Study on the Regional Carbon Emissions Reduction Effect of Green Manufacturing—A Policy Experiment Based on the Construction of Green Parks in China

Abstract

Green manufacturing is an effective means for regions to reduce carbon emissions. It is a crucial approach for improving modern environmental governance and lays the foundation for the Chinese government’s push for green transformation, sustainable development, and the realization of carbon neutrality. This paper utilizes data from 277 cities at the prefecture level from 2009 to 2022, with the creation of green parks under the China Green Manufacturing System Demonstration Construction Project serving as a case study. It deeply explores the effect of green manufacturing on local carbon output. The study reveals that green manufacturing, represented by the establishment of green parks, significantly reduces urban carbon emissions. The mechanism analysis shows that enhancing industrial intelligence is a key channel for green manufacturing to curb urban carbon emissions, and the advancement stages of the digital economy, market unification, and financial innovation further amplify green manufacturing’s carbon reduction effects. The diversity analysis suggests that green park establishment exhibits a stronger effect in the eastern regions, areas with abundant factor endowments, superior institutional environments, and those with a non-industrial base. Further analysis shows that the green park demonstration projects also contribute to elevating regional green innovation levels. This paper explores the effect of green park establishments on reducing carbon emissions from the perspective of green manufacturing system construction, providing important theoretical and empirical insights for understanding how green manufacturing can enhance the levels of carbon emissions reduction and promote sustainable green advancement within dual-carbon objectives.

1. Introduction

The “2023 Emissions Gap Report” released by the UNEP shows that, as of early October, temperatures in 2023 exceeded pre-industrial levels by 1.5 °C for 86 days, with the global average temperature being 1.8 °C higher than pre-industrial levels. In addition, the “2023 China Climate Bulletin” issued by the China Meteorological Administration indicates that the national ratio of extreme high-temperature events was 0.36, which is 0.24 higher than the average level over the years, while the national ratio of extreme low-temperature events was 0.27, 0.15 higher than the average level over the years. The recurring instances of extreme weather have heightened worries about global warming and carbon dioxide emissions [1]. China regards achieving carbon peaking and neutrality as key developmental objectives. China National Guidance Document “Made in China 2025” has emphasized that fully implementing green manufacturing is an essential path toward building an ecological civilization. Reducing carbon emissions is a fundamental requirement and goal of ecological civilization construction [2]. Therefore, scientifically evaluating green manufacturing’s influence on carbon emissions is vital for promoting green development comprehensively and ensuring the timely or early realization of carbon peaking and carbon neutrality, thus paving the way for the development of an ecological civilization.

Green manufacturing aims to create a system that fosters environmentally friendly, low-carbon, and circular growth. In 2015, the Ministry of Industry and Information Technology (MIIT) released “Made in China 2025”, which, for the first time, proposed setting up a sustainable manufacturing framework. The “14th Five-Year Plan for Industrial Green Development” issued in 2021 emphasizes that, by 2025, the green manufacturing system in key industries and regions should be fundamentally established and well developed. The 2024 “Guiding Opinions on Accelerating the Greening of the Manufacturing Industry” issued by the MIIT and six other departments once again emphasizes the importance of making green the foundation of industrial manufacturing. To expedite the process of green manufacturing and enhance the green manufacturing system, the Ministry of Industry and Information Technology has issued documents on multiple occasions to stress the significance of constructing a green manufacturing system. In 2017, the ministry announced the first batch of green manufacturing demonstration lists, covering multiple levels, including products, factories, parks, and supply chains.

The list encompasses entities like eco-friendly factories, sustainable design products, green parks, and supply chain management firms. Green parks, as an important form and driving force of industrial development in China, are a crucial measure to accelerate the construction of a green manufacturing system and to promote the path of ecological civilization. Green parks aim to showcase advanced green manufacturing technologies and exemplary practices through public channels, playing a demonstration role that encourages enterprises, parks, and key industries to comprehensively implement a new round of green, low-carbon technological transformation and upgrade, leading the green transformation of manufacturing in their regions. Ultimately, the goal is to perfect the green manufacturing and service systems, promote industrial green development, and contribute to the achievement of reaching carbon peaking and achieving neutrality in the industry. The application process for green parks is stringent, beginning with a self-assessment by the entity, followed by third-party inspections, then the evaluation and recommendation by provincial-level MIIT departments, and finally expert review, re-evaluation, and public disclosure online—all of these steps are conducted in a rigorous manner to determine the qualification as a green park. From the application requirements, a green industrial park must meet criteria such as a strong industrial base, complete infrastructure, and a high level of green manufacturing. The evaluation index system includes the following five primary indicators: Energy Utilization Greening (EG), Resource Utilization Greening (RG), Infrastructure Greening (IG), Industry Greening (CG), and Ecological Environment Greening (HG), as well as indicators related to green operational management. Based on these application requirements and the evaluation system, the establishment of green parks contributes will help promote industrial coupling in industrial parks, reducing carbon emissions, and achieving near-zero emission targets. Green parks function as primary spatial entities for practicing the concept of industrial green development and are crucial components of industrial green development. This pilot can serve as an external influence to enhance the level of green manufacturing, providing conditions for a quasi-experimental analysis of the carbon emissions reduction effects of green manufacturing.

2. Literature Review

As a key research topic, there are many studies on carbon emissions. Regarding the factors that impact carbon emissions, methods such as the Kaya identity, LMDI decomposition (an analytical tool to clearly identify and quantify the contribution of each factor by comparing the influence of various factors on the target index), and the STIRPAT model (It is a tool for evaluating environmental impact. It predicts its impact on the environment by analyzing three factors: Population Affluence and Technology) have been mainly used to study these influencing factors. At the macroeconomic scale, industrial structure upgrading [3], economic growth [4], market conditions [5], and financial development [6] can all influence carbon emissions. With the strong emphasis of the Chinese government on carbon emissions reductions, its role in emissions control has become significant. Existing research indicates that an increase in the urbanization rate [7] and a higher level of environmental regulation [8] can lead to reductions in carbon emissions. At the microscale, technological advancements can enhance the energy efficiency, thereby affecting carbon emissions [9]. Current studies find that total carbon dioxide emissions are still on the rise. To stimulate economic sustainability and social development, and achieve a holistic green transformation, China has implemented several key initiatives. Studies reveal that the low-carbon pilot policy fosters green economic growth and technological advancements [10], while the carbon trading mechanism significantly lowers carbon emissions and the energy consumption intensity, promoting low-carbon development [11]. Additionally, research considering the combined effects of market mechanisms and administrative policies has shown that carbon markets can significantly reduce carbon emissions [12].

Research on green manufacturing, both within and outside of China, remains limited. Current studies suggest that green manufacturing can significantly enhance the environmental information disclosure quality of enterprises [13]. For retailers, adopting green manufacturing strategies with an appropriate level of corporate social responsibility (CSR) can improve their profitability [14]. Green factories, as a crucial part of the green manufacturing framework, can form synergies with capital markets, significantly affecting stock prices and effectively advancing the transformation of traditional manufacturing industries [15]. It is clear that the current quantitative assessment of green manufacturing’s economic impact on various entities, such as enterprises, governments, and nations, is still insufficient. Additionally, while green manufacturing is a vital strategy for conserving energy and reducing emissions to meet the carbon peaking and carbon neutrality goals, there remains a lack of quantitative research on its impact on carbon emissions.

Addressing this research gap, this paper examines the effects of green manufacturing on urban carbon emissions based on the policy of establishing green parks. Specifically, this paper utilizes panel data covering 277 Chinese cities from 2009 to 2022, using the establishment of green parks as part of constructing a green manufacturing system as an external policy intervention. Employing a progressive difference-in-differences model (DID) model, this study systematically investigates the impact of green park establishment on urban carbon emissions and its underlying mechanisms. The findings indicate that green manufacturing, represented by the creation of green parks, has a significant effect on reducing urban carbon emissions. The reliability of these findings was confirmed through multiple robustness tests, including parallel trend tests, the exclusion of contemporaneous policy interference, adjustment for sample selection bias, the substitution of the explained variable, and the use of instrumental variable methods. Mechanism analysis revealed that enhancing industrial intelligence serves as a key channel through which green manufacturing reduces urban carbon emissions. The development of the digital economy, market integration, and fintech growth further contributes to amplifying the carbon reduction impact of green manufacturing. Heterogeneity analysis demonstrates that the influence of green park establishment is more pronounced in eastern regions, areas with abundant factor endowments, favorable institutional conditions, and non-industrial zones with an industrial base. Finally, the study observed that green park demonstration projects not only reduce carbon emissions but also foster regional green innovation.

This paper contributes in three main areas. First, in previous studies, we mainly paid attention to the influence of relevant factors at the city level or enterprise level on regional carbon emissions, and the construction of green parks played a more comprehensive role. Benchmarking management theory holds that we should constantly find and study the best companies in the same industry, and compare, analyze, and judge them with this enterprise as a benchmark, so that we can continuously improve ourselves. Its core is to learn from excellent enterprises. In terms of green development, the establishment of green parks has set an example for other industrial parks, which is helpful to give full play to its exemplary role and promote regional sustainable development. This research centers on green park demonstration projects within the green manufacturing system from the angle of carbon emissions reduction, supplementing the current research on policy effectiveness in the green manufacturing system, and supporting the achievement of carbon peaking and carbon neutrality goals. Second, drawing on data manually compiled from six batches of green park demonstration lists issued by the Ministry of Industry and Information Technology (MIIT) from 2017 to 2022, together with carbon emissions data from the Global Atmospheric Research Emissions Database, this research constructed a unique dataset, advancing studies on the environmental advantages of urban green park initiatives. Furthermore, this paper applies the progressive DID method, instrumental variables, and additional methods to provide robust evidence for a rational assessment of the impact of green park demonstration projects, offering empirical data to encourage local governments to apply for green park projects and drive green transformation. Third, this research confirms the pathway role of industrial intelligence and the moderating effects of digital economy, market integration, and fintech development levels, contributing to an in-depth understanding of the mechanisms through which green park demonstration projects aid in meeting carbon peaking and neutrality objectives. This study serves as a reference model for the green transformation and modernization of traditional industrial parks in China. Additionally, this research highlights the distinctive carbon reduction effects of green parks across various dimensions, such as the regional location, institutional environmental foundations, resource availability, and industrial development foundations, offering solid empirical evidence for the Chinese government to create realistic and regionally tailored green system development policies.

3. Theoretical Analysis and Research Hypotheses

In the context of the “carbon peaking and carbon neutrality” objective, building a green manufacturing system is essential for achieving the green transformation in manufacturing. It also represents a new direction toward low-carbon growth and high-quality, sustainable green development [16]. Initially, the green park demonstration construction initiative has a direct effect on reducing carbon emissions. The “Green Park Evaluation Requirements” stress the necessity for parks to implement resource-efficient and eco-friendly strategies in spatial planning, supply chain organization, and energy usage, leading to green parks characterized by clustered layouts, eco-structures, and environmental linkages.

These requirements indicate that the parks themselves are significant units for carbon reduction. Second, the demonstration construction project of green parks exerts an indirect effect on carbon reduction. On the one hand, green parks embody distinct green development principles and standards. As platforms aggregating production enterprises and infrastructure, green park demonstration projects can establish a positive image of sustainable green development and environmental responsibility, thus enhancing their recognition and influence within the industry and society. This attraction can draw green investors to invest in enterprises within the park, boost the corporate value, and further promote the green development of the park [15]. During the implementation of green manufacturing, even with imperfect incentive mechanisms, the positive feedback effect of attracting investment gives parks sufficient internal motivation to actively fulfill their environmental responsibilities and continuously improve their green development level. On the other hand, green parks play a “benchmark” role, guiding other enterprises and parks to learn from and emulate them, thereby exerting a “leading effect” and a “resource spillover effect”. According to learning theory, green parks, as “leaders” in green development, capture major industry attention. Parks in a disadvantageous position in market competition, in order to survive and develop, will absorb green technologies from benchmark parks through knowledge and technology spillover effects [17], thereby encouraging the entire industry to emulate and learn advanced green production methods and use clean energy, ultimately promoting urban low-carbon development [18]. Furthermore, several documents from the Ministry of Industry and Information Technology emphasize that green parks should publicly showcase and promote advanced green manufacturing technologies and best practices, encourage the release of annual green low-carbon development reports, and effectively share information to establish a demonstration effect in emissions reductions. Therefore, the following hypothesis is proposed:

Hypothesis 1: 

Green park demonstration projects can reduce regional carbon emissions.

The “Guiding Opinions on Expediting the Green Development of Manufacturing”, issued by the Ministry of Industry and Information Technology and seven other departments, reads the following: “Advance the high-end, intelligent, and green progression of manufacturing and accelerate the construction of a green manufacturing and service system”. Green manufacturing has a direct effect on enhancing industrial intelligence, which refers to fully integrating advanced manufacturing technology with information technology to form a intelligent system in which people, machines and matter can be interrelated [19]. Technological progress is a fundamental means to achieving energy savings and carbon reductions [20]. As a significant marker of the new wave of technological advancement, industrial intelligence brings about technological innovation and structural transformation effects, thereby improving energy efficiency, reducing emissions during the production process, and achieving energy savings and emissions reductions [21]. Additionally, industrial intelligence can not only enhance the production efficiency through the large-scale application of intelligent machinery as “production capital”, but also reshape the geographic pattern by changing factor endowments, utilizing the technological spillover effect to realize resource sharing among park enterprises, and ultimately reducing carbon emissions [22]. Furthermore, the competitive demonstration effect is an important way through which industrial intelligence promotes carbon reduction. Faced with external competition, parks that have not undergone intelligent transformation may adopt mimicry and emulate advanced parks to avoid elimination [18]. This process includes continuously upgrading the production technology and management practices, and applying low-carbon green technologies to improve the carbon productivity and subsequently reduce carbon emissions [22]. As a part of industrial intelligence, digital transformation not only fosters technological innovation to improve energy consumption patterns but also optimizes the structure to promote energy savings and emissions reductions [23]. The economic scale expansion resulting from digital technology application creates opportunities for the clean adjustment of the industrial structure [24]. Therefore, the following hypothesis is proposed:

Hypothesis 2: 

Green park demonstration projects promote carbon reduction effects by enhancing industrial intelligence levels.

As a new engine driving high-quality economic development, the digital economy is a key driver in promoting low-carbon growth. On the one hand, the development of the digital economy can increase the market share of high-tech industries, which are characterized by low energy consumption and high output. These industries are environmentally friendly and can exert an exclusion effect on high-emission, high-energy-consuming enterprises, thereby optimizing urban industrial structures [25]. On the other hand, a key aspect of the digital economy is the development of digital communication technology, which can effectively disseminate green environmental concepts to the public, reduce urban pollution, weaken information barriers, reduce information asymmetry, and strengthen regional cooperation to expand the coordinated utilization of green resources, thereby promoting green and high-quality urban development. Fintech generally refers to the application of the Internet and a series of new-generation information technologies, such as cloud computing, big data, artificial intelligence, and blockchain, in the financial sector. Existing studies have shown that fintech can promote the development of green technology innovation and energy-saving measures, which helps to reduce urban sulfur dioxide emissions and curb pollution. Fintech development can optimize regional industrial structures, alleviate financing constraints, and improve the resource allocation efficiency, thus contributing to the reduction in urban carbon emissions. Regions with higher levels of fintech development have better foundational conditions, making it more likely for local parks to be approved as green parks, with higher levels of green innovation within these parks, thereby contributing to regional carbon reduction. Market integration reflects the degree of free movement of goods and factors between different regions within a broader area [26]. Higher levels of market integration lead to complementary resource factors between regions, thus optimizing the resource allocation efficiency, reducing the total factor energy efficiency losses, and affecting the regional carbon reduction effect of green manufacturing [27]. Additionally, market integration influences carbon emissions by changing the competitive behavior between cities. R&D investment is the foundation of technological progress, and low-carbon technology development often requires significant human and material resources. Market segmentation causes cities to act individually, hindering the cooperative development of new technologies and slowing technological progress, which, in turn, slows the adoption of low-carbon production technologies. Joint development, on the other hand, is more conducive to concentrating efforts on significant initiatives, thereby accelerating the development of low-carbon and environmentally friendly technologies. Furthermore, higher levels of market integration increase the speed of knowledge and technology dissemination between regions, accelerating the promotion of green technology, and contributing to low-carbon urban development [28,29].

Hypothesis 3: 

In cities with a high level of market integration, financial technology development, and development level of digital economy, the carbon emissions reduction effect of green manufacturing will be more obvious.

4. Study Design

4.1. Empirical Model

This study examines whether green manufacturing significantly affects regional carbon emissions. Based on an analysis of relevant policies, this paper takes the time point when the green park was first approved as the treatment time. Since green parks were announced in six batches, this study uses a multi-period difference-in-differences model (DID) model to estimate the impact of green park demonstration projects on carbon emissions, using the following regression model:

$$\ln pco{2}_{it}={\beta }_0+{\beta }_{1}Gpar{k}_{it}+{\beta }_2\sum {CVS}_{it}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(1)

In Equation (1), $Gpark$ is the ratio of carbon dioxide emissions to population in a city in a year. The key independent variable is a binary variable that equals 1 if the city has an approved green industrial park in the year, and 0 otherwise; represents a set of control variables at the prefecture-level city, including the GDP(Gross Domestic Product) per capita (PGDP), population density (lnpop), industrial structure (Third), level of openness (FDI), human capital (human), financial development (ECO), and urbanization rate (urban); ${\gamma }_i$ represents the prefecture-level city fixed effects; ${\delta }_t$ represents the time fixed effects; ε_it represents random perturbation terms.

4.2. Data Sources

The sample in this research encompasses 277 prefecture-level cities spanning from 2009 to 2022. The samples used in this paper are representative. First of all, the time span selected in this paper is long. Due to the serious lack of data before 2009, the time span used in this paper starts from 2009, using data from the earliest possible year. At the same time, as far as the continuity of time is concerned, this paper uses data from the latest possible year, that is, the data from 2022. From the perspective of the year span, this sample is representative. Specific to the selected cities, the sample of 277 cities in China selected in this paper includes all of the cities in China as far as possible. From the perspective of the city scale, these 277 cities include large cities, such as Guangzhou, Shenzhen, and Chengdu, as well as small- and medium-sized cities, such as Yangquan and Liaoyang [30]. In terms of the industrial types, these cities include both old industrial bases like Anshan and non-old industrial base cities like Shijiazhuang. Geographically, these cities include both southern cities like Haikou and northern cities like Changchun. Judging from the scale of economic development, these cities include cities in economically developed areas such as the Yangtze River Delta and Pearl River Delta urban agglomerations, as well as some peripheral cities with relatively backward economic development. To sum up, the sample selection range of this paper is wide and very representative. Among them, 113 cities have approved green industrial parks, while 164 cities do not. The data regarding green industrial parks were compiled from the “Green Manufacturing List”, issued by the Ministry of Industry and Information Technology, starting from 2017. Carbon emissions data were retrieved from the Emissions Database for Global Atmospheric Research (EDGAR). Information on control variables was obtained from the “China City Statistical Yearbook” (2009–2022) or the National Economic and Social Development Statistical Bulletins of each city. Moreover, data on industrial robot installations were collected from reports of the International Federation of Robotics (IFR), and data on industrial enterprises were sourced from the second national economic census.

4.3. Variable Description and Descriptive Statistical Analysis

4.3.1. Dependent Variable

Carbon emissions (lnpco2). As there is no authoritative official institution in China that directly publishes carbon emissions data, differences in the methods of determining carbon emissions have become a key point in carbon emissions calculations. Existing research mainly employs the following three calculation methods: first, calculating carbon emissions by summing the carbon emissions from different types of energy based on various conversion coefficients [29,31]; second, using econometric models based on the factors influencing carbon emissions to calculate the emissions [32], with more specific approaches, including using remote sensing images or global night-time light data, to simulate and estimate carbon emissions [33,34,35]; third, obtaining data from city-level CO₂ emissions datasets [36]. In this study, carbon emissions data are sourced from EDGAR (Emissions Database for Global AtmosphericResearch) (available online: https://edgar.jrc.ec.europa.eu/emissions_data_and_maps (accessed on 25 July 2024)). The obtained carbon emissions data were converted into raster data using R software (Version R-4.2.2) and aggregated by region to obtain the carbon emissions data [37]. This study uses the natural logarithm of per capita carbon emissions as a proxy for carbon emissions.

4.3.2. Key Independent Variable

Green industrial park dummy variable (Gpark). This variable indicates whether the prefecture-level city has a green industrial park. Since green parks were announced in six different batches over different periods, there are differences in the prefecture-level cities listed as green parks for each year. Therefore, this variable is defined based on the six batches of green park demonstration lists released by the Ministry of Industry and Information Technology from 2017 to 2022. If the prefecture-level city has a green park in the year, then the variable equals 1 for that year and subsequent years; otherwise, it equals 0.

4.3.3. Mechanism Variable

Industrial intelligence. Following the common practice in the existing literature [38,39,40], this study uses the Bartik instrumental variable method to calculate the installation density and stock density of industrial robots to measure the industrial intelligence. The IFR dataset is matched one-to-one with the industry classification from China’s Second Economic Census, thereby obtaining data on the installation of industrial robots in various industries in China. Taking 2008 as the baseline year, the installation density and stock density of industrial robots at the city level were calculated according to the following formula:

$$A{R}_{it}={\displaystyle {\sum }_{s=1}^s\frac{emplo{y}_{s,i,t=2008}}{emplo{y}_{i,t=2008}}}\times {\displaystyle \frac{A{R}_{st}}{emplo{y}_{i,t=2008}}}$$

(2)

where s represents the set of industries, AR_it represents the number of robot installations or stock in a city in a year, AR_st represents the number of robot installations or stock in an industry in a year, $emplo{y}_{s,t=2008}$ represents the employment in an industry in 2008, ${employ}_{i,t=2008}$ represents the employment in a city in 2008, and $emplo{y}_{s,i,t=2008}$ represents the employment in an industry in a city in 2008.

4.3.4. Moderating Variables

Digital economy (digit): Referring to prior research, five pertinent indicators were selected [41]. A composite digital economy development index was obtained through principal component analysis to act as a proxy variable for the digital economy. The five indicators comprise Internet broadband users per hundred people, the ratio of employees in computer services and software to the total urban workforce, per capita telecommunication services, the density of mobile phone users per hundred individuals, and the China Digital Inclusive Finance Index developed by Peking University’s Digital Finance Research Center. Fintech development level (fin): The annual number of fintech companies in each prefecture-level city was calculated as an indicator of the regional fintech development. A higher value signifies a greater degree of fintech development [42]. Market integration (minteg): In line with previous approaches, market integration levels were determined using the relative price method based on seven categories of consumer price indices, reflecting residents’ clothing, food, housing, and transportation [43,44].

4.3.5. Control Variables

It is necessary to control for other factors that may affect urban carbon reduction. This research incorporates the following control variables in the model: PGDP, gauged by the regional per capita GDP; lnpop, determined by taking the natural logarithm of the ratio of the year-end total population to the administrative area; Third, quantified by the ratio of the value-added of the tertiary industry to that of the secondary industry; FDI, measured as the proportion of actual foreign capital utilized to the GDP in a given year; human, assessed according to the ratio of students in regular junior colleges to the total population at the year-end; ECO, evaluated by the balance of deposits and loans at the end of the year as a proportion of regional GDP; urban, appraised based on the share of the non-agricultural population in relation to the total population at the year-end.

This study focuses on prefecture-level and above cities from 2009 to 2022. Since the policies of municipalities are more biased and their development models are distinct compared to other prefecture-level cities, Beijing, Tianjin, Shanghai, and Chongqing were excluded from the sample data. After removing missing values for key variables, a final panel dataset of 3294 city–year observations was formed. A 1% winsorization was then applied to all of the continuous variables. The descriptive statistics for the key variables are presented in

Table 1. Descriptive statistics.

Var	Observations	Mean	SD	Min	Max
lnpco2	3294	1.676	0.888	−0.481	3.775
Gpark	3294	0.092	0.289	0.000	1.000
PGDP	3294	1.432	0.591	0.046	2.768
lnpop	3294	5.758	0.869	3.056	7.206
Third	3294	1.004	0.496	0.291	2.994
FDI	3294	0.015	0.015	0.000	0.070
human	3294	0.019	0.024	0.001	0.118
ECO	3294	2.411	1.085	0.934	6.497
urban	3294	0.544	0.152	0.250	0.950
AR1	3294	0.043	0.039	0.001	0.177
AR2	3294	0.239	0.285	0.004	1.310
minteg	3294	16.716	2.188	12.167	22.063
fin	3294	0.258	0.702	0.000	4.740
digit	3294	0.351	0.764	−1.350	2.681

. The mean carbon emissions are 1.676, with a standard deviation of 0.888, indicating significant differences in carbon emissions between regions. Regarding the control variables, PGDP, lnpop, Third, and ECO also exhibit the characteristic of “small mean, large standard error”. There are also significant differences in the digital economy (digit), fintech development level (fin), and market integration (minteg) among the different prefecture-level cities [45,46].

5. Empirical Results

5.1. Benchmark Results

First, this paper estimates the carbon emission effects of the green industrial park pilot project based on the baseline model (Equation (1)). A stepwise regression method was used, and the results are shown in

Table 2. Benchmark regression.

Var	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	lnpco2	lnpco2	lnpco2	lnpco2	lnpco2	lnpco2	lnpco2	lnpco2
Gpark	−0.065 ***	−0.063 ***	−0.053 **	−0.053 **	−0.053 **	−0.062 ***	−0.062 ***	−0.062 ***
	(−2.725)	(−2.666)	(−2.321)	(−2.331)	(−2.328)	(−2.673)	(−2.654)	(−2.683)
PGDP		0.062	0.072 *	0.061	0.063	0.062	0.072	0.062
		(1.548)	(1.834)	(1.363)	(1.418)	(1.410)	(1.425)	(1.312)
lnpop			−0.273 **	−0.281 **	−0.285 **	−0.289 **	−0.285 **	−0.286 **
			(−1.999)	(−1.994)	(−2.038)	(−2.118)	(−2.081)	(−2.134)
Third				−0.025	−0.024	−0.026	−0.030	−0.030
				(−0.748)	(−0.739)	(−0.784)	(−0.889)	(−0.892)
FDI					−0.300	−0.234	−0.237	−0.255
					(−0.551)	(−0.424)	(−0.427)	(−0.458)
human						2.873 **	2.873 **	2.836 **
						(1.989)	(1.998)	(2.030)
ECO							0.009	0.005
							(0.605)	(0.338)
urban								0.247
								(1.599)
_cons	1.471 ***	1.419 ***	2.978 ***	3.051 ***	3.079 ***	3.059 ***	3.013 ***	2.919 ***
	(118.636)	(40.314)	(3.767)	(3.737)	(3.799)	(3.844)	(3.737)	(3.607)
City/Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3294	3294	3294	3294	3294	3294	3294	3294
R2	0.310	0.314	0.321	0.322	0.322	0.329	0.329	0.333

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-values are in parentheses.

. Column (1) of Table 2 presents the results, including only the time and individual fixed effects, while Columns (2) to (8) gradually add city-level control variables. The results show that, without considering the other factors, when only regressing carbon emissions and green parks, the estimated coefficient of the impact of green industrial parks on the per capita dependent variable is −0.065, and significant at the 1% level. This indicates that carbon emissions in the treated prefecture-level cities are reduced compared to the control group, and this conclusion remains valid after gradually adding city-level control variables. The results in Column (8), which include all of the control variables, indicate that carbon emissions in prefecture-level cities with approved national green industrial parks are significantly reduced. The benchmark model’s estimation results preliminarily indicate that the national green industrial park pilot projects can significantly reduce regional carbon emissions. After the green park is approved, the green park can spread the concept of green development, share information efficiently, and play a full demonstration role to guide enterprises and parks to learn and imitate, and ultimately can reduce carbon emissions in the region and promote green development.

5.2. Parallel Trend Test

The parallel trend assumption is a prerequisite for using the difference-in-difference model method. Therefore, an event study was conducted to verify whether the parallel trend assumption holds, as follows:

$$\ln pco{2}_{it}={\beta }_0+{\displaystyle {\sum }_{k=-7,k\ne -1}^5{\beta }_k}Gpark(k)+{\beta }_2{\displaystyle \sum CV{S}_{it}}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(3)

In Equation (3), the variable still represents whether the prefecture-level city has been approved as a national green industrial park. Let denote the year in which the prefecture-level city was approved to establish its first national green industrial park. The first period is set as the baseline group, and the remaining parts of the model are kept in line with the benchmark model. The primary concern here is statistical significance. If the coefficient prior to time 0 lacks statistical significance, it implies that the estimation results fulfill the parallel trend assumption. The event study results are presented in Figure 1. The horizontal axis stands for the relative years from the policy event, while the vertical axis displays the policy effect. The dashed line represents the 95% confidence interval. The results reveal that, before the establishment of the national green industrial park, there is no statistically significant difference in carbon emissions between the treatment group and the control group, which indicates that the model employed in this paper meets the requirements of the parallel test. From the year of the national green industrial park’s construction onwards, the effect becomes notably negative, suggesting that the construction of green industrial parks has a carbon reduction effect with long-term persistence.

5.3. Robustness Test

5.3.1. Placebo Test

Building on the approach of Chetty and colleagues, this study constructed a “pseudo-policy” variable through random sampling and use this new independent variable to make regression to obtain the coefficients of the independent variables. If the coefficients and significance levels are not significantly different from those in the baseline regression, it would suggest that the results of this study might be driven by other random factors, thereby lacking robustness. Conversely, if the coefficients and significance levels differ, it would confirm the robustness of the study’s conclusions. This paper conducted 500 and 1000 random sampling regressions, and Figure 2a,b, respectively, illustrate the kernel density distribution of the estimated coefficients and p-values for the two sampling scenarios. As shown in the figure, the coefficients and p-values of the independent variables from the two random samplings differ significantly from those in the baseline regression (−0.062). This indicates that the results of the baseline regression in this study are robust and not obtained by chance.

5.3.2. Excluding Concurrent Policy Interference

At present, the major domestic policies with significant impacts mainly comprise the national low-carbon city pilot policy, the carbon emissions trading pilot policy, and the new energy demonstration city policy. These policies commenced in 2010, 2013, and 2014, respectively, with all of them falling within the sample period of this study. Hence, this paper incorporates dummy variables indicating whether a prefecture-level city was affected by these three policies in a given year so to eliminate potential policy interference. The results in Columns (1) to (3) of

Table 3. Robustness test.

Var	(1)	(2)	(3)	(4)	(5)
	Excluding the Low-Carbon City Pilot Policy	Excluding the Carbon Emissions Trading Pilot Policy	Excluding the New Energy Demonstration City Pilot Policy	PSM-DID	Replacement of the Dependent Variable
Gpark	−0.063 ***	−0.061 ***	−0.060 ***	−0.038 *	−0.023 **
	(−2.681)	(−2.629)	(−2.662)	(−1.758)	(−2.099)
DIDL	0.014
	(0.482)
DIDT		0.018
		(0.439)
DIDN			−0.034
			(−1.319)
_cons	2.922 ***	2.947 ***	2.919 ***	2.949 ***	7.144 ***
	(3.604)	(3.634)	(3.603)	(3.112)	(26.966)
City/Year	Yes	Yes	Yes	Yes	Yes
Control	Yes	Yes	Yes	Yes	Yes
N	3294	3294	3294	2786	3294
R2	0.333	0.333	0.335	0.323	0.426

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-values are in parentheses; The variable DIDL indicates whether the prefecture-level city was a low-carbon pilot city in the year; the variable DIDT indicates whether the prefecture-level city was a carbon emissions trading pilot city in the year; the variable DIDN indicates whether the prefecture-level city was a new energy demonstration city in the year.

suggest that the national low-carbon city pilot policy, the carbon emissions trading pilot policy, and the new energy demonstration city policy have some interference with the study’s results. However, the extent is limited and does not fundamentally alter the conclusion that green manufacturing can significantly reduce regional carbon emissions.

5.3.3. Sample Selection Bias

Considering that there may be systematic differences between cities with and without national green industrial parks, leading to non-comparability between the treatment and control groups, this paper further applies the PSM-DID (propensity score matching–difference-in-difference) method to alleviate the bias caused by non-random selection. All of the control variables were used as covariates, and a Logit model was employed to estimate the propensity score for each city to be approved as a national green industrial park. Subsequently, the treatment group (cities with green industrial parks) was paired with a control group (cities without green industrial parks) exhibiting similar characteristics using a 1:1 nearest neighbor matching technique. Finally, Equation (1) was re-estimated using the matched sample, and the regression results are shown in Column (3) of Table 3. The impact of national green industrial parks on carbon emissions remains significantly negative. Additionally, radius matching with a radius of 0.01 and kernel matching were performed [results retained for retrieval], and both confirmed the robustness of the benchmark regression results.

5.3.4. Replacing the Dependent Variable

Following the relevant research, the natural logarithm of total carbon dioxide emissions was used as the dependent variable for re-estimation. The results in Column (4) of Table 3 indicate that the conclusion remains consistent with the benchmark regression, thereby alleviating the concerns about severe measurement errors in the dependent variable that could significantly interfere with the reliability of the regression results.

5.3.5. Instrumental Variable Approach

Differences in emphasis on environmental sustainability may lead to non-randomness in the approval of green parks, thus introducing endogeneity issues that interfere with the persuasiveness and accuracy of the study’s conclusions. To alleviate endogeneity issues, and to accurately identify the impact of green manufacturing on carbon emissions, this study employed city topographic relief as an instrumental variable to address possible endogeneity issues. Specifically, city topographic relief is closely related to the level of infrastructure and economic development, which can influence regional green development. Cities with lower topographic relief are more likely to become pilot green park cities, indicating a correlation between topographic relief and the approval of green parks, thus meeting the requirement of instrumental variable relevance. Additionally, city topographic relief is an inherent characteristic of the prefecture-level city and does not directly affect urban carbon emissions, fulfilling the requirement of instrumental variable exogeneity. Therefore, using city topographic relief as an instrumental variable is reasonable. Since city topographic relief does not change over time, this paper uses the interaction term between city topographic relief and the year of green park approval as the instrumental variable (IV) for green manufacturing, and the two-stage least squares (2SLS) method is used for regression. The regression models are as follows:

$$Gpar{k}_{it}={\alpha }_0+{\alpha }_{1}i{v}_{it}+\beta {\displaystyle \sum CV{S}_{it}}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(4)

$$\ln pco{2}_{it}={\phi }_0+{\phi }_1{\overline{Gpark}}_{it}+{\phi }_2{\displaystyle \sum CV{S}_{it}}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(5)

Equation (4) represents the first-stage estimation model for the instrumental variable approach, and Equation (5) represents the second-stage estimation model, where $\overline{Gpar{k}_{it}}$ is the fitted value of $Gpar{k}_{it}$ obtained from the first-stage results. The results in Column (1) of

Table 4. Instrumental variable approach.

Var	(1)	(2)
	Gpark	lnpco2
Gpark		−0.262 **
		(−2.478)
iv	−0.132 ***
	(−6.510)
City/Year	Yes	Yes
Control	Yes	Yes
N	3292	3292
R2	0.338	0.270
Under-Identification Test	41.795 ***
Weak Instrument Test	42.087 > 16.38

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively; t-values are in parentheses.

show that the result of the instrumental variable is −0.132 and is statistically significant, indicating a strong correlation between green manufacturing and the instrumental variable. The instrumental variable successfully passes both the under-identification and weak instrument tests. The results presented in Column (2) of Table 4 reveal that the estimated coefficient for the carbon reduction effect of green parks, based on the first-stage results, is −0.262 and is statistically significant, indicating that cities with green parks have significantly lower carbon emissions.

5.3.6. Other Robustness Test Methods

This paper also conducted the following robustness tests:

(1)

Clustering standard errors at the province level. Cities within the same province may face similar policy environments and have strong economic linkages, so standard errors were clustered at the province level (results are available upon request).

(2)

Excluding abnormal years. Considering the impact of the pandemic on city operations, the years of the pandemic (2020 and 2021) were excluded from the regression.

(3)

Addressing potential “bad control” issues. All of the control variables were lagged by one period for re-estimation. All of the robustness tests confirm that the conclusions remain robust without fundamental changes.

(4)

In the related papers [47,48], both the urbanization rate and the actual expenditure on science and technology can have an important impact on regional carbon emissions. Therefore, this paper analyzed the urbanization rate and financial expenditure on science and technology in an empirical way to obtain more reliable results. Among them, the urbanization rate is expressed by the proportion of permanent residents in towns in the region to the total population in the region, and the financial expenditure on science and technology is measured by the ratio of urban expenditure on science and technology to financial expenditure. The results show that the carbon emissions effect of green manufacturing is still significant after considering other potential factors that may affect carbon emissions.

5.4. Mechanism Analysis

5.4.1. Mediation Mechanism Analysis

The above findings fully demonstrate that green manufacturing can reduce urban carbon emissions. As mentioned in the theoretical analysis, industrial intelligence plays a mediating role between green manufacturing and carbon reduction. To verify the existence of this mediating effect, this study introduces a proxy variable for industrial intelligence into the regression model. As shown in

Table 5. Mediation mechanism.

Var	(1)	(2)
	AR1	AR2
Gpark	0.013 ***	0.159 ***
	(5.203)	(5.770)
_cons	−0.058	−0.314
	(−0.726)	(−0.334)
City/Year	Yes	Yes
Control	Yes	Yes
N	3294	3294
R2	0.814	0.833

Note: *** indicate significance at the 1% levels, respectively; t-values are in parentheses.

, the coefficients for the stock density and installation density of industrial robots are both significantly positive at the 1% level, indicating that green manufacturing can enhance the level of industrial intelligence, and, when industrial intelligence is measured by the stock of industrial robots, the establishment of green parks can improve the level of industrial intelligence by 1.3%. As a representative of the new generation of information technology, industrial intelligence is an important direction for industrial transformation and upgrading. On the one hand, the deep integration of artificial intelligence with enterprise production can help enterprises transition from traditional manufacturing to smart manufacturing, improving the energy efficiency and providing new pathways to achieve the carbon peaking and carbon neutrality goals [49]. On the other hand, the scale expansion and industrial structure upgrading effects brought about by industrial intelligence can reduce the energy consumption and pollutant emissions, achieving energy conservation and carbon reductions. Additionally, at the enterprise external level, regulators such as the government can use artificial intelligence technologies like real-time monitoring to detect and track corporate carbon footprints, thereby enhancing their ability to supervise and perceive corporate carbon emissions, ultimately improving the accuracy of carbon emissions-related decisions and achieving the goal of carbon reductions. In summary, green manufacturing can reduce carbon emissions by enhancing the level of industrial intelligence, thus verifying Hypothesis 2.

5.4.2. Regional Characteristics as Promotion Mechanisms

This study further explores the impact mechanisms of green park approval on regional carbon emissions from the following three aspects: the digital economy, market integration, and fintech development level. It is expected that, in cities with higher levels of market integration, fintech development, and digital economy, the carbon reduction effect of green manufacturing will be more pronounced. Based on Equation (1), the model for analyzing the regional promotion mechanism is set as follows:

$$\ln pco{2}_{it}={\mu }_0+{\mu }_{1}Gpar{k}_{it}+{\mu }_{2}Gpar{k}_{it}\times M_{it}+{\mu }_3{M}_{it}+{\mu }_4{\displaystyle \sum CV{S}_{it}}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(6)

where M_it represents the mediator variable, specifically the proxy variable for market integration, the fintech development level, or the digital economy. The other symbols have the same meanings as in Equation (1).

This paper further investigates the strengthening effect of the digital economy on carbon emissions reductions in green park pilot policies. The results in Column (1) of

Table 6. Regional characteristics as promotion mechanisms.

Var	(1)	(2)	(3)
	lnpco2	lnpco2	lnpco2
Gpark	−0.041 *	−0.017	−0.054 **
	(−1.759)	(−0.771)	(−2.379)
minteg_Gpark			−0.009 *
			(−1.783)
minteg			−0.001
			(−0.793)
fin_Gpark		−0.033 **
		(−2.007)
fin		−0.036 ***
		(−3.014)
digit_Gpark	−0.042 *
	(−1.658)
digit	0.008
	(0.472)
_cons	2.873 ***	2.418 ***	2.932 ***
	(3.577)	(3.298)	(3.641)
City/Year	Yes	Yes	Yes
Control	Yes	Yes	Yes
N	3294	3294	3294
R2	0.336	0.352	0.334

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-values are in parentheses.

show that the cross-product coefficient of green manufacturing and the digital economy is significantly negative, indicating that the digital economy can enhance the carbon emissions reduction effect of green parks. According to the previous theoretical discussion, the environmental friendliness of digital technology itself and the dividend effect brought by digital technology development can enhance the carbon emissions reduction effect of green manufacturing and promote the green development of the region.

Financial technology can reduce urban carbon emissions by promoting green technology innovation, optimizing the industrial structure, and easing financing constraints, which are conducive to urban green development. Therefore, this paper further examines the strengthening effect of financial technology development on carbon emissions reductions in green park pilot policies. The results in Column (2) of Table 6 show that the coefficient of the intersection between financial technology and green manufacturing is significantly negative. Therefore, a high level of financial technology is helpful for strengthening the role of carbon emissions reductions in the establishment of green parks.

The level of market integration can reduce regional division, accelerate the flow of factors between regions, increase cooperation between cities, reduce the efficiency loss, and enhance the carbon emissions reduction effect of green manufacturing. Therefore, this paper adds the cross-product term of the market integration level and green manufacturing for regression. The results show that the cross-product term of the market integration level and green manufacturing is negatively significant, indicating that the carbon emissions reduction effect of green parks is greater in areas with a high market integration level. To sum up, Hypothesis 3 is verified.

5.5. Heterogeneity Analysis of Regional Characteristics

This paper further explores the optimal conditions for the impact of green park approval on regional carbon emissions, providing feasible policy ideas for promoting the establishment of green parks and leading to regional green development. The heterogeneity analysis is conducted from multiple aspects, as follows.

5.5.1. Regional Location

Given China’s extensive territory and large geographical expanse, there are notable differences in development among the different regions. Thus, this paper categorizes the sample into three regions—eastern, central, and western—according to the geographic distribution, and further investigates the impact of green manufacturing on regional carbon reductions in different areas. The results in Columns (1)–(3) of

Table 7. Regional location heterogeneity.

c	(1)	(2)	(3)
	East	Central	West
Gpark	−0.060 *	−0.057	−0.061
	(−1.715)	(−1.531)	(−1.487)
_cons	4.831 ***	3.047 *	2.050 ***
	(3.273)	(1.776)	(3.147)
City/Year	Yes	Yes	Yes
Control	Yes	Yes	Yes
N	1155	1240	899
R2	0.384	0.324	0.368

Note: *, and *** indicate significance at the 10%, and 1% levels, respectively; t-values are in parentheses.

show that green park demonstration projects in the eastern region have a more potent carbon reduction effect. The central and western regions in China face more acute contradictions between environmental issues and economic development. Compared to the eastern region, economic development in central and western cities is relatively sluggish, and they are behind the other regions in terms of policy support [50]. As a result, the green park demonstration projects in these areas have not achieved their maximum potential. In contrast, the eastern region is more economically developed, has a more comprehensive environmental protection system, and the local governments place greater emphasis on low-carbon development. Thus, green park demonstration projects are more likely to work synergistically with other supporting policies and facilities, yielding an effect greater than the sum of its parts. Therefore, the carbon reduction effect of green park demonstration projects is more significant in the eastern region.

5.5.2. Environmental Regulatory Basis

The effects of policy shocks may differ depending on the environmental regulatory basis. Therefore, this study explores how the effect of green manufacturing on urban carbon emissions varies across the different regions, focusing on cities designated as key environmental protection areas. In line with the “National Environmental Protection ’Eleventh Five-Year Plan’“ released by the State Council in 2007, the research sample is split into the following two groups: cities identified as key environmental protection areas and those that are not. The findings from the regression analysis for these groups are displayed in Columns (1) and (2) of

Table 8. Heterogeneity of regional characteristics.

Var	(1)	(2)	(3)	(4)	(5)	(6)
	Key Environmental Protection Cities	Non-Key Environmental Protection Cities	Resource-Based Cities	Non-Resource-Based Cities	Old Industrial Base Cities	Non-Old Industrial Base Cities
Gpark	−0.092 ***	0.020	0.001	−0.088 ***	−0.051	−0.065 **
	(−3.242)	(0.517)	(0.021)	(−3.135)	(−1.657)	(−2.127)
_cons	3.723 ***	2.399 **	1.281	3.162 ***	3.883 *	2.705 ***
	(3.310)	(2.586)	(0.754)	(3.429)	(1.669)	(3.072)
City/Year	Yes	Yes	Yes	Yes	Yes	Yes
Control	Yes	Yes	Yes	Yes	Yes	Yes
N	1248	2046	1340	1954	1106	2188
R2	0.380	0.338	0.371	0.340	0.393	0.319

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-values are in parentheses.

. It can be seen that green manufacturing is more effective in generating emissions reduction effects in key environmental protection cities. Key environmental protection cities are the focal point of national pollution control and emissions reductions, and their carbon reduction efforts receive attention from multiple entities, including the public, society, and the state. The environmental policies tend to favor these cities, and they may work synergistically with other environmental policies to achieve a stronger emissions reduction effect, thereby amplifying the positive impact of establishing green parks, making green manufacturing more effective in achieving emissions reduction effects in key environmental protection cities.

5.5.3. Resource Endowment

Considering the differences in urban natural resource endowment, cities are classified according to the “National Sustainable Development Plan for Resource-Based Cities (2013–2020)” issued by the State Council. The cities were divided into resource-based and non-resource-based cities, and a grouped regression was conducted. The regression results are shown in Columns (4) and (5) of Table 8. It can be concluded that, in the non-resource-based city group, the establishment of green parks significantly reduced urban carbon emissions. This suggests that green manufacturing can release multiple dividend effects in non-resource-based cities, effectively promoting environmental governance in these cities. Resource-based cities are often characterized by industries with “three highs”, and rely on the input and expansion of production factors to achieve economic growth. Their development model is relatively singular and dependent on traditional pathways, which hinders the introduction of emerging green technologies and the improvement of low-carbon energy efficiency [51]. On the other hand, non-resource-based cities develop more rapidly [52] and can align with national policies to introduce green and low-carbon technologies, accelerate the green transformation of industrial structures, and promote low-carbon economic development.

5.5.4. Industrial Development Basis

The characteristics of industrial institutions have an important impact on urban low-carbon governance. Heavy industries are characterized by a high energy consumption intensity and high carbon emissions, which can significantly hinder low-carbon urban development. Therefore, to examine the difference in the effect of green manufacturing on urban carbon reductions under different industrial development foundations, this study classifies the research sample into the following two categories: cities with old industrial bases and cities without old industrial bases, according to the “National Old Industrial Base Adjustment and Transformation Plan (2013–2022)”, and performs a grouped regression analysis. The results are shown in Table 8. The results in Columns (5) and (6) indicate that, in non-old industrial base cities, the establishment of green parks can more effectively reduce carbon emissions. A possible reason is that cities based on heavy industries rely on industrial development for economic growth, and some cities serve as key national energy bases, undertaking major technical equipment production projects. The establishment of green parks has limited demonstration effects on enterprises in these regions, making it difficult to change traditional production modes, thereby restricting the implementation effect of green parks and preventing them from achieving maximum carbon reduction benefits.

6. Further Analysis: Other Environmental Effects of Green Parks

We have confirmed that green manufacturing can reduce carbon emissions. Next, we will further discuss whether green manufacturing can improve the level of green innovation. Green innovation refers to activities aimed at achieving the coordinated development of resource utilization and environmental protection by leveraging new development concepts and emerging technologies to save resources, reduce pollution, and achieve corresponding economic effects [53]. As the nexus where innovation-driven forces and green development intersect, green innovation emerges as an efficacious approach for advancing sustainable development. Accurately understanding the impact of green manufacturing on urban green innovation levels can help formulate reasonable green innovation policies and reduce urban carbon emissions according to local conditions, thereby achieving low-carbon development. Urban green innovation levels are represented by the number of green patent grants per capita. Among them, invention patents, as a substantive innovation achievement, contain more independent intellectual property rights and can represent the quality aspect of green innovation. Meanwhile, green utility model patents, which are relatively easier to obtain and are often regarded as strategic innovations, can stand for the quantitative aspect of urban green innovation. Hence, this article takes the per capita quantity of green invention patents and the per capita number of green utility model patents as proxy indicators for urban green innovation levels. The results in Columns (1) and (2) of

Table 9. Green innovation level.

Var	(1)	(2)
	Pinvent	Pshiyong
Gpark	0.015 ***	0.073 ***
	(5.145)	(5.336)
_cons	−0.203 *	−0.954 *
	(−1.801)	(−1.873)
City/Year	Yes	Yes
Control	Yes	Yes
N	3294	3294
R2	0.423	0.529

Note: *, and *** indicate significance at the 10%, and 1% levels, respectively; t-values are in parentheses.

verify that the establishment of national green parks remarkably boosts urban green innovation levels. After the green park was approved, the green innovation level measured by the per capita green invention patents increased by 1.5% on average, and the green innovation level measured by the per capita green utility model patents increased by 7.3% on average. No matter which measurement method is used, it shows that the approved green park plays an important role in regional carbon emissions reductions.

7. Conclusions and Recommendations

Currently, the global push for carbon peaking and carbon neutrality is accelerating, and China is also advancing its carbon peaking and carbon neutrality goals. There is a consensus among scholars and policymakers regarding the significant role of factors such as environmental regulation, the industrial structure, and economic growth, as well as policies like low-carbon city pilots and carbon trading systems, in reducing carbon emissions and promoting low-carbon development. However, research on the environmental effects of green manufacturing remains relatively scarce, and the extent to which green manufacturing reduces carbon emissions is a question worth exploring. In addition, carbon reduction is not only reflected in the direct effects of green manufacturing but also includes the indirect effects of enhancing industrial intelligence. Existing research has not paid enough attention to the mechanisms through which green manufacturing reduces carbon emissions by improving industrial intelligence, which this study aimed to explore. The main conclusions are as follows:

(1)

Green park demonstration projects can significantly reduce urban carbon emissions.

(2)

Carbon reduction is not only reflected in the direct effects of green manufacturing but also includes the indirect effects of enhancing industrial intelligence. Green manufacturing promotes regional carbon reductions by improving the level of industrial intelligence, and higher levels of digital economy, fintech development, and market integration can strengthen the carbon reduction effects of green park demonstration projects.

(3)

The carbon reduction effect is stronger in the eastern region, in areas with more abundant resource endowment, in regions with a superior institutional environment, and in areas where the industrial base is not heavily reliant on heavy industries. In addition, green park demonstration projects can significantly improve regional green innovation levels.

Based on the above conclusions, this paper offers the following policy recommendations.

First, establish a more comprehensive green manufacturing ecosystem. Green manufacturing is a systematic engineering problem that focuses on environmental issues, economic development, and social harmony. The government should focus on establishing a more comprehensive green manufacturing ecosystem to improve the environmental quality, promote social and economic development, and improve the well-being of residents. Specifically, on environmental issues, it is necessary to make full use of the carbon emissions reductions brought by green manufacturing. The theme of green manufacturing should be expanded appropriately and in an orderly manner. A comprehensive green manufacturing standard, green management system, and long-term environmental protection mechanism should be established. More mechanisms, such as the selection, demonstration, and reward of green demonstration projects, should be promoted to encourage more regions to enhance their environmental awareness, focus on the green transformation of parks, and carry out more effective green manufacturing system construction to further reduce carbon emissions and strive toward the carbon peaking and carbon neutrality goals. At the same time, the long-term supervision mechanism for green parks should be further improved, a reasonable evaluation system should be established, and top-level design should be optimized. Under the principle of “re-evaluation every three years”, irregular inspections should be set, and non-compliant green parks should be promptly removed to ensure the sustainable development of the green manufacturing system. In economic development, we should actively promote recycling and intensification, improve the utilization efficiency of manufacturing resources, strengthen the green management of product life cycles, make rapid green technology innovation, and promote sustainable green economic development; at the social level, as green manufacturing demonstration projects, more feedback and information-sharing channels should be established, and relevant media should be encouraged to increase objective reporting. A combination of strict government supervision, public feedback, participation and supervision, and diligent media coverage can form a synergy for environmental governance, thereby enabling better policy implementation.

Second, promote regional industrial intelligence development and create a market environment for the green manufacturing system to function effectively. The government should increase investment in new infrastructure to stimulate the vitality of enterprises within parks for intelligent transformation and promote the development of industrial intelligence. Policies can be introduced to promote collaborative intelligent development strategies among parks and to balance regional development. According to the conclusions of this paper, the development of the digital economy, fintech, and market integration is particularly important for strengthening the carbon reduction effect of green park demonstration projects, and in promoting regional low-carbon and high-quality development. In the long term, the government must vigorously improve the market environment for the green manufacturing system to function effectively, enhance the responsiveness of local development to green park demonstration projects, coordinate and ensure the advancement of supporting policies, and ensure that local governments, enterprises, and relevant departments work together so that economic development and environmental protection can fully achieve synergistic effects. In the short term, local parks should be encouraged to introduce advanced technologies and experiences from green demonstration parks to create positive effects that are greater than the sum of their parts, and to enhance the overall regional development levels.

Third, each region should adopt differentiated approaches based on local conditions. Greater emphasis should be placed on leveraging regional advantages, formulating preferential policies to support enterprises in transitioning to low-carbon development, and promoting green park development. For example, many regions offer one-time rewards ranging from RMB 100,000 to 1 million to newly recognized national green industrial parks. In addition, financial institutions are encouraged to provide preferential financial support for green demonstration projects. For regions with many energy-intensive industries, local governments should improve the governance efficiency, encourage enterprises to actively adjust their energy structure and upgrade their industrial structure, develop low-carbon industries, and eliminate outdated production capacity.