, represents the consumption of the energy n in province i in period t, and represents the total population of province i in period t.
3.3.2. Core Explanatory Variable
The central explanatory variable examined in this paper is the digital economy (dig). We constructed an index system to measure the digital economy. The index system includes the length of long-distance fiber-optic cables; the number of people employed in the information transmission, software and information technology services industry; the turnover of the technology market; the total volume of telecommunications business; and the number of mobile phone subscribers. Then, to objectively measure the level of the digital economy, we applied the entropy method. The specific process of the entropy method is as follows.
where and are the maximum and minimum of , respectively. When the indicator is negative,
where k is a constant and , usually taken as . is greater than zero.
3.3.3. Mediator
3.3.4. Control Variables
In order to more comprehensively analyze the spillover effects of the development of the digital economy in carbon emissions, it is also necessary to study the control variables that may have an impact on carbon emissions. Therefore, the following control variables were selected in this paper: economic development (py), for which GDP per capita was used to control for possible non-linear effects of the level of economic development; industrial structure, which is expressed as the share of value added of the secondary industry in GDP (ig) and the share of value added of the tertiary industry in GDP (sg), respectively; technological progress (rd), characterized by patents granted per 100 R&D personnel; non-farm output (lp), represented by consumption expenditure per urban resident; urbanization (rm), characterized by urban population density; and talent development (pt), characterized by the human capital index in the China Human Capital Report (2022).
3.4. Data Sources and Descriptive Statistics
4. Empirical Results
4.1. Benchmark Regression
4.2. Mediating Effect Regression
Column (1) shows that the digital economy can significantly reduce the level of carbon emissions. Then, column (2) verifies that there is a significant negative correlation between the digital economy and economic agglomeration. In column (3), the mediating variable is added to the regression equation of the digital economy affecting carbon emissions. By observing the coefficient of dig, it can be seen that the regression coefficient of dig for ce in Model (3) is significant at the 1% level, and the value is increased compared with that of Model (1). This suggests that the increase in economic agglomeration is the mechanism of action of the digital economy to reduce the level of carbon emissions, and this result supports H1.
4.3. SDM
4.4. Dynamic SDM
5. Robustness Test
5.1. Change the Explanatory Variable
5.2. Change the Geographic Weighting Matrix
5.3. Change the Sample Interval
5.4. Endogeneity Treatment
6. Heterogeneity Analysis
Due to variations in resource allocation and developmental stages, both the advancement of the digital economy and carbon emissions exhibit pronounced heterogeneity in regional distribution. Consequently, the influence of the digital economy on carbon emissions is likely to manifest heterogeneously across regions, warranting an in-depth examination. We conducted a heterogeneity regression analysis for the eastern, central, and western regions. The eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning, Jilin and Heilongjiang. The central and western region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.
7. Discussion
1. This paper solely examines the impact of economic agglomeration as a mediating variable on the relationship between the digital economy and carbon emissions. Given that the digital economy can influence carbon emissions through many mediating variables, future research could delve deeper into analyzing different mediating transmission pathways of the digital economy on carbon emissions.
2. This paper employs the entropy weight method to gauge the level of the development of the digital economy. Consequently, the weight coefficients assigned to variables such as the length of long-distance fiber-optic cables, turnover of the technology market, and total volume of telecommunications business remain constant, indicating fixed weights for each variable. However, given the evolving nature of the context, dynamic weighting methods like the dynamic factor method offer a more nuanced reflection of variable weights across different time periods, rendering them more pertinent. Therefore, future research could explore the utilization of dynamic weighting methods to develop an approach that is both more objective and better aligned with changes in the digital economy’s development.
3. This paper examines the influence of China’s digital economy development on the level of carbon emissions from a provincial perspective, potentially obscuring variations in economic development within each province. Subsequently, further in-depth investigation into the interaction between these factors, taking into account the specific development levels of various prefecture-level cities within each province, could be conducted.
8. Conclusions and Policy Implications
8.1. Conclusions
The digital economy presents significant potential for reducing carbon emissions in China. This paper aimed to investigate the mechanisms through which the digital economy influences carbon emissions. Empirical analyses were conducted using fixed-effect models, intermediary effect models, an SDM, and a dynamic SDM by utilizing provincial panel data spanning from 2000 to 2021. The study sought to substantiate the impact of the digital economy on carbon emissions, elucidate the intermediary role of economic agglomeration, and assess the heterogeneity among different provinces. Ultimately, the following conclusions were derived: (1) The digital economy can effectively reduce carbon emissions. It can either directly reduce carbon emissions or indirectly affect carbon emissions through economic agglomeration, and the emission reduction effect is significant in the eastern, central and western regions. (2) The digital economy has significant spatial spillover effects. While developing the digital economy to reduce carbon emissions, different provinces can also influence the carbon emissions of neighboring provinces through the channels of spillover, demonstration, and competition effects. (3) Carbon emissions have a significant snowball effect, i.e., provinces with higher leveld of carbon emissions in the previous period will have significantly higher leveld of carbon emissions in the next period.
8.2. Policy Implications
In light of the foregoing discoveries, we propose the subsequent policy recommendations:
(1) There are spatial gradient differences in China’s digital economy development and carbon emissions. When setting carbon reduction targets, the government should set “dual-carbon” targets suitable for the level of development of different provinces according to the level of development of their digital economy.
(2) In the process of developing the digital economy, the government should pay close attention to the levels of economic agglomeration of different provinces. In regions characterized by limited economic agglomeration, such as the central and western areas, governmental intervention can be strategically employed to enhance the structure and density of economic agglomeration. This can be achieved by expediting the spillover impact of digital technology, leveraging the demonstrative influence of economic agglomeration observed in the eastern region, and fostering competitive dynamics.
(3) The government should consider the huge pressure of carbon reduction and emission reduction brought by the snowball effect on large carbon-emitting provinces. The aim of controlling the overall national carbon emissions can be realized through the development of the carbon market, the facilitation of carbon emission trading among different provinces, and the formulation of a carbon emission compensation policy. Ultimately, the challenge of climate risk arising from greenhouse gas emissions can be reasonably addressed.
Author Contributions
Conceptualization, Q.A. and L.Z.; methodology, L.Z.; software, L.Z.; validation, Q.A. and L.Z.; formal analysis, Q.A. and L.Z.; resources, Q.A.; writing—original draft preparation, L.Z.; writing—review and editing, Q.A., L.Z. and M.Y.; visualization, L.Z.; supervision, Q.A. and M.Y.; project administration, Q.A.; funding acquisition, Q.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Social Science Planning Project of Shandong Province, grant number 23CJJJ20.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The datasets used in this study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Statistical description of variables.
Table 1.
Statistical description of variables.
Variable | N | Mean | Std | Min | Max | |
---|---|---|---|---|---|---|
Dependent Variable | ce | 660 | 2.9794 | 2.8883 | 0.2857 | 25.6360 |
Independent Variable | dig | 660 | 0.0753 | 0.0809 | 0.0012 | 0.6186 |
Mediator Variable | ag | 660 | 4.8006 | 1.3008 | 1.3474 | 7.6828 |
Control Variable | py | 660 | 3.8482 | 3.0473 | 0.2662 | 18.398 |
ig | 660 | 0.4684 | 0.2994 | 0.0658 | 4.9961 | |
sg | 660 | 0.4531 | 0.2776 | 0.1008 | 4.1660 | |
lp | 660 | 9.4494 | 0.6145 | 8.1952 | 10.8453 | |
rd | 660 | 2.9684 | 0.8564 | 0.2109 | 6.1585 | |
rm | 660 | 7.5874 | 0.7407 | 3.2581 | 8.7495 | |
hc | 660 | 6.8544 | 0.8100 | 4.9562 | 8.8030 |
Table 2.
Benchmark regression and mediating effect test results.
Table 2.
Benchmark regression and mediating effect test results.
Variable | ce | ag | ce |
---|---|---|---|
(1) | (2) | (3) | |
dig | −11.1086 *** | −0.2105 ** | −10.4128 *** |
(−4.7854) | (−2.3031) | (−4.6981) | |
ag | \ | \ | 13.9921 *** |
(7.8503) | |||
sag | \ | \ | −1.3154 *** |
(−6.6533) | |||
py | 0.6586 *** | 0.0499 *** | 0.4567 *** |
(3.5633) | (6.9218) | (2.6485) | |
spy | −0.0364 *** | −0.0023 *** | −0.0241 *** |
(−3.9602) | (−6.8703) | (−2.9316) | |
ig | 2.4163 ** | 0.0915 * | 2.7003 ** |
(2.2936) | (1.8306) | (2.5162) | |
sg | −2.7732 ** | −0.1578 *** | −2.1980 ** |
(−2.5041) | (−2.7849) | (−1.9860) | |
lp | 3.2146 ** | 0.0098 | 1.2671 |
(2.4673) | (0.1933) | (1.0448) | |
rd | 0.0295 | −0.0375 *** | 0.3245 * |
(0.1554) | (−4.7546) | (1.7120) | |
rm | 0.4546 *** | 0.0083 | 0.2385 * |
(3.2785) | (0.9955) | (1.8079) | |
hc | −4.8212 * | −1.7014 *** | −7.6008 ** |
(−1.7176) | (−15.3865) | (−2.1707) | |
shc | 0.2366 | 0.0820 *** | 0.4572 ** |
(1.3558) | (12.6329) | (2.2070) | |
Constant | −9.9639 | 14.8443 *** | −14.7793 |
(−0.5432) | (24.2531) | (−0.6736) | |
IFE | YES | YES | YES |
TFE | YES | YES | YES |
R2 | 0.7815 | 0.9980 | 0.8037 |
F | 55.34 | 8187.65 | 65.76 |
Table 3.
Moran’s I test results.
Table 3.
Moran’s I test results.
ce | dig | |
---|---|---|
Moran’s I | 0.090 *** | 0.343 *** |
(7.477) | (27.997) |
Table 4.
Static and dynamic SDM results.
Table 4.
Static and dynamic SDM results.
Variable | SDM | Dynamic SDM | ||
---|---|---|---|---|
ce | ce | ag | ce | |
(1) | (2) | (3) | (4) | |
L.ce | \ | \ | \ | 1.4998 *** |
(24.97) | ||||
dig | −13.7636 *** | −16.6513 *** | −0.3427 *** | −3.7661 *** |
(−7.7547) | (−9.2088) | (−4.1404) | (−3.30) | |
ag | 11.9170 *** | \ | \ | 7.4042 *** |
(7.2632) | (5.45) | |||
sag | −1.0768 *** | \ | \ | −0.7460 *** |
(−5.5107) | (−5.18) | |||
W × dig | −106.8135 *** | −142.4802 *** | −2.7125 *** | 0.0049 *** |
(−5.8491) | (−7.7829) | (−3.2674) | (3.46) | |
W × ag | 41.8668 *** | \ | \ | −0.0107 |
(3.0521) | (−1.51) | |||
W × sag | −3.1848 ** | \ | \ | 0.0015 |
(−2.0496) | (1.50) | |||
Control variable | YES | YES | YES | YES |
Direct Effect | −10.8741 *** | −13.4744 *** | −0.3101 *** | \ |
(−6.2307) | (−7.5124) | (−3.7839) | ||
Indirect Effect | −55.5802 *** | −85.0672 *** | −2.1323 *** | \ |
(−4.6435) | (−5.6736) | (−2.9417) | ||
Total Effect | −66.4543 *** | −98.5416 *** | −2.4424 *** | \ |
(−5.2880) | (−6.2727) | (−3.2136) | ||
LogL | 50.9759 | 50.9759 | 50.9759 | \ |
R2 | 0.2313 | 0.2588 | 0.1476 | 0.2799 |
Table 5.
Robustness test results.
Table 5.
Robustness test results.
Variable | SDM | Dynamic SDM | IV Method | ||||
---|---|---|---|---|---|---|---|
Changing the Dependent Variable | Changing the Weight Matrix | Changing the Sample Period | Changing the Dependent Variable | Changing the Weight Matrix | Changing the Sample Period | (7) | |
(1) | (2) | (3) | (4) | (5) | (6) | ||
L.ce | \ | \ | \ | 1.5034 *** | 1.5068 *** | 1.5183 *** | \ |
(16.49) | (25.38) | (9.12) | |||||
dig | −1.2691 *** | −14.0533 *** | −10.8760 *** | −0.4520 ** | −3.8163 *** | −2.575 *** | −12.9828 *** |
(−5.5391) | (−7.7277) | (−7.4797) | (−2.19) | (−3.35) | (−2.69) | (−4.6132) | |
ag | 2.2049 *** | 12.2122 *** | 13.6653 *** | 1.2595 *** | 7.5027 *** | 5.9623 *** | 13.3144 *** |
(10.4523) | (7.4250) | (11.6922) | (5.12) | (5.53) | (6.26) | (8.1316) | |
sag | −0.1818 *** | −1.0755 *** | −1.4939 *** | −0.1109 *** | −0.7544 *** | −0.6329 *** | −1.2763 *** |
(−7.1967) | (−5.5049) | (−10.6148) | (−4.25) | (−5.24) | (−6.16) | (−6.8022) | |
W × dig | −5.5826 ** | −103.0876 *** | −78.6314 *** | 0.0006 ** | 0.0050 *** | 0.0028 ** | \ |
(−2.3485) | (−5.5331) | (−5.3996) | (2.16) | (3.50) | (2.31) | ||
W × ag | −2.7602 | 39.3231 *** | 20.1965 ** | 0.00003 | −0.0108 | −0.0119 ** | \ |
(−1.5107) | (2.7741) | (2.0648) | (0.02) | (−1.47) | (−2.35) | ||
W × sag | 0.1043 | −2.9065 * | −5.6515 *** | 0.0001 | 0.0015 | 0.0016 ** | \ |
(0.5023) | (−1.8424) | (−4.5997) | (0.78) | (1.50) | (2.31) | ||
ρ | −1.0550 *** | −0.8873 *** | −1.2116 *** | \ | \ | \ | \ |
(−5.2206) | (−4.1582) | (−5.3394) | |||||
Direct Effect | −1.1062 *** | −11.2082 *** | −8.1922 *** | \ | \ | \ | \ |
(−5.0863) | (−6.3790) | (−5.8521) | |||||
Indirect Effect | −2.2449 * | −51.4347 *** | −32.4968 *** | \ | \ | \ | \ |
(−1.8639) | (−4.4195) | (−4.2135) | |||||
Total Effect | −3.3511 *** | −62.6430 *** | −40.6889 *** | \ | \ | \ | \ |
(−2.6960) | (−5.1158) | (−5.0372) | |||||
Control variable | YES | YES | YES | YES | YES | YES | YES |
Fixed Effects | YES | YES | YES | YES | YES | YES | YES |
R2 | 0.1234 | 0.2252 | 0.2189 | 0.7372 | 0.3033 | 0.3191 | 0.8128 |
Table 6.
Statistical description of variables in different regions.
Table 6.
Statistical description of variables in different regions.
Variable | Region | N | Mean | Std | Min | Max |
---|---|---|---|---|---|---|
ce | Eastern Region | 286 | 5.3763 | 1.0023 | 0.0094 | 7.1497 |
Central and Western Region | 374 | 5.1925 | 0.9694 | −0.2053 | 7.6496 | |
dig | Eastern Region | 286 | 0.1008 | 0.1028 | 0.0015 | 0.6186 |
Central and Western Region | 374 | 0.0557 | 0.0512 | 0.0012 | 0.3019 | |
ag | Eastern Region | 286 | 5.5237 | 0.9597 | 3.4298 | 7.6828 |
Central and Western Region | 374 | 4.2476 | 1.2564 | 1.3474 | 5.8578 |
Table 7.
Heterogeneity test results.
Table 7.
Heterogeneity test results.
Variable | SDM | Dynamic SDM | ||
---|---|---|---|---|
Eastern Region | Central and Western Region | Eastern Region | Central and Western Region | |
(1) | (2) | (3) | (4) | |
L.ce | \ | \ | 1.3512 *** | 1.7597 *** |
(15.48) | (20.33) | |||
dig | −0.6698 ** | −19.7209 *** | 0.4356 | −15.1738 *** |
(−2.0675) | (−4.1556) | (1.19) | (−5.50) | |
ag | −4.5967 *** | −6.5964 *** | 0.7113 | 5.2898 *** |
(−7.3620) | (−2.5883) | (0.48) | (2.66) | |
sag | 0.3735 *** | 0.5610 * | −0.1476 | −0.2993 |
(5.9945) | (1.8350) | (−1.18) | (−1.12) | |
W × dig | −2.6689 | −14.1425 | 0.0005 | 0.0564 *** |
(−1.5383) | (−0.3391) | (0.29) | (5.21) | |
W × ag | −1.8186 | −94.5845 *** | 0.0826 *** | −0.0166 |
(−0.5570) | (−3.9273) | (3.61) | (−0.45) | |
W × sag | 0.3704 | 3.3912 | −0.0072 ** | 0.0080 |
(1.3101) | (1.1788) | (−3.22) | (1.49) | |
Control variable | YES | YES | YES | YES |
Fixed Effects | YES | YES | YES | YES |
R2 | 0.5862 | 0.1655 | 0.7629 | 0.7015 |
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