Navigating Urban Transformation: The Impact of Green Innovation on Sustainable Development Performance

Abstract

This paper examines the influence of green innovation on the sustainable development performance of Chinese companies listed on the Shanghai and Shenzhen A-shares market between 2010 and 2021. Utilizing manually collected green innovation patent data from the National Intellectual Property Administration, this paper finds that green innovation can enhance corporate sustainable development performance by improving the corporation’s reputation and increasing resource utilization efficiency within metropolitan areas. Heterogeneity analyses show that the impact of green innovation on sustainable development performance is more pronounced when the urban commercial credit environment is better, following the enactment of the new Environmental Protection Law, and during the implementation of digital transformation processes in cities. This paper enriches the research related to the economic consequences of green innovation and the influencing factors of sustainable development performance, offering theoretical support for policy refinement by regulatory authorities and the optimization of investment decisions by investors.

1. Introduction

Green innovation stands as the core of urban sustainable development, driving both corporate growth and the high-quality economic development of cities. While polluting industries have historically played a role in urban industrialization and national economic expansion, they now confront pressing challenges such as inefficient resource utilization and environmental degradation. Faced with environmental challenges in urban areas, China is actively implementing strategies that integrate environmental protection with economic prosperity (Zhang et.al., 2019) [1]. Defined by its balance of sustainability and growth, sustainable development has emerged as a crucial strategy for China to effectively manage the relationship between the urban environment and development. However, challenges such as the non-renewability of resources, unilateral resource pricing, and short-term evaluation indicators seem to pose inherent conflicts between urban sustainability and progress. Fortunately, with technological advancements, this dichotomy is fading, and green innovation has become vital for the sustainable development of urban enterprises at the micro level.

Green innovation refers to the process of creating new products or services that are environmentally friendly, or enhancing management methods to reduce negative environmental impacts (Aguilera-Caracuel, 2013) [2]. This strategy is pivotal for sustainable development, aiming to balance ecological sustainability with economic growth within urban contexts. This ensures that products and services not only meet human needs and enhance the quality of life in cities but also reduce negative environmental impacts while maintaining continuous operations. Current research defines the capabilities formed in the process of green innovation as green dynamic capabilities, suggesting that these capabilities can help enterprises break away from dependence on existing paths and gain a sustained competitive advantage in urban markets. Against this backdrop, systematically exploring the impact and mechanisms of green innovation on sustainable corporate development holds significant theoretical and practical relevance, especially for urban areas where the density of economic activities and environmental pressures are high.

Based on the aforementioned research context, this paper examines the impact of green innovation on the sustainable development performance of Chinese companies listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2021. By utilizing hand-collected green innovation patent data sourced from the National Intellectual Property Administration, empirical tests have confirmed that green innovation significantly boosts corporate sustainable development performance in urban areas. This conclusion remains robust after comprehensive checks, including variations in core indicator measurement methods, the consideration of different lag intervals, and the exclusion of other event impacts. Mechanism analysis further reveals that green innovation enhances sustainable development performance by improving corporate reputation and optimizing resource utilization within urban contexts. Additional tests highlight that the influence of green innovation on sustainable development performance becomes more pronounced in scenarios where the urban commercial credit environment is favorable, following the promulgation of the new Environmental Protection Law, and amidst digital transformation initiatives, particularly within urban settings.

This paper contributes in three main ways. Firstly, it offers a new perspective for research on factors influencing sustainable development by examining the impact of corporate innovation investment on sustainable development performance, specifically within the context of urban green innovation. Existing studies have primarily focused on the effects of company characteristics and executive traits on sustainable development, often overlooking the significant role of green innovation, a critical corporate investment activity in urban environments. Secondly, by focusing on corporate sustainable development performance in urban areas, this paper enriches the body of research concerning the economic consequences of green innovation. Previous studies on the economic outcomes of green innovation have mainly centered on its impact on regional development, with fewer exploring its effects on business development within cities, particularly in terms of operational performance, competitiveness, and financials. Lastly, this paper provides empirical evidence and theoretical support for regulatory authorities to refine policy guidance and for investors to optimize investment decisions, especially in urban areas where the concentration of economic activities and the urgency for sustainable development are the most pronounced.

2. Literature Review

2.1. Economic Consequences of Green Innovation

2.1.1. Regional Development

Focusing on regional development, existing studies have predominantly analyzed the impact of green innovation on industrial structure and the regional environment. Regarding industrial structure, it has been noted that the level of a corporation’s green innovation primarily influences its high-quality development by promoting the scientific and sophisticated advancement of the industrial structure [3]. Research by Huang and Li (2017) discovered that at the initial stages of research and development, corporate R&D investment could encourage green innovative behaviors among surrounding enterprises, thereby significantly enhancing the overall innovation capacity of the region [4]. The formation of innovation networks between cities has become a critical path for achieving the objectives of green innovation in manufacturing [5]. Wang et al. (2023) pointed out that corporate green innovation exhibits a significant regional clustering effect and spatial heterogeneity [6]. In terms of the regional environment, current studies indicate a notable inverted U-shaped relationship between green innovation and both network centrality and relationship strength. Cities attract high-end technical talents through their status factors to aid urban development. Research by Wang et al. (2022) demonstrates significant spatial disparities in corporate green innovation within China. Specifically, it is observed that there are lower levels of green innovation in the northern regions compared to the southern regions and higher levels in the eastern regions compared to the western regions [7]. Chen et al. (2024) highlighted that green innovation could directly reduce emissions of carbon dioxide, sulfur dioxide, and nitrogen oxides, producing both direct effects and spatial spillover effects, thereby enhancing the quality of the regional environment [8]. To broaden our understanding, it is essential to consider global perspectives on green innovation. For example, research from the European Union highlights the importance of green innovation in achieving sustainable development goals, while studies from Asia emphasize its role in enhancing environmental performance and economic competitiveness.

2.1.2. Corporate Development

From the perspective of corporate development, existing research suggests that green innovation significantly enhances a company’s performance levels. For instance, the paper by Huang and Li (2017) examined whether green innovation could impact corporate performance, finding that coordination capabilities and the degree of social reciprocity act as driving factors for the development of green innovation in businesses [4]. The trend towards greener products and the implementation of new processes can positively and effectively influence both environmental and organizational performance. Research by Klassen and Whybark (1999) discovered that certain innovations within green innovation, such as pollution prevention technologies, can lead to an increase in a company’s economic performance, while some technologies do not foster economic advancement [9]. Banerjee (2001) found that a company’s green innovation strategy can effectively reduce operational costs compared to previous levels and significantly develop new processes, thereby enhancing the eco-friendliness of existing products and improving product competitiveness, which in turn raises the company’s economic performance level [10]. However, the paper by Aguilera-Caracuel et al. (2013) observed that companies engaging in green innovation face stringent external environmental pressures, with high demands from environmental laws and regulations, resulting in no financial performance improvement compared to that of companies that do not engage in green innovation [2]. The level of green innovation and the company’s performance levels overall show a normal distribution.

2.2. Factors Influencing Corporate Sustainable Development Performance

In the context of China, the country has undergone a notable transition towards sustainable development policies over the past decade. Policies such as the 13th Five-Year Plan have emphasized ecological civilization and green development, significantly impacting urban industrial landscapes by promoting green industries and reducing pollution. Existing research primarily explores the impact of company characteristics and executive traits on corporate sustainable development performance [10]. This paper provides a comprehensive review of the aforementioned studies.

2.2.1. Company Characteristics

Regarding the impact of environmental awareness on corporate sustainable development performance, the paper by Seman et al. (2019) highlights that attention to environmental issues at various points in the supply chain, a focus on environmental protection, and the promotion of coordinated economic and environmental development significantly enhance the level of green innovation and environmental performance [11]. Additionally, in terms of the influence of regulatory intensity on corporate sustainable development performance, research by Wei et al. (2017) found that obtaining exclusive recognition from the government can effectively prevent excessive government intervention in business operations, similarly enabling companies to leverage their strengths for rapid expansion and development, thereby significantly improving sustainable development performance [12].

2.2.2. Executive Traits

Concerning the influence of executive traits on corporate sustainable development performance, the paper by Adu et al. (2022) indicates a significant negative correlation between executive compensation levels and corporate sustainable development performance, with this relationship significantly moderated by corporate governance mechanisms [13]. The research by Hao et al. (2019) suggests that executives with international experience tend to enhance corporate sustainable development performance through green innovation, although not all heterogeneity between such executives and local managers promotes sustainable development performance [14]. The paper by Huang (2013) notes that CEOs with MBAs or master’s degrees in science significantly boost corporate sustainable development performance, with CEO tenure and gender also having a significant impact [15].

3. Hypothesis Development

Green innovation theory posits that such innovation encompasses enhancements to products, processes, and services that not only add value to a business but also significantly reduce environmental pollution, serving as a means of innovation [16]. Compared to traditional innovation, green innovation effectively lowers energy consumption and pollution emissions, aiding in the improvement of the ecological environment and the harmonious coexistence of humanity and nature [17]. Under the theory of natural capitalism, firms can enhance their competitiveness by developing valuable, rare, inimitable, and non-substitutable capabilities, thus securing a sustained competitive advantage [18,19,20]. Green innovation is an effective means to achieve this sustained competitive advantage and is a vital component of a corporation’s sustainable development strategy [21], significantly impacting the sustainable development performance of a company. Therefore, based on green innovation theory and resource-based theory, this paper posits that green innovation can influence a company’s sustainable development performance in two ways.

Firstly, green innovation can bring about a more positive social reputation and brand image for a company, thereby enhancing its sustainable development performance. The eco-friendly attitude demonstrated through green innovation and production technology transformation can help firms establish a more positive green brand image [22], resulting in a favorable external reputation and further increasing brand loyalty [23] and customer satisfaction [24]. Customer loyalty and corporate reputation are critical factors affecting potential returns [24], ultimately contributing to favorable economic performance [25]. Empirical studies from a social responsibility perspective have found that corporate social responsibility fulfillment positively affects financial performance [26]. Therefore, green innovation can effectively improve a company’s sales and market value by helping establish a more active social reputation and brand image, thereby enhancing the company’s sustainable development performance.

Secondly, green innovation can grant companies substantial cost advantages from a resource-saving perspective, thus improving sustainable development performance. Green innovation can alleviate constraints from non-renewable resources through upgrades in manufacturing processes, reducing traditional resource usage and avoiding excessive energy consumption, thereby minimizing production waste and saving on resource costs from the outset [27,28,29,30,31]. In addition, green innovation can control pollution from the source, reducing the costs associated with the disposal of hazardous and toxic waste and thus, decreasing environmental costs [32]. Therefore, under the indirect pressure of stakeholders such as customers and suppliers, green innovation, as an effective tool, can transform this pressure into cost advantages, subsequently enhancing a firm’s performance in regards to sustainable development [33]. Based on these considerations, the following hypothesis is proposed:

H1. 

Green innovation can improve corporate sustainable development performance.

4. Research Design

4.1. Sample Selection and Data Sources

This paper focuses on Chinese A-share listed companies between 2010 to 2021. During the specific sample selection process, the following adjustments were made: (1) exclusion of samples from the financial industry; (2) exclusion of samples from ST companies; (3) exclusion of samples with incomplete data. Following the above criteria, this paper ultimately obtained 37,250 annual observations of company data. Following Sun et al. (2023) [34], this paper sourced from the China Stock Market & Accounting Research Database (CSMAR) (China Stock Market & Accounting Research Database (CSMAR): https://data.csmar.com/, accessed on 1 March 2023) and the Wind Information Inc. Database (WIND) (Wind Information Inc. Database (WIND): https://www.wind.com.cn/, accessed on 1 March 2023), which are both widely recognized for their comprehensive financial and economic data for China. To eliminate the influence of outliers, all continuous variables were winsorized at the 1st and 99th percentiles, and all regression standard errors underwent industry-level cluster processing.

Regarding data sources, the data on corporate green innovation were manually collected and organized from the website of the National Intellectual Property Office, based on the IPC classification numbers listed in the International Patent Classification Green List using the names of listed companies as keywords. The International Patent Classification Green List was introduced by the World Intellectual Property Organization in 2010, facilitating the search for information on environmentally friendly technology-related patents. Other data used in this paper were sourced from the WIND and CSMAR databases.

4.2. Variable Definitions

4.2.1. Dependent Variables

Following the research by Alexopoulos et al. (2018), this paper divides sustainable development performance into financial performance and environmental and social responsibility performance [35]. This paper uses the total asset turnover rate to measure sustainable development financial performance and the total score of corporate social responsibility ratings published by Hexun.com to measure sustainable development environmental performance. The results are shown in Table 1.

4.2.2. Independent Variables

Drawing on previous studies [36,37], this paper measures the number of green patents authorized in the previous period and the natural logarithm of one plus the number of green patents obtained as explanatory variables.

4.2.3. Control Variables

The control variables consist of a series of factors that may affect corporate sustainable development performance, primarily including: company size (Size), financial leverage (Lev), growth ability (Growth), board size (Board), proportion of independent directors (Indep), duality of roles (Dual), profitability status (Loss), enterprise value (TobinQ), shareholding of the largest shareholder (Top1), environmental management certification (ISO), Big Four accounting firms (Big4), audit opinions (Opinion), years of being listed (Age), and nature of the enterprise (SOE).

Table 1. Variable definitions.

Variables	Definition of Variables
FINAt	The rate of return on total assets.
ENVIRt	The natural logarithm of the Corporate Environmental and Social Responsibility Index scores released by Hexun.com.
GI_Applyt−1	The natural logarithm of one plus the number of patents applied in t − 1 year.
GI_Approvet−1	The natural logarithm of one plus the number of patents authorized in t − 1 year.
Sizet	The natural logarithm of total assets.
Levt	Total liabilities divided by total assets.
Growtht	Sales in year t minus sales in year t − 1, then divided by sales in year t − 1.
Boardt	The natural logarithm of the number of board directors.
Indept	The number of independent directors divided by the total number of board directors.
Dualt	An indicator variable that equals 1 if the chairman is also the general manager and 0 otherwise.
Losst	An indicator variable that equals 1 if the net profit is negative and 0 otherwise.
TobinQt	Market value of the business divided by the replacement cost.
Top1t	The number of shares held by the first largest shareholder divided by the number of shares outstanding.
ISOt	An indicator variable that equals 1 if the firm has passed ISO14000 [38] certification and 0 otherwise.
Big4t	An indicator variable that equals 1 if the firm’s auditor is KPMG, PWC, E&Y, or DTT and 0 otherwise.
Opiniont	An indicator variable that equals 1 if the firm has obtained a standard unqualified audit opinion and 0 otherwise.
Aget	The natural logarithm of the number of years since the firm’s IPO year.
SOEt	An indicator variable that equals 1 if the property right of the firm is state-owned and 0 otherwise.

Table 1. Variable definitions.

Variables	Definition of Variables
FINAt	The rate of return on total assets.
ENVIRt	The natural logarithm of the Corporate Environmental and Social Responsibility Index scores released by Hexun.com.
GI_Applyt−1	The natural logarithm of one plus the number of patents applied in t − 1 year.
GI_Approvet−1	The natural logarithm of one plus the number of patents authorized in t − 1 year.
Sizet	The natural logarithm of total assets.
Levt	Total liabilities divided by total assets.
Growtht	Sales in year t minus sales in year t − 1, then divided by sales in year t − 1.
Boardt	The natural logarithm of the number of board directors.
Indept	The number of independent directors divided by the total number of board directors.
Dualt	An indicator variable that equals 1 if the chairman is also the general manager and 0 otherwise.
Losst	An indicator variable that equals 1 if the net profit is negative and 0 otherwise.
TobinQt	Market value of the business divided by the replacement cost.
Top1t	The number of shares held by the first largest shareholder divided by the number of shares outstanding.
ISOt	An indicator variable that equals 1 if the firm has passed ISO14000 [38] certification and 0 otherwise.
Big4t	An indicator variable that equals 1 if the firm’s auditor is KPMG, PWC, E&Y, or DTT and 0 otherwise.
Opiniont	An indicator variable that equals 1 if the firm has obtained a standard unqualified audit opinion and 0 otherwise.
Aget	The natural logarithm of the number of years since the firm’s IPO year.
SOEt	An indicator variable that equals 1 if the property right of the firm is state-owned and 0 otherwise.

4.3. Empirical Model

On the basis of the reference to existing studies (Mccarthy et al., 2017; Avishek and David, 2017; Baskentli et al., 2019; Ali et al., 2019) [39,40,41,42], this paper established model (1) and used the OLS regression method to investigate the impact of green innovation on sustainable development performance and then tested the research hypothesis proposed in this paper, as follows:

$${\displaystyle \frac{{FINA}_{i,t}}{{ENVIR}_{i,t}}}={\beta }_0+{\beta }_1{\displaystyle \frac{{GI\text{\_}Apply}_{i,t-1}}{{GI\text{\_}Approve}_{i,t-1}}}+{\beta }_2{Controls}_{i,t}+{\epsilon }_{i,t}$$

(1)

5. Results and Discussions

5.1. Summary Statistics

In

Table 2. Summary statistics.

Variables	N	Mean	Std	Min	P25	Median	P75	Max
FINAt	37,250	0.0527	0.0761	−0.3302	0.0282	0.0537	0.0865	0.2510
ENVIRt	37,250	0.2294	0.1447	−0.0388	0.1611	0.2198	0.2663	0.7287
GI_Applyt−1	37,250	0.3967	0.8106	0.0000	0.0000	0.0000	0.6931	3.7377
GI_Approvet−1	37,250	0.3217	0.6944	0.0000	0.0000	0.0000	0.0000	3.3322
Sizet	37,250	22.0409	1.3361	19.1587	21.1004	21.8848	22.8032	26.0879
Levt	37,250	0.4254	0.2163	0.0497	0.2516	0.4136	0.5814	0.9778
Growtht	37,250	0.1760	0.4787	−0.6392	−0.0077	0.0867	0.2600	3.2722
Boardt	37,250	2.2383	0.1765	1.7918	2.0794	2.3026	2.3026	2.7726
Indept	37,250	0.3753	0.0533	0.3333	0.3333	0.3333	0.4286	0.5714
Dualt	37,250	0.3056	0.4606	0.0000	0.0000	0.0000	1.0000	1.0000
Losst	37,250	0.1103	0.3132	0.0000	0.0000	0.0000	0.0000	1.0000
TobinQt	37,250	2.1072	1.4243	0.8645	1.2736	1.6864	2.2961	9.7236
Top1t	37,250	0.3222	0.1692	0.0000	0.2077	0.3069	0.4348	0.7482
ISOt	37,250	0.2125	0.4091	0.0000	0.0000	0.0000	0.0000	1.0000
Big4t	37,250	0.0727	0.2596	0.0000	0.0000	0.0000	0.0000	1.0000
Opiniont	37,250	0.9542	0.2091	0.0000	1.0000	1.0000	1.0000	1.0000
Aget	37,250	1.9755	1.0031	0.0000	1.3863	2.1972	2.8332	3.3322
SOEt	37,250	0.3262	0.4688	0.0000	0.0000	0.0000	1.0000	1.0000

, the descriptive statistical results are presented. The standard deviations of sustainable development financial performance (FINA_t) and sustainable development environmental performance (ENVIR_t) are 0.0761 and 0.1447, respectively, indicating significant differences in sustainable development performance among different sample companies. The maximum values for the two indicators measuring green innovation, GI_Apply_t−1 and GI_Approve_t−1, are 3.7377 and 3.3322, respectively, suggesting that in the sample, the largest number of green patent applications was 42, and the largest number of green patents approved was 28.

5.2. Baseline Findings

Table 3. Green innovation and corporate sustainable development performance.

Variables	FINAt	ENVIRt	FINAt	ENVIRt
	(1)	(2)	(3)	(4)
GI_Applyt−1	0.0016 ***	0.0066 ***
	(3.09)	(4.76)
GI_Approvet−1			0.0019 ***	0.0076 ***
			(3.25)	(4.66)
Sizet	0.0070 ***	0.0344 ***	0.0070 ***	0.0345 ***
	(11.42)	(28.18)	(11.45)	(28.31)
Levt	−0.0499 ***	−0.0937 ***	−0.0499 ***	−0.0937 ***
	(−16.35)	(−15.84)	(−16.35)	(−15.84)
Growtht	0.0137 ***	0.0020	0.0137 ***	0.0020
	(16.74)	(1.58)	(16.75)	(1.60)
Boardt	0.0046	0.0136	0.0046	0.0134
	(1.44)	(1.59)	(1.42)	(1.57)
Indept	−0.0262 ***	0.0188	−0.0264 ***	0.0179
	(−2.63)	(0.71)	(−2.65)	(0.68)
Dualt	−0.0000	0.0014	0.0000	0.0014
	(−0.00)	(0.65)	(0.00)	(0.66)
Losst	−0.1328 ***	−0.1446 ***	−0.1328 ***	−0.1447 ***
	(−65.40)	(−59.58)	(−65.40)	(−59.60)
TobinQt	0.0075 ***	0.0082 ***	0.0075 ***	0.0082 ***
	(14.14)	(11.66)	(14.16)	(11.72)
Top1t	0.0193 ***	0.0186 **	0.0192 ***	0.0183 **
	(6.03)	(2.47)	(6.01)	(2.44)
ISOt	−0.0003	0.0358 ***	−0.0003	0.0358 ***
	(−0.31)	(13.78)	(−0.31)	(13.76)
Big4t	0.0096 ***	0.0374 ***	0.0096 ***	0.0374 ***
	(4.40)	(7.99)	(4.40)	(7.98)
Opiniont	0.0355 ***	0.0302 ***	0.0356 ***	0.0304 ***
	(11.64)	(8.32)	(11.66)	(8.38)
Aget	−0.0100 ***	−0.0142 ***	−0.0101 ***	−0.0143 ***
	(−16.77)	(−10.86)	(−16.79)	(−10.92)
SOEt	−0.0021 *	0.0112 ***	−0.0021 *	0.0114 ***
	(−1.70)	(3.36)	(−1.66)	(3.43)
Constant	−0.1009 ***	−0.5302 ***	−0.1010 ***	−0.5311 ***
	(−6.45)	(−15.30)	(−6.45)	(−15.32)
YEAR Effect	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES
N	37,250	37,250	37,250	37,250
Adj.R2	0.5138	0.3630	0.5138	0.3630

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

displays the relationship between green innovation and corporate sustainable development performance. Columns (1) and (3) do not include control variables, while columns (2) and (4) incorporate control variables. The regression results presented below all indicate a significant positive correlation between green innovation and corporate sustainable development performance, confirming hypothesis H1 presented previously, which posits that green innovation can enhance a corporation’s sustainable development performance. In addition, it is important to note that while all control variables are included in the model to account for potential confounding effects, only the coefficients for the independent variables of interest are discussed in detail in the text.

5.3. Robustness Checks

5.3.1. Alternative Metrics of Corporate Sustainable Development Performance

In the preceding analysis, this paper utilized the return on total assets to measure financial performance related to sustainable development and employed the overall score for the corporate social responsibility rating published by the Hexun website to assess sustainable environmental performance. In this section, a dual performance framework is adopted to evaluate corporate sustainable development performance. Specifically, the procedure involves standardizing both sustainable development financial performance (FINAt) and sustainable development environmental performance (ENVIRt). Thereafter, the average of the standardized values of sustainable development environmental performance is calculated to represent the corporate’s sustainable development performance as a dual metric.

Table 4. Alternative metrics of corporate sustainable development performance.

Variables	AMBIt
	(1)	(2)
GI_Applyt−1	0.0295 ***
	(4.28)
GI_Approvet−1		0.0332 ***
		(4.17)
Sizet	0.1659 ***	0.1663 ***
	(25.79)	(25.99)
Levt	−0.6517 ***	−0.6517 ***
	(−20.51)	(−20.51)
Growtht	0.0969 ***	0.0970 ***
	(12.70)	(12.71)
Boardt	0.0775 *	0.0767 *
	(1.91)	(1.89)
Indept	−0.1066	−0.1103
	(−0.85)	(−0.88)
Dualt	0.0048	0.0049
	(0.43)	(0.44)
Losst	−1.3728 ***	−1.3730 ***
	(−83.94)	(−83.97)
TobinQt	0.0776 ***	0.0778 ***
	(16.61)	(16.65)
Top1t	0.1907 ***	0.1894 ***
	(4.98)	(4.95)
ISOt	0.1223 ***	0.1224 ***
	(10.06)	(10.04)
Big4t	0.1926 ***	0.1926 ***
	(8.03)	(8.04)
Opiniont	0.3379 ***	0.3389 ***
	(14.17)	(14.21)
Aget	−0.1154 ***	−0.1157 ***
	(−17.08)	(−17.13)
SOEt	0.0246	0.0256
	(1.55)	(1.61)
Constant	−3.6535 ***	−3.6599 ***
	(−21.02)	(−21.09)
YEAR Effect	YES	YES
INDUSTRY Effect	YES	YES
N	37,250	37,250
Adj.R2	0.5298	0.5298

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

illustrates the relationship between green innovation and corporate sustainable development performance after transitioning to the new measure of performance. The regression results consistently demonstrate that, following the change in the measurement of sustainable development performance, there remains a significant positive correlation between green innovation and corporate sustainable development performance at the 1% level, thus maintaining the validity of the initially hypothesized test result H1.

5.3.2. Considering the Effect of the Length of the Lag Interval

Considering the impact of lag interval length, this paper measures green innovation by taking the natural logarithm of the number of green patents authorized and obtained in period t − 1 plus one, acknowledging that the effects of green innovation may exhibit latency. However, the manifestation of sustainable development performance effects might require a longer time interval. Therefore, the definition of green innovation in this paper shifts from t − 1 to t − 2 to ensure the robustness of the research findings.

Table 5. Considering the effect of the length of the lag interval.

Variables	FINAt	ENVIRt	FINAt	ENVIRt
	(1)	(2)	(3)	(4)
GI_Applyt−2	0.0017 ***	0.0066 ***
	(3.24)	(4.86)
GI_Approvet−2			0.0022 ***	0.0078 ***
			(3.77)	(4.86)
Sizet	0.0075 ***	0.0331 ***	0.0075 ***	0.0332 ***
	(12.19)	(27.97)	(12.17)	(28.02)
Levt	−0.0533 ***	−0.0955 ***	−0.0533 ***	−0.0954 ***
	(−17.20)	(−16.46)	(−17.20)	(−16.46)
Growtht	0.0142 ***	0.0014	0.0142 ***	0.0014
	(16.42)	(1.03)	(16.44)	(1.06)
Boardt	0.0043	0.0131	0.0042	0.0128
	(1.30)	(1.55)	(1.27)	(1.52)
Indept	−0.0259 **	0.0175	−0.0263 **	0.0163
	(−2.52)	(0.68)	(−2.56)	(0.63)
Dualt	0.0000	0.0016	0.0000	0.0016
	(0.01)	(0.74)	(0.02)	(0.75)
Losst	−0.1324 ***	−0.1446 ***	−0.1324 ***	−0.1446 ***
	(−64.28)	(−58.62)	(−64.27)	(−58.61)
TobinQt	0.0074 ***	0.0079 ***	0.0074 ***	0.0079 ***
	(13.86)	(11.58)	(13.87)	(11.63)
Top1t	0.0179 ***	0.0196 ***	0.0178 ***	0.0193 ***
	(5.52)	(2.71)	(5.50)	(2.67)
ISOt	−0.0003	0.0324 ***	−0.0004	0.0324 ***
	(−0.37)	(12.87)	(−0.40)	(12.85)
Big4t	0.0095 ***	0.0352 ***	0.0095 ***	0.0351 ***
	(4.27)	(7.81)	(4.27)	(7.79)
Opiniont	0.0384 ***	0.0293 ***	0.0385 ***	0.0295 ***
	(12.37)	(7.95)	(12.39)	(8.02)
Aget	−0.0106 ***	−0.0152 ***	−0.0106 ***	−0.0152 ***
	(−17.20)	(−11.99)	(−17.21)	(−12.04)
SOEt	−0.0014	0.0111 ***	−0.0013	0.0113 ***
	(−1.08)	(3.45)	(−1.03)	(3.52)
Constant	−0.1091 ***	−0.4796 ***	−0.1083 ***	−0.4795 ***
	(−6.90)	(−14.15)	(−6.86)	(−14.12)
YEAR Effect	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES
N	35,158	35,158	35,158	35,158
Adj.R2	0.5212	0.3649	0.5212	0.3649

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

demonstrates the relationship between green innovation and corporate sustainable development performance, accounting for the impact of lag interval length. Columns (1) and (3) do not include control variables, whereas columns (2) and (4) do. The regression results consistently reveal a significant positive correlation between green innovation and corporate sustainable development performance at the 1% level, maintaining the validity of hypothesis H1, tested previously.

5.3.3. Excluding the Impact of Other Events

Considering the potential impact of other event shocks, the Mass Entrepreneurship and Innovation initiative, initiated by the premier’s speech at the Summer Davos Forum in September 2014, led to a wave of innovation among businesses, spurred by supporting policies from local governments. Therefore, to isolate the effect of this initiative, the paper excludes data from 2014 and re-tests the research content with the remaining samples.

Table 6. Excluding the impact of other events.

Variables	FINAt	ENVIRt	FINAt	ENVIRt
	(1)	(2)	(3)	(4)
GI_Applyt−1	0.0017 ***	0.0068 ***
	(3.18)	(5.26)
GI_Approvet−1			0.0018 ***	0.0073 ***
			(2.99)	(4.75)
Sizet	0.0069 ***	0.0311 ***	0.0070 ***	0.0313 ***
	(11.18)	(28.02)	(11.26)	(28.25)
Levt	−0.0511 ***	−0.0908 ***	−0.0511 ***	−0.0908 ***
	(−16.24)	(−16.71)	(−16.24)	(−16.71)
Growtht	0.0138 ***	0.0028 **	0.0138 ***	0.0028 **
	(16.17)	(2.16)	(16.17)	(2.18)
Boardt	0.0053	0.0118	0.0053	0.0117
	(1.61)	(1.51)	(1.60)	(1.49)
Indept	−0.0252 **	0.0098	−0.0254 **	0.0091
	(−2.46)	(0.41)	(−2.47)	(0.38)
Dualt	0.0002	0.0022	0.0002	0.0022
	(0.16)	(1.10)	(0.17)	(1.11)
Losst	−0.1355 ***	−0.1449 ***	−0.1355 ***	−0.1450 ***
	(−64.37)	(−62.95)	(−64.39)	(−63.00)
TobinQt	0.0072 ***	0.0069 ***	0.0072 ***	0.0070 ***
	(13.44)	(10.70)	(13.46)	(10.79)
Top1t	0.0200 ***	0.0203 ***	0.0199 ***	0.0200 ***
	(6.21)	(2.98)	(6.19)	(2.94)
ISOt	−0.0005	0.0317 ***	−0.0005	0.0317 ***
	(−0.53)	(13.34)	(−0.51)	(13.34)
Big4t	0.0097 ***	0.0348 ***	0.0097 ***	0.0348 ***
	(4.34)	(8.10)	(4.35)	(8.10)
Opiniont	0.0364 ***	0.0298 ***	0.0364 ***	0.0300 ***
	(11.45)	(8.64)	(11.47)	(8.70)
Aget	−0.0103 ***	−0.0139 ***	−0.0103 ***	−0.0140 ***
	(−16.87)	(−11.64)	(−16.90)	(−11.72)
SOEt	−0.0025 **	0.0091 ***	−0.0025 **	0.0093 ***
	(−2.01)	(3.01)	(−1.96)	(3.08)
Constant	−0.1007 ***	−0.4512 ***	−0.1016 ***	−0.4541 ***
	(−6.31)	(−14.20)	(−6.36)	(−14.29)
YEAR Effect	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES
N	34,798	34,798	34,798	34,798
Adj.R2	0.5182	0.3657	0.5182	0.3656

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

shows the relationship between green innovation and corporate sustainable development performance after excluding the impact of the Mass Entrepreneurship and Innovation initiative. Columns (1) and (3) do not include control variables, whereas columns (2) and (4) do. The results continue to show a significant positive correlation between green innovation and corporate sustainable development performance at the 1% level after excluding the initiative’s impact, with the initial hypothesis H1 test results unchanged.

6. Mechanism Check Analyses

6.1. Enhancing Corporate Reputation

The research presented in this document demonstrates that green innovation can enhance a company’s social reputation and brand image, thereby improving its sustainable development performance. Existing studies suggest that corporate philanthropic donations convey positive signals of social responsibility to the public, significantly impacting the company’s reputation [43]. Therefore, when an organization has engaged in charitable giving, it is likely already perceived as having a high level of reputation. In this context, the potential of green innovation to further enhance reputation—and by extension, sustainable development performance—is limited, possibly exerting only a minimal impact. Conversely, when a company does not participate in philanthropy, there is greater scope for reputation enhancement. Under these circumstances, the role of green innovation in elevating a firm’s reputation becomes more pronounced, thus significantly affecting sustainable development performance.

To validate the mechanism by which reputation enhancement influences these outcomes, the paper segmented the full sample based on whether the companies had participated in charitable donations, conducting grouped tests of the previous content.

Table 7. Enhancing corporate reputation.

Variables	FINAt		ENVIRt		FINAt		ENVIRt
	Non-Charitable Donation Group	Charitable Donation Group	Non-Charitable Donation Group	Charitable Donation Group	Non-Charitable Donation Group	Charitable Donation Group	Non-Charitable Donation Group	Charitable Donation Group
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GI_Applyt−1	0.0032 ***	0.0003	0.0083 ***	0.0006
	(5.74)	(0.36)	(6.01)	(0.26)
p-value of Dif	0.0001 ***		0.0002 ***
GI_Approvet−1					0.0036 ***	0.0009	0.0099 ***	0.0012
					(5.51)	(0.88)	(5.95)	(0.42)
p-value of Dif					0.0010 ***		0.0003 ***
Sizet	0.0060 ***	0.0094 ***	0.0283 ***	0.0246 ***	0.0060 ***	0.0093 ***	0.0283 ***	0.0245 ***
	(9.25)	(8.66)	(23.38)	(10.28)	(9.35)	(8.55)	(23.51)	(10.20)
Levt	−0.0453 ***	−0.0795 ***	−0.0869 ***	−0.1034 ***	−0.0453 ***	−0.0794 ***	−0.0868 ***	−0.1033 ***
	(−14.47)	(−11.49)	(−15.75)	(−6.69)	(−14.47)	(−11.47)	(−15.75)	(−6.69)
Growtht	0.0135 ***	0.0153 ***	0.0032 ***	0.0045	0.0136 ***	0.0153 ***	0.0033 ***	0.0045
	(15.91)	(6.55)	(2.62)	(1.04)	(15.91)	(6.57)	(2.64)	(1.04)
Boardt	0.0064 *	−0.0076	0.0096	−0.0193	0.0062 *	−0.0077	0.0092	−0.0194
	(1.81)	(−1.45)	(1.20)	(−1.29)	(1.78)	(−1.46)	(1.16)	(−1.30)
Indept	−0.0259 **	−0.0194	−0.0049	−0.0312	−0.0262 **	−0.0195	−0.0059	−0.0314
	(−2.44)	(−1.11)	(−0.20)	(−0.68)	(−2.48)	(−1.12)	(−0.25)	(−0.69)
Dualt	0.0001	−0.0025	0.0016	−0.0020	0.0001	−0.0025	0.0016	−0.0020
	(0.14)	(−1.28)	(0.79)	(−0.44)	(0.15)	(−1.29)	(0.81)	(−0.44)
Losst	−0.1335 ***	−0.1218 ***	−0.1410 ***	−0.1580 ***	−0.1336 ***	−0.1219 ***	−0.1411 ***	−0.1580 ***
	(−62.98)	(−22.22)	(−58.84)	(−19.90)	(−62.98)	(−22.24)	(−58.81)	(−19.93)
TobinQt	0.0064 ***	0.0129 ***	0.0065 ***	0.0077 ***	0.0064 ***	0.0129 ***	0.0066 ***	0.0077 ***
	(11.65)	(13.10)	(9.93)	(5.33)	(11.70)	(13.10)	(10.02)	(5.33)
Top1t	0.0190 ***	0.0280 ***	0.0289 ***	−0.0123	0.0189 ***	0.0279 ***	0.0287 ***	−0.0124
	(5.64)	(4.91)	(4.13)	(−0.85)	(5.62)	(4.90)	(4.10)	(−0.85)
ISOt	−0.0010	−0.0008	0.0275 ***	0.0271 ***	−0.0010	−0.0009	0.0275 ***	0.0271 ***
	(−1.01)	(−0.48)	(10.60)	(6.52)	(−1.00)	(−0.51)	(10.56)	(6.49)
Big4t	0.0136 ***	−0.0036	0.0383 ***	0.0096	0.0137 ***	−0.0036	0.0383 ***	0.0095
	(5.48)	(−1.19)	(8.03)	(1.27)	(5.48)	(−1.21)	(8.03)	(1.26)
Opiniont	0.0344 ***	0.0435 ***	0.0276 ***	0.0437 ***	0.0346 ***	0.0434 ***	0.0279 ***	0.0436 ***
	(11.11)	(3.48)	(8.11)	(3.09)	(11.14)	(3.48)	(8.19)	(3.08)
Aget	−0.0100 ***	−0.0072 ***	−0.0131 ***	−0.0089 ***	−0.0100 ***	−0.0072 ***	−0.0131 ***	−0.0089 ***
	(−16.21)	(−5.99)	(−10.66)	(−3.28)	(−16.25)	(−5.99)	(−10.72)	(−3.29)
SOEt	−0.0015	−0.0071 ***	0.0108 ***	0.0144 **	−0.0014	−0.0071 ***	0.0111 ***	0.0144 **
	(−1.17)	(−2.97)	(3.57)	(2.21)	(−1.08)	(−2.96)	(3.67)	(2.21)
Constant	−0.0823 ***	−0.1331 ***	−0.4135 ***	−0.0010	−0.0831 ***	−0.1308 ***	−0.4136 ***	0.0013
	(−5.04)	(−4.47)	(−12.26)	(−0.01)	(−5.09)	(−4.40)	(−12.27)	(0.02)
YEAR Effect	YES	YES	YES	YES	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES	YES	YES	YES	YES
N	31,521	5729	31,521	5729	31,521	5729	31,521	5729
Pseudo.R2	0.5192	0.4818	0.3514	0.5981	0.5192	0.4819	0.3515	0.5981

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

displays the results of the analysis based on the reputation enhancement mechanism. Columns (1), (3), (5), and (7) test the group that did not engage in philanthropic activities and reveal that, regardless of whether FINA_t or ENVIR_t is used as the dependent variable, the coefficients for GI_Apply_t−1 and GI_Approve_t−1 are significantly positive at the 1% statistical level. This indicates a stronger effect of green innovation on reputation among industry peers, subsequently leading to enhanced sustainable development performance for companies that do not make charitable contributions. In contrast, columns (2), (4), (6), and (8) test the group that engaged in philanthropy, finding the coefficients for GI_Apply_t−1 and GI_Approve_t−1 to be insignificant across models, with significant inter-model coefficient differences at the 1% level. This implies that for companies involved in philanthropy, the influence of green innovation on enhancing reputation and sustainable development performance is weaker.

These findings corroborate the hypothesis that green innovation can improve sustainable development performance through the mechanism of reputation enhancement.

6.2. Improving Resource Utilization

Furthermore, the paper found that green innovation helps companies secure cost advantages through resource conservation, further boosting sustainable development performance. However, resource utilization could also be affected by a company’s research capabilities. Consequently, when an entity possesses strong research capabilities, resource utilization rates are likely already high. In these cases, the capacity for green innovation to further improve resource utilization—and consequently, sustainable development performance—may be negligible. On the other hand, entities with weaker research capabilities have more room to enhance resource utilization. Here, green innovation plays a more substantial role in increasing resource utilization rates, which in turn, has a more significant impact on sustainable development performance.

To verify the mechanism influencing resource utilization, the paper classified the full sample into high-tech and non-high-tech groups, based on their industry affiliations, and performed grouped tests. Specifically, drawing from existing research (Becker and Hall, 2013) [44], six industries from the Securities Regulatory Commission’s industry classification were identified as high-tech, i.e., petrochemicals, plastics, electronics, metals, machinery, and information technology. Companies within these sectors were assigned a value of 1; all others were assigned a value of 0.

Table 8. Improving resource utilization.

Variables	FINAt		ENVIRt		FINAt		ENVIRt
	Non-High-Tech Group	High-Tech Group	Non-High-Tech Group	High-Tech Group	Non-High-Tech Group	High-Tech Group	Non-High-Tech Group	High-Tech Group
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GI_Applyt−1	0.0048 ***	0.0005	0.0143 ***	0.0020
	(5.45)	(0.73)	(6.48)	(1.11)
GI_Approvet−1					0.0059 ***	0.0007	0.0161 ***	0.0019
					(5.56)	(0.94)	(6.04)	(0.91)
Sizet	0.0042 ***	0.0115 ***	0.0322 ***	0.0362 ***	0.0042 ***	0.0115 ***	0.0323 ***	0.0363 ***
	(5.02)	(13.25)	(20.71)	(18.03)	(5.04)	(13.18)	(20.90)	(18.04)
Levt	−0.0462 ***	−0.0594 ***	−0.0822 ***	−0.1033 ***	−0.0462 ***	−0.0594 ***	−0.0822 ***	−0.1032 ***
	(−11.34)	(−13.06)	(−10.73)	(−11.47)	(−11.33)	(−13.07)	(−10.75)	(−11.46)
Growtht	0.0114 ***	0.0176 ***	0.0000	0.0053 **	0.0115 ***	0.0176 ***	0.0001	0.0052 **
	(11.80)	(12.69)	(0.02)	(2.41)	(11.81)	(12.70)	(0.07)	(2.40)
Boardt	0.0098 **	−0.0031	0.0157	0.0070	0.0097 **	−0.0032	0.0157	0.0069
	(2.39)	(−0.63)	(1.40)	(0.55)	(2.37)	(−0.64)	(1.39)	(0.54)
Indept	−0.0058	−0.0398 ***	0.0392	0.0051	−0.0063	−0.0399 ***	0.0383	0.0048
	(−0.44)	(−2.77)	(1.12)	(0.13)	(−0.47)	(−2.78)	(1.09)	(0.12)
Dualt	−0.0005	−0.0004	−0.0012	0.0031	−0.0005	−0.0004	−0.0011	0.0032
	(−0.38)	(−0.27)	(−0.38)	(1.06)	(−0.36)	(−0.27)	(−0.37)	(1.07)
Losst	−0.1310 ***	−0.1330 ***	−0.1521 ***	−0.1344 ***	−0.1310 ***	−0.1330 ***	−0.1521 ***	−0.1343 ***
	(−47.35)	(−46.46)	(−46.91)	(−37.18)	(−47.31)	(−46.47)	(−46.91)	(−37.17)
TobinQt	0.0064 ***	0.0086 ***	0.0052 ***	0.0095 ***	0.0064 ***	0.0086 ***	0.0052 ***	0.0095 ***
	(7.61)	(12.98)	(5.62)	(9.71)	(7.62)	(12.96)	(5.66)	(9.73)
Top1t	0.0184 ***	0.0240 ***	0.0217 **	0.0143	0.0184 ***	0.0240 ***	0.0218 **	0.0141
	(4.54)	(4.99)	(2.28)	(1.21)	(4.55)	(4.98)	(2.28)	(1.19)
ISOt	−0.0038 ***	0.0034 ***	0.0357 ***	0.0385 ***	−0.0038 ***	0.0034 ***	0.0356 ***	0.0386 ***
	(−2.91)	(2.70)	(9.41)	(11.20)	(−2.95)	(2.68)	(9.36)	(11.21)
Big4t	0.0107 ***	−0.0017	0.0409 ***	0.0264 ***	0.0106 ***	−0.0017	0.0408 ***	0.0265 ***
	(4.11)	(−0.51)	(7.44)	(2.90)	(4.10)	(−0.50)	(7.41)	(2.92)
Opiniont	0.0373 ***	0.0347 ***	0.0323 ***	0.0256 ***	0.0374 ***	0.0348 ***	0.0325 ***	0.0257 ***
	(9.97)	(6.96)	(7.17)	(4.38)	(9.99)	(6.97)	(7.23)	(4.39)
Aget	−0.0112 ***	−0.0084 ***	−0.0153 ***	−0.0132 ***	−0.0112 ***	−0.0084 ***	−0.0154 ***	−0.0133 ***
	(−13.95)	(−9.73)	(−8.62)	(−7.18)	(−13.98)	(−9.72)	(−8.66)	(−7.23)
SOEt	−0.0013	−0.0041 **	0.0071 *	0.0176 ***	−0.0012	−0.0040 **	0.0072 *	0.0177 ***
	(−0.79)	(−2.13)	(1.66)	(3.48)	(−0.76)	(−2.11)	(1.70)	(3.50)
Constant	−0.0566 ***	−0.1769 ***	−0.4899 ***	−0.5236 ***	−0.0564 ***	−0.1763 ***	−0.4932 ***	−0.5258 ***
	(−2.70)	(−7.49)	(−11.35)	(−9.25)	(−2.70)	(−7.45)	(−11.43)	(−9.26)
YEAR Effect	YES	YES	YES	YES	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES	YES	YES	YES	YES
N	20,112	17,138	20,112	17,138	20,112	17,138	20,112	17,138
Pseudo.R2	0.5082	0.5363	0.3868	0.3349	0.5083	0.5363	0.3866	0.3349

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

presents the results of the analysis based on the influence mechanism of improving resource utilization. Columns (1), (3), (5), and (7) test the non-high-tech group and find that, regardless of whether FINA_t or ENVIR_t is used as the dependent variable, the coefficients for GI_Apply_t−1 and GI_Approve_t−1 are significantly positive at the 1% statistical level. This suggests a strong effect of green innovation in increasing resource utilization and enhancing sustainable development performance among non-high-tech companies. Conversely, columns (2), (4), (6), and (8) test the high-tech group, finding no significant coefficients for GI_Apply_t−1 and GI_Approve_t−1, with significant differences between model coefficients at the 1% level. For high-tech companies, the impact of green innovation on resource utilization and sustainable development performance is less pronounced.

Overall, these findings confirm that green innovation can boost sustainable development performance through the mechanism of improved resource utilization.

7. Heterogeneity Analyses

7.1. Urban Commercial Credit Environment

Finally, this paper ultimately examines whether the urban commercial credit environment impacts the effectiveness of green innovation in enhancing sustainable development performance among heavily polluting enterprises.

Firstly, a better commercial credit environment in urban areas enhances the extent of corporate signaling. When the urban commercial credit environment is stronger, the credit system is more robust, and vertical integration of the supply chain boosts corporate reputation, leading to a greater marginal impact on enhancing credit ratings through positive signals for good business performance. Secondly, a favorable urban commercial credit environment, as an informal institution, significantly curbs executives’ opportunistic behavior, allowing more resources to be allocated to expanding production scale, upgrading equipment, and improving resource utilization. Based on these analyses, this paper concludes that the urban commercial credit environment effectively strengthens the impact of green innovation on enhancing sustainable performance in heavily polluting enterprises.

To validate the aforementioned analysis, this paper divides the sample into two groups based on whether the urban commercial credit environment index of the prefecture-level city where the listed company is located is above or below the median level, creating a group with a better urban commercial credit environment and a group with a poorer urban commercial credit environment.

Table 9. Urban commercial credit environment.

Variables	FINAt		ENVIRt		FINAt		ENVIRt
	Better Credit Environment	Worse Credit Environment	Better Credit Environment	Worse Credit Environment	Better Credit Environment	Worse Credit Environment	Better Credit Environment	Worse Credit Environment
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GI_Applyt−1	0.0041 ***	0.0004	0.0089 ***	0.0016
	(5.58)	(0.61)	(5.19)	(0.77)
GI_Approvet−1					0.0052 ***	−0.0009	0.0115 ***	0.0008
					(6.39)	(−1.06)	(5.76)	(0.34)
Sizet	0.0046 ***	0.0095 ***	0.0320 ***	0.0371 ***	0.0046 ***	0.0097 ***	0.0319 ***	0.0372 ***
	(6.23)	(10.78)	(20.54)	(21.42)	(6.19)	(11.09)	(20.46)	(21.56)
Levt	−0.0481 ***	−0.0526 ***	−0.0780 ***	−0.1036 ***	−0.0479 ***	−0.0525 ***	−0.0776 ***	−0.1036 ***
	(−11.47)	(−12.91)	(−9.60)	(−13.45)	(−11.44)	(−12.88)	(−9.56)	(−13.43)
Growtht	0.0140 ***	0.0134 ***	0.0022	0.0019	0.0140 ***	0.0133 ***	0.0023	0.0018
	(10.18)	(13.54)	(1.12)	(1.12)	(10.18)	(13.48)	(1.14)	(1.11)
Boardt	0.0037	0.0042	0.0136	0.0155	0.0033	0.0043	0.0128	0.0156
	(0.81)	(1.02)	(1.23)	(1.35)	(0.73)	(1.06)	(1.16)	(1.36)
Indept	−0.0277 *	−0.0206	0.0040	0.0371	−0.0287 **	−0.0201	0.0019	0.0373
	(−1.91)	(−1.61)	(0.11)	(1.06)	(−1.98)	(−1.58)	(0.06)	(1.07)
Dualt	−0.0011	0.0006	−0.0000	0.0025	−0.0010	0.0005	0.0001	0.0025
	(−0.81)	(0.44)	(−0.01)	(0.84)	(−0.77)	(0.43)	(0.05)	(0.83)
Losst	−0.1371 ***	−0.1286 ***	−0.1469 ***	−0.1422 ***	−0.1371 ***	−0.1285 ***	−0.1468 ***	−0.1422 ***
	(−45.73)	(−48.84)	(−41.25)	(−43.69)	(−45.68)	(−48.76)	(−41.29)	(−43.48)
TobinQt	0.0066 ***	0.0084 ***	0.0070 ***	0.0089 ***	0.0066 ***	0.0084 ***	0.0070 ***	0.0089 ***
	(9.21)	(11.68)	(8.02)	(8.68)	(9.23)	(11.77)	(8.02)	(8.72)
Top1t	0.0207 ***	0.0196 ***	0.0238 **	0.0109	0.0204 ***	0.0196 ***	0.0232 **	0.0108
	(4.90)	(4.52)	(2.33)	(1.12)	(4.84)	(4.51)	(2.27)	(1.10)
ISOt	0.0000	−0.0007	0.0312 ***	0.0392 ***	−0.0000	−0.0006	0.0311 ***	0.0393 ***
	(0.00)	(−0.58)	(8.64)	(11.56)	(−0.03)	(−0.49)	(8.57)	(11.58)
Big4t	0.0034	0.0222 ***	0.0347 ***	0.0398 ***	0.0034	0.0223 ***	0.0347 ***	0.0398 ***
	(1.44)	(6.01)	(5.93)	(5.64)	(1.44)	(6.05)	(5.91)	(5.63)
Opiniont	0.0342 ***	0.0362 ***	0.0333 ***	0.0281 ***	0.0343 ***	0.0362 ***	0.0337 ***	0.0282 ***
	(7.22)	(9.19)	(5.81)	(6.19)	(7.23)	(9.19)	(5.86)	(6.20)
Aget	−0.0097 ***	−0.0105 ***	−0.0128 ***	−0.0157 ***	−0.0097 ***	−0.0106 ***	−0.0129 ***	−0.0158 ***
	(−12.62)	(−12.48)	(−7.26)	(−9.01)	(−12.65)	(−12.56)	(−7.30)	(−9.05)
SOEt	−0.0020	−0.0019	0.0155 ***	0.0075 *	−0.0018	−0.0018	0.0158 ***	0.0076 *
	(−1.19)	(−1.10)	(3.43)	(1.76)	(−1.09)	(−1.08)	(3.51)	(1.78)
Constant	−0.0620 ***	−0.1526 ***	−0.4809 ***	−0.5841 ***	−0.0599 ***	−0.1576 ***	−0.4759 ***	−0.5882 ***
	(−3.13)	(−6.87)	(−10.91)	(−12.02)	(−3.02)	(−7.14)	(−10.74)	(−12.13)
YEAR Effect	YES	YES	YES	YES	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES	YES	YES	YES	YES
N	16,772	20,478	16,772	20,478	16,772	20,478	16,772	20,478
Pseudo.R2	0.5235	0.5117	0.3838	0.3453	0.5239	0.5117	0.3844	0.3453

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

presents the results from the grouped tests based on the urban commercial credit environment index. Columns (1), (3), (5), and (7) tested the better credit environment group, revealing that regardless of using FINA_t or ENVIR_t as the dependent variable, the coefficients of GI_Apply_t−1 and GI_Approve_t−1 were significantly positive at the 1% level of significance. This indicates that a better urban commercial credit environment amplifies the positive impact of green innovation on enhancing sustainable performance. Columns (2), (4), (6), and (8) tested the worse credit environment group, showing that the coefficients of GI_Apply_t−1 and GI_Approve_t−1 were not significant when using FINA_t or ENVIR_t as the dependent variable. Thus, when a company does not implement digital transformation, the effect of green innovation on improving sustainable development performance is weaker. The results above indicate that a poorer urban commercial credit environment weakens the positive impact of green innovation on enhancing sustainable performance.

The above results show that when the urban commercial credit environment is better, green innovation has a more obvious effect on the improvement of sustainable development performance.

7.2. New Urban Environmental Protection Law

In the aforementioned analysis, green innovation enhances corporate sustainable development performance through two mechanisms: enhancing corporate reputation and improving resource efficiency. This paper further examines whether the enactment of the new Environmental Protection Law influences the effect of green innovation on enhancing corporate sustainable development performance. Following the enactment of the new Environmental Protection Law of China in 2014 [45], which aimed to strengthen the country’s environmental governance framework, there has been a significant increase in the number of green innovation projects undertaken by companies. The new law introduced more stringent emission standards, enhanced legal liability for violators, and promoted a shift towards sustainable development.

The revision of the new Environmental Protection Law strengthened the information disclosure mechanism, improved the accountability system, and intensified government regulation. Based on this, the paper argues that after the implementation of the new Environmental Protection Law, the mechanism for enhancing corporate reputation can play a more effective role due to the strengthened information disclosure mechanism, thereby strengthening the relationship between green innovation and corporate sustainable development performance. At the same time, with a more robust supervisory mechanism, the opportunistic behaviors of executives can be effectively curbed, allowing for more funds and efforts to be invested in expanding the production scale, improving equipment, and enhancing the resource utilization rate, which in turn strengthens the relationship between green innovation and corporate sustainable development performance. Based on the above analysis, this paper concludes that the implementation of the new Environmental Protection Law can effectively enhance the impact of green innovation on corporate sustainable development performance. To verify the above analysis, the paper divides the samples into groups of those that implemented the new Environmental Protection Law post-2015 and those that did not, conducting group testing.

Table 10. New urban Environmental Protection Law.

Variables	FINAt		ENVIRt		FINAt		ENVIRt
	Implement New Policy	Do not Implement New Policy	Implement New Policy	Do not Implement New Policy	Implement New Policy	Do not Implement New Policy	Implement New Policy	Do not Implement New Policy
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GI_Applyt−1	0.0031 ***	0.0010	0.0071 ***	0.0038
	(5.49)	(1.13)	(6.61)	(1.21)
GI_Approvet−1					0.0035 ***	0.0008	0.0085 ***	0.0041
					(5.57)	(0.87)	(6.64)	(1.12)
Sizet	0.0079 ***	0.0074 ***	0.0215 ***	0.0661 ***	0.0079 ***	0.0075 ***	0.0216 ***	0.0661 ***
	(11.95)	(7.77)	(23.63)	(23.96)	(12.04)	(7.81)	(23.71)	(23.93)
Levt	−0.0609 ***	−0.0416 ***	−0.0846 ***	−0.1377 ***	−0.0609 ***	−0.0416 ***	−0.0846 ***	−0.1378 ***
	(−17.24)	(−9.22)	(−17.77)	(−11.42)	(−17.24)	(−9.23)	(−17.75)	(−11.43)
Growtht	0.0135 ***	0.0130 ***	0.0013	0.0025	0.0135 ***	0.0130 ***	0.0013	0.0025
	(13.98)	(9.09)	(0.95)	(0.96)	(13.99)	(9.09)	(1.00)	(0.97)
Boardt	0.0045	0.0044	−0.0012	0.0242	0.0044	0.0044	−0.0016	0.0242
	(1.19)	(0.99)	(−0.18)	(1.51)	(1.14)	(0.99)	(−0.25)	(1.51)
Indept	−0.0212 *	−0.0314 **	−0.0254	0.0674	−0.0217 *	−0.0315 **	−0.0267	0.0670
	(−1.75)	(−2.33)	(−1.30)	(1.34)	(−1.80)	(−2.34)	(−1.36)	(1.33)
Dualt	0.0008	−0.0017	0.0021	−0.0018	0.0008	−0.0017	0.0021	−0.0018
	(0.70)	(−1.15)	(1.26)	(−0.39)	(0.71)	(−1.14)	(1.28)	(−0.39)
Losst	−0.1390 ***	−0.1095 ***	−0.1431 ***	−0.1527 ***	−0.1391 ***	−0.1095 ***	−0.1432 ***	−0.1527 ***
	(−59.57)	(−35.16)	(−60.68)	(−27.44)	(−59.59)	(−35.16)	(−60.75)	(−27.43)
TobinQt	0.0066 ***	0.0087 ***	0.0041 ***	0.0175 ***	0.0066 ***	0.0087 ***	0.0041 ***	0.0176 ***
	(12.40)	(8.35)	(7.14)	(9.74)	(12.43)	(8.37)	(7.20)	(9.74)
Top1t	0.0194 ***	0.0226 ***	0.0257 ***	0.0216	0.0193 ***	0.0226 ***	0.0253 ***	0.0215
	(5.68)	(4.65)	(5.07)	(1.28)	(5.64)	(4.64)	(4.99)	(1.27)
ISOt	−0.0009	−0.0002	0.0165 ***	0.0844 ***	−0.0009	−0.0002	0.0165 ***	0.0844 ***
	(−0.91)	(−0.15)	(8.55)	(13.07)	(−0.90)	(−0.12)	(8.52)	(13.06)
Big4t	0.0117 ***	−0.0001	0.0241 ***	0.0418 ***	0.0117 ***	−0.0001	0.0240 ***	0.0420 ***
	(4.79)	(−0.05)	(7.28)	(3.58)	(4.77)	(−0.02)	(7.23)	(3.60)
Opiniont	0.0385 ***	0.0234 ***	0.0268 ***	0.0306 ***	0.0386 ***	0.0234 ***	0.0270 ***	0.0307 ***
	(11.18)	(4.53)	(7.95)	(3.84)	(11.21)	(4.53)	(8.02)	(3.84)
Aget	−0.0120 ***	−0.0048 ***	−0.0150 ***	−0.0001	−0.0121 ***	−0.0048 ***	−0.0151 ***	−0.0001
	(−18.24)	(−4.97)	(−15.20)	(−0.04)	(−18.33)	(−4.97)	(−15.33)	(−0.04)
SOEt	−0.0026 *	−0.0056 ***	0.0023	0.0128 **	−0.0025 *	−0.0056 ***	0.0025	0.0129 **
	(−1.90)	(−3.20)	(0.96)	(2.04)	(−1.82)	(−3.20)	(1.08)	(2.05)
Constant	−0.1242 ***	−0.1169 ***	−0.2340 ***	−1.2773 ***	−0.1245 ***	−0.1179 ***	−0.2334 ***	−1.2784 ***
	(−6.89)	(−5.15)	(−8.99)	(−18.55)	(−6.90)	(−5.19)	(−8.97)	(−18.53)
YEAR Effect	YES	YES	YES	YES	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES	YES	YES	YES	YES
N	25,273	11,977	25,273	11,977	25,273	11,977	25,273	11,977
Pseudo.R2	0.5553	0.4121	0.3984	0.3670	0.5552	0.4120	0.3986	0.3669

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

shows the results of the group testing based on whether the new Environmental Protection Law was enacted. Columns (1), (3), (5), and (7) tested the group with the law implemented, revealing that using either FINA_t or ENVIR_t as the dependent variable, the coefficients of GI_Apply_t−1 and GI_Approve_t−1 are significantly positive at the 1% statistical level. This indicates a stronger impact of green innovation on enhancing sustainable development performance when the new Environmental Protection Law is implemented. Columns (2), (4), (6), and (8) tested the group without the law implemented, finding that using either FINA_t or ENVIR_t as the dependent variable, the coefficients of GI_Apply_t−1 and GI_Approve_t−1 are not significant. This means that the impact of green innovation on enhancing sustainable development performance is weaker when the new Environmental Protection Law is not implemented.

The results indicate that the effect of green innovation on improving sustainable development performance is more pronounced after the implementation of the new Environmental Protection Law.

7.3. Urban Digital Transformation Processes

In the previous tests, this paper finds that green innovation can enhance corporate sustainable development performance through the mechanism of improving the resource utilization rate. In this part, the paper will focus on analyzing whether digital transformation strategies influence the effect of green innovation on enhancing corporate sustainable development performance.

Previous research has shown that after implementing a digital transformation strategy, companies can effectively enhance their production efficiency [45]. Therefore, this paper argues that when implementing a digital transformation strategy, companies will be better able to leverage the enhancement effect of green innovation on resource utilization efficiency, thereby reinforcing the role of green innovation in improving sustainable development performance. In conclusion, it is believed that the impact of green innovation on enhancing the sustainable development performance of companies will be effectively strengthened when implementing a digital transformation strategy. To verify the above analysis, the samples were divided into a digital transformation group and a non-digital transformation group, based on whether the management discussion and analysis section contains digitization-related roots, and a group test was conducted. The method of obtaining the digitization-related roots was to establish a corporate digital transformation dictionary and then extract digitization-related roots from the management discussion and analysis section of annual reports using the Python-3.7.0 text extraction function.

Table 11. Urban digital transformation processes.

Variables	FINAt		ENVIRt		FINAt		ENVIRt
	Implement Digital Transformation	Do not Implement Digital Transformation	Implement Digital Transformation	Do not Implement Digital Transformation	Implement Digital Transformation	Do not Implement Digital Transformation	Implement Digital Transformation	Do not Implement Digital Transformation
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GI_Applyt−1	0.0047 ***	0.0004	0.0147 ***	0.0015
	(5.38)	(0.80)	(6.15)	(0.99)
GI_Approvet−1					0.0053 ***	0.0003	0.0153 ***	0.0023
					(5.44)	(0.55)	(6.06)	(1.29)
Sizet	0.0020 **	0.0095 ***	0.0284 ***	0.0374 ***	0.0021 **	0.0096 ***	0.0287 ***	0.0374 ***
	(2.24)	(14.59)	(15.86)	(26.80)	(2.29)	(14.65)	(16.20)	(26.76)
Levt	−0.0390 ***	−0.0564 ***	−0.0801 ***	−0.0990 ***	−0.0393 ***	−0.0564 ***	−0.0810 ***	−0.0990 ***
	(−7.68)	(−16.90)	(−8.68)	(−15.60)	(−7.73)	(−16.90)	(−8.79)	(−15.61)
Growtht	0.0150 ***	0.0135 ***	0.0045	0.0012	0.0150 ***	0.0135 ***	0.0046	0.0013
	(8.50)	(14.53)	(1.58)	(0.86)	(8.51)	(14.54)	(1.61)	(0.88)
Boardt	0.0089 *	0.0035	0.0110	0.0151 *	0.0088 *	0.0035	0.0108	0.0150 *
	(1.69)	(1.02)	(0.86)	(1.68)	(1.68)	(1.02)	(0.84)	(1.66)
Indept	−0.0142	−0.0231 **	−0.0012	0.0311	−0.0142	−0.0232 **	−0.0014	0.0307
	(−0.89)	(−2.15)	(−0.03)	(1.11)	(−0.90)	(−2.15)	(−0.04)	(1.09)
Dualt	−0.0015	−0.0005	−0.0022	0.0017	−0.0015	−0.0004	−0.0022	0.0017
	(−0.96)	(−0.43)	(−0.65)	(0.72)	(−0.96)	(−0.42)	(−0.65)	(0.73)
Losst	−0.1367 ***	−0.1311 ***	−0.1212 ***	−0.1508 ***	−0.1369 ***	−0.1311 ***	−0.1218 ***	−0.1508 ***
	(−34.01)	(−59.20)	(−22.67)	(−57.25)	(−34.06)	(−59.20)	(−22.79)	(−57.29)
TobinQt	0.0084 ***	0.0076 ***	0.0050 ***	0.0094 ***	0.0084 ***	0.0076 ***	0.0051 ***	0.0094 ***
	(10.97)	(13.16)	(4.62)	(12.07)	(11.03)	(13.17)	(4.70)	(12.06)
Top1t	0.0122 ***	0.0282 ***	−0.0050	0.0319 ***	0.0119 **	0.0281 ***	−0.0061	0.0318 ***
	(2.62)	(7.88)	(−0.52)	(3.69)	(2.55)	(7.88)	(−0.62)	(3.68)
ISOt	−0.0030 **	0.0008	0.0306 ***	0.0377 ***	−0.0030 **	0.0008	0.0307 ***	0.0376 ***
	(−2.06)	(0.81)	(7.67)	(13.12)	(−2.07)	(0.82)	(7.63)	(13.10)
Big4t	0.0172 ***	−0.0015	0.0377 ***	0.0312 ***	0.0170 ***	−0.0015	0.0374 ***	0.0312 ***
	(6.10)	(−0.62)	(6.70)	(4.83)	(6.08)	(−0.61)	(6.64)	(4.83)
Opiniont	0.0186 ***	0.0378 ***	0.0549 ***	0.0236 ***	0.0187 ***	0.0378 ***	0.0551 ***	0.0236 ***
	(2.91)	(11.62)	(7.47)	(6.05)	(2.92)	(11.63)	(7.55)	(6.06)
Aget	−0.0088 ***	−0.0097 ***	−0.0113 ***	−0.0149 ***	−0.0088 ***	−0.0097 ***	−0.0112 ***	−0.0149 ***
	(−9.99)	(−14.15)	(−5.73)	(−10.57)	(−9.97)	(−14.17)	(−5.69)	(−10.58)
SOEt	−0.0053 ***	−0.0020	0.0122 ***	0.0108 ***	−0.0051 **	−0.0020	0.0126 ***	0.0109 ***
	(−2.62)	(−1.51)	(2.60)	(3.08)	(−2.55)	(−1.50)	(2.67)	(3.09)
Constant	−0.0039	−0.1547 ***	−0.4066 ***	−0.5821 ***	−0.0046	−0.1553 ***	−0.4126 ***	−0.5803 ***
	(−0.16)	(−9.39)	(−7.98)	(−15.17)	(−0.19)	(−9.42)	(−8.11)	(−15.10)
YEAR Effect	YES	YES	YES	YES	YES	YES	YES	YES
INDUSTRY Effect	YES	YES	YES	YES	YES	YES	YES	YES
N	8852	28,398	8852	28,398	8852	28,398	8852	28,398
Pseudo.R2	0.4805	0.5266	0.3165	0.3772	0.4804	0.5266	0.3156	0.3772

***, **, and * indicate 1%, 5%, and 10% significance, respectively.

shows the results of the group tests based on whether or not digital transformation was implemented. Columns (1), (3), (5), and (7) tested the digital transformation group, revealing that regardless of using FINA_t or ENVIR_t as the dependent variable, the coefficients of GI_Apply_t−1 and GI_Approve_t−1 were significantly positive at the 1% level of significance. This indicates that when a company undergoes digital transformation, the impact of green innovation on enhancing sustainable development performance is stronger. Columns (2), (4), (6), and (8) tested the non-transformation group, showing that the coefficients of GI_Apply_t−1 and GI_Approve_t−1 were not significant when using FINA_t or ENVIR_t as the dependent variable. Thus, when a company does not implement digital transformation, the effect of green innovation on improving sustainable development performance is weaker.

The results above indicate that the enhancing effect of green innovation on sustainable development performance becomes more apparent after the implementation of digital transformation.

8. Research Conclusions and Research Suggestions

8.1. Research Conclusions

In recent years, China has stepped up its crackdown on corporate environmental pollution while vigorously promoting green innovation and sustainable development. Against this backdrop, exploring the impact of green innovation on the sustainable development of enterprises is of great theoretical significance and practical value. This paper selects companies listed on China’s Shanghai and Shenzhen A-share markets as research subjects and empirically tests the impact of green innovation on the sustainable development performance of enterprises, with the following main conclusions:

Firstly, green innovation plays a positive role in promoting the sustainable development performance of enterprises. This conclusion is still valid after conducting robustness tests such as changing the measurement method of core indicators, considering the length of lag periods, and excluding other temporal shocks. Secondly, the results of the mechanism test show that green innovation mainly improves the sustainable development performance of enterprises through two mechanisms, i.e., enhancing corporate reputation and improving resource utilization efficiency. Thirdly, the promotion effect of green innovation on sustainable development performance is more significant when the urban commercial credit environment is better, after the issuance of the new Environmental Protection Law, and during the implementation of digital transformation processes.

8.2. Research Suggestions

Based on the above conclusions, this paper mainly offers the following suggestions:

Firstly, for government departments, it is necessary to fully recognize the important role of green innovation in enterprises and to formulate related policies to guide the input and transformation of green innovation research and development, thereby enhancing the sustainable development performance of enterprises. In addition, the government should continue to deepen the process of marketization, strengthen the role and position of the market mechanism in socio-economic development, and continuously improve the level of economic development, thus enhancing the sustainable development performance of enterprises through these two dimensions.

Secondly, for listed companies, to effectively enhance their own sustainable development performance, they should take green innovation as the orientation, continuously strengthen the research and development investment in green invention patent innovation, and continuously improve the level of green invention patent innovation, thereby driving the enterprise into a virtuous cycle of development. In addition, enterprises should pay attention to protecting and enhancing their reputations in the production and operation process and engage in partnerships with academic institutions and industry peers to share knowledge and resource based on case studies.

Thirdly, for investors and other stakeholders, they should understand that enterprises can greatly improve their sustainable development capabilities through green innovation. Based on this information, stakeholders can make more accurate investment decisions. Stakeholders can actively pay attention to enterprises that have made progress in green innovation, as these enterprises exhibit better sustainable development prospects and business competitiveness. At the same time, stakeholders can also evaluate and monitor the environmental performance of enterprises, choosing to invest in those enterprises that actively fulfill their social responsibilities, displaying good reputations and resource utilization efficiency.

Finally, for urban planners, it is crucial to integrate green innovation strategies into urban development plans to foster sustainable growth. This can be achieved by creating incentives for green technology adoption, such as tax breaks or subsidies, and by designing urban infrastructure that supports environmentally friendly practices. Corporate managers, especially in less technologically advanced cities, should focus on building collaborative networks with research institutions and other companies to overcome barriers to green innovation. Strategies include investing in research and development, participating in industry clusters, and leveraging digital technologies to enhance resource efficiency and reduce environmental impact.