Does the Innovative City Pilot Policy Promote Urban Energy Use Efficiency? Evidence from China

Abstract

The innovative city pilot policy (ICPP) is a key policy practice in China’s innovation-driven economic strategy, yet its influence on urban energy use efficiency (UEUE) has yet to be assessed. This study used balanced panel data from Chinese cities from the period of 2006 to 2022 to investigate the impact of innovative cities on urban energy consumption efficiency. The double-difference method, which treats creative cities as quasi-natural experiments, was applied to identify the impact mechanism of these cities. Additionally, this study looked at heterogeneity from several angles and assessed the effects of the policy on the environment. Following thorough testing to guarantee the reliability of the findings of this study—such as changing variables, ruling out further policy interferences, and running placebo tests—it can be concluded that the pilot program significantly improves urban energy consumption. The mechanism analysis performed in this study shows that, via talent concentration, utilizing technology, and optimizing the industrial structure, the creative city pilot policies increase energy consumption efficiency. Additionally, a heterogeneity analysis shows that eastern cities and cities with a high degree of digital finance benefit most from the legislation in terms of energy consumption efficiency. The application of creative city pilot programs has a more noticeable effect on increasing the energy consumption efficiency in eastern cities, as well as in cities with a high degree of digital infrastructure and digital finance, according to the heterogeneity analysis. Furthermore, an environmental consequence test shows that, by encouraging the growth of UEUE, the development of innovative cities can successfully help to reduce carbon emissions.

1. Introduction

Energy use efficiency is the ratio of useful energy gained to the total energy invested in the conversion and use of energy; it measures the extent to which energy is used efficiently in production, transmission, and consumption processes. High energy utilization efficiency means that more useful work or output can be produced when a given amount of energy is used, while a low level of efficiency means that energy is wasted and lost [1]. The Chinese government emphasize how critical it is to aggressively and persistently advance the objectives of carbon peak and carbon neutrality. It also highlights how important it is to advance the energy revolution, optimize the clean and effective use of coal, and raise energy efficiency. These actions are essential for fostering modernization and transformation in the national energy structure and attaining high-caliber development. China’s large population, high-carbon energy consumption structure, and significant economic output all contribute to an ongoing rise in national energy demand, which is not expected to change anytime soon, thus seriously hampering the objectives of the ‘dual-carbon’ goals.

Innovation is the primary driving force of development and is a powerful initiative to promote an improvement in energy use efficiency [2]. As an important policy practice in the process of innovation-driven development in China, the national innovation city pilot policy (ICPP) has underpinned six rounds of pilot city approvals since 2008. The development of energy use efficiency, as one of the most important factors in the transformation and application of innovation results, is conducive to reducing greenhouse gas emissions and mitigating global climate change [3]. Therefore, the innovative city pilot policy also pays significant attention to the development of energy use efficiency. Currently, China’s innovative city pilot policy aims to promote the sustainable development of cities through scientific and technological innovation, especially in the enhancement and optimization of energy use efficiency, with an emphasis on the use of advanced smart grids and clean energy technologies to improve the overall energy efficiency of cities [4]. In European countries, the innovation policies implemented a focus on the transformation of the green economy, emphasizing the use of renewable energy and the improvement of energy efficiency while focusing on social equity and environmental protection [5]. By comparing China’s innovative city pilot policies with European countries’ practices in energy efficiency, important similarities and differences in policy objectives, technology application, financial support, and social participation can be identified. While rapidly advancing energy efficiency, China needs to learn from the successful experience of Europe to overcome the challenges in the implementation process and achieve a higher level of sustainable development.

Today, China is the world’s largest carbon emitter and therefore has a significant international role in promoting improvements in energy use efficiency. Moreover, China is currently in a critical period of structural economic transformation, and improving energy efficiency is an important way to optimize this process [6]. Studying the successful experiences of innovative cities can provide a reference for nationwide economic transformation; moreover, high energy efficiency is usually accompanied by a better quality of life and increased environmental well-being. Innovative cities, by offering more efficient energy use and more comfortable living environments, are likely to attract a greater inflow of people, especially young talent and highly skilled labor [7], which can thus foster economic development and innovation. However, can energy efficiency be improved through the adoption of national creative city pilot programs? If yes, how can cities use their influence? Do the location of the city, the degree of digital financial growth, and the quality of the urban infrastructure affect how effective national creative city pilot initiatives are at reducing energy use? As these questions have not been sufficiently answered in the literature, this research uses panel data from 266 Chinese cities between 2006 and 2022 to investigate innovative cities as quasi-natural experiments, utilizing the double-difference method to assess how creative cities affect energy efficiency. Additionally, many robustness tests are carried out in this study to offer fresh empirical support for the connection between China’s creative cities and energy-efficient usage.

The marginal contributions of this research work are as follows: Firstly, this study analyzes the impact of innovative city construction on China’s energy efficiency from a macro-policy perspective, which not only enriches the existing research literature but also provides a powerful reference for China’s ‘dual-carbon’ goal. Secondly, this study introduces the TOE theoretical framework to identify the impacts of innovative city pilot policies on urban energy efficiency from the three dimensions of science and technology, structure, and environment, which further expands the use of the TOE theoretical framework. Thirdly, we further examine the environmental consequences of the ICPP on urban energy transition, which provides empirical evidence that cities are able to reduce pollution and carbon emissions to cope with the environmental crisis.

2. Literature Review

2.1. Energy Efficiency

Improving energy efficiency not only helps conserve resources and reduce environmental impacts but also brings significant economic and social benefits, representing an important strategy for achieving sustainable development and addressing global challenges. The research process on energy use efficiency is different at home and abroad; most foreign scholars focus on the construction of energy efficiency measurement systems and the exploration of energy-saving technology [8], and the research has a certain degree of maturity.

The research on energy efficiency in China mainly focuses on the relationship between economic and social variables and energy use efficiency, including such variables as technological innovation, factor market distortion, foreign investment, industrial structure, social assets, and energy use efficiency [9,10,11]. Another strand of the literature explores the impact of environmental policy variables on energy use efficiency. Based on data from China, Fang et al. [11] found that environmental regulation reduces firms’ production costs, which inhibits energy efficiency. Wu et al. [12] used spatial econometric modeling to find a significant non-linear relationship between environmental regulation and green total factor energy use efficiency. Curtis and Lee [13] found that command-and-control environmental regulation decreases energy efficiency by increasing electricity purchased from off-site utilities. The third aspect is the impact of social development variables on energy use efficiency. Chen et al. [14] stated that an energy transition contributes to energy use efficiency, a process that not only contributes to climate change mitigation but also effectively reduces the emission of air pollutants. According to a report by the International Energy Agency (IEA), the global energy mix is transitioning to renewable energy, and the share of renewable energy is expected to increase significantly by 2030. Currently, many European countries are achieving improvements in air quality by implementing stringent emission standards and promoting renewable energy. For example, Germany’s energy transition policy promotes the use of wind and solar energy, reducing the dependence on fossil fuels and significantly reducing air pollution levels in cities. Not only that, but the increased efficiency of energy use reduces pollutant emissions and lowers the concentration of air pollutants. Studies have shown that the implementation of clean energy sources such as wind and solar energy can reduce PM2.5 emissions by up to 20 percent in certain areas. In turn, reducing air pollution can help reduce health problems caused by pollution [15], which in turn reduces healthcare expenditures and improves quality of life. Studies have shown that China’s energy transition policies have been effective in improving urban air quality and are expected to prevent tens of thousands of air pollution-related deaths each year [16].

2.2. Innovative City Pilot Policy Evaluation

In the context of broad ICPP implementation, aiming to evaluate the success of laws supporting innovation, many scholars have started to methodically analyze the policy’s implementation impacts, with a focus on urban innovation. The national creative city pilot program is believed to have greatly boosted technological innovation in industries and enterprises. This has been made feasible by increasing the number of highly skilled people in a given area, emphasizing urban industrial agglomeration and pushing important actors to innovate, an approach expected to continue to produce positive results over time [14]. Moreover, the pilot technique has a distinct spatial spillover effect that can raise the level of innovation capability in nearby cities. Furthermore, the development of national creative cities improves territorial innovation capacity in the near and long term; remarkably, the influence of the policy on enhancing territorial innovation potential rises as a city develops. Simultaneously, numerous evaluation results have demonstrated that the pilot programs implemented in national innovative cities not only dramatically increase the level of innovation in the city but also foster entrepreneurship, reduce carbon emissions in the surrounding area, raise the total factor productivity in the manufacturing sector, and support high-quality local economic development [14,17,18].

2.3. Impact of Innovative City Pilots on Energy Use Efficiency

With the acceleration of global urbanization, cities are facing great challenges regarding energy use and environmental protection. As an important strategy to address these challenges, innovative city pilots aim to enhance the sustainable development capacity of cities through technological innovation, policy guidance, and social participation. On the one hand, innovative cities are usually committed to optimizing their energy mix, reducing their dependence on traditional fossil fuels, and increasing the proportion of renewable energy [19]. On the other hand, by introducing technologies such as smart grids and smart buildings, the construction of innovative cities can achieve efficient energy use and management [20]. It has been shown that innovative city pilots have significantly improved energy use efficiency through technological innovation and management optimization; for example, many cities have reduced energy consumption through the promotion of efficient smart buildings [21]. In addition, the application of smart grids has led to more flexible energy distribution, reducing energy waste and improving overall efficiency [22]. Undifferentiated innovative cities have implemented integrated energy management systems during the construction process to optimize energy use strategies through data analysis [23]. Innovative city pilots are usually accompanied by the widespread promotion of renewable energy, with research data showing a significant increase in the use of renewable energy sources such as solar and wind energy in innovative city pilots [24]. Many cities have installed solar photovoltaic systems in public buildings to achieve a self-sufficient energy supply, while some coastal cities have increased their share of renewable energy by building offshore wind farms [25].

In summary, although there have been studies systematically assessing the implementation effect of national innovative city pilot policies from the perspective of urban innovation, and others focusing on their impact on renewable energy, few scholars have paid attention to the impact of innovation-driven policies on energy utilization efficiency. Energy utilization efficiency, as an important technical component of national innovation development, is the key to promoting industrial transformation and upgrades, improving industrial competitiveness, promoting energy conservation and emission reduction, and mitigating climate change. Therefore, it has become important to study the impact of national innovative city pilot policies on energy utilization efficiency with the construction of innovative cities as the base point.

3. Policy Background and Research Hypotheses

3.1. Policy Background

The national innovative city pilot policy is an important policy innovation for China to address the insufficient level of scientific and technological innovation and the rough mode of economic development, and it is also an important driving force for the realization of high-quality development and Chinese-style modernization in the context of the new economic normal. According to the series of pilot policy documents, as the center of regional economic output, social development, and factor concentration, the development mode of cities has a bearing on the economic potential and social stability of the region and even the country. As such, the construction of national innovative cities should be gradually promoted as an important mechanism to enhance the capability of independent innovation, promote the upgrading of the economic development mode, and drive the rapid and high-quality development of the region. Indeed, the construction of innovative cities is an important measure for the implementation of China’s innovation-driven development strategy. In 2008, China started the practice of progressive innovative city construction, and Shenzhen was established as the first pilot innovative city in China because of its pioneering system and advantages in independent innovation. In 2010,China points out that a number of cities should be promoted to take the lead in entering the ranks of innovative cities, so as to provide a model for more cities to embark on the road of innovation and development. In the same year, the National Development and Reform Commission (NDRC) and other administrations approved 44 cities (districts), including Dalian and Xiamen, as pilot innovative cities, which is the largest number of approvals in the entire progressive reform process. By 2022, after seven rounds of pilot city approvals, the number of national innovative pilot cities (districts) had reached 103, with China’s innovative city pilots going through the ‘pilot–propagation–coverage’ development process. In short, the core objective of the innovative city pilot policy is to improve the independent innovation ability of cities, considering that the improvement of energy efficiency is related to the high-quality transformation of China’s economy and the development of scientific and technological innovation; therefore, it is of practical urgency and academic importance to investigate the impact of China’s innovative city policy on energy efficiency.

3.2. Research Hypotheses

3.2.1. Direct Impact: Innovative City Pilot Policy and Energy Use Efficiency

The initial expansion of an innovative city has a positive effect on energy enterprises’ technological advancement and can facilitate the movement and concentration of resource components on a small scale. Concurrently, the early stages of development in trailblazing cities have significant macroeconomic effects, which can significantly increase urban innovation and entrepreneurial activity, thereby promoting the modernization and enhancement of the urban energy infrastructure. When developing creative cities, urban areas employ energy-saving measures and environmentally beneficial building designs. These measures for reducing energy consumption include using energy-efficient equipment, strengthening building structures, and employing renewable energy sources such as solar and wind power. Meanwhile, the concept of a circular economy is typically promoted through the progressive construction of cities. By reusing and recycling materials, this technique aims to reduce energy waste and increase energy efficiency. Innovative city construction enhances energy efficiency and establishes the groundwork for long-term urban growth by using cutting-edge technology, promoting green concepts, and optimizing urban design.

3.2.2. Indirect Impacts Based on the TOE Theoretical Framework

The TOE theory refers to the study of the interaction between technology, organization, and environment by placing the three levels of factors in the same framework and has been widely applied to organizations’ technology integration and adoption behaviors [26]. As a systematic analysis framework based on the application of technology, the TOE framework is used to explore the mechanisms by which technology achieves its effects in multilevel application scenarios. Since the TOE framework does not stipulate the specific explanatory variables of the three categories of technology, organization, and environment, scholars can make appropriate adjustments to it, and thus, the TOE framework is highly flexible and can well explain the causes and influences of complex social phenomena. Currently, the TOE framework is widely used in many research fields, such as urban economic development and social productivity improvement, and is also applicable to the improvement of energy efficiency in China, which is affected by many factors. Based on the TOE theory, this study analyzes the reasons why the ICPP affects energy use efficiency from three dimensions: technology (technology agglomeration effect), organization (talent concentration effect), and environment (industrial structure optimization effect).

Innovative City Pilot Policies, Technology Agglomeration Effect, and Energy Use Efficiency

Innovative city pilot policies can enhance energy use efficiency through the technology clustering effect, and their mechanism of action is mainly reflected in the following aspects. The first is an intelligent management system. Through IoT technology, cities can achieve real-time monitoring and management of energy consumption, optimize resource allocation, and help users and managers understand energy consumption so that they can take measures to reduce energy waste [27]. The second is the innovation of renewable energy technology. Technological innovation has promoted the efficient use of renewable energy sources, such as solar photovoltaic power generation and wind power generation. The popularity of these technologies has enabled cities to diversify their energy supply and reduce their dependence on traditional fossil energy sources [28]. The development of new energy storage technology can effectively store renewable energy, balance supply and demand, and improve the efficiency of energy use. Again, this is the use of green energy-saving technology, and technological innovations have made building design more energy-efficient, with the urban construction process reducing the energy consumption of buildings by optimizing their thermal insulation performance and adopting high-efficiency lighting and air-conditioning systems [29]. The development and application of new energy-saving materials can also significantly improve the energy efficiency of buildings. Finally, it is digital and data driven. On the one hand, by analyzing large amounts of energy consumption data, cities can identify areas and time periods of high energy consumption so that targeted energy-saving measures can be taken. On the other hand, AI technology can help predict energy demand, optimize scheduling, reduce peak loads, and improve energy use efficiency [30]. From this, it can be inferred that the national innovative city pilot policy promotes energy utilization efficiency through the technology clustering effect.

Innovative City Pilot Policy, Talent Concentration Effect, and Energy Use Efficiency

The level of talent concentration, a critical component of promoting technological innovation, has a substantial impact on a country’s ability to innovate independently and absorb cutting-edge technology from other countries. The creative national city pilot program can improve energy efficiency through talent concentration. The realms of technological invention and application are where its manner of action is most visible. The government can boost the city’s innovation vitality by bringing in top individuals with extensive research backgrounds and innovative skills that can encourage technology innovation and application [31], and energy efficiency can be improved by distributing and utilizing experts’ innovative energy technology and research findings. Next, let us consider cooperation and exchange. The talent agglomeration effect enables individuals from various professions and backgrounds to collaborate and share ideas, while cross-border collaboration among varied talent types in innovative cities frequently fosters energy sector innovation and improves energy use efficiency. Finally, we have the setting that fosters policy. Due to the skill advantage provided by the talent agglomeration effect, it is expected that the government will implement more focused policy assistance to boost innovation in the energy sector [32]. Furthermore, talent concentration will foster a creative, open, and welcoming atmosphere, attracting more talent to participate in energy-related innovation activities. This means that the skill agglomeration effect is the mechanism driving the national innovative city pilot program’s energy efficiency gains [33].

Innovative City Pilot Policies, Industrial Structure Optimization Effect, and Energy Use Efficiency

The innovative city pilot policy can enhance energy use efficiency through the optimization effect of industrial structure. The first aspect is the development of high-tech industries. Innovative cities are usually committed to the development of high-tech and high-value-added industries such as information technology, biotechnology, and new energy, which usually have higher energy use efficiency than traditional industries. Moreover, the R&D conducted by high-tech industries and their resulting products and services promote the popularization of advanced technologies, such as smart manufacturing, the internet of things, and artificial intelligence, which can significantly improve energy efficiency in the production process. Secondly, the ICPP promotes industrial synergy and agglomeration [34]. Innovative cities promote the clustering of related industries through the construction of industrial parks and innovation centers. Such clustering can improve the sharing and optimal allocation of resources and reduce energy waste. At the same time, cooperation and technology exchanges between enterprises can promote the effective use of resources and reduce energy consumption, and once again, there are policy incentives. The government gives incentives such as financial subsidies and tax incentives to highly energy-efficient enterprises through innovative city pilot policies, which prompts enterprises to adopt more efficient production technologies and equipment [35]. At the same time, the flow of funds tends to prioritize industries with high energy efficiency and environmental protection characteristics, thus promoting the development of these industries and improving the overall energy use efficiency. Lastly, the level of infrastructure intelligence is improved. Innovative cities focus on building infrastructure such as smart transport, smart grids, and smart buildings, which improve the efficiency of energy management and deployment through information technology [36]. Additionally, intelligent systems can monitor energy use in real time, optimize energy distribution, and reduce waste, thus improving the efficiency of urban energy use. From this, it can be inferred that the national innovative city pilot policy promotes energy use efficiency through the industrial structure optimization effect.

Based on the above analysis, we propose the following research hypotheses:

Hypothesis 1: 

The ICPP can promote UEUE.

Hypothesis 2: 

The ICPP promotes UEUE through the technology agglomeration effect.

Hypothesis 3: 

The ICPP promotes UEUE through the talent agglomeration effect.

Hypothesis 4: 

The ICPP promotes UEUE through industrial structure optimization.

4. Research Design and Data Description

4.1. Model Setting

It should be noted that, due to the fact that some cities in Tibet, Xinjiang, and other provinces have more missing data on key variables, the missing information cannot be accurately restored via interpolation and other means. Therefore, to ensure the reliability of this study’s results, the areas mentioned above were excluded, and the panel data of cities from 2006 to 2022 were finally obtained. Meanwhile, this study examined creative cities as quasi-natural experiments and created a multi-period double-difference model to assess the success of policy implementation, taking into account both regional and temporal variables. The precise formula is given below:

$${\mathrm{EUEU}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{DID}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(1)

In Equation (1), i stands for the city and t stands for the year; EUEU stands for energy use efficiency; ${\mathrm{X}}_{\mathrm{it}}$ is a control variable. ${\mathsf{\mu }}_{\mathrm{i}}$ stands for the city-fixed effect; ${\mathsf{\gamma }}_{\mathrm{t}}$stands for the year-fixed effect; ${\mathsf{\epsilon }}_{\mathrm{it}}$ is a random error term; ${\mathrm{DID}}_{\mathrm{it}}$is a policy dummy variable; and ${\mathsf{\alpha }}_1$captures the specific impact of the innovative city pilot policy on the efficiency of energy use.

4.2. Indicator Construction

4.2.1. Explained Variables

The urban energy use efficiency (UEUE) is calculated using the SBM-Malmquist–Luenberger index method. We chose labor, money, and energy as input factors. Labor refers to the number of people employed in the city, capital indicates the amount of capital stock in the city, and energy denotes the city’s total energy usage. The planned objective was the city’s total gross domestic product (GDP), but the unintended consequences included the release of sulfur dioxide, industrial soot, and industrial effluent from the city’s industrial sector.

4.2.2. Core Explanatory Variables

The primary independent variable chosen was the innovative city pilot policy (DID), represented as a binary variable. It was allocated a value based on the list of policy pilots, specifically to the pilot city. If city I becomes innovative in year t, then the variable ${\mathrm{DID}}_{\mathrm{it}}$assumed a value of 1 starting from year t. Alternatively, it assumed a value of 0.

4.2.3. Mechanism Variables

This research discusses three main mechanisms: the talent concentration effect (HCE), the technology concentration effect (TCE), and the industrial structure optimization effect (ISOE). The research findings cited are based on the computation of three ratios [8,25,30]. These ratios include the percentage of the urban population with at least a bachelor’s degree, the proportion of science and technology expenses compared to the total budget expenses of the local government, and the value-added ratio of the service sector to the combined value-added of the agricultural and manufacturing industries.

4.2.4. Control Variables

To limit the impact of unexplained factors on estimation results, important findings from previous research are integrated into the control of energy consumption variables [37,38]. More specifically, the following variables are included: The percentage of government spending on education relative to GDP is referred to as government public education expenditure (PEE). The phrase “foreign direct investment” (FDI) refers to the relationship between the actual amount of foreign investment and GDP. The degree of financial development (FIN) is calculated by dividing the total amount of loans and deposits held by financial institutions in the city at the end of the year by GDP (gross domestic product). The per capita GDP represents the average economic output per person and is used to measure economic development. The level of urbanization is determined by the proportion of the total population that lives in urban areas in comparison to the total population. It should be noted that, as some cities in Tibet, Xinjiang, and other provinces have more missing data information on key variables, and as the missing information could not be accurately restored via interpolation and other means, to ensure the reliability of this study’s results, those cities were excluded, and the panel data of 266 cities from 2006 to 2022 were finally obtained.

Table 1. Data descriptive statistics.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Count	Mean	Sd	Min	P50	Max
UEUE	4191	0.314	0.110	0.021	0.315	1.166
DID	4191	0.126	0.331	0.000	0.000	1.000
HCE	4191	1.558	1.854	0.004	0.944	14.988
TCE	4191	1.392	1.318	0.000	0.999	16.560
ISOE	4191	0.747	0.412	0.094	0.640	4.897
PEE	4191	0.194	0.045	0.011	0.193	0.387
FDI	4191	0.171	0.182	0.000	0.112	2.066
FIN	4191	0.094	0.094	0.008	0.074	3.558
PGDP	4191	1.043	0.069	0.800	1.046	1.228
URBAN	4191	0.517	0.154	0.153	0.502	1.000

shows the results of descriptive statistics for key variables.

4.3. Analysis of Empirical Results

4.3.1. Parallel Trend Test

Figure 1 depicts the findings of this study’s parallel trend test, which was conducted using an event analysis. The coefficients are tiny and negligible prior to the policy’s implementation, implying that there is no discernible difference between the energy efficiency trends in pilot cities and non-pilot cities, consistent with the parallel trend assumption. However, once the policy was implemented, the coefficient gradually improved and began to show a significant positive trend, indicating that pilot city development has a long-term promotional effect.

4.3.2. Benchmark Regression

Table 2. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	UEUE	UEUE	UEUE	UEUE	UEUE	UEUE	UEUE	UEUE
DID	0.0638 ***	0.0169 *	0.0180 **	0.0176 *	0.0176 *	0.0175 *	0.0176 *	0.0174 *
	(0.0054)	(0.0089)	(0.0088)	(0.0091)	(0.0091)	(0.0091)	(0.0091)	(0.0091)
PEE			−0.0491	−0.0504	−0.0504	−0.0506	−0.0511	−0.0501
			(0.0666)	(0.0662)	(0.0662)	(0.0662)	(0.0662)	(0.0659)
FDI				−0.0049	−0.0049	−0.0047	−0.0045	−0.0067
				(0.0138)	(0.0138)	(0.0138)	(0.0138)	(0.0139)
FIN						0.0146	0.0110	0.0069
						(0.0105)	(0.0123)	(0.0128)
PGDP							−0.0700	−0.1067
							(0.0918)	(0.0943)
URBAN								0.0615
								(0.0524)
City Fe		YES	YES	YES	YES	YES	YES	YES
Year Fe		YES	YES	YES	YES	YES	YES	YES
Cons	0.3055 ***	0.2370 ***	0.2460 ***	0.2473 ***	0.2473 ***	0.2462 ***	0.3131 ***	0.3222 ***
	(0.0049)	(0.0041)	(0.0124)	(0.0121)	(0.0121)	(0.0122)	(0.0889)	(0.0881)
N	4191	4191	4191	4191	4191	4191	4191	4191
r2		0.1930	0.1932	0.1933	0.1933	0.1935	0.1938	0.1944

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

displays the results of estimating the influence of the national innovative city pilot policy on energy use efficiency. To be more specific, column (1) displays the estimation findings in their original form, without any adjustments or the inclusion of control variables. The estimation findings in columns 2 through 7 include the gradual inclusion of control variables, while accounting for individual-level and temporal variability. The data from column (8) are utilized in the subsequent analysis.

The estimated coefficients for columns (1) through (8) in Table 2’s regression findings are both statistically significant and positive. This suggests that the execution of the pilot program, namely at a minimum of 10% level, greatly enhances energy consumption efficiency. Therefore, Hypothesis 1 is confirmed.

4.3.3. Placebo Test

The replacement test serves as a kind of placebo test to evaluate the statistical significance of the expected outcomes compared to a random generation. The replacement test is based on the initial hypothesis that the ICPP has no appreciable impact on energy consumption efficiency. According to the initial premise, the estimated coefficients obtained from the actual data can be viewed as random samples with an envelope distribution. As a result, statistical inference can be based on the envelope distribution of the anticipated coefficients from the permutation test. In this work, the permutation test serves as a sort of indirect placebo test. The results of the placebo test, shown in Figure 2, indicate that the calculated values have a normal distribution with a centering point of 0. Since the estimated coefficients really fall outside of the expected range, it is clear that they are outliers. This demonstrates this study’s repeatability and aligns with the anticipated outcome of the placebo test.

4.3.4. Substitution of Variables

In this article, EUEU is measured in a benchmark regression using the SBM method. Additionally, the super-efficiency CCR technique is used to ensure result stability.

Table 3. Substitution of variables.

	(1)	(2)
VARIABLES	CCR	CCR
DID	0.0296 **	0.0284 **
	(0.0115)	(0.0122)
Control Variables	NO	YES
City Fe	YES	YES
Year Fe	YES	YES
Cons	0.4463 ***	0.5331 ***
	(0.0060)	(0.1260)
N	4191	4191
R2	0.1779	0.1793

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the impact of creative cities on energy efficiency, accounting for the existence or absence of control mechanisms. The results corroborate the robustness of the benchmark regression results, as they show that all difference-in-differences (DID) coefficient values are statistically significant at the 5% level, and the benchmark regression results have been validated as accurate.

4.3.5. Propensity Score Matching

In this study, we refer to the studies [38,39], using propensity score matching for robustness testing, and the specific model is constructed as follows:

$$P\left(treat=1\right)=f\left(FDI,RD,PGDP,URBAN,FIN\right)$$

(2)

Given the propensity score value P, we initially use k-nearest zero matching with k equal to four. The regression findings reported in

Table 4. PSM-DID.

	(1)	(2)	(3)
VARIABLES	UEUE	UEUE	UEUE
DID	0.0103 *	0.0103 *	0.0174 *
	(0.0056)	(0.0056)	(0.00915)
Constant	0.236 **	0.258 **	0.322 ***
	(0.104)	(0.114)	(0.0881)
Control Variables	YES	YES	YES
City Fe	YES	YES	YES
Year Fe	YES	YES	YES
Observations	3196	3286	4191
R2	0.146	0.146	0.194

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

show that the variable DID continues to have a substantial impact on the variable UEUE. We also used caliper matching and kernel matching to perform more robustness testing. The regression analysis results show that the conclusions of our benchmark regression are currently being evaluated.

4.3.6. Excluding Interference from Other Policies

Four primary policy categories are gathered and organized here to reduce the impact of incentives from other policies on carbon emissions in urban areas: big data pilot cities (DID1), smart cities (DID2), broadband China (DID3), and low-carbon cities (DID4). These categories are part of the base regression analysis and were selected to coincide with the sample period.

Table 5. Excluding interference from other policies.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	UEUE	UEUE	UEUE	UEUE	UEUE
DID	0.0175 *	0.0180 **	0.0179 *	0.0171 *	0.0182 *
	(0.0092)	(0.0091)	(0.0100)	(0.0092)	(0.0098)
Control Variables	YES	YES	YES	YES	YES
City Fe	YES	YES	YES	YES	YES
Year Fe	YES	YES	YES	YES	YES
Cons	0.3220 ***	0.3216 ***	0.3222 ***	0.3233 ***	0.3225 ***
	(0.0883)	(0.0882)	(0.0882)	(0.0879)	(0.0883)
N	4191	4191	4191	4191	4191
R2	0.1944	0.1945	0.1944	0.1945	0.1946

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

shows that the estimated coefficients of the difference-in-differences (DID) analysis are still statistically significant, with a significance level of at least 10%, even after accounting for the impact of each policy type separately and collectively. Furthermore, the fact that these coefficients are positive lends credence to the findings’ long-term reliability.

4.4. Mechanism Test

Theoretical research indicates that the talent concentration impact, technology concentration effect, and industrial structure optimization effect are the variables that determine the energy consumption efficiency in the national innovative city pilot program. The findings of the mechanism test are presented in

Table 6. Mechanism test.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	TCE	TCE	HCE	HCE	ISOE	ISOE
DID	0.6103 ***	0.3817 ***	0.3068 ***	0.2983 **	0.0848 **	0.0705 *
	(0.1421)	(0.1151)	(0.1043)	(0.1165)	(0.0343)	(0.0361)
Control Variables	NO	YES	NO	YES	NO	YES
City Fe	YES	YES	YES	YES	YES	YES
Year Fe	YES	YES	YES	YES	YES	YES
Cons	0.2174 ***	−7.1766 ***	1.0654 ***	0.8989	0.5759 ***	1.5931 ***
	(0.0631)	(1.2121)	(0.0570)	(0.8840)	(0.0162)	(0.5014)
N	4191	4191	4191	4191	4191	4191
R2	0.2463	0.3746	0.3100	0.3136	0.5965	0.6062

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Columns (1) and (2) of Table 6 analyze the impact of DID on the impacts of technological agglomeration, both with and without the addition of control factors. The analytical results indicate that the pilot program of the national innovative city enhances the effect of technological agglomeration, leading to a subsequent rise in energy efficiency. The positive and statistically significant calculated coefficient of the difference-in-differences (DID) indicates this. This result therefore confirms Hypothesis 2.

Columns (3) and (4) of Table 6 analyze the impact of DID on the talent agglomeration effect, including both the presence and absence of control variables. The results of the difference-in-differences (DID) study indicate that the national innovative city pilot program successfully improves energy consumption efficiency by boosting the talent pooling effect. The calculated coefficients of the difference-in-differences (DID) analysis are found to be statistically significant and positive. This outcome provides support for Hypothesis 3.

Columns (5) and (6) of Table 6 analyze the impact of DID on the optimization effect of industrial structure, both when control factors are included and when they are not. The investigation reveals that the calculated coefficient of the difference-in-differences (DID) is statistically significant and positive. This suggests that the national innovative city pilot program effectively enhances energy consumption efficiency by optimizing industrial structure. This discovery thus confirms the validity of Hypothesis 4.

4.5. Heterogeneity Analysis

This research investigates the various influences of policy shocks on city location, digital finance level, and digital infrastructure in order to give empirical evidence for promoting creative cities.

4.5.1. Location Heterogeneity

In this study, a location dummy variable named “EAST” was created, and cities in the eastern region were assigned a value of 1, whereas cities in the middle and western areas were assigned a value of 0. Furthermore,

Table 7. Heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	EAST = 1	EAST = 0	DF = 1	DF = 0	IF = 1	IF = 0
	UEUE	UEUE	UEUE	UEUE	UEUE	UEUE
DID	0.0073	0.0229 **	0.0037	0.0220 *	0.0242	0.0267 ***
	(0.0151)	(0.0115)	(0.0156)	(0.0129)	(0.0199)	(0.0072)
Control Variables	YES	YES	YES	YES	YES	YES
City Fe	YES	YES	YES	YES	YES	YES
Year Fe	YES	YES	YES	YES	YES	YES
Cons	0.2484	0.4054 ***	0.3178 ***	0.3715 **	0.1066	0.3036 ***
	(0.2631)	(0.1137)	(0.1111)	(0.1643)	(0.2389)	(0.1126)
N	1202	2989	1871	2310	890	3281
R2	0.5862	0.6249	0.6635	0.7067	0.7511	0.6251

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

’s column (1) presents the findings, with the regression coefficients passing the significance test at the 1% level. The fact that the coefficients are positive suggests that the central and western regions are more affected by the pilot policy’s effects on the efficiency of renewable energies.

4.5.2. Heterogeneity of Digital Financial Development (DF)

To assess the level of digital financial development in cities, we created a binary variable called DF in this study. Cities that surpass the average level were assigned a rating of 1, and cities that are at or below the average level were assigned a rating of 0. The findings of the statistical analysis are displayed in column 2 of Table 7. Based on the estimated coefficient of the difference-in-differences (DID) study, there is a statistically significant positive impact of having low levels of residential development in flood-prone zones (DF building) in cities. This effect is observed at a significance level of 10%.

4.5.3. Heterogeneity of Digital Infrastructure Development (IF)

In this study, we generated a binary variable referred to as IF to represent the expansion of urban digital infrastructure. We assigned a value of 1 to cities that are above the average and a value of 0 to those that are not. The estimation findings in column (3) are displayed in Table 7. In urban areas with a notably lower level of infrastructure development, the derived coefficient, known as DID, has a positive and statistically significant relationship at the 1% significance level.

4.6. Testing the Environmental Consequences: The Innovation-Driven Environmental Dividend

To evaluate the policy’s environmental implications, this study undertakes a thorough examination of the impact of the national innovative city pilot program on carbon emission intensity (CO2_GDP). In

Table 8. Environmental consequences.

	(1)	(2)
	CO2_GDP	CO2_GDP
DID	−0.0095 ***	−0.0099 ***
	(0.0023)	(0.0024)
Control Variables	NO	YES
City Fe	YES	YES
Year Fe	YES	YES
Cons	0.0251 ***	0.1173 ***
	(0.0009)	(0.0272)
N	4191	4191
R2	0.1000	0.1224

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

, the estimation results are shown.

The effects of the ICPP on CO2 can be observed in Table 8. Column (1) demonstrates the effect in the absence of any control variables, but column (2) illustrates the impact when control variables are considered. At the 1% significance level, it is found that the estimated coefficients of the difference-in-differences (DID) analysis are significantly negative. This suggests that the establishment of innovative urban centers can effectively decrease the level of carbon emissions in large cities.

5. Discussion

5.1. Discussion of Benchmark Regression Results

The results of the benchmark regression performed in this study show that the estimated coefficient of the construction of innovative cities is significantly positive, being at least at the 10 per cent level, indicating that the implementation of this pilot policy can significantly improve the efficiency of energy use. This finding validates the findings of other scholars. On the one hand, Xiao et al. [17] argued that innovative city policies tend to encourage system integration and optimization by integrating resources and energy use in various areas of the city, and innovative cities usually develop renewable energy sources, such as solar and wind power, to reduce their dependence on fossil fuels, thus improving the overall energy efficiency to achieve systemic energy efficiency improvement. On the other hand, innovative city policies can help to promote the establishment of information sharing and cooperation mechanisms, promote the joint efforts of all parties in the city, and at the same time, by raising the public awareness of energy conservation and sustainable developments, motivate residents and enterprises to actively participate in energy conservation measures, and form a favorable energy conservation atmosphere to jointly achieve the enhancement of energy use efficiency [27].

5.2. Discussion of Mechanism Analysis

Based on the TOE theoretical framework, this study empirically tests that the pilot policy of innovative national cities affects energy use efficiency through the technology agglomeration effect, talent agglomeration effect, and industrial structure optimization effect.

Firstly, innovative city construction enhances energy use efficiency through the technology agglomeration effect, a conclusion which verifies the research of existing studies. Liu and Zhang [39] argue that technology agglomeration brings about the concentration of resources, and enterprises can share facilities and services to reduce costs and improve efficiency. Han et al. [40] argue that innovative cities attract many startups and high levels of investment, which promote the emergence of new energy-saving technologies and business models, and that technology clustering drives competition among companies, which pushes them to continuously improve their technology and service quality, thus increasing the overall energy efficiency. Secondly, the construction of innovative cities has been a major contributor to the development of energy-saving technologies.

Secondly, the construction of innovative cities improves energy efficiency through the talent agglomeration effect. This conclusion verifies the research of existing studies. Yu et al. [41] argue that the implementation of the innovative city pilot policy can attract many innovative talents for the pilot cities, and talent agglomeration will accelerate the spillover diffusion of knowledge and technology, which will optimize the efficiency of energy use. He et al. [42] argue that talent agglomeration promotes the rapid dissemination and sharing of knowledge through training and cooperation, thus improving the overall energy use efficiency. Professional networks and social platforms within the city make it easier for talents to establish connections with each other, forming a virtuous cycle of knowledge sharing and resource complementarity, which improves energy efficiency while optimizing the allocation of talent resources.

Finally, the construction of innovative cities improves energy efficiency through the optimization effect of industrial structure. This conclusion verifies the research of existing studies. Wan et al. [43] argue that the optimization and upgrading of industrial structure reduce the proportion of traditional industries, cause reverse changes in energy consumption, reduces the proportion of traditional energy sources such as petroleum, expands the anticipated scenario of renewable energy sources, and then improves the level of energy use. Zhao et al. [44] argue that the optimization of the industrial structure means more resources invested in R&D and innovation and promotes the development of new technologies and products, which are usually accompanied by higher levels of energy use efficiency. Moreover, the optimized industrial structure promotes the diffusion and application of new technologies among industries and promotes the energy efficiency of the whole region.

5.3. Discussion of the Heterogeneity Analysis

In terms of locational heterogeneity, this study shows that the impact of pilot policies on the efficiency of renewable energy use is more pronounced in the central and western regions. The reason for this is that the central and western regions may be at a lower starting point in terms of economic and technological development compared to the eastern regions and therefore have more room for improvement in terms of energy use efficiency improvement [28]. Through the guidance and support of innovative city pilot policies, these areas can achieve energy efficiency improvements more quickly and produce more significant results. At the same time, the government may divert more policy resources and funds to the central and western regions to promote their economic development and social progress [27,45]. This policy tilt may increase the implementation of innovative city policies in the central and western regions, which will lead to more significant results in energy use efficiency.

Within the digital finance heterogeneity, this study shows that the impact of pilot policies on energy role efficiency is more pronounced in cities with lower levels of digital finance. This is because innovative cities usually resort to digital technologies to improve the efficiency of urban management and services, including the digital monitoring and optimization of energy management systems. In cities with a lower level of digital financial construction, the introduction and application of digital technologies can improve energy use efficiency more significantly [46]. Meanwhile, in cities with a lower level of digital financial construction, the quality of the urban environment can be improved, and the image of the city can be enhanced through the improvement of energy utilization efficiency [47], which in turn attracts more investment and talent and enhances the overall competitiveness of the city.

In relation to digital infrastructure heterogeneity, this study shows that the impact of pilot policies on energy role efficiency is more pronounced in cities with lower levels of digital infrastructure. This is because innovative city policies typically advocate the promotion of infrastructure innovation and upgrading, including energy infrastructure. In cities with lower levels of infrastructure development, the introduction of innovative city policies may motivate cities to improve energy infrastructure and increase energy use efficiency [27]. At the same time, innovative city policies may push cities to explore more clean and low-carbon energy utilization and reduce their reliance on traditional energy sources. Cities with a low level of infrastructure development, guided by innovative city policies, can transform and upgrade their energy use methods more quickly and improve energy use efficiency [48].

5.4. Discussion of Environmental Consequence Test

The environmental consequence test performed in this study shows that innovative city buildings can reduce urban carbon emission intensity while improving energy efficiency. This is because on the one hand, innovative city buildings usually focus on promoting energy efficiency improvements, including measures such as constructing energy-efficient buildings and promoting energy-efficient equipment. Improving energy efficiency can reduce carbon emission intensity by providing more energy services without increasing energy consumption. On the other hand, innovative city buildings usually advocate for the use of clean energy, such as wind and solar energy, to replace traditional fossil fuels. By promoting the use of clean energy, cities can reduce their dependence on high-carbon energy sources, thereby reducing carbon emissions.

6. Conclusions and Recommendations

Improving energy efficiency is critical to meeting carbon peaking and neutrality targets. This analysis uses balanced panel data for Chinese cities at the prefecture level, spanning the years 2006–2022. Using the double-difference method, the influence of China’s innovative urban planning on energy efficiency is evaluated. The findings show that (1) the implementation of the innovative city pilot policy significantly improves urban energy use efficiency; (2) the mechanism study reveals that the policy improves energy use efficiency through the effects of talent agglomeration, technology agglomeration, and industrial structure optimization; and (3) the analysis identifies heterogeneity in the results. The stringent tests, which included variable replacement, the removal of other policy interferences, and placebo tests, had no effect on the results. According to the findings, cities with lower levels of digital infrastructure and financing, as well as those in central and western China, are more likely to profit from the creative city pilot program’s energy efficiency. The environmental assessment also demonstrates how creative city planning may effectively reduce pollutants and carbon emissions by promoting energy efficiency.

First and foremost, it is necessary to increase the number of progressive cities while also supporting the growth of the sustainable energy industry in terms of production and consumption. The government should continue to increase its list of creative cities, considering both supply and demand. Stricter environmental policies that encourage businesses and individuals to use more renewable energy sources are required to increase demand for renewable energy. As a result, the development of renewable energy technology will receive an indirect boost. Promoting the renewable energy quota system and the green power certificate trading mechanism on the supply side is critical for increasing energy efficiency. This will enhance research and development innovations in the industry while also encouraging enterprises to invest more in high-efficiency energy usage projects.

Increased funding for talent development and technological R&D will aid in the improvement and modernization of the industrial framework. To promote the city’s creative growth while increasing energy efficiency, local governments can boost talent development efforts and provide additional incentives to attract highly skilled professionals, thus helping to ensure an adequate supply of human capital. Concurrently, it is necessary to enhance funding for technology-related research and development (R&D), support the Science and Technology Innovation Fund, and increase support for initiatives focusing on new digital infrastructure.

Additionally, the heterogeneity analysis performed the government’s policy focus. First, regional cooperation and exchange should be strengthened. The central and western regions should be given greater preference in terms of energy policy and resources to attract renewable energy enterprises and talents and stimulate urban innovation vitality by means of improving infrastructure construction and fostering a favorable market environment; in addition, encouraging cooperation and resource sharing between cities in the central and western regions is necessary to form an intra-regional network of technological and information exchanges and to enhance the overall innovation capacity. In regions with a lower level of digital financial development, the construction of digital financial infrastructure should be strengthened to optimize the popularity of digital finance to small- and medium-sized energy enterprises and to optimize the supportive environment. Not only that, but the government should also encourage financial institutions to launch digital financial products suitable for small- and medium-sized enterprises, lower the threshold of financing, improve the liquidity of enterprises, establish experimental zones for financial technology innovation, promote the application of blockchain, artificial intelligence, and other technologies in the financial sector, and enhance the efficiency and coverage of financial services. In cities with low levels of infrastructure, the government should further increase investment in infrastructure facilities; transform the structure of energy consumption; increase investment in transport, energy, communications, and other infrastructure, especially smart grids and renewable energy facilities; and improve energy transmission and management capabilities. Finally, it will be necessary to encourage the adoption of green building standards for new construction and renovation projects, improving energy efficiency and reducing energy consumption.