1. Introduction

Since the Industrial Revolution, the overuse of fossil fuels has been the main reason for many greenhouse gas emissions. The greenhouse effect has led to global warming, restricting the sustainability of economies and society. According to the WMO Provisional State of the Global Climate 2022, the global mean temperature continued to rise in 2022, triggering a series of climate crises, such as glacier melting, sea-level rise, and extreme weather, causing severe economic losses in the world. In response to global climate change, many countries have successively signed international treaties, such as the United Nations Framework Convention on Climate Change, the Kyoto Protocol, and the Paris Agreement, establishing the principle of common but differentiated responsibility (CBDR). China has committed to peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. Later, emission peak and carbon neutrality as essential goals in the fight against pollution were written in the 14th Five-Year Plan, reflecting the firm determination of the Chinese government to improve the ecological environment.

Energy in China’s New Era points out that China has made a remarkable achievement in optimizing the energy consumption structure since China has adhered to the new development concept of innovation, including coordination, green, openness, and sharing. In 2019, coal consumption accounted for 57.7% of total energy consumption, down 10.8% from 2012; clean energy consumption, such as hydropower, wind power, and nuclear power, accounted for 23.4%, an increase of 8.9% compared with 2012; and non-fossil energy consumption accounted for 15.3%, an increase of 5.6% from 2012. However, China’s energy and industrial structure can still not eliminate its high dependence on carbon. Industrialization and urbanization are still occurring at high speed, so a significant gap exists between the reality and “net-zero” carbon emissions.

In order to transform the extensive mode of economic development and achieve sustainable development that combines environmental and economic benefits, the Chinese government proposed a new development concept in 2015. The new development concept includes five dimensions: innovation, coordination, green, openness, and sharing. Among them, innovation is the first driving force of development; coordination focuses on solving imbalance problems; green focuses on solving ecological and environmental problems; openness focuses on solving external problems; and sharing focuses on the distribution of development fruits [1]. The new development concept is comprehensive and diverse. It integrates various aspects, showing China’s practical and feasible path to sustainable economic development [2]. Successfully implementing a development concept depends on its compatibility with the natural environment and on whether there is solid policy support. In China, the vocabulary in the government work report can broadly reflect a region’s development concept, path, and status [3]. According to the data we calculated, the mean value of the word frequency related to the new development concept in the local government work report before 2015 was 0.875. After 2015 it was 0.938; the difference before and after is significant at the 1% level. After the central government proposed the new development concept, local governments have incorporated the new development concept into regional governance practice actively.

China’s carbon economy is facing severe challenges in terms of emission reduction. The implementation of the new development concept provides a path for the realization of the dual-carbon goal. Scholars have pointed out that the implementation of the new development concept is conducive to promoting technological innovation, optimizing the industrial structure, and improving resource utilization, which not only helps to promote high-quality economic and social development but also empowers high-quality ecological development [4,5]. Nevertheless, these analyses are based on theoretical research and need more empirical analysis. Can local governments reduce carbon emissions by implementing the new development concept? What are the impact mechanisms? Are there differences in different regions? Discussion of the above issues clarifies the practical impact of implementing the new development concept on carbon emissions and provide a theoretical basis for applying the new development concept to achieve carbon emission reduction.

The subsequent arrangement of this article is as follows: Section 2 presents a literature review; Section 3 presents the theoretical analysis and research hypotheses; Section 4 presents the research design, including the econometric model and variable definition; Section 5 shows the results of empirical study; Section 6 presents further analysis; and Section 7 provides conclusions, discussions, and policy implications.

2. Literature Review

Global warming caused by greenhouse gas emissions has brought enormous pressure on ecological security, food security, and water security, seriously hindering the sustainable development of human society. As carbon emission reduction has become a big concern worldwide, scholars have researched the calculation methods, impact factors, and reduction strategies of carbon emissions. The carbon footprint is a quantitative expression of GHG emissions and an essential indicator of global warming potential (GWP) [6]. The calculation methods of carbon footprint include input–output (IO) [7], life-cycle assessment (LCA) [8], and IPCC. Based on the calculation, many scholars have discussed the impact factors of carbon emissions, such as economic growth [9], population [10], industrial structure [11], energy consumption [12], international trade [13], and foreign direct investment [14]. As for reduction strategies, using clean and renewable energy, developing low-carbon technologies, and a low-carbon economy can effectively reduce carbon emissions [15]. The circular economy encourages the implementation of clean and recycling technologies, which is also conducive to carbon emission reduction [16].

In 2015, the Chinese government proposed the new development concept. Later, many scholars discussed the relationship between this and carbon emissions. Some studies have pointed out that there are misunderstandings, poor coordination, and even bad practices of carbon emission reduction in some areas, which is due to a lack of thorough understanding of the new development concept [17]. The new development concept can solve the misunderstandings and incorrect practices of emission peaks and carbon neutrality [18]. It also plays a vital role in accurately identifying and solving various contradictions and challenges when promoting dual-carbon goals [19]. Implementing the new development concept is conducive to transforming the industrial structure, forming green production and lifestyle, and cultivating green consumption, which can effectively reduce carbon emissions [20,21]. More specifically, some scholars have taken the Beijing–Tianjin–Hebei region and the Kubuqi Desert as examples to specifically analyze the role of the new development concept in curbing carbon emissions and improving the ecological environment [22,23]. In addition, a few scholars have explored the guiding role of the new development concept in the fields of a better life, ecological environment security, and green development systems [24,25,26].

The existing literature has explored the logical path of the new development concept supporting the dual-carbon goal, deepened our understanding of the relationship between the new development concept and carbon emissions, and provided a necessary theoretical basis for this study. However, there are also shortcomings in existing research: most studies have used normative analysis to illustrate the inhibitory effect of the new development concept on carbon emissions, and a few scholars have used case studies to test the effect of the new development concept on energy consumption and emission reduction. Empirical analysis of the relationship between the new development concept and carbon emissions is insufficient, making it difficult to understand to what extent the new development concept can reduce carbon emissions and the paths to reduce carbon emissions. Research on these issues can enrich the literature on carbon emissions.

3. Research Hypotheses

3.1. The Impact of the New Development Concept on Carbon Emissions

Unlike developed countries, China’s urbanization and industrialization are still in a rapid development stage, and the accumulation of capital stock driven by production and infrastructure investment has released many carbon emissions. Overusing fossil fuels is the primary source of carbon emissions. In China’s fossil energy structure, coal accounts for nearly 2/3, and 60% of thermal power comes from coal-fired power, the largest carbon emission source. Implementing the new development concept, transforming economic development mode, and optimizing energy utilization and structure has become the key to achieving emission peak and carbon neutrality in China. The concept is the forerunner of action, and certain development practices are guided by certain development concepts. The new development concept requires the economic system to shift from simply pursuing “economic growth” to pursuing “green growth”, comprehensively considering the quantity and quality of economic growth and building a green, low-carbon, and circular economic system based on technological innovation and industrial upgrading [27]. Meanwhile, the ecosystem has shifted from “growth first” to “protection first” [28], and the economic system and ecosystem can realize win–win development by improving resource utilization and reducing energy consumption.

The new development concept includes five elements: innovation, coordination, green, openness, and sharing, each of which can have a significant impact on carbon emissions:

Firstly, innovation is the primary engine of development and the key to improving production technology and resource utilization. Green and low-carbon technology innovation not only helps to promote technological progress and improve productivity but also reduce resource waste and environmental pollution, promoting the development of a circular and low-carbon economy. For example, developing green-energy technology can effectively improve the productivity of clean energy while reducing the use of coal, petroleum, and fossil fuels.

Secondly, coordination development focuses on solving the imbalance problems. Coordination between departments and regions can solve issues such as fragmentation and administrative barriers in environmental work, achieve overall planning for carbon emission reduction, and reduce unnecessary losses. Coordinated development of the industrial and supply chain can promote matching supply and demand, improve industrial efficiency, and achieve low-carbon transformation. Furthermore, coordinated development of the energy structure is conducive to strengthening the overall optimization of the energy system and promoting green and low-carbon economic development.

Thirdly, green development emphasizes the harmonious coexistence of human beings and nature, cultivates a social atmosphere that respects and protects nature, and implements ecological protection practices, which help improve the ecological environment’s natural carbon sequestration capacity. Popularizing green development concepts can lead the public to form a green consumption idea and promote enterprises to adopt green production methods, such as reducing over-packaging.

Fourthly, openness development focuses on internal and external links of development. Enterprises can acquire advanced green and low-carbon technologies through international cooperation to promote green productivity. Enterprises can also use comparative advantages to gain market share and improve green technological progress when exploring foreign markets.

Fifthly, sharing is the ultimate goal of development. People’s desire for a better life includes the need for a beautiful, low-carbon, and healthy living environment. A good ecological environment is the most inclusive welfare of people’s livelihood. It is necessary to address environmental issues, promote green production and lifestyle formation, and reduce the negative environmental externalities caused by carbon emissions. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 1. 

The implementation of the new development concept is conducive to significantly reduce carbon emissions.

3.2. The Impact Mechanism of the New Development Concept on Carbon Emissions

3.2.1. The Mediating Effect of Technological Progress

To adhere to the new development concept in the new development stage, it is necessary to firmly implement the innovation-driven strategy, accelerate the improvement of innovation capability, and reduce carbon emissions through technological progress. Technological progress plays a mediating role between the new development concept and carbon emissions.

On the one hand, the implementation of the new development concept helps to accelerate technological progress. Innovation is the key to solving the problem of insufficient development momentum and intensified constraints on resources and the environment. The new development concept strengthens the position of innovation as the primary driving force, highlighting the importance of scientific and technological innovation as a guide for comprehensive innovation. Implementing the new development concept will help deepen economic system reform, remove institutional barriers that restrict sustainable development, improve resource allocation, mobilize the enthusiasm of the entire society, gradually enhance the driving force and vitality of development, stimulate innovation vitality of talents, enhance the technological innovation ability of enterprises, and accelerate technological progress.

On the other hand, technological progress can effectively reduce carbon emissions. Research has shown that technological progress has improved industrial energy efficiency, reducing China’s carbon dioxide emissions [29,30]. The improvement of production technology can improve energy efficiency. For example, clean technologies, terminal pollution control technologies, and waste recycling technologies have the advantages of high conversion efficiency, less waste generation, short process flow, high degree of automation, fast chemical reaction speed, and low energy consumption, which can effectively curb carbon emissions in industrial production [31,32]. In addition, improving total factor productivity is one of the manifestations of technological progress, and it also can improve the utilization efficiency of natural resources and reduce carbon emissions [33,34,35]. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 2. 

Technological progress plays a mediating role between the implementation of the new development concept and carbon emissions.

3.2.2. The Mediating Effect of Industrial Structure Upgrading

Implementing the new development concept is of great significance for accelerating the strategic transformation of industrial and economic structure and implementing the dual-carbon strategy under the new development concept should take a low-carbon industrial structure as a scientific method [20]. Industrial structure upgrading plays a mediating role between the new development concept and carbon emissions.

On the one hand, implementing the new development concept helps to promote upgrading industrial structures. The new development concept emphasizes development quality and requires upgrading the height of the economic structure and building a modern industrial system. It also focuses on taking the real economy as the core of economic development, improving the quality of the supply system, taking innovation as the first driving force to lead development, improving labor productivity, and promoting the evolution of development results. Specific measures, such as optimizing and upgrading traditional industries, clustering advanced manufacturing, developing strategic emerging industries, and accelerating agricultural modernization, are helpful in laying a modern industrial system foundation for economic development [36].

On the other hand, the industrial structure adjustment will affect carbon emissions. The energy consumption demands of the three major industries are significantly different. The pollution intensity of the secondary industry is higher than that of the primary and tertiary industries. Therefore, if the industrial structure of a city is optimized, that is, the proportion of the tertiary industry is increased and the proportion of the secondary industry is reduced, carbon emissions will be reduced [37]. Many heavy chemical enterprises are China’s primary sources of energy consumption and carbon emissions [31]. Adjusting the industrial structure, reducing the proportion of high-energy-consuming industries, and promoting low-carbon transformation of traditional industries can help to reduce carbon emissions [32]. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 3. 

Industrial structure upgrading plays a mediating role between the implementation of the new development concept and carbon emissions.

4. Research Design

4.1. Sample Selection and Data Source

This paper selects 285 cities in China from 2010 to 2019 as research samples. The carbon emission data are from the China Carbon Emission Accounting Database, and the data on the implementation of the new development concept are from the 2010–2019 city government work reports, which are collected manually on cities’ government websites. Other data are from the China City Statistical Yearbook. In order to avoid the impact of extreme values, we conducted a 1% tail reduction on all continuous variables. There are 2450 city year observations in this study. We use Stata16.0 to process and analyze the data.

4.2. Variable Definitions

4.2.1. Explained Variable

Carbon emissions. The China Carbon Emission Accounting Database (CEADs) created by Tsinghua University has constructed multi-scale carbon emission accounting methodologies covering national, regional, urban, infrastructure, and other levels of carbon accounting lists. It has the advantages of full coverage, multi-scale, and high accuracy [38]. Therefore, this study uses the total carbon dioxide emissions in the CEADs as the proxy indicator of the carbon emission scale, recorded as CE1; the ratio of total carbon dioxide emissions to GDP is used as a proxy indicator for carbon emission intensity, recorded as CE2.

4.2.2. Explanatory Variable

The implementation of the new development concept. We use a textual analysis method to calculate the word frequency related to the new development concept in the government work reports. The steps are as follows:

(1)

Convert the government work reports from 2010 to 2019 into txt format;

(2)

Use the Jieba thesaurus in Python to segment government work reports while eliminating inactive words without practical meaning;

(3)

Use the word-bag method to calculate the frequency of keywords in the five dimensions of the new development concept and use TF-IDF to calculate the weighted frequency;

(4)

Add the TF-IDF weighted word frequency of each keyword in each dimension as the score for each dimension;

(5)

Add up the scores for each dimension to obtain the implementation level of the new development concept, recorded as New.

In the above steps, keyword dictionary construction is a crucial point. This study uses Li’s idea of constructing text indicators to build a keyword dictionary [39]. The steps are as follows:

(1)

Randomly select 500 government work reports for manual reading, perceive and extract Chinese words representing innovation, coordination, green, openness, and sharing, and build a seed word set for new development concept (see Table 1);

(2)

Based on the seed word set, use the CBOW model in the Word2Vec machine-learning method proposed by Mikolov et al. to conduct corpus training on government work report texts [40];

(3)

Use the trained model to output the top 20 words in the seed word set with the highest similarity to each seed word set and merge them to form a new development concept keyword dictionary.

Table 1.
Seed word set of the new development concept.

Table 1.
Seed word set of the new development concept.

DimensionsSeed WordsInnovationInnovation, R&D, technology, invention, creation, research, new technology, patents, etc.CoordinationBalance, coordination, integration, overall planning, overall consideration, duality, etc.GreenGreen, environmental protection, pollution, ecology, ecological protection, low-carbon, etc.OpennessInternal and external linkage, opening up, international cooperation, foreign trade, etc.SharingFairness, justice, injustice, disparity, efficiency, redistribution, secondary distribution, common prosperity, etc.

4.2.3. Mediating Variables

(1)

Technological progress. Technological progress is reflected in the emergence of new technologies and the improvement of factor utilization efficiency [41]. Therefore, we use the following two indicators to measure technological progress:

First, refer to Griliches [42]. The number of invention patent applications per capita is taken as the proxy variable of technological innovation outcome, recorded as PA. Second, refer to Sun and Guo [43]. Take total factor productivity as the proxy variable for factor use efficiency, recorded as TFP, and the calculation formula is:

T
F
P

i
t

=

ln

?

Y

i
t

/

L

i
t

?
?
ln
?
(

K

i
t

/

L

i
t

)

where

Y

i
t

is the output of city i in period t, measured by GDP;

K

i
t

is the total fixed assets investment of city i in period t, and is the capital stock calculated by the perpetual inventory method;

L

i
t

is the labor of city i in period t, measured by the total number of employees; and

?

is the capital output elasticity, taken as 1/3. We take this value, because a simple verification was conducted based on China’s industry data, and the result indicated that 1/3 was broadly consistent with the actual situation [44,45].

(2)

Industrial structure upgrading. Generally, industrial structure rationalization and advancement are used to measure industrial structure upgrading [46]. The former refers to production factors that are reasonably allocated and interrelated among various industries, while the latter reflects the evolution process of industrial structure from low to high. Therefore, we use the following two indicators to measure industrial structure upgrading. First, based on the research of Yu [47], the reciprocal of the Theil index is used to measure the level of industrial structure rationalization, recorded as SR, and the calculation formula is as follows:

S
R

i
t

=

1

T
h
e
i
l

i
t

=

1

?

j
=
1

3

(

Y

i
t
,
j

Y

i
t

)
ln
?
(

Y

i
t
,
j

Y

i
t

/

L

i
t
,
j

L

i
t

)

where

Y

i
t

represents the gross product;

Y

i
t
,
j

represents the added value of the j industry;

L

i
t

represents the total number of employees; and

L

i
t
,
j

represents the number of employees in the j industry.

Second, refer to Sun et al. [48]. The advanced level of industrial structure is measured by giving higher weight to the proportion of added value of the three industries in turn, recorded as SA. The calculation formula is as follows:

S
A

i
t

=

Y

i
t
,
1

Y

i
t

 
?
 
1
+

Y

i
t
,
2

Y

i
t

 
?
 
2
+

Y

i
t
,
3

Y

i
t

 
?
 
3

The meanings of the symbols in the formula are the same as those in Formula (2).

4.2.4. Control Variables

Drawing on the research of Hu and Wang [49] and Xu et al. [50], we select economic development, population density, foreign direct investment, financial development, infrastructure, and residents’ education level as control variables. In addition, we also set up city virtual and annual virtual variables. The symbols of each variable and their measurement methods are shown in Table 2.

4.3. Econometric Models

In order to verify Hypothesis 1, this paper uses carbon emission scale and intensity as the explained variables, and the implementation of the new development concept is used as the core explanatory variable. A baseline regression model is constructed as shown in Equation (4):

C
E
1
(
C
E
2
)

i
t

=

?

0

+

?

1

N
e
w

i
t

+
?

C
o
n
t
r
o
l
s

i
t

+

?

i

+

?

t

+

?

i
t

where

C
E
1

i
t

and

C
E
2

i
t

represent the scale and intensity of carbon emissions, respectively;

N
e
w

i
t

represents the new development concept;

C
o
n
t
r
o
l
s

i
t

is a group of control variables, including economic development, population density, foreign direct investment, financial development, infrastructure, and residents’ education level;

?

i

is an individual fixed effect;

?

t

is a time fixed effect; and

?

i
t

is a random disturbance term. If hypothesis 1 is established, that is, the implementation of the new development concept has a significant inhibitory effect on the scale and intensity of carbon emissions,

?

1

should be significantly negative.

In order to test the mediating effect of technological progress and industrial structure upgrading in the new development concept affecting carbon emissions, this paper draws on the research of Baron and Kenny [51] and Wen and Ye [52] to construct the following models based on the benchmark model:

M
e
d
i
a
t
o
r

i
t

=

?

0

+

?

1

N
e
w

i
t

+
?

C
o
n
t
r
o
l
s

i
t

+

?

i

+

?

t

+

?

i
t

C
E
1
(
C
E
2
)

i
t

=

?

0

+

?

1

N
e
w

i
t

+

?

2

M
e
d
i
a
t
o
r

i
t

+
?

C
o
n
t
r
o
l
s

i
t

+

?

i

+

?

t

+

?

i
t

where

M
e
d
i
a
t
o
r

i
t

is the mediating variable. The steps for the mediating effect test are as follows: Firstly, the new development concept (New) regresses with the carbon emission scale (CE1) or carbon emission intensity (CE2), as shown in Equation (4); Secondly, the new development concept (New) regresses with the mediating variables of technological innovation outcome (PA), total factor productivity (TFP), industrial structure rationalization (SR), and industrial structure advancement (SA), as shown in Equation (5); Finally, the new development concept and mediating variables regress with the scale and intensity of carbon emissions, as shown in Equation (6). If

?

1

,

?

1

and

?

2

are significant,

?

1

is not significant, indicating a completely mediating effect; if

?

1

,

?

1

, and

?

2

are significant,

?

1

is significant and |

?

1

|<|

?

1

|, indicating a partial mediating effect.

5. Empirical Results

5.1. Descriptive Statistics

Table 3 shows descriptive statistical results for variables in the baseline regression model. The mean value of CE1 is 39.799, the standard deviation is 37.877, the minimum value is 2.620, the maximum value is 206.630, the mean value of CE2 is 0.321, the standard deviation is 0.313, the minimum value is 0.028, and the maximum value is 2.136, indicating that there is a significant gap between cities in terms of both carbon emission scale and carbon emission intensity. The mean value of New is 0.914, the standard deviation is 0.233, the minimum value is 0.460, and the maximum value is 1.550, which indicates that after the central government proposed the new development concept, local governments actively incorporated it into their governance and paid more attention to implementing the new development concept in the economic transformation, with small differences between regions. As for control variables, there are also significant differences in GDP, Pop, FDI, Fin, Infra, and Edu among different cities, showing the objective reality of uneven development among cities in China.

5.2. Correlation Analysis

Table 4 reports the correlation coefficients between variables in the baseline regression model. From the Pearson correlation coefficient matrix in the lower left section, the correlation coefficients between New and CE1, CE2 are ?0.348 and ?0.001, and both are significant at the 1% level, preliminarily indicating that there is a significant negative correlation between the implementation of the new development concept and carbon emissions. The correlation coefficients between GDP, Pop, Fin, Infra and CE1 and CE2 are significantly positive, indicating that factors such as economic development and population size have a significant positive correlation with carbon emissions; The correlation coefficients between FDI and Edu and CE1 and CE2 are significantly negative, indicating a significant negative correlation between these factors. From the Spearman correlation coefficient matrix in the upper right section, the correlation coefficients between New and CE1, CE2 are significantly negative, and the correlation coefficients between the control variables and the explained variable are in line with the Pearson correlation coefficient matrix results. The above results preliminarily confirm hypothesis 1, that is, in the new development stage, implementing the new development concept can significantly curb carbon emissions, but further regression analysis is still needed to verify the accuracy of this conclusion.

To avoid multicollinearity affecting the estimation accuracy, we further calculate the variance inflation factor (VIF) of explanatory variables in the baseline regression model, and Table 5 reports the results. The maximum value of the VIF is 2.300, the minimum value is 1.110, and the mean value is 1.510, which are all less than the empirical standard of 10, showing that these variables can be included in one regression model.

5.3. Baseline Regression

Table 6 shows the results of the Hausman test for model selection. The Chi2 values are 69.78 and 27.00, with p-values less than 0.01, rejecting the null hypothesis that “there is no fixed effect”. Therefore, the fixed effect model is selected for coefficient estimation.

Table 7 shows the baseline regression results, where columns (1) and (3) only control individual fixed effects, while columns (2) and (4) control individual-fixed effects and time-fixed effects. The following conclusions can be drawn:

When the explained variable is CE1, the coefficients of New are ?0.783 and ?0.129, both significant at the 1% level, indicating that implementing the new development concept can significantly inhibit the carbon emission scale. When the explained variable is CE2, the coefficients of New are ?0.015 and ?0.013, both significant at the 1% level, indicating that the implementation of the new development concept can significantly reduce carbon emission intensity. The findings strongly confirm hypothesis 1, showing the negative effect of implementing the new development concept on carbon emissions.

As for the impact of control variables on carbon emissions, the coefficients of GDP are significantly positive, indicating that economic development has a significant promoting effect on carbon emissions. The coefficients between Pop and CE2 are significantly positive, indicating that population density has a significant promoting effect on carbon emission intensity. Excessive population gathering brings “congestion effects” [53], and the increase in population density creates pressure on urban transportation infrastructure, which brings a negative impact on energy consumption. The coefficients of FDI are significantly negative, indicating that foreign direct investment significantly inhibits carbon emissions. Knowledge, technology, and experience brought about by FDI have improved resource utilization and management efficiency, which help reduce carbon emissions. The coefficients of Fin are significantly positive, indicating that financial development significantly improves carbon emissions. In traditional financial development, the lack of attention to the environmental review of enterprises (or projects) is not conducive to promoting environmentally sound projects and encourages extensive development methods. Financial development should move closer to the direction of green finance, reducing carbon emissions through green loans and green investments. The coefficients between Infra and CE2 are significantly positive, indicating that infrastructure construction has a significant promoting effect on carbon emission intensity. Generally speaking, transportation infrastructure construction increases the accessibility of the provincial space, which may negatively impact carbon emission reduction. The coefficients between Edu and CE1 are significantly negative, indicating that residents’ education level has a significant inhibitory effect on the carbon emission scale. High-quality talents have more ability and willingness to supervise ecological environment construction, which helps reduce carbon emission scale.

5.4. Robustness Test

5.4.1. Alternative Measures of Core Explanatory Variables

Referring to the accounting methods of Ren et al. [54], we use the amount of natural gas, liquefied petroleum gas, and electricity consumption to calculate the scale and intensity of carbon emissions. Then, we use the recalculated CE1 and CE2 as proxies for carbon emissions, and columns (1) and (2) of Table 8 show the re-estimation results. The coefficient between New and CE1 is ?0.005, significant at the 1% level, and the coefficient between New and CE2 is ?0.117, significant at the 1% level, indicating that the implementation of the new development concept can significantly inhibit the carbon emission scale and carbon emission intensity, consistent with the baseline regression analysis results. In addition, this article also uses the natural logarithm of total carbon dioxide emissions to measure the scale of carbon emissions and the per capita carbon dioxide emissions to measure the intensity of carbon emissions. Columns (3) and (4) of Table 8 show the results. The regression coefficients of New are significantly negative, indicating that the new development concept can significantly reduce the scale and intensity of carbon emissions, in line with previous conclusions.

5.4.2. Excluding Samples from Municipalities

Due to the fact that Beijing, Tianjin, Shanghai, and Chongqing are municipalities directly under the central government, they have a high degree of resource concentration in economics and trade, science and education, culture, medical care, and other fields. These four cities also have strong strengths in economic scale, industrial value, population size, and technological innovation, which is significantly different from other cities. Therefore, we eliminate these four samples and then estimate the coefficients of the core explanatory variable. Columns (5) and (6) of Table 8 show the results. The coefficients of New are ?0.550 and ?0.012, which are significant at the 1% level, showing that implementing the new development concept can significantly curb carbon emissions after excluding municipalities directly under the central government. The conclusion is robust.

5.4.3. Investigation by Sample Interval

In 2015, China proposed the new development concept, providing new theoretical guidance for the new stage of development. The new development concept conforms to China’s national conditions and has significant guiding meaning in enhancing economic sustainability. Considering that there may be differences in the impact of the new development concept on carbon emissions before and after 2015, this paper divides the sample interval based on 2015, and Table 9 shows the results. Before 2015, the effects of New are ?1.232 and ?0.003, significant at least at the 5% level; after 2015, the effects of New are ?4.539 and ?0.019, both significant at the 1% level. These findings indicate that after proposing the new development concept, city governments have paid more attention to implementing the new development concept in their governance practice to keep consistent with the central government, with a more significant effect on reducing carbon emissions.

5.4.4. Eliminate the Sample of Innovative City Pilot Policies

Cities are spatial carriers of innovation activities and gather a high number of innovation factors. They are indispensable responsibility subjects and action units in the development of the low-carbon economy. In 2008, China established Shenzhen as the first national innovative city to accelerate the construction of innovative cities. Since then, innovative city policy pilot projects have been continuously expanded. As of 2022, there are 103 innovative city pilots. Some studies show that innovative cities have achieved significant results in ecological environment improvement and carbon emission performance [55,56]. In order to eliminate the inhibitory effect of innovative city pilot policy on carbon emissions, this study eliminates the innovative cities, and columns (1) and (2) of Table 10 show the results. The coefficients of New are ?3.504 and ?0.018, significant at the 5% and 10% levels, respectively, indicating that the new development concept still has a significant inhibitory effect on carbon emissions scale and intensity after excluding the impact of innovative city pilot policy, in line with the results of baseline regression.

5.4.5. Explanatory Variables Lag by One Period

According to our baseline regression results, implementing the new development concept has a significant inhibitory effect on carbon emissions. However, carbon emissions may also affect the new development concept, that is, cities with high carbon emission scales and intensities are more likely to actively implement the new development concept to reduce air pollution and improve environmental sustainability. Therefore, to avoid the endogeneity of mutual interference, all explanatory variables are lagged by one period, and columns (3) and (4) in Table 10 show the results. The coefficients of New (lagging one period) are ?0.904 and ?0.003, significant at least at the 5% level, indicating that the new development concept can significantly inhibit the scale and intensity of carbon emissions. The conclusion is valid after considering the endogenous problem of mutual causality.

5.4.6. Instrumental Variable Method

Because implementing the new development concept is not random, it may be affected by some factors. When these factors also affect carbon emissions, it leads to potential endogenous problems and affects estimation accuracy. In order to alleviate endogenous problems, this study uses the average value of New of other cities in the same province as an instrumental variable (IV). Generally speaking, a reasonable instrumental variable needs to meet two requirements: one is that the instrumental variable is related to the core explanatory variable, and the other is that the instrumental variable is unrelated to the explained variable. Cities in the same province have similarities in history, culture, customs, and language, and other cities in the same area influence this city’s actions. The higher the implementation level of the new development concept in other cities in the same area, the higher the implementation level of this city. At the same time, the implementation degree of the new development concept of other cities in the same province will affect the carbon emissions of other cities but has nothing to do with the scale and intensity of carbon emissions of this city. Columns (1) to (3) of Table 11 show the two-stage regression results. In the first stage, the results show that IV meets the requirements of relevance and exogeneity, and we can regress IV under CE1 and CE2 of the second stage. The coefficient between IV and New is 0.505, significant at the 1% level, indicating that the better the implementation of the new development concept in other cities in the same province, the higher the implementation level of the new development concept in this city. In the second stage, the estimation between New and CE1 is ?4.070, significant at the 1% level, and the coefficient between New and CE2 is ?0.020, significant at the 1% level, consistent with the benchmark conclusion.

In addition, we also select the age of officials as the instrumental variable of the new development concept. On the one hand, the younger an official is, the bigger their promotion space is. As such, the incentive effect of competition for young officials is more pronounced than for elderly officials. Young officials are more inclined to keep in line with the central government in their tenure and pay more attention to implementing the new development concept. On the other hand, age is a natural attribute unaffected by carbon emissions. To explain the results conveniently, we take the reciprocal of the mayor’s age as the instrumental variable of the new development concept, and columns (4) to (6) of Table 11 show the results. In column (4), we can see that this instrument variable also meets the requirements of relevance and exogeneity, and the coefficient between IV and New is significantly positive, which means the younger the city officials, the higher the implementation level of the new development concept. In the second stage, the coefficients between New and CE1, New and CE2 are negative and significant at least at the 5% level, in line with our basic conclusion.

5.5. Mechanism Test

5.5.1. Technological Progress

Table 12 reports the mediating effect of technological progress in the new development concept affecting carbon emissions. There is a significant partial mediating effect when the mediating variable is PA. In column (1), the coefficient between New and PA is 1.098, significant at the 1% level, indicating that the new development concept can significantly increase technological innovation output. In column (2), the coefficient of New is ?0.072, significant at the 5% level, and the coefficient of PA is ?0.052, significant at the 1% level, indicating that the new development concept can curb the scale of carbon emissions by increasing technological innovation achievements, and the indirect effect accounts for about 44.26% of the total effect (1.098 × 0.052/0.129 × 100%

?

44.26%). Column (3) shows that the coefficients of New and PA are ?0.009 and ?0.004, respectively, both significant at the 1% level, showing that the new development concept can reduce carbon emission intensity through technological innovation, and the indirect effects brought by technological innovation account for about 30.77% of the total effects (1.098 × 0.004/0.013 × 100%

?

30.77%). In sum, implementing the new development concept can promote the transformation of development momentum, stimulate enthusiasm for innovation and creation, and promote the replacement of traditional technologies with new technologies, which is helpful to reducing the scale and intensity of carbon emissions.

Columns (4) to (6) in Table 12 report the mediating role of total factor productivity in the new development concept curbing carbon emissions. In column (4), the coefficient between New and TFP is 0.050, significant at the 10% level, indicating that the new development concept has a significant role in improving total factor productivity. In column (5), the coefficients of New and TFP are ?0.060 and ?1.382, respectively, both significant at the 5% level, indicating that the new development concept can reduce the scale of carbon emission by improving total factor productivity, and the indirect effect brought by TFP accounts for about 53.49% of the total effect (0.050 × 1.382/0.129 × 100%

?

53.49%). In column (6), the estimations of New and TFP are ?0.011 and ?0.040, significant at levels of 1% and 5%, respectively, indicating that total factor productivity plays a significant partial mediating role in the new development concept reducing carbon emission intensity, and the indirect effect produced by TFP accounts for about 15.38% (0.050 × 0.040/0.013 × 100%

?

15. 38%) of the total effect. In sum, the new development concept can inhibit carbon emission scale and intensity by improving total factor productivity, promoting resource utilization efficiency, and reducing resource waste.

5.5.2. Industrial Structure Upgrading

Table 13 reports the mediating effect of industrial structure upgrading in the new development reducing carbon emissions. There is a significant partial mediating effect when the mediating variable is SR. In column (1), the coefficient between New and SR is 0.006, significant at the 1% level, indicating that implementing the new development concept can significantly promote the rationalization of industrial structure. In column (2), the coefficients of New and SR are ?0.121 and ?1.403, significant at least at the 5% level, respectively, indicating that the new development concept can curb the scale of carbon emissions through industrial structure rationalization, and the indirect effect brought by SR accounts for about 6.20% of the total effect (0.006 × 1.403/0.129 × 100%

?

6.20%). In column (3), the estimations of New and SR are ?0.009 and ?0.694, significant at least at the 10% level, respectively, indicating that the new development concept can reduce the carbon emission intensity by promoting the rationalization of industrial structure, and the indirect effect produced by the rationalization of industrial structure accounts for about 30.77% of the total effect (0.006 × 0.694/0.013 × 100%

?

30.77%).

Columns (4) to (6) in Table 13 report the results of the mediating effect of industrial structure advancement. In column (4), the coefficient between New and SA is 0.635, significant at the 1% level, indicating that implementing the new development concept can significantly promote the advancement of industrial structure. In column (5), the coefficients of New and SA are ?0.076 and ?0.084, respectively, both significant at the level of 5%, indicating that the new development concept can reduce the scale of carbon emissions through the advancement of industrial structure. For every additional unit of New, CE1 is directly reduced by 0.076 units, and the industrial structure is advanced by 0.635 units, thus indirectly reducing CE1 by 0.053 units (0.635 × 0.084

?

0.053), and the total effect is reduced by 0.129 units. The indirect effect brought by SA accounts for about 41.09% of the total effect. In column (6), the coefficients of New and SA are ?0.010 and ?0.006, significant at the levels of 5% and 1%, respectively, indicating that the new development concept can reduce carbon emission intensity through industrial structure advancement, and the indirect effect produced by industrial structure advancement accounts for about 23.08% of the total effect (0.635 × 0.006/0.013 × 100%

?

23.08%). In sum, implementing the new development concept is helpful to promoting the transformation and upgrading of industrial structures, building a rational and advanced industrial structure system, and promoting the development of modern service industry and high-tech industry, which contribute to reducing environmental pollution and achieving energy conservation and emission reduction.

6. Further Analysis

6.1. Impact of Five Development Concepts on Carbon Emissions

To investigate the impact of the five sub-dimensions of the new development concept on the carbon emissions scale, we take each dimension’s weighted word frequency as the core explanatory variables, and Table 14 shows the results. The coefficients of innovation, coordination, green, and sharing are all negative and significant at least at the 10% level, suggesting that implementing the innovation, coordination, green, and sharing development concepts helps reduce the carbon emission scale. The coefficient of openness is ?7.321, which is not significant, indicating that the concept of openness development has no significant inhibitory effect on the scale of carbon emissions. The possible reasons for this are as follows: China’s foreign trade is considerable, but it lacks high-value-added products, resulting in domestic environmental pollution, which cannot play an active role in reducing the scale of carbon emissions.

To investigate the impact of five sub-dimensions of the new development concept on carbon emission intensity, this paper takes the weighted word frequency of each dimension as the core explanatory variable, and the results are shown in Table 15. The coefficients of innovation, coordination, green, and sharing are all significantly negative, showing that implementing these four development concepts is conducive to curbing carbon emission intensity. The coefficient of openness is ?0.038, which is not significant, indicating that the impact of the open development concept on carbon emission intensity is not obvious. A possible reason for this is that there is a big carbon deficit in China’s foreign trade, and China bears huge carbon-emission-reduction pressures caused by the consumption demand of other countries [57]. At the same time, China’s carbon productivity is lower than the global average level, and the inhibitory effect on carbon emission intensity is limited.

6.2. Group Regression by Region

A notable feature of China’s economic development is regional imbalance. The eastern region is superior to the central and western areas regarding financial strength, talent reserve, and technological innovation. With the implementation of the “Rise of Central China” and “Western Development” strategies, the central and western areas strive to develop under their advantages, but there are still obvious regional gaps. We divide the 285 cities into 3 groups based on their locations, and then conduct group regression to investigate the differentiated impact of New on carbon emissions. Table 16 shows the results. The coefficients of New in the eastern region are ?0.175 and ?0.020, significant at the levels of 1% and 5%, respectively; the coefficients in the central region are ?0.065 and ?0.014, both significant at the 1% level; and in the western region, the coefficients are ?0.209 and ?0.038, both not significant, indicating that the inhibitory effect of New on carbon emission scale and intensity presents a spatial difference pattern of decreasing from east to west. The possible reasons for this are as follows: The eastern region is at the forefront of institutional innovation in soft environment, and it is an experimental and demonstration area for reform in many fields. Emerging and knowledge-intensive industries are developing rapidly and have strong strengths. There are many institutional innovations in its business environment, such as Zhengjiang’s “Running at Most Once” and Jiangsu’s “No Meet for Approval”, which contribute to improving the institutional environment and implementing the new development concept. Most cities in central or western areas lacks funds, talents, and technologies and still rely on natural resources to develop the economy. As such, the impact of New on carbon emissions is more pronounced in the eastern region than in central and western regions.

6.3. Group Regression by City Size

To investigate the impact of New on carbon emissions in cities of different sizes, this paper divides the 285 cities into 3 groups according to their population. Table 17 shows the results of group regression. When the explained variable is CE1, the coefficients of New in small-sized, medium-sized and large-sized cities are ?4.986, ?0.281, and ?1.728, which are insignificant, significant at the 10% level and significant at the 1% level, respectively. When the explained variable is CE2, the coefficients of New in small-sized, medium-sized, and large-sized cities are ?0.010, ?0.001, and ?0.013, which are insignificant, insignificant, and significant at the 5% level, respectively. These results show that in large-sized cities, the new development concept has a more significant inhibitory effect on carbon emission scale and intensity than in small-sized and middle-sized cities. Large-sized cities, such as Beijing, Shanghai, and Guangzhou, have high factor utilization efficiency, technological innovation levels, production efficiency, and industrial structure transformation. Hence, New has a stronger effect on restraining the scale and intensity of carbon emissions. In contrast, the population of small- and medium-sized cities is relatively small, and the internal driving force and talent-gathering ability required for intensive development are low. Hence, New has no noticeable effect on carbon emission reduction.

6.4. Group Regression by Whether It Is a Resource-based City or Not

According to the National Sustainable Development Plan of Resource-based Cities (2013–2020) issued by the State Council, cities are divided into resource-based and non-resource-based groups, and Table 18 shows the results of group regression. The coefficients of New in non-resource-based cities are ?1.057 and ?0.010, i.e., not significant. The coefficients in resource-based cities are ?2.278 and ?0.011, significant at the levels of 1% and 5%, respectively, indicating that in resource-based cities, the inhibitory effect of New on carbon emission scale and intensity is significantly stronger than that in non-resource-based cities. The possible reasons for this are that the initial resource dependence of non-resource-based cities is low, the innovation level is relatively cutting edge, and the transformation space driven by New is smaller than that of resource-based cities. At the same time, the total carbon emissions and carbon emission intensity of resource-based cities are at a high level, and the marginal effect of implementing new development concept is more significant.

6.5. Group Regression by City Administrative Level

According to the administrative level of the cities, this paper divides the 285 cities into a high-administrative-level group (including municipalities directly under the central government, sub-provincial cities, and provincial capitals) and a general prefecture-level group, and Table 19 reports the results. The coefficients of New in general prefecture-level cities are ?0.333 and ?0.015, i.e., both are not significant. The coefficients in high-administrative-level cities are ?0.563 and ?0.018, significant at the levels of 1% and 5%, respectively, indicating that in high-administrative-level cities, the inhibitory effect of New on carbon emission scale and intensity is stronger than that of general prefecture-level cities. The possible reasons for this are that cities with higher administrative levels have accumulated more factor stocks, and compared with general prefecture-level cities with scarce resources, they can flexibly and independently allocate resources in economic and social development, concentrate on implementing the new development concept, and reduce carbon emissions.

7. Conclusions and Discussion

7.1. Conclusions

The new development concept provides a scientific guide for the strategic goals of emission peak and carbon neutrality. Taking 285 cities in China from 2010 to 2019 as samples, this paper measures the implementation of the new development concept by textual analysis and analyzes the relationship between the new development concept and the scale and intensity of carbon emission. The results show the following: (1) Implementing the new development concept can significantly curb the scale and intensity of carbon emissions, and the above conclusion is still valid after the robustness test; (2) Technological progress, total factor productivity, rationalization, and advancement of the industrial structure have played significant mediating roles between the implementation of the new development concept and carbon emissions; and (3) Implementing the innovation, coordination, green, and sharing development concepts has a significant inhibitory effect on carbon emissions, while implementing the openness development concept is of no help. The negative effect of the implementation of the new development concept is more significant in eastern, large-size, resource-based, and high-administrative-level cities compared with other cities.

7.2. Discussion

Promoting emission peak and carbon neutrality is an urgent need for China to solve the environmental problems. Natural resources are the foundation of national development. Since the reform and opening up, China’s economy has achieved a remarkable growth miracle and has become the second-largest economy in the world. At the same time, China faces the dilemma of limited resources and unsustainable traditional extensive growth mode. Implementing the new development concept in an all-around way has become the strategic choice for China’s further development. The existing research theoretically discusses the emission reduction effect of the new development concept but needs more empirical evidence. To explore the impact of implementing the new development concept on carbon emissions, the impact mechanisms, and the regional differences, we conduct empirical research based on 285 cities in China. The possible contributions of this paper are as follows: Firstly, it uses the textual analysis method to analyze government work reports and measures the implementation of the new development concept. Based on this, it explores the impact of implementing the new development concept on the scale and intensity of carbon emissions. Secondly, this paper enriches the research on the impact factors of carbon emissions. The existing literature in this field is mainly from the perspectives of economic growth, population, industrial structure, and energy consumption but lacks the perspective of development concepts. Hence, this study has an incremental contribution to this field. Thirdly, this paper uses the mediating effect model to verify the impact mechanisms of the new development concept on carbon emissions and explores regional heterogeneity by group regression, which can provide a theoretical basis for policymakers to formulate carbon-emission-reduction strategies.

The highlight of this paper is that it uses the textual analysis method to capture the implementation of the new development concept, which is also the basis of empirical research. Different from the practice of economic growth and technological innovation, the new development concept is an idea that is very difficult to quantify. Scholars have not yet conducted quantitative research on the implementation level of the new development concept. Textual analysis is a qualitative and quantitative content analysis method initially used in information science [58]. It has gradually developed into an important research method in modern social science. It is generally believed that people’s cognitive tendency is mainly reflected by the words they often use, and the frequency of using words reflects the importance people attach to things. As an official document, the government work reports show the importance attached by government officials to specific goals, represent the trend in future work, and embody their governance philosophy [59]. Based on this theory, this paper makes statistics on the frequency of keywords related to the new development concept in government work reports and measures the implementation of the new development concept with this, which represents a good attempt in this field and provides a reference for other related research.

However, there are also some shortcomings in this paper: Firstly, from a research perspective, this paper takes 285 cities in China as research samples, lacks research at the provincial or county level, and does not go deep into the more micro-enterprise level, so the universality of the conclusion may be lacking. Secondly, in the research content, this paper only explores the mediating role of technological progress and industrial structure upgrading. The impact of implementing the new development concept exists in every aspect of the economy, and it is possible to achieve “decoupling” through other ways. Finally, regarding research methods, this paper uses textual analysis to measure the implementation of the new development concept, which can only roughly measure the importance attached by the government to this concept. Nevertheless, measuring how much of this concept has been applied in administrative activities takes work. Therefore, we can make a more in-depth study from the above three aspects in future research.

7.3. Policy Implications

First, in the future, China should implement the new development concept completely, accurately, and comprehensively; accelerate the implementation of the innovation-driven strategy, and enhance cities’ innovation power; promote regional coordinated development, and enhance cooperation between cities; take the green and low-carbon urban development road, and form a green and low-carbon production mode and lifestyle; adhere to a high level of opening up, and improve the quality of foreign trade and investment cooperation; and adhere to shared development, and ensure the fruits of urban development can benefit the people more.

Second, China should seize the opportunity of a new round of scientific and technological revolution and industrial transformation, give full play to the strategic supporting role of scientific and technological innovation, make good use of existing technologies, increase forward-looking and disruptive green and low-carbon technology research and development, and accelerate the popularization and application of pollution and carbon reduction technologies. Furthermore, China should promote industrial optimization and upgrading; promote the deep integration of emerging technologies, such as the Internet, big data, artificial intelligence, and fifth-generation mobile communication (5G) with green and low-carbon industries; and increase the proportion of green and low-carbon industries in the total economic output by developing strategic emerging industries and building green manufacturing systems and service systems.

Third, China should adhere to the construction principles of adapting to local conditions and explore the development model of low-carbon cities in line with the local characteristics. In implementing the new development concept, decision makers should combine the actual situation of the city, proceed from the geographical location, scale, resource endowment, and administrative level of the city, and steadily push forward the dual-carbon goal.

Author Contributions

Conceptualization, H.W. and Z.Z.; methodology, H.W. and Z.Z.; software, H.W.; validation, H.W., and Z.Z.; formal analysis, H.W.; investigation, Z.Z.; resources, H.W. and Z.Z.; data curation, H.W.; writing—original draft preparation, H.W. and Z.Z.; writing—review and editing, H.W. and Z.Z.; visualization, H.W. and Z.Z.; supervision, H.W. and Z.Z.; project administration, H.W. and Z.Z.; funding acquisition, H.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Guangxi Science and Technology Development Strategy Research Project: Research on statistical monitoring and evaluation of science and technology innovation and strategies for improving innovation ability in Guangxi. The funder was Guangxi Science and Technology Department. Grant number “ZL22064020”. This research was also funded by Project of Tibet Science and Technology Department: Research on the current situation, problems, and countermeasures of science and technology investment in Tibet. The funder was Tibet Science and Technology Department. Grant number “XZ202301ZY0004F”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 2.
Definitions of variables.

Table 2.
Definitions of variables.

SymbolsDefinitionsExplained Variables CE1Total carbon dioxide emissionsCE2Carbon dioxide emissions per unit of GDPExplanatory Variable NewFirst, use textual analysis to calculate the weighted word frequency of innovation, coordination, green, openness, and sharing, and then add up the scores of each dimension to obtain the implementation degree of the new development conceptMediating Variables PAPer capita domestic invention patent applicationsTFPCalculated by Formula (1)SRCalculated by Formula (2)SACalculated by Formula (3)Control Variables GDPNatural logarithm of GDP per capitaPopPopulation/land areaFDIActual utilized foreign capital investment/GDPFinYear-end deposit balance of financial institutions/GDPInfraBus per capitaEduNumber of students in colleges per capitaCityLocated in this city, the value is 1, otherwise it is 0YearIn this year, the value is 1, otherwise it is 0

Table 3.
Descriptive statistics of variables in the baseline model.

Table 3.
Descriptive statistics of variables in the baseline model.

VariablesObservationsMeanSdMinMedianMaxCE1245039.79937.8772.62028.080207.630CE224500.3210.3130.0280.2322.136New24500.9140.2330.4600.8901.550GDP245010.9360.6779.09410.93012.425Pop24500.1030.0800.0060.0820.399FDI24502.5312.3170.0131.84610.925Fin24501.7080.7330.6261.5564.762Infra24508.3384.8460.9957.34725.072Edu24505.5934.5050.2534.23920.265

Table 4.
Correlation coefficients of variables in the baseline model.

Table 4.
Correlation coefficients of variables in the baseline model.

VariablesCE1CE2NewGDPPopFDIFinInfraEduCE110.547 ***?0.349 ***0.477 ***0.292 ***?0.131 ***0.159 ***0.420 ***?0.282 ***CE20.488 ***1?0.019 ***0.178 ***0.079 ***?0.042 **0.086 ***0.179 ***?0.130 ***New?0.348 ***?0.001 ***10.330 ***0.167 ***0.230 ***0.158 ***0.348 ***0.239 ***GDP0.437 ***0.188 ***0.353 ***10.141 ***0.194 ***0.057 ***0.651 ***0.364 ***Pop0.188 ***0.056 ***0.095 ***0.075 ***10.319 ***0.226 ***0.327 ***0.335 ***FDI?0.102 ***?0.010 *0.223 ***0.160 ***0.209 ***10.126 ***0.287 ***0.234 ***Fin0.196 ***0.041 **0.165 ***0.081 ***0.254 ***0.115 ***10.349 ***0.398 ***Infra0.323 ***0.135 ***0.331 ***0.630 ***0.297 ***0.217 ***0.378 ***10.572 ***Edu?0.134 ***?0.016 **0.169 ***0.330 ***0.351 ***0.178 ***0.393 ***0.502 ***1

Table 5.
Variance expansion factors of explanatory variables.

Table 5.
Variance expansion factors of explanatory variables.

VariablesInfraGDPEduFinPopNewFDIMean VIFVIF2.3001.8801.5201.3401.2301.2101.1101.5101/VIF0.4350.5330.6580.7480.8130.8260.902

Table 6.
Hausman test results.

Table 6.
Hausman test results.

Explained VariablesCE1CE2Chi269.7827.00p-value0.0000.000

Table 7.
Baseline regression results.

Table 7.
Baseline regression results.

VariablesCE1CE2(1)(2)(3)(4)New?0.783 ***?0.129 ***?0.015 ***?0.013 *** (?3.447)(?3.062)(?4.914)(?4.741)GDP11.005 ***0.430 ***0.037 ***0.176 *** (8.504)(3.121)(2.992)(6.132)Pop?2.051?12.6240.631 ***0.398 ** (?0.181)(?1.016)(3.417)(2.423)FDI?0.214 *?0.075 *?0.002 *?0.004 * (?1.681)(?1.726)(?1.858)(?1.918)Fin3.085 **0.247 *0.022 *0.072 *** (2.124)(1.757)(1.865)(3.809)Infra?0.055?0.0580.004 *0.003 * (?0.310)(?0.326)(1.944)(1.682)Edu?0.816 **?0.581 *0.0070.002 (?2.222)(?1.673)(1.632)(0.554)CityYesYesYesYesYearNoYesNoYesConstant?79.330 ***?37.837?0.243 *?1.679 *** (?5.380)(?1.069)(?1.847)(?5.562)Observations2450245024502450R-squared0.1410.1660.1090.152F13.970 ***18.530 ***17.560 ***19.930 ***

Table 8.
Robustness test I.

Table 8.
Robustness test I.

VariablesVariable Replacement 1Variable Replacement 2Excluding Samples(1)(2)(3)(4)(5)(6)CE1CE2CE1CE2CE1CE2New?0.005 ***?0.117 ***?0.828 **?0.601 ***?0.550 ***?0.012 *** (?5.127)(?4.299)(?2.307)(?3.098)(?3.270)(?3.667)GDP0.192 ***1.4213.575 *0.032 ***0.7190.177 *** (2.696)(1.202)(1.941)(2.675)(0.205)(6.137)Pop0.39410.270 **11.0260.101 **11.6280.400 ** (1.469)(2.558)(1.322)(2.179)(0.942)(2.426)FDI?0.010 *?0.130 **?0.324 *?0.001?0.101?0.004 * (?1.878)(?2.224)(?1.694)(?1.243)(?0.303)(?1.875)Fin0.0091.979 ***?1.5980.0110.0340.072 *** (0.291)(3.179)(?1.425)(1.474)(0.036)(3.802)Infra?0.0020.069 *0.206 *0.002 ***0.0670.003 * (?0.580)(1.768)(1.813)(2.656)(0.384)(1.678)Edu?0.015 **?0.092?0.379 *0.002?0.582*0.002 (?2.497)(?0.675)(?1.679)(1.035)(?1.675)(0.523)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant1.14320.392 *47.983 **?0.323 **39.274?1.688 *** (1.553)(1.729)(2.520)(?2.503)(1.126)(?5.557)Observations245024502450245024022402R-squared0.2750.3750.3420.3720.1630.153F14.240 ***13.140 ***13.530 ***16.610 ***19.180 ***19.880 ***

Table 9.
Robustness test II.

Table 9.
Robustness test II.

VariablesCE1CE2Before 2015After 2015Before 2015After 2015New?1.232 **?4.539 ***?0.003 ***?0.019 *** (?2.348)(?6.541)(?2.929)(?3.570)GDP3.934 *3.220 *0.200 ***0.125 * (1.957)(1.913)(5.468)(1.902)Pop16.472?11.4300.352 **0.214 * (1.031)(?0.818)(2.319)(1.903)FDI?0.078?0.165 *?0.004 *?0.001 * (?0.268)(?1.886)(?1.712)(?1.807)Fin?1.8770.7510.063 ***0.040 * (?1.195)(0.640)(2.752)(1.894)Infra0.008?0.0450.0020.001 (0.058)(?0.242)(1.316)(0.501)Edu?0.626 **?1.102 *0.0020.007 (?1.978)(?1.800)(0.447)(1.609)CityYesYesYesYesYearYesYesYesYesConstant?6.64811.065?1.918 ***?1.258 (?0.155)(0.183)(?4.984)(?1.648)Observations18506001850600R-squared0.2100.0360.1880.043F10.110 ***12.640 ***12.290 ***11.490 ***

Table 10.
Robustness test III.

Table 10.
Robustness test III.

VariablesEliminate the Influence of Innovative City PolicyExplanatory Variables Lagged by One Period(1)(2)(3)(4)CE1CE2CE1CE2New?3.504 **?0.018 *?0.904 ***?0.003 ** (?2.396)(?1.911)(?3.458)(?2.327)GDP?0.3570.185 ***?0.6810.141 *** (?0.070)(4.516)(?0.220)(4.795)Pop6.2660.478 *10.2880.414 *** (0.417)(1.907)(0.954)(2.738)FDI0.150?0.007 **?0.148?0.003 * (0.301)(?2.435)(?0.517)(?1.802)Fin?0.4880.073 **?1.2820.045 ** (?0.391)(2.547)(?1.052)(2.450)Infra0.1310.003 **0.1920.002 ** (0.579)(1.986)(0.863)(2.067)Edu?0.432 *0.000?0.659 *0.000 (?1.794)(0.055)(?1.702)(0.131)CityYesYesYesYesYearYesYesYesYesConstant29.126?1.669 ***44.709?1.277 *** (0.575)(?3.934)(1.430)(?4.081)Observations1344134422752275R-squared0.1100.1350.1420.095F17.320 ***17.750 ***18.180 ***17.080 ***

Table 11.
Robustness test IV.

Table 11.
Robustness test IV.

Variables(1)(2)(3)(4)(5)(6)First StageSecond StageSecond StageFirst StageSecond StageSecond StageNewCE1CE2NewCE1CE2IV0.505 *** 0.291 ** (6.830) (2.316) New ?4.070 ***?0.020 *** ?1.867 **?0.972 *** (?5.251)(?7.151) (?2.112)(?3.464)GDP0.0401.054 **0.177 ***0.059 *1.093 *0.746 ** (1.240)(2.322)(6.226)(1.740)(1.911)(2.148)Pop0.01711.230 *0.398 **0.00812.538 *0.476 * (0.112)(1.893)(2.427)(0.052)(1.882)(1.928)FDI?0.001?0.099 **?0.004 *?0.001?2.713 **?0.017 ** (?0.374)(?2.301)(?1.850)(?0.395))(?2.109)(?2.136)Fin0.0160.0510.073 ***0.029 *0.3510.355 ** (1.091)(0.050)(3.734)(1.867)(0.111)(1.982)Infra0.0010.0880.003 *?0.001?0.6430.001 (0.053)(0.502)(1.676)(?0.213)(?0.092)(0.013)Edu?0.001?0.567 *0.002?0.003?0.115 **?0.032 (?0.550)(?1.764)(0.491)(?1.259)(?2.128)(?0.105)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant?0.062
(?0.170)26.905
(0.700)?1.577 ***
(?4.980)?0.047
(?0.230)3.872 *
(1.932)?2.754 *
(?1.883)Observations239023902390245024502450R-squared0.5830.1120.1310.5430.1430.158Kleibergen–Paap rk LM statistic35.312 *** 12.503 ** Cragg–Donald Wald F statistic96.730 26.304 Kleibergen–Paap rk Wald F statistic46.652 18.673 Hansen J statistic0.000 0.000 Endogeneity test0.760 * 0.822 ***

Table 12.
Mechanism test of technological progress.

Table 12.
Mechanism test of technological progress.

VariablesTechnological Innovation OutcomeTotal Factor Productivity(1)(2)(3)(4)(5)(6)PACE1CE2TFPCE1CE2New1.098 ***?0.072 **?0.009 ***0.050 *?0.060 **?0.011 *** (6.428)(?2.146)(?3.751)(1.860)(2.042)(?3.166)PA ?0.052 ***?0.004 *** ?1.382 **?0.040 ** (3.422)(?3.212) (?2.157)(?1.972)GDP5.712 **0.848 **0.175 ***0.458 ***2.045 *0.206 *** (2.120)(2.244)(6.142)(5.516)(1.928)(6.511)Pop10.70512.0170.397 **?0.20118.3150.446 *** (0.967)(0.965)(2.412)(?1.024)(1.041)(3.139)FDI?0.364?0.050 *?0.004 *?0.008 *?0.082 *?0.004 * (?0.946)(?1.856)(?1.899)(?1.883)(?1.929)(?1.712)Fin?1.086?0.2960.072 ***?0.052?2.2490.049 *** (?1.010)(?0.311)(3.815)(?1.467)(?1.433)(2.685)Infra0.574 ***0.0220.003 *?0.010 ***?0.0400.001 ** (3.068)(0.121)(1.890)(?3.483)(?0.352)(1.986)Edu?0.242?0.564 *0.002?0.009?0.334 *0.003 (?0.936)(?1.817)(0.555)(?1.630)(?1.884)(0.849)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant?61.402 **42.470?1.676 ***3.789 ***3.015?1.622 *** (?2.159)(1.222)(?5.565)(4.514)(0.081)(?5.079)Observations244924492449181618161816R-squared0.3290.1690.1530.4050.2070.235F19.650 ***18.510 ***19.450 ***63.520 ***18.360 ***10.990 ***

Table 13.
Mechanism test of industrial structure upgrading.

Table 13.
Mechanism test of industrial structure upgrading.

VariablesRationalization of Industrial StructureAdvancement of Industrial Structure(1)(2)(3)(4)(5)(6)SRCE1CE2SACE1CE2New0.006 ***?0.121 ***?0.009 **0.635 ***?0.076 **?0.010 ** (7.508)(?3.357)(?2.523)(5.838)(?2.036)(?2.256)StructureR ?1.403 **?0.694 * ?0.084 **?0.006 *** (?2.227)(?1.952) (?2.107)(?3.483)GDP?0.086 ***0.038 ***0.162 ***0.2180.383 ***0.175 *** (?3.333)(3.010)(6.245)(0.151)(3.108)(6.117)Pop?0.219 ***13.9710.389 **24.618 ***14.8150.388 ** (?2.741)(1.029)(2.261)(3.287)(1.166)(2.422)FDI?0.002?0.132 *?0.004 **0.112?0.069 *?0.004 * (?1.088)(?1.761)(?2.268)(1.058)(?1.807)(?1.922)Fin?0.025 ***?0.4310.060 ***3.563 ***0.0910.070 *** (?2.910)(?0.408)(3.652)(4.604)(0.086)(3.778)Infra0.000?0.0030.002 *?0.0140.0560.003 * (0.187)(?0.016)(1.836)(?0.279)(0.315)(1.686)Edu?0.003?0.492 **0.0020.347 ***?0.560 **0.002 (?1.423)(?2.521)(0.675)(2.786)(?2.514)(0.523)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant1.102 ***35.342?1.488 ***221.999 ***56.167?1.760 *** (4.155)(0.933)(?5.519)(14.653)(1.298)(?5.105)Observations220522052205244924492449R-squared0.1010.1850.1850.5770.1670.153F15.760 ***18.200 ***19.210 ***60.710 ***18.360 ***19.470 ***

Table 14.
The impact of sub-indicators on carbon emission scale.

Table 14.
The impact of sub-indicators on carbon emission scale.

Variables(1)(2)(3)(4)(5)CE1CE1CE1CE1CE1Innovation?2.301 *** (?4.534) Coordination ?7.503 ** (?2.083) Green ?5.034 *** (?7.934) Openness ?7.321 (?1.332) Sharing ?0.163 * (?1.951)GDP0.371 ***0.351 ***0.484 ***0.408 **0.439 *** (5.106)(5.099)(6.135)(2.114)(5.123)Pop12.690 *12.223 *13.00212.02312.619 *** (1.924)(1.987)(1.036)(0.968)(3.014)FDI?0.077 **?0.086 *?0.077 *?0.080?0.074 * (?2.234)(?1.863)(?1.935)(?0.242)(?1.825)Fin?0.224?0.206?0.300?0.232?0.252 (?0.232)(?0.215)(?0.314)(?0.243)(?0.263)Infra0.0560.0560.0590.0550.058 (0.318)(0.318)(0.332)(0.309)(0.326)Edu?0.586 *?0.585 *?0.576 *?0.579 *?0.581 * (?1.686)(?1.691)(?1.654)(?1.667)(?1.671)CityYesYesYesYesYesYearYesYesYesYesYesConstant37.80237.95137.27636.67437.768 (1.076)(1.080)(1.069)(1.047)(1.069)Observations24502450245024502450R-squared0.1660.1670.1670.1670.166F18.510 ***18.480 ***18.580 ***18.750 ***18.630 ***

Table 15.
The impact of sub-indicators on carbon emission intensity.

Table 15.
The impact of sub-indicators on carbon emission intensity.

Variables(1)(2)(3)(4)(5)CE2CE2CE2CE2CE2Innovation?0.014 *** (?5.385) Coordination ?0.099 *** (?3.595) Green ?0.029 ** (?2.508) Openness ?0.038 (?0.821) Sharing ?0.043 * (?1.954)GDP0.175 ***0.176 ***0.175 ***0.175 ***0.175 *** (6.175)(6.138)(6.117)(6.135)(6.137)Pop0.398 **0.393 **0.396 **0.395 **0.399 ** (2.420)(2.391)(2.405)(2.382)(2.420)FDI?0.004 *?0.004 *?0.004 *?0.004 *?0.004 * (?1.914)(?1.968)(?1.916)(?1.922)(?1.918)Fin0.071 ***0.072 ***0.071 ***0.071 ***0.071 *** (3.789)(3.834)(3.829)(3.805)(3.803)Infra0.003 *0.003*0.003 *0.003*0.003 * (1.682)(1.671)(1.679)(1.676)(1.680)Edu0.0020.0020.0020.0020.002 (0.556)(0.553)(0.558)(0.571)(0.572)CityYesYesYesYesYesYearYesYesYesYesYesConstant?1.684 ***?1.682 ***?1.681 ***?1.690 ***?1.679 *** (?5.577)(?5.578)(?5.584)(?5.621)(?5.555)Observations24502450245024502450R-squared0.1520.1540.1520.1520.152F19.780 ***19.880 ***19.820 ***19.710 ***19.760 ***

Table 16.
Further analysis. Group regression by region.

Table 16.
Further analysis. Group regression by region.

VariablesEasternCentralWesternEasternCentralWesternCE1CE1CE1CE2CE2CE2New?0.175 ***?0.065 ***?0.209?0.020 **?0.014 ***?0.038 (?3.055)(?3.661)(?0.992)(?2.113)(?3.850)(?1.187)GDP5.942 *2.838 **3.0840.221 ***0.217 ***0.004 (1.936)(2.529)(1.046)(4.915)(4.632)(0.080)Pop8.2276.3014.348 **0.3120.3530.746 ** (0.356)(0.619)(2.311)(1.224)(1.592)(2.108)FDI?0.011?0.363?4.180 *0.002?0.009 **?0.001 (?0.031)(?1.282)(?1.723)(0.657)(?2.338)(?0.240)Fin2.172?2.3043.884 *0.118 ***0.043?0.003 (1.076)(?1.288)(1.746)(4.606)(1.047)(?0.232)Infra0.168?0.170?0.1620.0030.0010.002 (0.660)(?1.036)(?0.293)(1.353)(0.285)(0.773)Edu?0.872?0.634 **?0.4110.007?0.001?0.005 (?1.230)(?2.140)(?0.704)(1.042)(?0.270)(?1.642)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant?25.0945.315157.564?2.331 ***?1.951 ***0.197 (?0.540)(0.139)(1.259)(?4.840)(?3.943)(0.440)Observations10579674261057967426R-squared0.2070.1730.2820.2580.1320.129F16.340 ***16.780 ***11.800 ***17.510 ***15.160 ***12.610 ***

Table 17.
Further analysis. Group regression by city size.

Table 17.
Further analysis. Group regression by city size.

VariablesSmall SizeMedium SizeLarge SizeSmall SizeMedium SizeLarge SizeCE1CE1CE1CE2CE2CE2New?4.986?0.281 *?1.728 ***?0.010?0.001?0.013 ** (?0.816)(?1.898)(?3.612)(?0.249)(?0.026)(?2.364)GDP?8.089?0.2661.518 *0.0940.106 **0.084 ** (?0.844)(?0.047)(1.891)(1.630)(2.004)(1.987)Pop70.860 **7.81411.7421.921 ***0.0380.281 (2.181)(0.535)(0.481)(2.804)(0.251)(0.922)FDI?0.329?0.108?0.685?0.011 ***?0.001 ***?0.003 ** (?0.427)(?0.363)(?1.428)(?2.902)(?2.936)(?2.303)Fin?3.794?1.601?1.0220.0010.0290.026 * (?1.588)(?0.661)(?0.630)(0.017)(1.270)(1.798)Infra0.0780.263?0.2790.004 **0.003 *0.001 ** (0.609)(0.683)(?1.178)(2.102)(1.844)(2.470)Edu0.016?0.858 **?0.787 ***?0.001?0.0060.004 (0.025)(?2.064)(?2.637)(?0.173)(?0.751)(1.093)CityYesYesYesYesYesYesYearYesYesYesYesYesYesConstant104.51733.24735.420?0.733?0.814?0.772 (1.056)(0.576)(0.673)(?1.135)(?1.525)(?1.657)Observations65379210056537921005R-squared0.1610.1380.2590.1960.0930.160F13.270 ***14.930 ***15.720 ***17.480 ***14.260 ***14.380 ***

Table 18.
Further analysis. Group regression by whether it is a resource-based city or not.

Table 18.
Further analysis. Group regression by whether it is a resource-based city or not.

VariablesNon-Resource-BasedResource-BasedNon-Resource-BasedResource-BasedCE1CE1CE2CE2New?1.057?2.278 ***?0.010?0.011 ** (?0.484)(?5.510)(?0.616)(?2.291)GDP1.186 **1.946 *0.177 ***0.182 *** (2.379)(1.871)(5.057)(3.774)Pop29.228 **10.2350.387 ***0.370 (2.292)(0.492)(3.185)(1.068)FDI?0.216 **?0.193?0.001?0.009 * (?2.385)(?0.260)(?0.743)(?1.943)Fin0.1081.2480.075 ***0.077 ** (0.117)(0.551)(3.656)(2.108)Infra0.1880.3020.003 **0.004 *** (0.891)(1.116)(2.343)(2.920)Edu?0.545 **?0.853 *?0.006 **?0.007 (?2.528)(?1.925)(?2.203)(?0.777)CityYesYesYesYesYearYesYesYesYesConstant14.66165.290?1.827 ***?1.540 *** (0.453)(0.916)(?4.809)(?3.134)Observations15858651585865R-squared0.2190.1390.2100.130F17.150 ***13.900 ***17.640 ***13.760 ***

Table 19.
Further analysis. Group regression by city administrative level.

Table 19.
Further analysis. Group regression by city administrative level.

VariablesGeneral Prefecture-Level CitiesCities with High Administrative LevelGeneral Prefecture-Level CitiesCities with High Administrative LevelCE1CE1CE2CE2New?0.333?0.563 ***?0.015?0.018 ** (?0.143)(?3.121)(?0.735)(?2.556)GDP0.645 *7.4470.200 ***0.047 (1.934)(0.626)(6.442)(1.118)Pop0.4760.389 *0.406 *0.220 (0.034)(1.708)(1.937)(1.207)FDI?0.141 *0.058?0.005 **0.000 (?1.875)(0.086)(?2.104)(?0.055)Fin0.016?0.2090.091 ***0.025 ** (0.013)(?0.116)(4.192)(2.165)Infra0.0490.0560.003 *0.002 * (0.254)(0.145)(1.940)(1.869)Edu?0.560 **?0.899 *0.0020.005 (?2.299)(?1.873)(0.473)(1.558)CityYesYesYesYesYearYesYesYesYesConstant36.431?24.009?1.895 ***?0.523 (0.990)(?0.195)(?5.882)(?1.224)Observations20673832067383R-squared0.1540.2780.1640.226F19.230 ***12.960 ***19.470 ***16.280 ***

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Hua Wang

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