The Impact of Digital–Financial Dual Pilot Policy on Carbon Emission Efficiency: Evidence from Chinese Cities

Abstract

Enhancing carbon emission efficiency is crucial for achieving carbon reduction and economic growth. This paper focuses on the digital–financial dual pilot policy formed by the Broadband China strategy pilot (BCP) policy and the Promoting Science and Technology to Combine with Finance pilot (TFCP) policy. Using the panel data of 284 prefecture-level cities in China from 2007 to 2022 and nighttime light data, this paper adopts the super-efficiency SBM model to calculate urban carbon emission efficiency. Based on this efficiency, this paper employs the staggered difference-in-differences model to discuss the impact of the dual pilot policy on urban carbon emission efficiency. The research results indicate that the dual pilot policy significantly improves urban carbon emission efficiency, and compared to the single pilot policy, the dual pilot policy has a greater effect on improving carbon emission efficiency. This conclusion still holds after the parallel trend test, heterogeneous treatment effects test, and other robustness tests. Mechanism analysis demonstrates that the dual pilot policy enhances urban labor, capital, and credit resource allocation efficiency and green technological innovation by generating resource allocation and collaborative innovation effects, thereby improving urban carbon emission efficiency. Further analysis reveals that implementing the TFCP policy first, followed by the BCP policy, can more effectively maximize the dual pilot policy’s positive impact on urban carbon emission efficiency. The impact of the dual pilot policy on urban carbon emission efficiency exhibits heterogeneity, depending on the resource endowment, digital infrastructure level, administrative hierarchy, economic and financial development level, and intellectual property protection intensity of cities. This paper provides valuable insights for effectively implementing the dual pilot policy and achieving a win–win outcome in carbon reduction and economic development.

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.

2. Policy Background and Theoretical Analysis

2.1. Policy Background

In 2013, the State Council of China issued the Broadband China strategy and implementation plan, elevating the BCP policy to a national strategy. According to the Management Measures for Creating Broadband China Demonstration Cities, the Chinese Ministry of Industry and Information Technology launched three batches of BCP policy pilot cities in 2014, 2015, and 2016, respectively, to improve the level of broadband development and promote the development of digital facilities in pilot cities. From the perspective of BCP policy implementation, the BCP policy is not only focused on promoting the construction of infrastructure such as the Internet but also further applies digital infrastructure to both the production and consumption sides of economic and social development, driving the digital, networked, and intelligent transformation of the economy [17]. Therefore, the BCP policy has become an important policy in promoting China’s digital economy [18].

Finance is the cornerstone of the modern economy and a key driving force for innovative activities. Faced with the common difficulties in obtaining funding and cost issues in the low-carbon innovation field, the Chinese government launched the TFCP policy to promote the deep integration of technology and finance to solve the problem of “difficult and expensive financing”. In 2011, the first batch of TFCP policy pilot projects was launched in forty-one cities, including Tianjin, Shanghai, and Wuhan. According to preliminary statistics, since implementing the first batch of TFCP policy pilot projects, more than 350 science and technology finance policy documents have been intensively issued in pilot cities, providing sufficient financial support and a favorable innovation environment for low-carbon development. To further promote the TFCP policy, the Chinese government launched the second batch of TFCP policy pilot projects in Zhengzhou, Xiamen, and other cities in 2016, providing solid financial support for cultivating low-carbon industries and promoting energy conservation and emission reduction in the pilot areas.

As of 2022, thirty-two cities in China have become dual pilot cities. In fact, the BCP and TFCP policies each have their own focus in terms of goals, locations, and timelines, yet they are closely interconnected and highly integrated. Specifically, the BCP policy emphasizes the combination of network infrastructure construction and application service innovation, which provides strong technical support and a wide range of application scenarios for the TFCP policy. Meanwhile, the BCP policy can provide more data support for financial institutions in TFCP policy pilot cities by strengthening public information sharing and improving information integration and utilization efficiency, thereby promoting a more efficient flow of financial resources to the technology sector. The TFCP policy can provide financial support for constructing and upgrading BCP policy pilot cities through fiscal subsidies, tax reductions, and credit incentives. At the same time, the TFCP policy encourages the creation of a science and technology-finance ecosystem by establishing technology–finance alliances to promote communication and cooperation between financial institutions and technology intermediary service organizations. This helps to provide more precise financial services for technology companies related to broadband networks and supports the research and innovation of broadband technologies. Therefore, the BCP and TFCP policies mutually reinforce each other on multiple levels, jointly advancing the development of the digital economy and technology–finance integration. Under the synergistic effect of the BCP and TFCP policies, the dual pilot policy will significantly impact urban carbon emission efficiency. Given that both the BCP and TFCP policies have been confirmed to significantly impact urban carbon emissions [5,6,8,9], and based on the analysis mentioned above, it is clear that the BCP and TFCP policies mutually promote each other on multiple levels. Therefore, this study focuses on the dual pilot policy, combining both the BCP and TFCP policies, and explores its synergistic effects on urban carbon emission efficiency. Based on this, we will theoretically analyze how the dual pilot policy affects carbon emission efficiency and propose research hypotheses.

2.2. Theoretical Analysis and Research Hypotheses

As an important measure to promote the coordinated development of the digital economy and finance, the dual pilot policy will improve the efficiency of urban resource allocation and green technology innovation by generating resource allocation effects and collaborative innovation effects, thereby enhancing urban carbon emission efficiency.

On the one hand, the dual pilot policy can improve the resource allocation efficiency of urban labor, capital, and credit by generating resource allocation effects. By integrating and efficiently utilizing various resources, the dual pilot policy can ensure the optimal allocation and application of resources within cities, helping cities to move closer to the “Pareto optimal” state of resource allocation. The resource allocation effects generated by the dual pilot policy are mainly reflected in the following three aspects. First, the dual pilot policy can synergistically improve the efficiency of urban labor resource allocation. The BCP policy promotes Internet infrastructure construction and helps pilot cities to build more intelligent and transparent recruitment and job search platforms using Internet technology. The payment and financing solutions provided by the TFCP policy can give financial support for platform operation, which helps to accelerate the development of digital recruitment platforms, further reducing information asymmetry between job seekers and employers and effectively improving the matching efficiency of the labor market. In addition, the BCP policy strengthens the network coverage in pilot cities, which can increase opportunities for remote work and flexible employment [18]. The financial services provided by the TFCP policy can provide the necessary support for remote work, such as online payments, cross-border payments, etc., further enhancing the labor market’s mobility in pilot cities. Second, the dual pilot policy can synergistically enhance the efficiency of urban capital allocation. Specifically, the BCP policy improves the speed of information acquisition and transmission, promoting information transparency. Based on this, investors can timely obtain key details such as the financial status, market dynamics, and industry trends of enterprises, reducing the investment risks caused by information asymmetry [19]. Meanwhile, the TFCP policy can combine the big data and artificial intelligence analysis technologies brought about by the BCP policy to provide a basis for investment decisions. For example, financial institutions can more accurately assess investment projects’ potential returns and risks by analyzing market data and consumer behavior. This data-driven decision-making approach reduces investment uncertainty and improves the effectiveness of capital allocation [20]. Third, the dual pilot policy can synergistically optimize the efficiency of urban credit resource allocation. Implementing the BCP policy will help to improve the online financing environment in pilot cities, enabling more enterprises to participate in the online credit market and provide them with more credit options. At the same time, through technologies such as big data, artificial intelligence, and blockchain brought by the BCP policy, financial institutions can more accurately evaluate the credit status of borrowers, reduce information lag and communication barriers between borrowers and lenders, and thereby improve the efficiency and accuracy of credit approval. Moreover, the TFCP policy promotes decentralized financial markets (such as P2P lending, crowdfunding platforms, etc.), providing new financing channels for capital demanders and reducing the dependence on traditional financial systems [21]. Through these channels, the allocation of funds becomes more market oriented, and credit resources can flow freely according to market demand, thereby effectively improving the efficiency of credit resource allocation.

On the other hand, the dual pilot policy promotes urban green technology innovation through collaborative innovation effects. First, the dual pilot policy accelerates the dissemination and application of green technologies. The BCP policy reduces R&D and promotion costs by providing high-speed Internet and cloud computing platforms to build technical support and resource-sharing platforms for green technology innovation. At the same time, the BCP policy helps green technology companies to quickly reach potential users and markets by optimizing information dissemination channels and shortening technology’s commercialization cycle. The TFCP policy provides financial support for the research and promotion of green technologies through customized financing schemes, further reducing the threshold for technology diffusion [8]. For example, green technology companies can obtain financing support from the TFCP policy while utilizing the digital platform provided by the BCP policy for technology development, effectively bridging the entire chain from research and development to marketization. The synergistic effect promotes the rapid diffusion and widespread application of green technologies. Second, the dual pilot policy provides sufficient financial support for green technology innovation by optimizing the resource allocation efficiency of the green finance market. The BCP policy attracts more social capital into green technology by improving information transparency and reducing information asymmetry [22,23]. The TFCP policy utilizes the big data and artificial intelligence technology provided by the BCP policy to optimize the information transmission mechanism of the financial market, provide accurate matching of green projects for investors, significantly improve the efficiency of capital allocation, and thus provide sufficient financial support for green technology innovation. For example, the BCP policy’s digital platform provides real-time data and risk analysis of green projects, helping investors to quickly identify high-quality projects. At the same time, the TFCP policy provides diversified financing channels for green technology enterprises through financial tools such as green bonds and carbon trading financing, promoting the improvement and upgrading of the green technology industry chain. The deep integration of finance and digital technology provides a solid financial guarantee for green technology innovation. Third, the dual pilot policy is conducive to promoting the industrialization of green technologies. The TFCP policy provides financial support for green technology enterprises to expand their production scale and reduce financing costs through financial instruments such as green loans and carbon finance. The BCP policy helps these enterprises to quickly bring their products to market and expand their application scope through broadband infrastructure and digital platforms. For example, after receiving financing support from the TFCP policy, green technology companies can leverage cloud-based data management and intelligent logistics systems provided by the BCP policy to optimize production and distribution processes, thereby accelerating the large-scale production and market penetration of green technology products. Fourth, the dual pilot policy can support the continuous optimization of green technologies by strengthening environmental monitoring and data management. The BCP policy provides the infrastructure for environmental monitoring of green technologies through big data and artificial intelligence technology. Enterprises can monitor pollution emissions and energy consumption in real time and optimize technology design and process flow based on data feedback. At the same time, the TFCP policy provides continuous innovation funding support for enterprises through financial instruments such as green loans and environmental insurance. Further, it stimulates the driving force of enterprise technological upgrading through policy incentives and market-oriented mechanisms [24]. For example, after utilizing the efficient monitoring system provided by the BCP policy to discover emission reduction potential, enterprises can invest in more environmentally friendly technology research and development through the TFCP policy’s green credit support, thereby achieving synchronous improvement of technology and environmental performance. This mutual empowerment of digital and financial technology guarantees green technologies’ long-term iteration and upgrading.

In summary, the dual pilot policy improves the labor, capital, and credit resource allocation efficiency and the level of green technology innovation in dual pilot cities through resource allocation and collaborative innovation effects. Improving the resource allocation efficiency and green technology innovation level will significantly enhance carbon emission efficiency [25,26,27,28,29]. Therefore, this paper proposes the following hypotheses.

Hypothesis 1 (H1): 

The dual pilot policy can improve urban carbon emission efficiency.

Hypothesis 2 (H2): 

The dual pilot policy enhances urban carbon emission efficiency by improving the allocation efficiency of labor, capital, and credit resources, as well as the level of green technological innovation, through resource allocation effects and collaborative innovation effects.

Based on the above analysis, the specific impact mechanisms are shown in Figure 1 below.

3. Materials and Methods

3.1. Variable Description

3.1.1. Dependent Variable

In calculating urban carbon emission efficiency, urban carbon emission data are required. The scientific and rational construction of carbon emission efficiency requires the consideration of comprehensive input and output indicators. Therefore, this paper includes energy consumption as an input indicator and considers carbon emissions as an undesired output. The input variables for carbon emission efficiency (CEE) selected in this paper include labor, capital, and energy, with the desired output being urban GDP and the undesired output being urban carbon emissions. However, data availability issues make the urban carbon emission indicator difficult to measure directly. Therefore, this paper first introduces the method for calculating urban carbon emissions and then measures urban carbon emission efficiency using the super-efficiency SBM model.

(1)

Calculation of urban carbon emissions (CE)

The calculation method for carbon emissions typically involves multiplying the consumption of various energy sources by their respective carbon emission factors and then summing them to obtain the total carbon emissions. However, due to the lack of comprehensive data on urban energy consumption by type in China, using the urban energy consumption and carbon emission coefficient method to measure city-level carbon emissions may result in significant measurement errors.

Considering that province-level data on various types of energy consumption are complete in China, carbon emissions at the provincial level can be measured using the carbon emission coefficient method. Additionally, since nighttime light data are strongly correlated with carbon emissions [30], and nighttime light data at the province and city levels are relatively complete, carbon emission data at the city level can be obtained using province- and city-level nighttime light data, along with province level carbon emission data [31]. The logic for the calculation is as follows: First, measure the carbon emissions at the province level based on the consumption of various types of energy at the province level. Then, a regression between provincial carbon emission data and provincial nighttime light data is performed to establish the correlation between carbon emissions and nighttime light data. Finally, based on the correlation between carbon emissions and nighttime light data, as well as nighttime light data at the city level, the carbon emissions at the city level are obtained. Specifically, the calculation process of urban carbon emissions is as follows:

The first step was to measure carbon emissions at the provincial level. Based on the consumption data of nine types of energy, such as coal and coke, at the provincial level in the China Energy Statistical Yearbook and their carbon emission coefficients, the carbon emissions at the province level were measured.

The second step was to calibrate the nighttime lighting data. The nighttime light data were obtained from two satellites: the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The data from the DMSP-OLS satellite are available up to 2013, while the data from the NPP-VIIRS satellite are available from 2012 to the present. Given that the nighttime light data were derived from two satellites and could not be compared, we extracted the DMSP-OLS-like raster data constructed by Wu et al. [32], which integrated both the DMSP-OLS and SNPP-VIIRS data. Based on this, nighttime light data at the province and city levels were obtained.

The third step was to obtain the correlation between nighttime light data and carbon emissions. We fit the province level carbon emissions obtained in the first step and the provincial-level nighttime light data obtained in the second step. The specific model construction is as follows:

$${CE}_{it}^p=\gamma \times {NL}_{it}^p+{\phi }_i+{\omega }_t+{\epsilon }_{it}$$

(1)

where p represents the province; ${CE}_{it}^p$ represents the carbon emissions of province i in year t; ${NL}_{it}^p$ represents the total nighttime light of province i in year t; ${\phi }_i$, ${\omega }_t, \mathrm{a}\mathrm{n}\mathrm{d} {\epsilon }_{it}$ represent the individual fixed effect, time fixed effect, and error term, respectively. By using a two-way fixed effects model, the estimated value of coefficient $\gamma$, estimated value of ${CE}_{it}^p$, and individual and time fixed effects were obtained. This paper found a positive and highly significant $\gamma$ (t-value greater than 100) through regression analysis of Equation (1), indicating that there was indeed a significant correlation between nighttime light data and carbon emissions. The specific results are shown in

Table A1. Regression results of provincial carbon emissions and nighttime lighting fitting.

Variables	Provincial Level Carbon Emissions
Provincial nighttime light	0.699 ***
	(0.003)
City/Year fixed effect	YES
Observations	496
Adj. R2	0.999

Note: Nighttime lighting and carbon emissions are both logarithmic values. Robust standard errors are shown in parentheses. *** divulges 1% significance levels.

.

The fourth step was to measure the carbon emissions at the city level based on the correlation between nighttime light data and carbon emissions, as well as nighttime light data at the city level. Specifically, the following model was constructed using a top–down approach to measure the carbon emissions of cities:

$${CE}_c={CE}_p\times \left(\widehat{{CE}_c}/\widehat{{CE}_p}\right)={CE}_p\times \left[\right(\widehat{\gamma }\times {NL}_c+\widehat{{\phi }_p}+\widehat{{\omega }_t})/(\widehat{\gamma }\times {NL}_p+\widehat{{\phi }_p}+\widehat{{\omega }_t}\left)\right]$$

(2)

In Equation (2), ${CE}_c$ represents the carbon emissions at the city level. ${CE}_p$ represents the carbon emissions of the province where the city is located (obtained from the first step). ${NL}_c$ and ${NL}_p$ are the nighttime light data of the city and the nighttime light data of the province where the city is located, respectively (obtained from the second step). $\widehat{\gamma }$ is the regression coefficient obtained from Equation (1) in the third step. $\widehat{{\phi }_p}$ and $\widehat{{\omega }_t}$ are the individual and time fixed effects of the province where the city is located, respectively. $\widehat{{CE}_c}$ is the estimated carbon emissions at the city level based on ${NL}_c$. $\widehat{{CE}_p}$ is the estimated carbon emissions of the province where the city is located based on ${NL}_p$. Based on ${CE}_p$, $\widehat{{CE}_c},$ and $\widehat{{CE}_p}$, the carbon emissions at the city level were calculated. According to the calculations, taking 2022 as an example, the total urban carbon emissions in 2022 were approximately 11 billion tons, which was consistent with China’s real total carbon emissions in 2022 (11.477 billion tons). This indicated that the carbon emission calculation in this paper was highly reasonable.

(2)

Measurement of urban carbon emission efficiency (CEE)

In summary, the specific indicators for measuring carbon emission efficiency are shown in

Table 1. Descriptions of input and output indicators of urban carbon emission efficiency.

First Level Indicator	Second Level Indicator	Third Level Indicator
Inputs	Labor	Number of employees in prefecture-level city.
	Capital	$\mathrm{Capital} \mathrm{is} \mathrm{represented} \mathrm{by} \mathrm{the} \mathrm{stock} \mathrm{of} \mathrm{capital}. \mathrm{This} \mathrm{paper} \mathrm{adopts} \mathrm{the} \mathrm{perpetual} \mathrm{inventory} \mathrm{method} \mathrm{to} \mathrm{calculate} \mathrm{capital} \mathrm{stock}, \mathrm{and} \mathrm{the} \mathrm{formula} \mathrm{is}: K_{i,t}=K_{i,t-1}(1-{\delta }_{i,t})+I_{i,t}$$, \mathrm{where} K_{i,t}$$\mathrm{represents} \mathrm{the} \mathrm{capital} \mathrm{stock} \mathrm{of} \mathrm{city} \mathrm{i} \mathrm{in} \mathrm{year} \mathrm{t};{\delta }_{i,t}$$\mathrm{represents} \mathrm{the} \mathrm{capital} \mathrm{depreciation} \mathrm{rate} (9.6\%); \mathrm{and} I_{i,t}$ represents investment.
	Energy	Energy is calculated by the total energy consumption based on nighttime light data.
Desired outputs	Urban GDP	Urban GDP calculated at 2007 constant price.
Undesired outputs	Urban carbon emissions	Urban carbon emissions are calculated based on nighttime light data.

. For the calculation of urban energy consumption, considering the high correlation between energy consumption and nighttime light values [25], this paper first measured the total energy consumption at the province level. Then, based on the same method, we replaced the above carbon emissions with energy consumption. The province-level energy consumption data and the nighttime light data were used to fit the city-level energy consumption, thereby obtaining the total energy consumption at the city level. The specific indicators for calculating energy consumption efficiency are shown in Table 1.

In terms of the selection of measurement methods, considering that the general SBM model often results in multiple cities having an efficiency value of 1 after calculation, this paper is based on the super-efficiency model proposed by Andersen and Petersen [33]. The specific model is as follows:

$$\rho =min{\displaystyle \frac{1+\frac{1}{m}{\sum }_{i=1}^m\frac{s_i^x}{x_{ik}}}{1-\frac{1}{s_1+s_2}({\sum }_{r=1}^{s_1}\frac{s_r^y}{y_{rk}}+{\sum }_{t=1}^{s_2}\frac{s_t^z}{z_{tk}})}}$$

(3)

$$\mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}{x}_{ik}\ge {\displaystyle \sum _{j=1,j\ne k}^n}{x}_j{\lambda }_j-s_i^x,\forall i\\ y_{rk}\le {\displaystyle \sum _{j=1,j\ne k}^n}{y}_j{\lambda }_j+s_r^y,\forall r\\ z_{tk}\ge {\displaystyle \sum _{j=1,j\ne k}^n}{z}_j{\lambda }_j-s_t^z,\forall t\\ 1-{\displaystyle \frac{1}{s_1+s_2}}({\displaystyle \sum _{r=1}^{s_1}}{\displaystyle \frac{s_r^y}{y_{rk}}}+{\displaystyle \sum _{t=1}^{s_2}}{\displaystyle \frac{s_t^z}{z_{tk}}})>0\\ s_i^x,s_r^y,s_t^z,{\lambda }_j\ge 0,\forall i,r,t,j\end{array}\right.$$

where $x_{ik}$, $y_{rk},$ and $z_{tk}$ denote the inputs, desired outputs, and undesired outputs, respectively; $s^x$, $s^y,$ and $s^z$ denote the slack of inputs, desired outputs, and undesired outputs, respectively; $m$, $s_1,$ and $s_2$ denote the number of inputs, desired outputs, and undesired outputs, respectively; $\mathrm{n}$ denotes the number of decision-making units; ${\lambda }_j$ denotes the weight vector; and $\rho$ denotes CEE.

The SBM model is specifically designed to measure efficiency. The smaller the input, the greater the expected output and the lower the undesired output, the higher the efficiency value. In the traditional SBM model, efficiency is defined as a function of the slack variables of inputs and outputs, allowing for the simultaneous consideration of input redundancy and output insufficiency. The super-efficiency SBM model builds upon the traditional SBM model by allowing efficiency values to exceed 1, further distinguishing efficiency differences among cities. Therefore, the super-efficiency SBM model is well-suited for measuring urban carbon emission efficiency.

3.1.2. Independent Variable

The dual pilot policy (DF) formed by the BCP and TFCP policies is the core independent variable, which is obtained by multiplying the policy dummy variable (${\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}$) and the time dummy variable (${\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$). If a city is both a BCP policy pilot city and a TFCP policy pilot city, it will be listed as the treatment group and ${\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}$ is assigned a value of 1; otherwise, the value of ${\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}$ is 0. ${\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$ is a variable that distinguishes the time before and after implementation of the dual pilot policy. ${\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{i}\mathrm{t}}$ of the treatment group is assigned a value of 1 in the year when the dual pilot policy is implemented and in the following years; otherwise, it is 0.

3.1.3. Selection of Control Variables

In light of the studies by Zhang and Fan [15], Gao et al. [34], and Ai and Yan [35], the control variables were selected as follows: ① Urban economic development level (LPGDP). It is expressed in the logarithm of regional per capita GDP. ② Fiscal intervention (GOV). It is measured as the percentage of government fiscal expenditure to GDP. ③ Technology expenditure level (STE). It is measured as the percentage of technology expenditure to government fiscal expenditure. ④ Financial Development (FDE). It is expressed as the ratio of total deposits and loans of financial institutions to GDP. ⑤ Urbanization rate (URBAN). It is expressed as the percentage of urban permanent residents to the total permanent population. ⑥ Environmental Regulation (ENREG). It is expressed as the ratio of industrial pollution control investment to industrial added value in the province where the city is located. ⑦ Industrial structure. It includes industrial structure upgrading (LUPGRADE) and industrial structure rationalization (RATIONAL). LUPGRADE is given by the logarithmic value of the sum of the product of each of the three industries’ output share and their respective productivity. Productivity is calculated as the ratio of industry output to the number of employees in that industry. RATIONAL is the rationalization index measured based on the Theil index, where a smaller value represents a more rational industrial structure.

3.2. Model Construction

Implementing the dual pilot policy led to differences in carbon emission efficiency between pilot cities before and after policy implementation and simultaneously to differences in carbon emission efficiency between pilot and non-pilot cities. Based on the above differences and considering that the pilot policy was implemented in multiple batches, this paper used the staggered difference-in-differences model to explore the impact of the dual pilot policy on urban carbon emission efficiency. We applied the following regression model:

$${CEE}_{it}=\alpha +\beta {DF}_{it}+\rho X_{it}+{\mu }_i+{\lambda }_t+{\xi }_{it}$$

(4)

where i and t represent the city and year, respectively; and ${CEE}_{it}$ is the dependent variable that represents the carbon emission efficiency of city i. ${DF}_{it}$ represents the dummy variable of the dual pilot policy. If city i implemented the dual pilot policy in year t, ${DF}_{it}$ is equal to 1; otherwise, ${DF}_{it}$ is 0. $X_{it}$ represents a set of control variables. ${\mu }_i$ and ${\lambda }_t$ represent the city and year fixed effects, respectively. ${\xi }_{it}$ is the error term. $\beta$ is the coefficient that this paper is most concerned about. If $\beta$ is significantly greater than 0, it indicates that the dual pilot policy will improve the urban carbon emission efficiency.

3.3. Data Sources

Considering that the BCP policy was implemented in three batches in 2014, 2015, and 2016, and the TFCP policy was implemented in two batches in 2011 and 2016, this paper selected panel data from 284 prefecture-level cities from 2007 to 2022 to evaluate the impact of the dual pilot policy on urban carbon emission efficiency. The data were obtained from the annual China Urban Statistical Yearbook, China Energy Statistical Yearbook, provincial and municipal statistical yearbooks, and DMSP-OLS and SNPP-VIIRS nighttime light data.

The descriptive statistics of the variables are shown in

Table 2. Descriptive statistics of variables: number of observations, mean, standard deviation, minimum, 25th percentile, 50th percentile, 75th percentile, and maximum.

Variable	Number	Mean	Standard Deviation	Minimum	25th Percentile	50th Percentile	75th Percentile	Maximum
CEE	4544	0.383	0.163	0.090	0.288	0.350	0.430	1.477
DF	4544	0.056	0.231	0.000	0.000	0.000	0.000	1.000
LPGDP	4544	10.585	0.692	4.595	10.144	10.608	11.055	13.056
GOV	4544	18.986	10.245	4.262	12.457	16.371	22.416	148.516
STE	4544	1.628	1.646	0.052	0.587	1.122	2.080	21.184
FDE	4544	2.404	1.195	0.560	1.590	2.086	2.839	21.302
URBAN	4544	54.822	15.842	16.413	43.500	53.005	64.500	100.000
ENREG	4544	0.003	0.003	0.000	0.001	0.002	0.004	0.031
LUPGRADE	4544	14.054	1.197	10.939	13.229	13.857	14.699	21.418
RATIONAL	4544	0.295	0.244	0.000	0.111	0.243	0.417	3.675

. The mean value of CEE was 0.383, with a minimum value of 0.090 and a maximum value of 1.477. The 75th percentile value was 0.430, indicating that the CEE of most cities was around 0.4, with only a small number of cities having a CEE greater than 1, suggesting a right-skewed distribution. The mean value of DF was 0.056, indicating that only a small proportion of cities in the sample were dual pilot cities. Given that this study aimed to explore the impact of DF on CEE, we preliminarily calculated the change in CEE for dual pilot cities during implementation of the dual pilot policy. Specifically, the average CEE of dual pilot cities increased by approximately 0.03 between 2014 and 2022, while no significant changes were observed in the CEE of non-pilot cities during the same period. This preliminary finding suggested that the dual pilot policy may have a policy effect on carbon emission efficiency. However, since various factors influence CEE, econometric models were needed for further rigorous empirical analysis to assess the policy effect of the dual pilot policy on carbon emission efficiency.

4. Results and Discussion

4.1. Benchmark Regression

Table 3. Benchmark regression results of the impact of the digital–financial dual pilot policy on carbon emission efficiency.

Variables	(1)	(2)	(3)	(4)
DF	0.039 ***	0.043 ***	0.042 ***	0.037 ***
	(0.012)	(0.012)	(0.013)	(0.012)
LPGDP		0.096 ***		0.072 ***
		(0.022)		(0.026)
GOV		−0.001 **		−0.003 ***
		(0.001)		(0.001)
STE		0.003 *		0.004 *
		(0.002)		(0.002)
FDE		−0.024 ***		−0.020 **
		(0.008)		(0.008)
URBAN		0.000		0.001
		(0.000)		(0.000)
ENREG		1.477 **		2.059 **
		(0.630)		(0.925)
LUPGRADE		0.016 ***		0.014 **
		(0.005)		(0.006)
RATIONAL		−0.088 ***		−0.081 **
		(0.029)		(0.032)
Constant	0.380 ***	−0.764 ***	0.376 ***	−0.499 *
	(0.002)	(0.231)	(0.002)	(0.280)
City/Year fixed effect	YES	YES	YES	YES
Observations	4544	4544	3056	3056
Adj. R2	0.689	0.730	0.729	0.767

Note: Robust standard errors are shown in parentheses. ***, **, and * divulge 1%, 5%, and 10% significance levels, respectively.

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:

$${CEE}_{it}=\alpha +{\sum }_{v\ge -6(v\ne -2)}^6{\beta }_v{D}_{it}^v+{\rho X}_{it}+{\mu }_i+{\lambda }_t+{\xi }_{it}$$

(5)

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), $D_{it}^v$ is a set of dummy variables. $n_i$ is the implementation year of the dual pilot policy (0 for cities without a pilot policy), if t-$n_i$ ≤ −6, then set $D_{it}^{-6}=1$; if t-$n_i$=v (v≠−6, 6), then set $D_{it}^v=1$; if t-$n_i$ ≥ 6, then set $D_{it}^6=1$. 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. Bacon decomposition results of the impact of the dual pilot policy on carbon emission efficiency.

	(1)	(2)	(3)	(4)
	DID Estimator	Weight	DID Estimator	Weight
earlier treated group vs. later treated group	0.089	0.007	0.088	0.011
later treated group vs. earlier treated group	−0.001	0.007	−0.001	0.011
time-varying treated group vs. never treated group	0.039	0.986	0.042	0.978

. 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. Robustness tests of PSM-DID method and excluding interference from other policies.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	PSM-DID	Excluding Interference from Other Policies
DF	0.048 ***	0.033 ***	0.037 ***	0.039 ***	0.037 ***	0.036 ***
	(0.012)	(0.012)	(0.012)	(0.012)	(0.012)	(0.012)
IPC		0.029
		(0.028)
SPC			0.004
			(0.006)
BAR				0.019 ***
				(0.007)
GFR					0.035
					(0.050)
LCB						0.006
						(0.007)
Control	Yes	Yes	Yes	Yes	Yes	Yes
City/Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	1756	3056	3056	3056	3056	3056
Adj. R2	0.773	0.767	0.767	0.767	0.767	0.767

Note: Robust standard errors are shown in parentheses. *** divulges 1% significance level.

, 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. Robustness tests of excluding interference from regional characteristics and non-random selection of pilot cities.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Excluding Regional Characteristics		Excluding Non-random Selection of Pilot Cities
DF	0.030 **	0.035 ***	0.038 ***	0.028 **	0.037 ***	0.036 ***
	(0.013)	(0.012)	(0.012)	(0.013)	(0.013)	(0.012)
TLK			−0.000
			(0.001)
TSH				0.005 ***
				(0.002)
TZX					0.000
					(0.002)
TTQ						0.006 ***
						(0.002)
Year × GP	YES
Year × EMW		YES
Control	YES	YES	YES	YES	YES	YES
City/Year fixed effect	YES	YES	YES	YES	YES	YES
Observations	3056	3056	3056	3056	3056	3056
Adj. R2	0.774	0.769	0.767	0.768	0.767	0.767

Note: Robust standard errors are shown in parentheses. *** and ** divulge 1% and 5% significance levels, respectively.

, 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:

$${LnY}_{it}=c+\alpha {LnL}_{it}+\beta {LnK}_{it}+{\xi }_{it}$$

(6)

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 ${disL}_{it}=\left|\alpha Y_{it}/{w_{it}L}_{it}-1\right|$ and ${disK}_{it}=\left|\beta Y_{it}/{r_{it}K}_{it}-1\right|$, respectively.

Thirdly, we calculated $\alpha$, $\beta$, ${disL}_{it}$, and ${disK}_{it}$ 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 $FD=\left|(ECU-INCU)/INCU\right|$. $ECU$ 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:

$$M_{it}=\alpha +{\beta DF}_{it}+{\rho X}_{it}+{\mu }_i+{\lambda }_t+{\xi }_{it}$$

(7)

In Equation (7), M represents the mechanism variables mentioned above ($disL$, $disK$, $FD,FD2$, 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. Mechanism analysis of urban labor, capital, and credit resource allocation efficiency and green technological innovation.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	disL	disK	FD	FD2	ngpat	qgpat
DF	−0.585 ***	−0.278 ***	−0.192 **	−0.147 ***	0.268 ***	0.038 ***
	(0.129)	(0.049)	(0.075)	(0.052)	(0.019)	(0.007)
Control	Yes	Yes	Yes	Yes	Yes	Yes
City/Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	3056	3056	21,010	1910	3056	1365
Adj. R2	0.659	0.153	0.016	0.265	0.764	0.621

Note: Robust standard errors are shown in parentheses. *** and ** divulge 1% and 5% significance levels, respectively.

. 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 impact of mechanism variables on carbon emission efficiency.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
disL	−0.013 ***
	(0.004)
disK		−0.008 **
		(0.003)
Fd			−0.001 *
			(0.001)
Fd2				−0.021 ***
				(0.006)
ngpat					0.111 ***
					(0.015)
qgpat						0.138 ***
						(0.040)
Control	Yes	Yes	Yes	Yes	Yes	Yes
City/Year fixed effect	Yes	Yes	Yes	Yes	Yes	YES
Observations	4544	4544	28894	2989	4544	2194
Adj. R2	0.735	0.729	0.930	0.751	0.733	0.776

Note: Robust standard errors are shown in parentheses. ***, **, and * divulge 1%, 5%, and 10% significance levels, respectively.

. 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. Comparative analysis of the synergistic effects of the dual pilot policy on urban carbon emission efficiency.

Variables	(1)	(2)	(3)
	Control Group: Single Pilot Policy Cities	Control Group: BCP Policy Pilot Cities	Control Group: TFCP Policy Pilot Cities
DF	0.053 ***	0.058 ***	0.030 *
	(0.012)	(0.012)	(0.016)
Control	YES	YES	YES
City/Year fixed effect	YES	YES	YES
Observations	2000	1712	800
Adj. R2	0.735	0.746	0.787

Note: Robust standard errors are shown in parentheses. *** and * divulge 1% and 10% significance levels, respectively.

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. Heterogeneity analysis of the implementation time sequence of the dual pilot policy.

Variables	(1)	(2)	(3)	(4)
	Confirm that the Second Batch of TFCP Policy is Implemented Earlier Than the Third Batch of BCP Policy		Confirm that the Third Batch of BCP Policy is Implemented Earlier Than the Second Batch of TFCP Policy
	Implement TFCP First and Then BCP	Implement BCP First and Then TFCP	Implement TFCP First and Then BCP	Implement BCP First and Then TFCP
DF	0.040 ***	0.029 **	0.046 ***	0.009
	(0.014)	(0.012)	(0.014)	(0.020)
Control	YES	YES	YES	YES
City/Year fixed effect	YES	YES	YES	YES
Observations	2976	2624	2944	2656
Adj. R2	0.767	0.716	0.771	0.715

Note: Robust standard errors are shown in parentheses. *** and ** divulge 1% and 5% significance levels, respectively.

.

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. Heterogeneity analysis results based on urban resource endowment, digital infrastructure construction, and administrative level.

Variables.	(1)	(2)	(3)	(4)	(5)	(6)
	Resource-Based	Non-Resource-Based	High Digital Infrastructure Construction	Low Digital Infrastructure Construction	High Administrative Level	Low Administrative Level
DF	−0.031	0.049 ***	0.035 **	0.012	0.086 ***	−0.013
	(0.040)	(0.013)	(0.016)	(0.014)	(0.014)	(0.020)
Inter-group differences	0.000 ***		0.000 ***		0.048 **
Control	YES	YES	YES	YES	YES	YES
City/Year fixed effect	YES	YES	YES	YES	YES	YES
Observations	1168	1888	1670	1372	336	2720
Adj. R2	0.740	0.761	0.794	0.784	0.912	0.691

Note: Inter-group differences are obtained using Fisher’s permutation test with 500 bootstrap samples. Robust standard errors are shown in parentheses. *** and ** divulge 1% and 5% significance levels, respectively.

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. Heterogeneity analysis results based on economic development level, financial development level, and intellectual property protection intensity.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	High Economic Development	Low Economic Development	High Finance Development	Low Finance Development	Strong Intellectual Property Protection	Weak Intellectual Property Protection
DF	0.046 ***	0.004	0.035 **	−0.010	0.070 ***	0.048
	(0.015)	(0.011)	(0.014)	(0.036)	(0.015)	(0.044)
Inter-group differences	0.000 ***		0.000 ***		0.000 ***
Controls/Cons	YES	YES	YES	YES	YES	YES
City/Year fixed effect	YES	YES	YES	YES	YES	YES
Observations	1501	1542	1523	1522	1523	1526
Adj. R2	0.772	0.758	0.851	0.783	0.819	0.880

Note: Inter-group differences are obtained using Fisher’s permutation test with 500 bootstrap samples. Robust standard errors are shown in parentheses. *** and ** divulge 1% and 5% significance levels, respectively.

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.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This paper uses panel data from 284 prefecture-level cities in China from 2007 to 2022. It considers the digital–financial dual pilot policy formed by combining the BCP and TFCP policies as a quasi-natural experiment. And based on measuring urban carbon emission efficiency using nighttime light data and the super efficiency SBM model, the paper explores the impact of the dual pilot policy on urban carbon emission efficiency through a staggered difference-in-differences model. The main conclusions are as follows: (1) The dual pilot policy significantly improves urban carbon emission efficiency, and compared to a single pilot policy, the dual pilot policy has a greater impact on improving carbon emission efficiency. (2) The dual pilot policy can improve the resource allocation efficiency of urban labor, capital, and credit and the level of green technology innovation by generating resource allocation and collaborative innovation effects, thereby enhancing urban carbon emission efficiency. (3) Implementing the TFCP policy first and then the BCP policy can more effectively enhance the impact of the dual pilot policy on improving urban carbon emission efficiency. (4) The impact of the dual pilot policy on carbon emission efficiency exhibits heterogeneity, with the policy effects being more significant in non-resource-based cities, high digital infrastructure-level cities, high administrative-level cities, high economic and financial development-level cities, and cities with strong intellectual property protection.

5.2. Policy Recommendations

Based on the research findings of this paper, the following policy recommendations are put forward for reference.

Firstly, further enhance the role of the digital–financial dual pilot policy in improving carbon emission efficiency. On the one hand, the government should strengthen communication and coordination among multiple departments and optimize the policy environment to integrate the BCP and TFCP policies effectively. On the other hand, carbon emission efficiency should be incorporated into the dual pilot policy evaluation system, and a dedicated policy effect evaluation department should be established to continuously refine and summarize the construction experiences of dual pilot cities while promoting advanced experience models.

Secondly, it is essential to ensure the integration of the BCP and TFCP policies at the city level to achieve more efficient resource allocation and green technological innovation. Local governments should promote collaboration between different departments to break down information silos and ensure more efficient utilization of labor, capital, and credit resources. At the same time, the government should increase fiscal policy support for green technology innovation activities with “low-carbon, zero-carbon, and negative-carbon” characteristics. And it should fully leverage the role of financial markets in incentivizing and supervising, thereby promoting the steady development of green technological innovation.

Thirdly, the government should promote the implementation of the dual pilot policy according to actual development situations. On the one hand, we should not only expand the coverage of the dual pilot policy in low administrative-level cities but also advocate for a fair factor competition environment and stronger intellectual property protection. At the same time, efforts should be made to accelerate the construction and improvement of urban digital infrastructure. On the other hand, we should first consider implementing the TFCP policy to encourage the integration of technology and finance, followed by the BCP policy, to achieve a more efficient win–win situation for economic development and environmental protection.