Can the New Energy Demonstration City Policy Promote Green and Low-Carbon Development? Evidence from China

Abstract

Developing new energy is critical to China’s green and low-carbon development. Therefore, in 2014, the Chinese government promulgated a vital innovation policy, namely, the New Energy Demonstration City Policy (NEDCP), which is expected to reduce energy consumption and carbon emissions in Chinese cities. Is the NEDCP facilitating green and low-carbon development in China, and if so, how? Based on unbalanced panel data from 2003 to 2017 at the city level in China, in this study we evaluate the impact of the NEDCP on green and low-carbon development using the staggered difference-in-differences (DID) method. We find that the NEDCP can significantly contribute to green and low-carbon development in China. After various robustness tests, our results are still valid. According to the heterogeneity analysis, non-resource and non-old industrial-base cities have a greater positive impact from this policy. The mechanism analysis denotes that the positive policy effect works by upgrading the industrial structure and stimulating urban innovation. The substantial empirical evidence presented in this paper supports the continued promotion and implementation of new-energy demonstration cities.

1. Introduction

China has been developing rapidly in the past 40 years, and its economic strength has increased dramatically. At present, China is rated number two among the world’s major economies, attracting worldwide attention. In just over 40 years, China’s GDP has soared nearly 300-fold. However, China’s economic miracle has led to tremendous pressure for energy consumption [1,2]. The total energy consumption of China was 4980 million tons (Mt) of standard coal in 2019. Specifically, the share of coal consumption is 56.8%, whereas the share of clean energy consumption is 24.3%. This high-carbon energy structure not only stimulates China’s booming economic development but also makes China the largest carbon emitter in the world [3]. With continuously increasing emissions, the issue of global climate change is becoming increasingly prominent. The call for emission reduction has become increasingly apparent. Countries worldwide actively adopt various measures to reduce carbon emissions, which naturally results in China facing tremendous international pressure [4]. In addition, due to the non-renewability and vast consumption of traditional fossil energy, the risk of traditional-energy depletion continues to increase. China is facing severe energy-security issues [5].

To decrease carbon emissions and ensure energy security, China has turned its attention to new energy. The Chinese authorities hope to change the high-carbon energy structure by vigorously developing new energy [6]. In 2016, China became the largest new-energy producer and consumer as a result of the rapid advancement of new energy [7]. However, on account of coal-rich energy endowment as well as the considerable consumption of traditional energy in China, the development of new energy still cannot transform the Chinese consumption pattern of high-carbon energy in the short run [8]. To promote new-energy development and accelerate the energy transition in China, the authorities have proposed many new-energy-related plans and regulations. In 2014, the Chinese government presented a list of NEDCs. There are 81 cities and 8 industrial parks on this list. To promote local sustainable development, the NEDCP aims to actively boost the popularity of new-energy-related technologies in daily economic activity and increase new-energy consumption. The pilot program of the NEDCP encompasses approximately 20% of China’s landmass, and its policies affect upward of 300 million individuals. As such, it is crucial to China’s energy-development strategy that the policy effectively facilitates green and low-carbon economic development. Therefore, this study delves into the impact of the NEDCP on green and low-carbon development in China. In contrast to other energy policies, the NEDCP currently stands as one of the most impactful initiatives for promoting new energy in China. Analyzing this policy can meaningfully enhance the existing research on the subject while facilitating further worldwide advancement toward green and low-carbon growth.

The major aims of the research are as follows. First, extant studies on green and low-carbon development have mainly focused on the national or provincial level, with little attention given to prefecture-level indicators. Compared with national and provincial-level carbon-emission-intensity indicators, this paper adopts the prefecture-level carbon-emission-intensity indicator, which could more precisely reflect the NEDCP’s impact on green and low-carbon policy. Second, the environmental effects of new-energy consumption, such as reductions in SO₂ and NO_x emissions, are the primary focus of related studies [9]. In contrast, considering both the environmental and economic effects of new-energy promotion is the highlight of this paper. Moreover, to better address potential endogeneity issues, in this article we employ the DID approach to examine how the NEDCP affects green and low-carbon development. We also employ multiple methods to perform robustness tests, making the research results more convincing. Third, we take the heterogeneity of cities into account and verify the promoting role of the NEDCP in green and low-carbon development, which could provide an empirical basis and theoretical guidance to improve and promote the NEDCP in the future.

The following form the structure of the remainder of this study. Section 2 summarizes the background and related research. Mechanisms and hypotheses are proposed in Section 3. Data and methods are introduced briefly in Section 4. The empirical findings are reported and discussed in Section 5. The last section provides a summary and implications.

2. Literature Review

2.1. Policy Background

The NEDCP is primarily designed to improve and highlight new energy in adjusting the energy structure and protecting the environment in China. In 2014, the Chinese government issued the NEDCP. The pilot area includes 8 industrial parks and 81 cities. The spatial distribution of NEDCs is depicted in Figure 1. It is obvious that most selected cities are situated east of the Hu Line. The “Hu Line”, also called Heihe–Tengchong Line, is a geographical line that is frequently used to divide China into two areas with distinct demographic and economic conditions.

To accelerate the construction of the NEDCs, the Chinese government ordered local governments to make accompanying development plans for the NEDCs. First, to achieve specific obligatory goals local governments must incorporate NEDCs into economic and social development programs. For example, the industrial structure of target cities should be optimized to make their energy intensity lower than the provincial average. The industrial structure of the target city should be optimized so that the energy intensity is below the average level of their provinces. Second, local governments should strengthen protections and support innovative activities. Technological advancements should reinforce the progress and application of new energy to realize the comprehensive utilization of both new and traditional energy and reduce carbon emissions from production and consumption.

2.2. Related Literature

We focus on whether this pilot policy could spur green and low-carbon development in China. Although very few previous studies have focused on the relationship between them, we can better investigate the NEDCP’s effects on green and low-carbon development by using the previous literature on the economic effects of new energy.

Some researchers believe that new energy can significantly uplift the economy. Apergis and Payne [10] investigated the connection between the consumption of renewable energy and economic growth by employing the heterogeneous panel co-integration method. They claimed that renewable-energy use could have sparked an expansion of the economy in the sample countries between 1980 and 2006. Similarly, the correlation between Brazil’s actual GDP and various forms of energy consumption from 1980 to 2006 was examined by Pao and Fu [11]. They confirmed that renewable-energy consumption could slow environmental degradation, improve national competitiveness, and promote the economic growth of Brazil. Dai et al. [9] investigated the influence of renewable-energy use on the economy by employing the computable, general dynamic equilibrium method. They reported that new-energy use would have a significant green-growth effect and could significantly reduce air pollutants, such as CO₂, NO_x, and SO₂, substantially improve the atmospheric environment, and promote local green growth in China. Using data from Turkey from 1980 to 2017, Sohag et al. [12] employed the autoregressive distribution lag (ARDL) method to analyze the linkage between technological innovation, militarization, clean energy, and green economic growth. They concluded that clean energy and technological innovation could effectively promote green development in Turkey, and that the long-term relationship between them is asymmetric. Using data from the seven major industrialized countries during 1991–2014, Destek and Aslan [13] studied the linkage of renewable energy, economic growth, and environmental pollution. They found that biomass power, hydropower, and wind energy could significantly reduce carbon emissions in these target countries. Considering economic security in Ukraine, Materyna et al. [14] found that the circular economy not only helps to reduce waste and environmental pollution but also helps to ensure the stability of national technological development.

However, some studies have reached different conclusions. Using Börzel’s theoretical framework on Europeanisation, Maris and Flouros [15] examined European Union Member States’ Green Deal responses, strategies, and compliance. They confirmed that considerable variation exists in Member States’ strategies, which is not conducive to achieving a climate-neutral European economy by 2050. Ocal and Aslan [16] used the ARDL method to investigate the economic impact of clean energy in Turkey during 1990–2010. They think that clean energy is an expensive energy resource for Turkey, and its consumption would be detrimental to the country’s economic growth. Similarly, Destek [17] performed asymmetric causality tests to study this problem in six emerging industrialized countries from 1971 to 2011. According to the findings of the study, renewable-energy use will have a negative impact on India’s economic growth among these sample countries. Chen et al. [18] analyzed the influence of clean energy use on economic development in 103 countries during 1995–2015. They found that new-energy consumption cannot achieve economic growth in developed countries. In contrast, in developing countries, the utilization of renewable energy is bad news for the booming economy. Xie et al. [19] analyzed this topic using annual panel data from 27 European Union member nations during 2008–2017. They confirmed that, limited by the current technological level, renewable-energy consumption is relatively low or high, and it is bad for green economic booming. We are curious about whether the NEDCP can encourage low-carbon development due to the unstable link between new-energy development and green development.

In addition, policies related to new energy have begun to attract some research interest. Related policy research focuses primarily on policies such as pilot plans for low-carbon-city plans and carbon trading. The policy effects of NEDCs, which are essential comprehensive policies for developing new energy, have received little attention [20]. Khanna et al. [21] conducted a preliminary comparative evaluation of the plans for low-carbon and complementary measures of eight pilot cities in China by reviewing the historical development and background of low-carbon cities. They believe that the release of the low-carbon-city policy (LCCP) and the vague definition of low-carbon cities, interference with related policies, and insufficient policy and market support will affect urban development. Cheng et al. [22] investigated the influence of LCCP on sustainable growth by employing panel data from 194 prefecture-level cities during 2007–2016. They inferred that this strategy has further developed green total factor productivity (TFP) in low-carbon cities with technological progress. In addition, they confirmed that this kind of promotion is more evident in larger-scale cities with better infrastructure and better technical foundations. Similarly, Yu et al. [6] used 251 cities in China during 2003–2018 to investigate the effects of LCCP on carbon efficiency. They found that it significantly increased emission efficiency and promoted green development. In addition, this effect also had an obvious positive effect on spatial spillover, enhancing the emission efficiency in the surrounding area.

As a major pilot policy to promote new-energy development, it is essential to analyze the policy effects of the NEDCP. First, it provides the government with a crucial point of reference for adjusting its energy-development strategy and spreading the use of new energy. Second, it can also provide guidance for other countries to formulate development strategies for reasonable new energy. We look forward to seeing how the NEDCP affects green development.

3. Mechanism and Hypothesis

The construction of NEDCs is based on the idea of ‘new city, new energy, and new life’, aiming to spur urban sustainable development. The primary purpose of the NEDCP is to promote the application of technologies for new energy in urban areas. In the selected cities, the new-energy use should either exceed 100,000 tons per year or the proportion of total energy use should exceed 3%. To achieve this, the local governments will formulate appropriate new-energy development strategies and introduce policies conducive to new-energy-industry development. All these measures are beneficial to the energy transition and subsequently encourage low-carbon development. Accordingly, we propose the following hypothesis:

H1: 

The NEDCP can effectively promote urban green and low-carbon development.

3.1. Industrial-Structure Effects of the NEDCs

An unreasonable industrial structure is a key culprit for high carbon emissions in cities. Adjusting the industrial structure is considered to be a necessary way to make urban green and low-carbon development happen [23,24]. One of the targets of NEDCs is to optimize the urban energy mix, boost new-energy consumption, and achieve industrial structure upgrading [25]. In this context, the local governments will issue a series of related incentive policies. For example, supporting the low-carbon tertiary industry to raise the share of the high-end service industry by giving preferential treatment to land, taxation, and credit resources [26]. Furthermore, local authorities will also strengthen environmental regulations, set higher entry barriers for industries that produce much pollution and use much energy, and reduce the space available to heavily polluting enterprises [22]. Increasingly stringent environmental regulations have significantly inhibited the growth of industrial enterprises. With the limited use of fossil energy and rising operating costs of industrial companies, some companies will choose to transform from high- to low-emission industries with comparative advantages [27]. Furthermore, the public authorities will also adopt green credit policies to reduce the financial backing for energy-intensive industries, limit the reckless expansion of industrial enterprises’ production scales, and alter the industrial structure [28]. Accordingly, we propose the following hypothesis:

H2: 

By upgrading the industrial structure, the NEDCP will achieve urban green and low-carbon development.

3.2. Technological Effects of the NEDCs

Technological progress is the primary driver for achieving green development in cities [29]. Therefore, to achieve the stated goals of the NEDCP and reduce fossil energy consumption as soon as possible, local governments will inevitably support technological innovation and provide new path options for urban energy utilization [30]. Specifically, the NEDCP can promote technical innovation through the following two channels. First, NEDCs have increased the application requirements for green technologies. To spread the popularity of new energy, local governments will provide subsidies to the new-energy industry, reduce the pressure of insufficient R&D funds for new-energy-related companies, and encourage corporate innovation [22]. Second, the NEDCP has raised the environmental regulatory requirements. When faced with strict environmental regulations, companies will adjust their business strategies, update green production technologies, and increase technological innovations to enhance environmental performance, resource utilization, emission reduction, and utilization efficiency [31]. As a result, our hypothesis is as follows:

H3: 

The NEDCP realizes urban green and low-carbon development through technological progress.

4. Variables, Methods, and Data

4.1. New Energy Demonstration Cities

The list of NEDCs was issued by the Chinese government in 2014. The pilot area includes 8 industrial parks and 81 cities. Among the 286 sample cities in this study, 57 cities are NEDPCs. In this article, we take the NEDCs as the treatment group, and the others serve as the control group.

4.2. Green Development

Green and low-carbon development generally refer to a long-term growth pattern marked by low emissions, low pollution, and low energy use. Suppose a region’s economy is growing while the carbon-emission intensity is declining. This indicates that this region has developed a low-carbon and environmentally friendly development model [32,33]. As a result, we estimate green and low-carbon development (CARGDP) by utilizing the carbon-emission intensity at the city level in this article. Emission of carbon dioxide per unit of GDP serves as the representation for this indicator.

4.3. Control Variables

According to relevant studies [25,34,35], in this paper we also take the following indicators as control variables, which are used to control other possible factors influencing green and low-carbon development. (1) Population density (POP) is denoted as the ratio of a city’s total population to its administrative region at the end of the year; (2) per capita real GDP is used to represent the level of economic development (PGDP), taking 2003 as the base year; (3) the proportion of added value of secondary industry to GDP is referred to as industrial structure (IND); (4) a city’s innovation capability (INCA) is expressed using the quantity of green patents; (5) the rate of foreign direct investment (FDI) to GDP demonstrates openness (OPEN); and (6) fiscal pressure (PRES) is calculated as the ratio of fiscal surplus to fiscal revenue. Except for industrial structure, openness, and fiscal pressure, all variables are in logarithmic form. We used Stata17 to perform our analysis.

4.4. Empirical Method

The most direct approach to assessing the impact of the NEDCP on green and low-carbon development is by comparing the difference in a city’s carbon intensity before and after policy implementation. However, this difference may be influenced by other factors beyond the policy itself over time. To account for these additional factors, this study adopts the difference-in-differences (DID) method, referencing Beck et al. [36], to examine the impact of the NEDCP on green and low-carbon development. The DID approach circumvents the endogeneity complications of simultaneity bias and missing variables and obtains an accurate policy analysis [37]. The specifications are as follows:

$$Y_{it}={\beta }_0+{\beta }_{1}Trea{t}_i\times Pos{t}_t+{\beta }_{n}Control{s}_{it}+C_i+T_t+{\epsilon }_{it}$$

(1)

where Y_it is the intensity of city i’s carbon emissions in year t, which is used to denote the level of low-carbon development. Post represents a time dummy variable. It is assigned as 0 before 2014 and 1 after 2014. Treat is a city dummy variable. The NEDCs are assigned as 1, denoting the experimental group. The others are given 0, denoting the control group. This article focuses on the coefficient ${\beta }_1$, which measures the influence of the NEDCP on green and low-carbon development. Controls represents the control variables, including population density, industrial structure, innovation capabilities, economic-development level, openness, and fiscal pressure. They are potential factors affecting green and low-carbon development. C and T denote the city and year fixed effects, respectively, and ${\epsilon }_{it}$ is the error term.

Before using the DID method, a relatively stable trend in the carbon intensity of the two groups needed to be confirmed before the establishment of the NEDCs. Therefore, according to Yang et al. (2021) [25], we conducted parallel trend testing. The following are the specific model settings:

$$Y_{it}=\alpha +\sum _{j=-5}^3{\beta }_{j}Trea{t}_i\times Pos{t}_{t+j}+{\beta }_{n}Control{s}_{it}+C_i+T_t+{\epsilon }_{it}$$

(2)

where Y_it expresses city i’s carbon emission intensity in year t. The definition of Treat in Equation (2) is the same as in Equation (1). Post_t+j is a time dummy variable. For example, Post_t−5 represents the seventh year before the policy was implemented, and Post_t+3 represents the third year after policy implementation. This article focuses on ${\beta }_j$, which measures the policy effect. Parallel trends hold when no obvious difference is shown between the two groups before policy implementation. In contrast, there should be a considerable difference after the policy is put into action.

4.5. Data

The impact of the NEDCP on green development is examined using unbalanced panel data from 286 cities during 2003–2017 in this paper. We calculated the city-level intensity of carbon emissions in accordance with Chen et al. [38]. In addition, the initial data of the remaining variables selected in the article are obtained from the official Statistical Yearbook of China, provinces and cities, as well as the China Energy Statistical Yearbook, and the Chinese Research Data Services (CNRDS) databases. The descriptions of each variable are presented in

Table 1. Descriptive statistics of variables.

Variable	Unit	Obs	Mean	S.D.	Min	Max
CARGDP	Tons/104 yuan	4290	0.74	0.71	−1.99	3.38
POP	Person/km2	4290	5.72	0.93	1.55	9.98
PGDP	Yuan/person	4290	9.90	0.86	6.22	12.91
IND	%	4290	2.83	1.85	0.00	9.50
INCA	Number	4290	48.31	11.01	9.00	90.97
OPEN	%	4170	1.98	2.61	0.00	90.51
PRES	%	4290	1.73	1.83	−0.19	26.10

.

5. Empirical Results

5.1. Parallel Trend Test

The parallel trend test results are depicted in Figure 2. The X-axis denotes the year relative to the implementation of NEDCP, and the interaction term’s coefficient is shown on the Y-axis. The red vertical line represents the 95% confidence interval and the blue line represents the trend of the coefficients of Treat×Post. Additionally, the vertical and horizontal dashed lines represent $x=0$ and $y=0$, respectively. It can be found that in the years before the establishment of the NEDCs, these coefficients were not significant. The coefficients remained significantly negative after the first year of this policy, denoting that the parallel trend assumption holds. In addition, these coefficients also confirm that the NEDCP can substantially reduce the carbon emission intensity.

5.2. Baseline Regression

The coefficient of Treat×Post is −0.0248 at a significance level of 5% without the control variables, as shown in column (1) of

Table 2. Baseline regression.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	CARGDP	CARGDP	CARGDP	CARGDP	CARGDP	CARGDP	CARGDP
Treat×Post	−0.0248 **	−0.0247 **	−0.0271 **	−0.0339 ***	−0.0332 ***	−0.0362 ***	−0.0344 ***
	(0.0115)	(0.0115)	(0.0108)	(0.0101)	(0.0101)	(0.0100)	(0.0100)
POP		−0.0208	−0.4780 ***	−0.3412 ***	−0.3408 ***	−0.3787 ***	−0.3606 ***
		(0.0131)	(0.0232)	(0.0228)	(0.0227)	(0.0228)	(0.0231)
PGDP			−0.5152 ***	−0.3551 ***	−0.3557 ***	−0.3952 ***	−0.3757 ***
			(0.0222)	(0.0222)	(0.0222)	(0.0223)	(0.0226)
IND				0.0101 ***	0.0102 ***	0.0064 ***	0.0070 ***
				(0.0016)	(0.0016)	(0.0018)	(0.0018)
IND2				−0.0002 ***	−0.0002 ***	−0.0001 ***	−0.0002 ***
				(0.0000)	(0.0000)	(0.0000)	(0.0000)
INCA					−0.0139 ***	−0.0135 ***	−0.0128 ***
					(0.0032)	(0.0032)	(0.0032)
OPEN						0.0013	0.0013
						(0.0010)	(0.0009)
PRES							0.0122 ***
							(0.0025)
Constant	0.7398 ***	0.8591 ***	8.5759 ***	6.2038 ***	6.2419 ***	6.9059 ***	6.5686 ***
	(0.0021)	(0.0749)	(0.3405)	(0.3353)	(0.3347)	(0.3384)	(0.3442)
City effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	4290	4290	4290	4290	4290	4170	4170
R2	0.9646	0.9646	0.9688	0.9726	0.9727	0.9733	0.9734

Notes: **, and *** indicate statistical significance at the 5% and 1% levels, respectively. Standard errors in parentheses. The same below.

. From column (2) to column (7) in Table 2, we added all the control variables one by one. Column (7) indicates that when all the control variables are included, the factor of Treat×Post is −0.0344 at a significance level of 1%. It can be preliminarily noted that the establishment of the NEDCs helps reduce the carbon intensity significantly and supports urban green and low-carbon development. In column (7), we used population density, economic development level, industrial structure (where IND2 represents the square term of industrial structure), innovation capability, openness, and fiscal pressure as control variables. We confirmed that the intensity of carbon emissions can be significantly reduced by increasing population density. In general, as the urban population increases, various urban infrastructures improve gradually, especially public transportation and other public systems. Furthermore, this has the potential to facilitate urban sustainable development and increase energy-use efficiency [39]. In addition, we further found that the linkage between carbon intensity and industrial structure shows an obvious inverted-U relation. As this ratio increases, the emission intensity presents earlier increasing and later decreasing trends. In the initial stage of industrial development, the secondary industry often adopts a rough development mode, which only focuses on economic benefits and ignores the externalities of the environment. As the proportion of secondary industry increases, it often results in a rapid rise in carbon intensity. With the development of technology and the social attention on environmental-pollution issues, energy-utilization efficiency has gradually increased, which has resulted in the carbon intensity decreasing as the ratio of secondary industry has increased. Therefore, the relationship between carbon intensity and industrial structure shows an inverted U shape [40]. We also confirmed that green patents have positive effects, while fiscal pressure has a negative effect. Moreover, we found that the degree of openness has no connection with the carbon-emission density, which does not support the “pollution halo” or “pollution haven” hypothesis. In general, green patents can promote green production technologies and reduce emission intensity. Finally, the authorities will expand the industrial scale to alleviate fiscal pressure, which greatly increases carbon-emission intensity and will not benefit low-carbon development [34].

5.3. Heterogeneity Analysis

There are significant differences between China’s cities. Therefore, city heterogeneity may affect the policy impacts of the NEDCP. To this end, we analyzed the impacts of city heterogeneity from two aspects: resource endowment and industrial characteristics.

5.3.1. Resource-Endowment Heterogeneity

Energy is an essential strategic resource, and its development and utilization will be restricted by regional resource endowments. With reference to the State Council’s classification standards, we classified cities into two categories: cities with and without resources (the detailed classification standard can be found on http://www.gov.cn/zwgk/2013-12/03/content_2540070.htm, accessed on 3 December 2013).

Table 3. Heterogeneity analysis.

	(1)	(2)	(3)	(4)
	Resource-based	Non-resource-based	Old industrial base	Non-old industrial base
Treat×Post	−0.0145	−0.0523 ***	−0.0293 *	−0.0441 ***
	(0.0170)	(0.0121)	(0.0159)	(0.0127)
Control	Yes	Yes	Yes	Yes
City effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	1676	2494	1387	2783
R2	0.9649	0.9766	0.9735	0.9742

Notes: *, and *** indicate statistical significance at the 10% and 1% levels, respectively. Standard errors in parentheses.

describes the outcomes of applying the DID approach to each kind of city. The NEDCP does not significantly reduce the carbon intensity for resource cities, as shown in columns (1) and (2). At the 1% level, we can observe a negative effect for non-resource-based cities. Resource cities rely heavily on their own resources. Under this path dependence, the changes from traditional energy to new energy have the characteristics of slowness and high cost. However, the transition of non-resource-based cities is relatively easy. Therefore, under the impetus of the NEDCP, it is possible to foster rapid transactions for green and low-carbon development.

5.3.2. Heterogeneity in Industrial Characteristics

The plan for adjusting and renovating the old industrial bases throughout the country (2013–2022) in 2013 identified 120 cities as old industrial bases (the list of cities can be found on http://www.gov.cn/gongbao/content/2013/content_2441018.htm, accessed on 18 March 2013). Most of them are experiencing fast economic growth. Nevertheless, their development methods are relatively extensive, with the distinctive features of high energy consumption, which has become an obstacle to the switch of old industrial cities toward sustainable development. Can the carbon intensity from old industrial base cities be decreased by the establishment of NEDCs? To solve this problem, we chose cities that belong to the NEDCs and the remaining cities for the old industrial bases. Then, we performed a similar analysis as above in this subsample. Furthermore, for non-old industrial-base cities, we classified prefecture-level cities under the NEDCP and the other cities. Then, we studied the impact of the NEDCP on non-old industrial-base cities.

According to columns (3) and (4) in Table 3, the NEDCP has an obvious negative influence on the emission density of both old and non-old industrial-base cities. The horizontal comparison shows that the NEDCP has a greater impact on the emission density of non-old industrial-base cities than old industrial-base cities. This phenomenon may occur for the following reasons. First, an old industrial base has an energy-intensive industrial structure and extensive development pattern. Due to the path dependence of production technology, the carbon-emission-intensity reduction is slight, making the NEDCs more ineffective. Therefore, the impact of motivating green development is not obvious. Second, northeast China has the majority of old industrial cities, which have the slowest economic growth and the most serious population loss in China. (According to China’s seventh population census, the outflow of the population in Northeast China from 2010 to 2020 was as high as 11.01 million, which is more than 10% of the region’s total population. In addition, the three provinces of Liaoning, Jilin, and Heilongjiang in Northeast China are ranked relatively backward in terms of total GDP and GDP growth rate among Chinese provinces.). As a result, the old industrial base cities lack the necessary funds and talent pool, which are crucial to the accomplishment of economic transformation, thereby leading to the slower speed of transition to green and low-carbon development processes.

5.4. Robustness Test

5.4.1. Placebo Test

Due to data limitations, some unobservable potential factors may affect the results, leading to estimation bias. Following Chetty et al. [41], we performed a placebo test to check whether the potential variables influence the results. The estimated coefficient $\widehat{{\beta }_1}$ in the baseline model can be obtained as:

$$\widehat{{\beta }_1}={\beta }_1+\xi \times cov(Treat\_i,\epsilon \_it│\mathrm{X})/var(Treat\_i│\mathrm{X}),$$

(3)

where X includes all fixed effects as well as control variables, and $\xi$ denotes the influence of unobservable variables on the independent variable. If $\xi =0$, the estimated results are not affected by unobservable factors. However, $\xi =0$ cannot be directly verified. Therefore, this article uses the indirect placebo-test method. Specifically, we selected a variable that does not affect the independent variable theoretically, and then we replaced Treat for re-estimation. If $\widehat{{\beta }_1}=0$, then $\xi =0$. This approach randomly generated a list of new-energy pilot cities, obtained the estimated coefficient ${\widehat{\beta }}_{random}$, and repeated this step 500 times. Figure 3 displays the distribution of 500 estimated coefficients. In Figure 3, the dashed line represents the coefficient of Treat×Post in the baseline regression, the blue line represents the fitted trend of the estimated coefficient ${\widehat{\beta }}_{random}$ and the red circles denote the estimated coefficient ${\widehat{\beta }}_{random}$. We found that the mean value of ${\widehat{\beta }}_{random}$ is −0.0016, which is close to 0 and insignificant. It is far from the coefficient −0.0344 of the baseline regression. Thus, we verified the conclusions’ robustness.

5.4.2. Other Policies

We were concerned about how other environmental policies could affect the sample period covered in the research. To alleviate the potential problem, we summarized the environmental policies implemented since 2014, such as the LCCP. Then, the regression included an interaction between the time-trend item and the policy dummy variable. On the other hand, many provincial-level environmental policies have been implemented in various provinces. However, due to the availability of data, we were not able to consider all provincial-level environmental policies. Therefore, referring to Lai et al. [42], we added an interaction of the time-trend item and a provincial dummy variable into the regression to control the influence of the provincial-level factors on the estimated results. Column (1) of

Table 4. Robustness test.

	(1)	(2)	(3)	(4)
	CARGDP	CARGDP	SURGDP	PSM-DID
Treat×Post	−0.0361 ***	−0.0390 ***	−0.0919 **	−0.0350 ***
	(0.0089)	(0.0097)	(0.0423)	(0.0100)
Control	Yes	Yes	Yes	Yes
City effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	4170	4170	4090	4156
R2	0.9799	0.9753	0.8609	0.9735

Notes: **, and *** indicate statistical significance at the 5%, and 1% levels, respectively. Standard errors in parentheses.

shows the results. We found that the coefficient of Treat×Post is still significantly negative at the statistical level of 1%. This result indicated that other environmental policies have not caused bias in the estimated results in this article.

5.4.3. Other Control Variables

In an ideal setting, the DID mandates that the control and treatment groups be chosen at random. However, the selection of NEDCs, in fact, is not completely random. In the policy-making process, many external environmental factors, such as the city’s economic and social development level and geographical location, are usually taken into consideration. The above factors may have different effects on a city’s development, thus leading to estimation bias. To alleviate the possible influence of these factors, on the basis of Lu et al. [43], in this research we added the interaction term Z_c × Trend_t of these factors with the trend term into the regression. Among them, Z_c represents the economic and social characteristics as well as the geographical location of the city. Specifically, these factors include whether the city is a provincial capital, a special economic zone, or a northern city. Moreover, we also take the cities’ gradients into consideration. Trend_t refers to the time trend. The outcomes are presented in Table 4 in Column (2). The conclusion of this study remains stable after controlling for the aforementioned factors.

5.4.4. Alternative Measure of Green and Low-Carbon Development

China’s energy structure is led by fossil energy. The consumption of fossil energy produces large amounts of carbon dioxide while also emitting a large amount of sulfur dioxide. Therefore, we replaced the independent variable with SO₂-emission intensity, which is represented by SO₂ emissions per unit of gross domestic product, and then the regression was performed again. Column (3) in Table 4 shows that the regression factors of Treat×Post are negative at a 5% significance level, indicating that the establishment of NEDCs could significantly promote green development. This further makes our findings more convincing.

5.4.5. PSM-DID

Additionally, we verified the reliability of our findings by employing the PSM-DID strategy. It can overcome the selectivity-bias issue of the DID model. Our sample contains 286 prefecture-level cities, and there may be significant economic differences between these cities. Therefore, we utilized the propensity score matching approach to match the two groups and use the matched sample to perform the DID regression again.

Before executing the PSM-DID procedure, we needed to carry out a balance test on the variables to guarantee the precision of the propensity-score-matching result. The outcomes of the balance test are shown in

Table 5. Applicability test of the PSM-DID method.

	Unmatched	Mean		%Reduction		t-Test
Variable	Matched	Treated	Control	%Bias	Bias	t	p > |t|
POP	U	5.7724	5.7542	2.0		0.52	0.6050
	M	5.7724	5.7685	0.4	78.5	0.09	0.9290
PGDP	U	9.9284	9.9003	3.4		0.85	0.3930
	M	9.9284	9.9485	−2.4	28.6	−0.5	0.6160
IND	U	48.8950	48.156	6.9		1.79	0.0730
	M	48.8950	48.853	0.4	94.3	0.08	0.9340
INCA	U	3.1050	2.8111	16.1		4.13	0.0000
	M	3.1050	3.1471	−2.3	85.7	−0.47	0.6400
OPEN	U	1.7925	2.0215	−9.8		−2.27	0.0230
	M	1.7925	1.8487	−2.4	75.4	−0.61	0.5410
PRES	U	1.5613	1.6776	−6.7		−1.8	0.0720
	M	1.5613	1.5453	0.9	86.3	0.2	0.8390

.

Significant differences in industrial structure, innovation ability, openness, and financial pressure existed between the two groups before matching. The two groups’ differences in the above variables were eliminated after matching, which indicates that the matching method in this paper is effective. Figure 4 also depicts the propensity-score kernel-density function curves for the experimental and control groups prior to and following kernel matching. After matching, the propensity score distribution in the two groups was very close, indicating that the quality of the matching was also good.

Column (4) of Table 4 displays the PSM-DID outcomes. The factors of Treat×Post are all negative, indicating that the results are close to the benchmark case, which again verifies that our benchmark results are robust. The NEDCP has a certain effect of improving the green and low-carbon advancement of cities.

5.5. Mechanism Analysis

To verify the industrial-structure-upgrading effect of the NEDCP according to Chen and Zhao [44], we constructed two indicators, High1 and High2, to represent industrial-structure upgrading. Specifically, High1 is the number of employees belonging to the high-end service industry and High2 is the proportion of employees belonging to the high-end service industry that accounts for the quantity of staff in the tertiary industry. (Specifically, based on the classification standard of Industrial Classification of National Economic Activities, we classified high-end service industry as the following industries: telecommunications, financial intermediation, computer services and software, scientific research, leasing and business services, professional technical services, and geological prospecting.) As observed in columns (1) and (2) in

Table 6. Mechanism analysis.

	(1)	(2)	(3)	(4)
	High1	High2	Scores	Pscores
Treat×Post	0.0362 ***	0.4429 **	2.5075 ***	2.3207 ***
	(0.0130)	(0.1847)	(0.8181)	(0.6939)
Control	Yes	Yes	Yes	Yes
City effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	4170	4161	4095	4095
R2	0.9572	0.8375	0.8864	0.9221

Notes: **, and *** indicate statistical significance at the 5% and 1% levels, respectively. Standard errors in parentheses.

, the coefficients of Treat×Post are significantly positive, denoting that the establishment of the NEDCs has effectively changed the cities’ industrial structures.

In addition, the Innovation and Entrepreneurship Index of China created by Zhang [20] is utilized in this article to evaluate cities’ innovation capabilities. Specifically, this article uses city scores (Scores) and per capita scores (Pscores) as proxy variables for cities’ innovation capacities. The coefficients of Treat×Post all show significantly positive features. This denotes that the NEDCP can greatly improve the innovation capabilities of the pilot cities.

6. Conclusions and Policy Implications

Developing new energy is a pivotal move to promote China’s green economy. The NEDCP is a crucial pilot plan to accelerate the evolution of new energy. The DID method and unbalanced panel data from 286 prefecture-level cities during the period of 2003–2017 are used in this research to examine the influence of the NEDCP on green and low-carbon development in China. This article also takes into account the heterogeneity between cities. The conclusions are as follows. First, the NEDCP can dramatically reduce urban carbon-emission intensity and stimulate urban low-carbon development. We still confirm the positive policy effect of the NEDCP. Furthermore, we find that it can reduce emission intensity by spurring industrial-structure upgrading and urban innovation. Second, after considering the heterogeneity between cities, we confirm that resource endowments and urban industrial characteristics will significantly affect the policy effects. In terms of differences in resource endowment, for non-resource-based cities, the policy can dramatically decrease the local carbon intensity. In general, on account of less dependence on traditional energy, the energy structure transformation of non-resource-based cities is relatively easy. Regarding the heterogeneity of industrial characteristics, the NEDCP has more significant policy effects in non-industrial-base cities. This occurs because the old industrial base has a low industry level and a high intensity of energy consumption and emissions. Additionally, because of the path dependence of production technology, the reduction in emission intensity is slight, making such a new-energy demonstration city less effective.

According to our results, we provide the following suggestions: (1) Expanding the scope of the pilot program of the NEDCs. Their establishment can significantly advance urban green development. The central government should further expand the pilot range of the NEDCs in China, clarify the selection criteria, formulate a reasonable evaluation system, strengthen supervision of the NEDCs, and promote local green development. (2) Upgrading the industrial structure as well as improving the cities’ innovation abilities. The local governments should attach more importance to the upgrading effect and innovation effect of NEDCs. Specifically, the local authorities should actively create a good investment environment, spur the progress of local high-tech enterprises, and stimulate the innovation and application of high-tech enterprises. These measures can serve as a reference for other countries to help them enhance their energy efficiency to reduce carbon emissions. (3) Considering city heterogeneity when developing relevant policies. For cities with resource endowments, the local governments should further strengthen the application of new energy. They can take measures such as subsidies to lower dependencies on traditional fossil-energy resources. For cities in the old industrial base, the local governments should actively promote industrial transformation.

China has become the world’s largest producer and consumer of new energy and is still prioritizing the high-quality development of new energy. Further research can be conducted from the following perspectives. First, given China’s remarkable progress in the field of new-energy vehicles, it is highly significant to analyze the impact of the NEDCP on new-energy-vehicle companies. Not only can this expand the analysis’ object to the enterprise level, providing more detailed results, but it can also offer a novel explanation for the rapid development of China’s NEV companies. Second, carbon intensity is merely one metric of green and low-carbon development in China. By constructing additional explanatory indicators upon collecting ample data, we can obtain more precise measurements of green and low-carbon development to yield accurate results.