1. Introduction
Climate change is a common challenge human society faces, and reducing greenhouse gas emissions has become a global consensus [
1]. As the world’s largest carbon emitter, China has consistently placed great importance on and actively addressed climate change. In September 2020, China set the goal of peaking carbon dioxide emissions by 2030 and striving to achieve carbon neutrality by 2060. However, achieving these goals inevitably poses significant economic and social development challenges for a developing country like China. Meanwhile, against the backdrop of sluggish global economic recovery and weak trade growth, China’s economic development faces long-term downward pressure. Therefore, under the dual constraints of carbon reduction and economic growth requirements, improving carbon emission efficiency, namely stabilizing economic development while reducing carbon emissions, has become an urgent and critical task for China [
2].
The Chinese government has implemented multiple pilot policies to promote carbon reduction and high-quality economic development. Among them, the BCP policy and TFCP policy are two distinctive policy experiments launched by the Chinese government to improve urban digital infrastructure construction and alleviate financing constraints on urban technological innovation, respectively. They play a key role in achieving the dual goals of carbon reduction and high-quality economic development through digital driving and financial support. As a key policy for promoting digital infrastructure development, the BCP policy aims to establish a high-speed, ubiquitous, integrated, and secure broadband network. The broadband infrastructure it provides can support emerging industries such as cloud computing, big data, and artificial intelligence, creating new economic growth opportunities. Meanwhile, the BCP policy facilitates traditional industries’ digitalization and intelligent transformation through high-speed broadband networks. For instance, adopting smart manufacturing and remote office solutions can enhance production efficiency, optimize resource allocation, and reduce resource waste and energy consumption. The TFCP policy is a crucial policy in China that fosters deep integration between technological innovation and financial resources. It seeks to support technological innovation through financial instruments, accelerating the transformation and commercialization of scientific and technological achievements. The TFCP policy provides targeted financial support, such as special loans and venture capital, for researching and developing low-carbon technologies and promoting innovation in clean energy, carbon capture and storage (CCS), and energy-saving technologies to reduce carbon emissions. Additionally, the development of financial derivatives such as green bonds and carbon options under the TFCP policy facilitates the growth of the carbon finance market. It drives the advancement of industries like new energy and green buildings. It is worth noting that the BCP and TFCP policies are not independent or unrelated but interact and overlap in the same geographic space. For example, cities such as Beijing and Tianjin have simultaneously implemented the BCP and TFCP policies, and their urban development has been fully supported by digital technology and financial resources. Furthermore, from the perspective of their implementation plans, the BCP and TFCP policies exhibit a significant synergistic effect on urban carbon emission efficiency. For instance, the BCP policy promotes the development of big data, providing essential data support for TFCP policy. This enables the government to assess corporate carbon emissions through big data analytics, offering precise decision-making references for green loans. At the same time, the TFCP policy supports broadband infrastructure construction under the BCP policy through financial instruments such as low-interest loans and special bonds. This financial support accelerates the application of digital technologies, including cloud computing, big data, and artificial intelligence, further enhancing production efficiency and reducing carbon emissions. Therefore, some important and urgent questions are whether the dual pilot policy formed by the BCP and TFCP policies can improve carbon emission efficiency. Can the dual pilot policy synergistically affect carbon emission efficiency compared to a single pilot policy? If the answer is yes, how does it affect carbon emission efficiency? What are the heterogeneous characteristics of its impact? Clarifying the above issues has important theoretical and practical significance for effectively leveraging the policy effects of the dual pilot policy, promoting carbon emission reduction, and facilitating high-quality economic development.
This paper aims to explore whether the combination of digital economy and financial development policies formed by the BCP and TFCP policies has a synergistic effect on carbon emission efficiency. However, the current literature mainly focuses on the impact of single policies related to the digital economy or financial development on carbon emission reduction. In terms of the digital economy, existing research based on different policies mostly indicates that the digital economy will promote carbon emission reduction. The studies conducted by Wei et al. and Hu based on the policy of China’s big data comprehensive pilot zone showed that the digital economy significantly reduces carbon emissions by achieving industrial upgrading, promoting technological innovation, and improving energy utilization efficiency [
3,
4]. Luo and Yuan and Dong et al. used the BCP policy as a quasi-natural experiment to explore the carbon emission reduction effect of digital infrastructure construction, and they found that digital infrastructure will also reduce carbon emission intensity through industrial upgrading, promoting green technology innovation, and improving energy utilization efficiency [
5,
6]. Shu et al. found that Smart City pilot policy significantly reduces urban carbon emissions through technological innovation and industrial structure mechanisms [
7]. Regarding financial development, the existing literature indicates that financial development positively impacts carbon emission performance. Xu et al. found that the TFCP policy reduces urban carbon emission intensity through the effects of total factor productivity improvement, innovation factor agglomeration, and industrial structure optimization, and the impact of the TFCP policy has significant spatial spillover effects [
8]. Lu et al. found that the TFCP policy can reduce carbon emissions scale and intensity in pilot cities by promoting green technology innovation and optimizing industrial structure [
9]. Xu et al. found that green credit policies reduce corporate carbon emissions by lowering investment carbon intensity and strengthening environmental supervision [
10].
Additionally, some literature has focused on the impact of China’s Digital Inclusive Finance Index on carbon emissions. The Digital Inclusive Finance Index reflects the overall development level of integrating digital technology and financial services, and related research usually only focuses on the impact of digital finance development on carbon emissions. However, the synergistic effect of the combination of digital and financial has not been considered. Lee and Wang found that digital inclusive finance can reduce carbon intensity by optimizing industrial structure and promoting green technology [
11]. Li et al. (2023) found that digital inclusive finance improves carbon emission efficiency by promoting capital-biased technological progress [
12].
Moreover, some scholars have explored the impact of the dual pilot policy on carbon emissions, and their research primarily focused on environmental policies rather than the digital economy or financial development. Li and Zhang explored the impact of the dual pilot policy of low-carbon pilot cities and new energy demonstration cities on carbon emissions, and they found that the dual pilot policy achieves carbon reduction by promoting green technology innovation [
13]. Jiang et al. found that the combination of low-carbon city pilot policies and carbon emission trading pilot policies can reduce carbon emissions by promoting technological progress and optimizing energy allocation [
14]. Zhang and Fan and Zhao et al. explored the carbon emission reduction effects of policy combinations, specifically the Low-Carbon City pilot policy with the Smart City pilot policy and Low-Carbon City pilot policy with the Energy Conservation and Emission Reduction fiscal policy, respectively [
15,
16].
In summary, current research mainly focuses on the impact of single policies, such as digital economy policies represented by the BCP policy or financial development policies represented by the TFCP policy on carbon emissions. Research on digital inclusive finance also only focuses on the impact of the overall development level of digital finance on carbon emissions without considering the synergistic effect of digital and finance. However, due to overlap between the BCP and TFCP policies within cities, exploring the impact of a single policy, such as the BCP or TFCP policy, while ignoring the effects of another policy can lead to estimation bias to a certain extent. At the same time, considering that the application of digital technology will improve the efficiency of financial services and financial development can also provide necessary financial support for the digital economy, the combination of the digital economy and financial development will inevitably have a synergistic effect on carbon emissions. Therefore, exploring whether there is a synergistic effect between the development of the digital economy and finance development on carbon emissions is of great theoretical significance. Moreover, the above research indicates that the existing literature has mostly focused on the impact of the digital economy or financial development on carbon emissions. However, given China’s dual pressure of carbon reduction and high-quality economic development, examining whether the digital–financial dual pilot policy can simultaneously promote economic growth while reducing carbon emissions holds greater practical significance. In other words, the impact of the dual pilot policy on urban carbon emission efficiency is more worthy of investigation in the current economic context.
Based on the above analysis, this paper focuses on the impact of the digital–financial dual pilot policy formed by the BCP and TFCP policies on urban carbon emission efficiency. The contributions of this paper are as follows: Firstly, based on nighttime light data and the super-efficiency SBM model, we calculate urban carbon emission efficiency and examine the impact of the combination of digital and finance on carbon emission efficiency from the perspective of the dual pilot policy, verifying the synergistic effect of the dual pilot policy. On the one hand, using nighttime light data to calculate carbon emissions avoids the measurement error caused by missing key data. At the same time, by using labor, capital, and energy consumption as input variables, carbon emissions as undesirable outputs, and gross domestic product as the desired output, and applying the super-efficiency SBM model to measure carbon emission efficiency, this approach effectively addresses the limitations of traditional models, such as the efficiency value being capped at 1 and the difficulty of comparing efficiency values. On the other hand, this paper focuses on the synergistic effect of the digital–financial dual pilot policy, which not only examines the unique effects of the dual pilot policy compared to a single pilot policy, avoiding the evaluation limitations of the single perspective, but also provides new insights for the government to coordinate pilot policies better and achieve more efficient carbon reduction goals. Secondly, we deeply explore the impact mechanism of the dual pilot policy on carbon emission efficiency from the perspectives of resource allocation effect and collaborative innovation effect, providing a theoretical basis for further improving the dual pilot policy and enhancing carbon emission efficiency. Thirdly, the heterogeneity of the dual pilot policy’s effects on carbon emissions was explored from the perspectives of differences in resource endowment, administrative level, digital infrastructure, economic and financial development level, and intellectual property protection intensity of cities, enriching the research conclusions and providing practical evidence for the differentiated implementation of relevant policies and improving the policy’s effects.
The remainder of this paper is organized as follows.
Section 2 outlines the policy background, theoretical analysis, and research hypotheses.
Section 3 shows the empirical strategy.
Section 4 presents the empirical results, discusses the influential mechanism of dual pilot policy on carbon emission efficiency, and presents a further analysis. Finally, the conclusions and policy recommendations are presented in
Section 5.
4. Results and Discussion
4.1. Benchmark Regression
Table 3 presents the regression results of the impact of the dual pilot policy on carbon emission efficiency. Columns (1) and (2) show the baseline regression results, indicating that, regardless of whether control variables were included in the model, dual pilot policy had significantly improved urban carbon emission efficiency. Considering that non-dual pilot cities included cities that only implemented single pilot policies, such as the BCP or TFCP policy, we excluded cities that only implemented a single pilot policy, and we obtained a research sample with dual pilot policy cities as the treatment group and non-pilot policy cities as the control group to examine the net effect of the dual pilot policy. The results in columns (3) and (4) indicated that, after excluding the policy effects of the single pilot policy, the promoting effect of the dual pilot policy was still significantly positive. And the regression coefficient showed that the dual pilot policy increased the carbon emission efficiency of the dual pilot cities relative to the non-policy cities by an average of approximately 9.7% (0.037/0.383) during the policy implementation period. The results showed that implementing the dual pilot policy significantly improved the carbon emission efficiency of cities, and hypothesis 1 was verified.
The carbon emission efficiency improvement effect (9.7%) of the dual pilot policy indicated that the dual pilot policy plays a significant role in promoting green technological innovation and optimizing resource allocation. On the one hand, by providing high-speed Internet, cloud computing platforms, and big data technology, the BCP policy provides strong technical support for the R&D, application, and monitoring of green technology and reducing the cost of technology optimization and promotion. On the other hand, the TFCP policy provides sufficient financial support for the innovation and industrialization of green technologies through diversified financial services such as customized financing, green financial tools, and carbon trading support, motivating enterprises to actively participate in carbon reduction actions. The combination of the two policies not only promotes urban economic development but also promotes urban carbon reduction. In addition, the regression results validated the policy design’s effectiveness, indicating that the dual pilot policy provides a practical and feasible path for urban carbon reduction while promoting economic development. This result provides an important reference for other regions to explore green and low-carbon development models. Especially in the current context of the “dual carbon” goal, the synergistic effect of the BCP and TFCP policies has broad application potential. Future policy design can further strengthen the deep integration of the two policies to maximize their driving role in green technology innovation and carbon reduction.
4.2. Robustness Test
4.2.1. Parallel Trend Test
Using the difference-in-differences (DID) model requires satisfying the parallel trend assumption, which means that the changes in carbon emission efficiency between dual pilot cities and non-pilot cities were parallel before the policy implementation. We referred to Jacobson et al. [
36] and constructed the following model for the parallel trend test:
The principle of the parallel trend test based on Equation (5) is as follows: Create dummy variables for the period before and after the implementation of the dual pilot policy. If the dummy variable for the period before the dual policy implementation does not significantly affect carbon emission efficiency, there is no significant difference in carbon emission efficiency between dual pilot and non-pilot cities before the policy implementation. In other words, the changes in carbon emission efficiency in dual pilot and non-pilot cities were parallel, and the parallel trends test was passed. In Equation (5),
is a set of dummy variables.
is the implementation year of the dual pilot policy (0 for cities without a pilot policy), if
t- ≤ −6, then set
; if
t-=v (
v≠−6, 6), then set
; if
t- ≥ 6, then set
. Considering that the time range before and after dual pilot policy implementation was too long (from 9 years before the pilot policy to 8 years after the pilot policy), and there were relatively few data in the 6 years before and after policy implementation, the periods of 6 years or more before the policy and after the policy were aggregated into 6-year periods before and after the policy. So the minimum value of v was −6, and the maximum value was 6. In addition, we took the second year before the implementation of the dual pilot policy as the base period, and the parallel trend test results are shown in
Figure 2. It can be seen that there was no significant difference in carbon emission efficiency before policy implementation but a significantly positive difference after policy implementation. This indicated that there was no systematic difference in carbon emission efficiency between dual pilot cities and non-pilot cities before implementing the dual pilot policy, and the parallel trend was verified.
4.2.2. Treatment Effect Heterogeneity Test
Considering that the dual pilot policy was implemented in multiple batches, there may be treatment effect heterogeneity. That is, there are differences in the impact of the same treatment on different individuals, which may be manifested in two dimensions: different duration after treatment or different timing of treatment. Treatment effect heterogeneity may lead to biased estimation results in the traditional two-way fixed effects model (TWFE). Therefore, this paper referred to Goodman-Bacon [
37] and used the Bacon decomposition method to conduct a robustness test on the impact of the dual pilot policy on carbon emission efficiency to avoid the impact of treatment effect heterogeneity. According to the Bacon decomposition, the treatment effects of staggered difference-in-differences can be decomposed into three groups: the earlier treated group as the treatment group and the later treatment group as the control group, the later treated group as the treatment group and the earlier treatment group as the control group, and time-varying treated group as the treatment group and never treated group as the control group. Among them, when the “earlier treated group” is used as the control group, it may cause bias in the estimation results of TWFE. If the weight of this decomposition result in the TWFE estimator is small, the treatment effect heterogeneity test is passed. The Bacon decomposition results are shown in
Table 4. Columns (1) and (2) show the results of all city samples, while columns (3) and (4) show the results when excluding the cities that only implemented a single pilot policy. The weight of the difference-in-differences estimator for “later treated group vs. earlier treated group” did not exceed 1.1%. Therefore, the estimators of TWFE in this paper were not affected by treatment effect heterogeneity, and the benchmark regression results were robust.
The Bacon decomposition results preliminarily indicated that the estimators of TWFE in this paper were not affected by treatment effect heterogeneity. To further enhance the robustness of the research conclusions, this paper referred to De Chaisemartin and d’Haultfoeuille [
38]. They diagnosed the potential heterogeneous treatment effects in the baseline regression by calculating the group–period average treatment effect. The results showed that the average treatment effect of the dual pilot policy was 0.040, which was significant at the 5% level.
Figure 3 presents the dynamic estimation results that satisfied the parallel trend assumption. It suggests that after considering heterogeneity in treatment effects, the benchmark results of this paper remained valid.
4.2.3. Placebo Test
Another concern with using the DID method is the possibility of unobservable omitted variables affecting the regression results [
39]. Referring to Li et al. [
39], we randomly selected dual pilot policy cities as the treatment group. This paper conducted 500 and 1000 placebo tests in sequence.
Figure 4 and
Figure 5 show that the regression results of the dual pilot policy dummy variable were around 0 and showed a normal distribution. Most of the regression results were insignificant, and the benchmark regression’s estimated coefficient (0.037) was an outlier in placebo tests. Therefore, we could conclude that the impact of the dual pilot policy on carbon emission efficiency was unlikely to be affected by unobserved factors, indicating that the previous benchmark regression results were robust.
4.2.4. Other Robustness Tests
- (1)
PSM-DID method
Given that the results may have endogeneity issues due to the selection bias of urban samples, this paper applied the PSM-DID method to solve this problem. We used the six control variables mentioned as covariates and selected the control group cities by nearest neighbor matching using the caliper method. Then, we conducted regression based on the matched samples. The regression results of the model are shown in column (1) of
Table 5, which indicated that, after using the PSM-DID model, the dual pilot policy still significantly improved carbon emission efficiency.
- (2)
Excluding interference from other policies
Considering that other policies may affect urban carbon emission efficiency during the implementation of the dual pilot policy, this paper collated five policies that may have affected urban carbon emission efficiency during the sample period, including the Innovative City pilot policy (IPC), the Smart Construction Pilot City policy (SPC), Cities along the Belt and Road (BAR), Green Financial Reform and Innovation pilot zones (GFR), and the Low-Carbon City pilot policy (LCB). To avoid the impact of the above policies on the estimation results of the dual pilot policy, this paper successively added these five policy dummy variables to the benchmark model. The regression results are shown in columns (2) to (6) of
Table 5. The results indicated that, after considering the impact of other policies, the dual pilot policy still significantly improved urban carbon emission efficiency.
- (3)
Excluding interference from regional characteristics
Considering that there may be factors, such as regional economic conditions and industrial policies, that affect urban carbon emission efficiency, this paper replaced the time fixed effect with the interactive fixed effect of time and the seven major geographical regions of China (Year × GP) and of time and the three major regions of East, Central, and West (Year × EMW) to control for interference from these regional characteristics. As shown in columns (1) and (2) of
Table 6, after controlling for regional characteristics, the impact of the dual pilot policy on carbon emission efficiency was still significantly positive.
- (4)
Excluding interference from non-random selection of pilot cities
The selection of dual pilot policy cities may be related to factors such as urban development level and regional location, which may have differentiated impacts on urban carbon emission efficiency. To avoid the influence of these factors, this paper drew on the method by Lu et al. [
40] and added the interaction term of these city characteristic factors and the time trend in the regression model. The city’s characteristic factors included whether it belongs to two control areas (TLK), the provincial capital city (TSH), a municipality directly under the central government (TZX), and special economic zone (TTQ). As shown in columns (3) to (6) of
Table 6, it can be seen that the dual pilot policy still significantly improved urban carbon emission efficiency.
4.3. Mechanism Analysis
Based on the theoretical analysis, the dual pilot policy improves the resource allocation efficiency of urban labor, capital, and credit and the level of green technology innovation by generating resource allocation effects and collaborative innovation effects, thereby enhancing urban carbon emission efficiency. Therefore, this paper conducted a mechanism analysis.
For the resource allocation efficiency of labor and capital, this paper referred to Liu and Xia [
41]. We calculated the labor factor distortion degree (disL) and the capital factor distortion degree (disK). The smaller the values of disL and disK, the higher the labor and capital allocation efficiency. The specific calculation process is as follows.
Firstly, we set the Cobb Douglas production function and took its logarithm to obtain the following model:
Secondly, we assumed that the labor price was w and the capital price was r. The market distortion was calculated based on the deviation between the marginal output of factors and their prices. Then, disL and disK were obtained, represented as and , respectively.
Thirdly, we calculated , , , and based on actual city data. Y represents the city’s GDP. L represents the number of employed persons in the city. K represents the urban capital stock, measured by the perpetual inventory method. W represents the labor price, expressed as the average wage of employed persons in the city. r is the capital price set at 10%, representing a depreciation rate of 5% and an actual interest rate of 5%.
For the credit resource allocation efficiency, considering the lack of data at the city level, this paper referred to Wang et al. [
42] and used the deviation of listed companies from the industry average cost of capital (FD) to estimate it, where
.
is the using cost of enterprise funds, expressed as the ratio of enterprise interest expenses to the total liabilities after deducting the accounts payable.
INCU is the average using cost of enterprise funds in the industry to which the enterprise belongs. The smaller the FD, the higher the resource allocation efficiency of credit. In addition, we measured the credit resource allocation efficiency (FD2) of cities based on the average FD of listed companies within the city to enhance the robustness of the research conclusions.
For calculating urban green technology innovation, this paper used the green technology innovation quantity (ngpat) and green technology innovation quality (qgpat). The quantity of green technology innovation was measured by the total number of green patent applications, and the quality of green technology innovation was measured by the citation count of green patent. Considering that the citation count of green patents at the city level is difficult to obtain, this paper collected the green patent citation data of listed companies and aggregated it at the city level.
We constructed the model expressed in Equation (7) to explore the mechanism of the dual pilot policy effect on urban emission efficiency:
In Equation (7),
M represents the mechanism variables mentioned above (
,
,
,
ngpat, and
qgpat). The other variable settings remained consistent with the baseline regression model (Equation (4)). The results of the mechanism analysis are shown in
Table 7. The estimated coefficients of DF in columns (1) and (2) were significantly negative, indicating that the dual pilot policy significantly reduced the distortion of urban labor and capital resources and significantly improved the allocation efficiency of labor and capital resources. The results in columns (3) and (4) indicated that the dual pilot policy significantly improved the resource allocation efficiency of credit. Therefore, the results in columns (1) to (4) indicated that the dual pilot policy generated resource allocation effects to improve the resource allocation efficiency of urban labor, capital, and credit, thus increasing urban carbon emission efficiency. The estimated coefficients of
DF in columns (5) and (6) were significantly positive, indicating that the dual pilot policy improved urban green technology innovation and thus enhanced urban carbon emission efficiency. On this basis, this paper examined the impact of resource allocation efficiency and green technological innovation on carbon emission efficiency to further supplement the evidence on the correlation between mechanism variables and carbon emission efficiency. The results indicated that distortion of labor, capital, and credit resources reduces carbon emission efficiency. At the same time, improving the quantity and quality of green technology innovation enhances carbon emission efficiency. The results are shown in
Table A2. The above results indicated that the dual pilot policy improves urban carbon emission efficiency by generating resource allocation and collaborative innovation effects. Overall, hypothesis 2 was validated.
4.4. Comparative Analysis of Synergistic Effects of the Dual Pilot Policy
To further explore whether the dual pilot policy had a greater effect on improving carbon emission efficiency than a single pilot policy, this paper excluded the sample of cities without a pilot policy and constructed a research sample with dual pilot policy cities as the treatment group and single pilot policy cities as the control group for capturing the effect of the dual pilot policy on improving carbon emission efficiency relative to the single pilot policy. Column (1) of
Table 8 shows that dual pilot cities had significantly improved urban carbon emission efficiency compared to single pilot cities. The regression coefficient indicated that the dual pilot policy led to an average increase of approximately 0.053 in carbon emission efficiency during the policy implementation period. This result suggested that the dual pilot policy effectively integrates the advantages of the two single pilot policies, fully leveraging the complementary effects of digital infrastructure and technology-finance support. By working together across multiple dimensions, such as technological innovation and resource allocation, the dual pilot policy generates a synergistic effect, resulting in a stronger policy impact than a single pilot policy.
In addition, to examine whether the dual pilot policy had heterogeneous carbon emission efficiency improvement effects on different single pilot policy cities, we first set the cities that were TFCP policy pilot cities but had not yet become dual pilot policy cities as the control group and then set the cities that were BCP policy pilot cities but had not yet become dual pilot policy cities as the control group. The results in columns (2) and (3) of
Table 8 indicated that dual pilot policy significantly improved urban carbon emission efficiency. The results in columns (2) and (3) of
Table 8 suggested that, regardless of whether single BCP policy pilot cities or single TFCP policy pilot cities were used as the control group, the dual pilot policy significantly improved urban carbon emission efficiency.
Furthermore, we explored the optimal path for implementing the dual pilot policy from the perspective of the chronological order of single pilot policy implementation. We removed the cities that implemented the BCP policy first among the dual pilot policy cities to examine the impact of dual pilot policy with the TFCP policy implemented first on carbon emission efficiency. Similarly, by removing the cities that implemented the TFCP policy first, we analyzed the impact of the dual pilot policy with the BCP policy implemented first on carbon emission efficiency. Considering that the second batch of the TFCP policy was implemented in 2016 at the same time as the third batch of the BCP policy, this paper first identified that the implementation time of the second batch of the TFCP policy was earlier. The results are shown in columns (1) and (2) of
Table 9.
Then, we assumed that the the third batch of the BCP policy was implemented earlier, and the results are shown in columns (3) and (4). All of the results indicated that, with the same control group, implementing the TFCP policy first and then the BCP policy more effectively enhanced the effect of the dual pilot policy on improving urban carbon emission efficiency.
4.5. Heterogeneity Analysis
4.5.1. Heterogeneity of Resource Endowment
This paper shows that the dual pilot policy enhances urban carbon emission efficiency by optimizing resource allocation and promoting green technological innovation. Meanwhile, green technological innovation and resource allocation efficiency are related to urban resource endowment. On the one hand, resource-based cities are typically dominated by natural resource exploitation and energy-intensive industries. Their economic growth model heavily relies on resource-based industries, resulting in insufficient incentives for green technological innovation. By contrast, non-resource-based cities have a more diversified industrial structure with lower dependence on natural resources, allowing them to better adapt to the demand for green technological innovation. On the other hand, resource-based cities have long concentrated resource allocation in resource-based and energy-intensive industries, leading to rigid resource allocation that hinders a swift transition to green and low-carbon industries. By contrast, non-resource-based cities exhibit greater flexibility in resource allocation. Local governments in these cities can quickly adjust resource flows in response to policy guidance, facilitating the development of green and low-carbon industries. Therefore, the impact of the dual pilot policy on carbon emission efficiency varies depending on resource endowment. It is likely to be more effective in improving carbon emission efficiency in non-resource-based cities. Therefore, we divided the cities into resource-based and non-resource-based cities based on the “Notice of the State Council on Issuing the National Sustainable Development Plan for Resource-based Cities (2013–2020)” to explore the heterogeneous impact of the dual pilot policy on urban carbon emission efficiency.
The results in columns (1) and (2) of
Table 10 indicated that the dual pilot policy had no significant impact on the carbon emission efficiency of resource-based cities but had a considerably positive effect on the carbon emission efficiency of non-resource-based cities. This suggested that the dual pilot policy significantly improved non-resource-based cities’ carbon emission efficiency compared to resource-based cities. The reason is that resources and high energy-consuming industries have long constrained the development of resource-based cities. And abundant natural resources may even lead to the “resource curse” effect, resulting in insufficient innovation motivation for cities. Non-resource-based cities rely less on natural resources, and local governments can promptly adjust policy guidance to promote the development of low-carbon and green industries in cities, thereby enabling the dual pilot policy to exert positive effects.
4.5.2. Heterogeneity of Digital Infrastructure
The resource allocation effects and collaborative innovation effects of the dual pilot policy largely depend on the digital infrastructure conditions of cities. A high level of digital infrastructure enables real-time data collection, transmission, and analysis, helping local governments and enterprises to accurately identify inefficiencies in resource allocation, optimize the distribution of energy, capital, and technology, and promote the development of low-carbon industries. At the same time, this high-level infrastructure provides strong technological support for green technological innovation, such as optimizing energy use, developing low-carbon technologies, and improving production processes through big data and artificial intelligence. By contrast, cities with a low level of digital infrastructure often lack the necessary technical support. They may face issues such as information asymmetry and inefficient decision making in the resource allocation process. Moreover, insufficient digital infrastructure can limit the scope of green technology diffusion and application, weakening the overall impact of technological innovation. Therefore, we referred to Bi [
43] and measured the degree of urban digital infrastructure construction using the entropy weight method according to the long-distance optical cable density, Internet broadband access ports per capita, information transmission, the proportion of computer service and software industry employees, telecom business income per capita, and mobile phone penetration rate. Then, the cities were divided into groups with high and low levels of digital infrastructure construction based on the median urban digital infrastructure construction degree. The results in columns (3) and (4) indicated that, compared to the group with a low level of digital infrastructure construction, the dual pilot policy had a greater effect on improving carbon emission efficiency in the group with a high level of digital infrastructure. This result was consistent with the above theoretical analysis. The construction of digital infrastructure has a significant technological spillover effect, accelerating the flow of knowledge and information. This technological spillover effect helps to improve the overall economic efficiency of resource allocation and innovation capacity, enabling the dual pilot policy to enhance carbon emission efficiency more effectively.
4.5.3. Heterogeneity of Administrative Level
The object of this study was the dual pilot policy, and the implementation effectiveness of the policy is strongly correlated with the administrative level of the city. Generally speaking, cities with a higher administrative level tend to receive more policy support and resource allocation, including financial funds, technological resources, and human resources. This enables these cities to better utilize the support and funding provided by the BCP and TFCP policies. Moreover, governments in high administrative-level cities have stronger administrative capabilities and execution power, allowing them to respond quickly to national policy requirements and develop and implement detailed low-carbon development plans and supporting measures. Additionally, these cities typically have more well-developed regulatory systems, ensuring the effective implementation of the dual pilot policy. By contrast, cities with low administrative levels have relatively limited resource acquisition capabilities and may struggle to fully absorb and utilize the resources provided by the dual pilot policy. Furthermore, constrained by administrative levels and resource allocation, cities with low administrative levels often have lower policy execution efficiency, making it difficult to promote the implementation of the dual pilot policy efficiently. Therefore, we divided the cities into high administrative-level cities and low administrative-level cities, and we examined whether the dual pilot policy had heterogeneous effects. If a city belonged to a provincial capital, municipality directly under the central government, special economic zone, sub-provincial city with separate planning status, or deputy provincial capital, it was considered a high administrative-level city; otherwise, it was considered a low administrative-level city. The impact of the dual pilot policy on carbon emission efficiency in cities of different administrative levels is shown in columns (5) and (6). The results indicated that compared to low administrative-level cities, the dual pilot policy significantly improved the carbon emission efficiency of high administrative-level cities, which was consistent with the theoretical analysis.
4.5.4. Heterogeneity of Economic Development Level
The impact of the dual pilot policy on carbon emission efficiency is also closely related to the city’s economic development level. Cities with a high level of economic development can provide sufficient funding and resource support for implementing the BCP and TFCP policies, such as building high-quality digital infrastructure and promoting green finance projects. At the same time, these cities typically have a more developed technological innovation system and stronger R&D capabilities, allowing them to fully utilize the technical resources provided by the BCP policy, such as broadband networks, big data, and cloud computing, to promote the research and application of green technologies. By contrast, cities with lower economic development levels face the challenge of insufficient resource investment. Due to limited financial resources, these cities find it difficult to invest sufficient funds in digital infrastructure construction and green finance projects, resulting in weaker implementation effects of the dual pilot policy. Moreover, relatively insufficient technological innovation capabilities and a lack of high-quality education and research resources also make it hard for cities with lower economic development levels to effectively absorb the technical support provided by the BCP policy, with a clear lack of momentum for green technological innovation. To explore the heterogeneity of the dual pilot policy effects based on economic development level, this paper divided the cities into two groups based on the median per capita GDP. The results in columns (1) and (2) of
Table 11 show that, compared to cities with low level of economic development, the dual pilot policy significantly improved the carbon emission efficiency of cities with high-level economic development, which aligned with the above theoretical analysis.
4.5.5. Heterogeneity of Financial Development Level
The level of urban financial development can also affect the implementation effects of the dual pilot policy. Cities with a high level of financial development have more well-established financial markets and sufficient capital supply, enabling them to provide strong financial support for implementing the BCP and TFCP policies. At the same time, these cities typically possess more efficient resource allocation capabilities, allowing them to quickly direct funds and technology toward low-carbon industries and technological innovation. Financial institutions in these cities can better identify and support low-carbon projects, optimize fund allocation, and promote the research and application of green technologies. Moreover, cities with a high level of financial development have stronger technological innovation capabilities and better conditions for technology diffusion, allowing them to fully utilize the digital infrastructure provided by the BCP policy and the technology-finance integration mechanism supported by the TFCP policy to advance the research and commercialization of green technologies. By contrast, due to insufficient capital supply, cities with a low level of financial development struggle to attract and utilize the financial resources provided by the dual pilot policy, limiting the effectiveness of the implementation of the dual pilot policy. Additionally, these cities often have imperfect financial markets, resulting in lower resource allocation efficiency and difficulty in effectively promoting low-carbon transformation. To explore the heterogeneity of the dual pilot policy effects based on financial development level, this study divided the cities into two groups based on the median of the ratio of the total deposits and loans of financial institutions to GDP: cities with a high level of financial development and cities with a low level of financial development. Then, we conducted group testing. The results in columns (3) and (4) of
Table 11 show that, compared to cities with a low level of financial development, the dual pilot policy significantly improved the carbon emission efficiency of cities with a high level of financial development, which aligned with the above theoretical analysis.
4.5.6. Heterogeneity of Intellectual Property Protection Intensity
Green technological innovation is the core driving force for a city’s low-carbon transformation. A city’s low-carbon transformation development depends on technological breakthroughs and the institutional environment, such as the intensity of intellectual property protection. Therefore, the effect of the dual pilot policy on improving urban carbon emission efficiency is significantly influenced by the level of local intellectual property protection. Cities with high levels of intellectual property protection can offer better institutional guarantees for green technological innovation. In cities with strong intellectual property protection, enterprises and research institutions are more willing to invest resources and effort into developing green technologies. This institutional environment fosters continuous innovation in green technologies, accelerating the development and application of low-carbon technologies. By contrast, cities with weak intellectual property protection face challenges in safeguarding innovative achievements, leading to lower motivation for enterprises and research institutions to engage in green technology research and development, which limits technological innovation momentum and scale. Additionally, cities with strong intellectual property protection can facilitate the diffusion and application of green technologies through standardized technology transactions and licensing mechanisms. These cities typically have well-established technology transaction markets and technology transfer platforms, enabling low-carbon technologies to be more efficiently spread and applied in actual production, thus enhancing overall carbon emission efficiency. Conversely, cities with weak intellectual property protection face obstacles in the diffusion and application of technologies due to imperfect technology transaction mechanisms, limiting the full potential of the dual pilot policy. To explore the heterogeneity of the dual pilot policy effects based on intellectual property protection intensity, this paper measured the intensity of intellectual property protection by the number of intellectual property-related cases per capita at the city level. The cities were divided into two groups based on the median. The results in columns (5) and (6) of
Table 11 show that the dual pilot policy had a greater impact on improving carbon emission efficiency in cities with strong intellectual property protection. By contrast, it had no positive effect in cities with weak intellectual property protection, which aligned with the theoretical analysis.
4.6. Limitations and Future Research
This study provides new insights into the impact of the digital–financial dual pilot policy on urban carbon emission efficiency, but there are still some limitations. For example, considering that carbon reduction efforts vary across different industries, the impact of the dual pilot policy on carbon emission efficiency may be closely related to industry type, but this paper fails to explore the differences in policy effects across industries. When more data are available, future research can explore the impact of the dual pilot policy on carbon emission efficiency in different industries in order to further enhance the significance of the research.