Environmental Information Disclosure and Firms’ Green Total Factor Productivity: Evidence from New Ambient Air Quality Standards in China

Abstract

Green total factor productivity (GTFP) is a key factor for achieving sustainable development and enhancing economic competitiveness. Environmental information disclosure plays a significant role in improving the corporate GTFP. Using A-share-listed company data in China from 2009 to 2019, this study employs the Ambient Air Quality Standards (GB3095-2012) promulgated by China in 2012 as a quasi-natural experiment. This study employs difference-in-differences (DID) to examine the impact of environmental information disclosure on corporate GTFP. The findings reveal that on average, environmental information disclosure positively affects firms’ GTFP. Mechanism analyses show that environmental information disclosure promotes GTFP by increasing total corporate costs, alleviating corporate financing constraints, and promoting green technological innovation. Environmental information disclosure mainly affects non-state-owned smaller, and young enterprises. These conclusions provide theoretical support and empirical evidence for governments to leverage environmental information disclosure to promote green and sustainable development, thereby achieving high-quality economic growth.

1. Introduction

Since the beginning of its reform and opening up, China has made tremendous progress in its economic development. However, China has faced severe environmental pollution owing to economic development. China now urgently needs to transition from high emissions and pollution to green development. GTFP is an important indicator of green development. It considers both the environmental and economic benefits. Hence, increasing the contribution of GTFP to economic growth has become a pressing concern for China.

Environmental information disclosure has emerged as an essential regulatory tool to promote green development. China has been progressively increasing its level of disclosure of environmental information, which enhances transparency and pressures firms to improve their environmental performance [1,2]. In 2012, China’s Ministry of Environmental Protection (MEP) issued the Ambient Air Quality Standards (GB3095-2012) [3] (hereafter referred to as AAQSs). This air pollution control policy aims to enhance environmental information transparency and promote the air pollution control process. The AAQSs provide an ideal context to examine the impact of environmental information disclosure on corporate GTFP.

This study employs the implementation of the AAQSs as a quasi-natural experiment and utilizes panel data from A-share-listed company data in China for the period 2009–2019. It applies the DID method to examine the impact of environmental information disclosure on firms’ GTFP and investigates the underlying mechanisms. Furthermore, it analyzes the heterogeneous effects of environmental information disclosure on the GTFP across various firm characteristics, including ownership, size, and age.

Compared to the existing literature, the potential marginal contributions of this study are as follows. First, it expands research on the impact of environmental regulations on firms’ GTFP from the perspective of environmental information disclosure. Offering new insights for enterprises to achieve a win–win situation in economic growth and environmental protection. Second, most existing research on the GTFP has been conducted at the provincial, municipal, and industrial levels, with relatively scarce literature on the impact on firms’ GTFP. Based on data from listed companies, this study employed two distinct measurement methods to assess firms’ GTFP, thereby studying the impact of environmental information disclosure. Third, this study enriches the micro-mechanisms through which environmental information disclosure affects the GTFP by examining three aspects: firms’ total costs, financing constraints, and green technological innovation. This finding provides further empirical evidence for the formulation of environmental information disclosure policies, thereby refining the national environmental regulatory policy framework.

The remainder of this paper is organized as follows. Section 2 summarizes the pertinent literature on environmental information disclosure and GTFP. Section 3 provides the background of the AAQSs and analyzes the impact of environmental information disclosure on the corporate GTFP and its mechanisms. Section 4 introduces the research design, including the data and empirical strategy. The main results and mechanism analyses are presented in Section 5. Section 6 discusses the policy implications, GTFP welfare effects and limitations, and future research. The final section presents the conclusions of this study.

2. Literature Review

Current research on environmental information disclosure primarily focuses on two aspects: the factors influencing environmental information disclosure and its economic effects. Factors that influence environmental information disclosure are multifaceted. The social performance theory proposed by Wartick and Cochran (1985) [4] suggests that companies have a social responsibility to disclose environmental information to the public, and they can profit by doing so. That is, companies engage in environmental information disclosure to fulfill their social responsibilities and maintain their competitive edge. The factors that affect the quality or level of environmental information disclosure can be divided into two parts: external and internal. External factors include government regulations [5,6], media attention [7], and culture and institutions [8,9]. Internal factors include internal controls [10,11], firm governance [12,13] and executive characteristics [14,15]. From an economic perspective, studies have shown that environmental information disclosure can affect firms’ financial performance [16,17], financing costs [18,19], business value [20], environmental pollution [21,22], emission reduction [23], energy efficiency [24,25], green innovation [26], and firm exports [27].

Concurrently, the GTFP has garnered widespread attention in the academic community. Recent research has focused on the GTFP measurement and its influencing factors. Pittman (1983) [28] applied data envelopment analysis (DEA) to consider undesirable outputs for the first time. Chung et al. (1997) [29] further expanded on this by forming a directional distance function and proposing a modified ML (Malmquist–Luenberger) index, which was more compatible with environmental concepts. Researchers have extensively investigated the factors influencing GTFP. Some scholars have found that the Belt and Road Initiative [30], green-credit policy [31], low-carbon policy [32,33], and carbon emissions trading [34,35,36] all have a significant positive impact on China’s GTFP. Some scholars have also considered the relationship between foreign direct investment (FDI) [37,38], human capital structure [39], green innovation [40], digital entrepreneurial activity [41], digital transformation [42], and GTFP.

As a significant factor affecting corporate GTFP, there is no consensus on the relationship between environmental regulation and GTFP. The research conclusions can generally be categorized into three types, including the following: environmental regulation can enhance the GTFP. This perspective is primarily based on the innovation compensation effect, which suggests that appropriate environmental regulations can stimulate innovation. This not only compensates for the cost losses incurred by complying with environmental regulations, but also improves a firm’s competitiveness, thereby enhancing GTFP [43]. Subsequently, scholars have used inter-provincial, urban, and industrial data to confirm that a certain level of environmental regulation can stimulate innovation and increase the GTFP [44,45,46,47,48,49]. Environmental regulations hinder GTFP improvement. This view is primarily based on the compliance cost hypothesis, which posits that environmental supervision increases production costs, crowds out research and development (R&D) investment [50], and may affect ecological efficiency [48], energy efficiency [51], and GTFP [52,53]. The third type suggests a U-shaped or inverted U-shaped relationship and other non-linear relationships between environmental regulation and GTFP. Some scholars believe that environmental regulations have a U-shaped effect on GTFP at the provincial and industrial levels [54], and only when the intensity of regulation exceeds the inflection point can the innovation compensation effect occur [45]. Other scholars argue that the impact of environmental regulations on GTFP is an inverted U-shape [55].

Despite the growing body of research, few studies examine how environmental information disclosure specifically influences firms’ GTFP at the micro level. This study addresses this gap by employing firm-level data. Specifically, it investigates whether environmental information disclosure influences GTFP through increased compliance costs, alleviated financing constraints, and enhanced green innovation.

3. Background and Theoretical Hypotheses

3.1. Background

The evolution of the air quality standards policy in China can be traced to 1982, when the initial Environmental Air Quality Standards were established. These standards were first revised in 1996 and then partially modified in 2000. However, these earlier policy documents primarily relied on the Air Pollution Index (API) to evaluate air quality conditions, which includes indicators such as SO₂, NO₂, and PM10. Prior to the implementation of the new air quality standards policy, owing to the lack of transparency in environmental data, local governments and enterprises had limited incentives for pollution reduction and environmental issues remained unresolved. In this context, in February and May 2012, the MEP issued AAQSs. AAQSs have imposed stricter emission limits on a broader range of pollutants, replacing the API with a new air quality index (AQI). This revised AQI incorporated measurements of PM2.5, PM10, SO₂, NO₂, CO, and O₃, offering a more comprehensive overview of air contaminants [56].

On 21 May 2012, the MEP announced the first-phase implementation plan for monitoring new air quality standards. This plan mandated that by the end of October 2012, 74 pilot cities (including key areas such as the Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, as well as municipalities directly under the Central Government, provincial capitals, and cities with independent planning status, totaling 74 pilot cities) would adopt new standards. Pilot city governments release environmental air quality data in real time to the public, higher-level governments, and social media, which increases the degree of environmental information disclosure. At the same time, it reduces and avoids environmental information asymmetry between central and local governments.

The implementation of the AAQSs significantly affects environmental management. In some cities, such as Beijing and Tianjin, AQI rankings and scores are considered when evaluating local officials. Officials are evaluated based on their achievement of appraisal goals, resulting in rewards, penalties, interviews, or accountability measures. The new standard prohibits local governments from directly or indirectly influencing local air quality while ensuring the transparency of air quality data. Consequently, local authorities are taking concrete steps to enhance environmental information disclosure. Furthermore, the implementation of this new standard has an impact on corporate behavior. They can engage in green innovation or optimize production processes, which will affect firms’ GTFP.

While the AAQSs provide a framework for monitoring and reporting air quality, the actual environmental disclosure by industries is closely linked to their compliance with specific emission limits. Industries and enterprises are required to adhere to strict emission limits set by the government to ensure that their operations do not exceed permissible levels of pollutants. These emission limits are a critical component of the environmental regulations and are directly tied to the overall air quality goals outlined in the AAQSs. The implementation of the AAQSs has significantly impacted environmental management and corporate behavior. The increased transparency in environmental information disclosure, driven by the AAQSs, has made it more challenging for enterprises to conceal their emissions. As a result, they are compelled to invest in cleaner technologies and more efficient production processes to meet the regulatory requirements and improve their environmental performance.

3.2. Theoretical Hypotheses

This study primarily discusses how environmental information disclosure affects firms’ GTFP from three aspects based on the AAQSs: cost effects, financing constraints, and the impact on green innovation.

The AAQSs and their implementation plan explicitly stipulate detailed requirements for the disclosure of atmospheric quality information: First, starting from 2013, the monitoring data in pilot cities are managed by nationally established monitoring stations and are made public to the entire society. Local governments were prohibited from interfering with the detection results, thereby enhancing data transparency. Second, the AAQSs are supervised and implemented by environmental protection administrative authorities at all levels, ensuring the continuity and effectiveness of the monitoring data. These measures increase environmental pressure on enterprises and incorporate the costs associated with clean production practices into the operational costs of enterprises, thus increasing their compliance costs. Consequently, AAQSs are expected to increase total costs for enterprises and affect their GTFP in several ways. First, considering the long term, the rise in total costs will force enterprises to optimize their resource allocation, directing resources from highly polluting and inefficient projects towards those that are less polluting and more efficient, thereby enhancing the GTFP. Second, the increase in total costs due to environmental information disclosure compels enterprises to engage in green technological innovation. It not only makes their production processes greener and allows for environmental regulatory requirements to be met, but also improves their GTFP. Finally, enterprises may seek to enhance their efficiency to offset the profit reduction caused by rising total expenses. This can be achieved through strategies such as streamlining production methods and minimizing resource wastage, which, in turn, can lead to an increase in their GTFP. Based on this, the following hypothesis is proposed:

H1: 

Environmental information disclosure can affect firms’ GTFP by increasing total costs.

Companies rely heavily on external funding sources. During the fundraising process, businesses often present favorable information to potential investors to enhance their confidence, which intensifies the information gap between investors and companies. For long-term and highly unpredictable innovative projects, managers frequently hesitate to make significant R&D investments because of potential risks. To mitigate the lending risks associated with information asymmetry, financial institutions and other external investors may limit loan amounts and raise interest rates, causing companies to halt or abandon innovation. Disclosing environmental information can decrease agents’ information advantages and curb opportunistic behavior through management. Following the implementation of the new air quality standards policy, more (negative) information about companies became publicly available. This enables faster incorporation of relevant company information into stock prices, thus addressing the information imbalance between market investors and companies, lowering investment risks for stakeholders, and consequently easing firms’ financing constraints, reducing the risks associated with green technological innovation, and improving firms’ GTFP.

This new air quality standards policy enhances the disclosure of firms’ environmental data by adopting a comprehensive AQI evaluation system and enforcing stricter regulations. Consequently, investors have access to more relevant information, reducing information costs and the asymmetry between investors and companies. This alleviates firms’ financing constraints and promotes the development of green technologies, thereby improving their GTFP. Based on this, we propose the following hypothesis:

H2: 

Environmental information disclosure can affect firms’ GTFP by alleviating their financing constraints.

Theoretically, the Porter Hypothesis posits that appropriate environmental regulations can stimulate technological innovation in firms [43]. The new air quality standards policy provides enterprises with comprehensive and timely market information, which contributes to reducing innovation costs and future uncertainties for businesses, thereby fostering motivation for green technological innovation. After increasing the transparency of environmental information, local governments comprehensively carry out environmental governance and constrain corporate pollution activities through subsidies and penalties, thereby passing the pressure of environmental governance on enterprises. Under the pressure of environmental regulation, enterprises will increase their investment in green technological innovation and enhance their capacity for green technological innovation to enhance firms’ GTFP. Meanwhile, environmental information disclosure reduces information asymmetry, enabling the market to assess firms’ environmental performance more accurately. Investors are more willing to provide financial support to firms with good environmental performance, and consumers are more inclined to choose green products. This market incentive encourages firms to increase their investments in green technological innovation to enhance their market competitiveness. Furthermore, the disclosure of negative environmental information can impose an additional penalty on enterprises violating environmental laws, beyond legal sanctions, increasing the impetus to innovate and advance green technology and compelling enterprises to reduce pollution costs and avoid penalties through technological innovation. Consequently, the implementation of the new air quality standards policy may prompt enterprises to accelerate investment in green technology R&D, reduce pollution emissions in the production process, and ultimately enhance their GTFP. Based on this, we propose the following hypothesis:

H3: 

Environmental information disclosure can affect firms’ GTFP by enhancing their level of green technological innovation.

4. Materials and Methods

4.1. Data

This study uses data from A-share-listed companies in China from 2009 to 2019 obtained from the China Stock Market and Accounting Research (CSMAR) as the primary dataset. In accordance with previous literature, the following samples were excluded: (1) annual samples of financial companies, (2) annual samples of companies in abnormal conditions, such as those designated with the Special Treatment (ST) label, and (3) annual samples of listed companies with severe missing data. Furthermore, to ensure the reliability of the sample, the following data treatments were applied: samples with missing values for certain variable indicators (such as operating income, total assets, and net value of fixed assets) were removed, as well as samples of companies with fewer than 10 employees.

Data on annual sulfur dioxide emissions for each listed company were manually compiled from publicly available documents, including the annual reports of listed companies, corporate social responsibility reports, and information from the official websites of listed companies. The city-level control variables are primarily sourced from the China Urban Statistical Yearbook and various city-specific statistical yearbooks. In addition, to address potential heteroskedasticity and dependency on the regression model setup, the natural logarithm of some continuous variables was calculated. The computation of the GTFP in this study was conducted using custom MATLAB programming, while regression analysis was performed using Stata18.

4.1.1. Dependent Variable: GTFP

The measurement of economic performance has consistently been a central focus in economic research, with the majority of literature utilizing Total Factor Productivity (TFP) as a metric. However, traditional TFP solely considers the input constraints of production factors, such as labor and capital, without accounting for resource and environmental constraints, thereby potentially distorting the evaluation of changes in social welfare and economic performance. Consequently, the Malmquist Productivity Index based on the traditional distance function cannot account for undesired outputs, whereas the Malmquist–Luenberger Productivity index based on the directional distance function can incorporate undesired outputs into calculations. In Data Envelopment Analysis (DEA), when there is excessive input or insufficient output (i.e., non-zero slacks), the radial DEA efficiency measurement overestimates efficiency, whereas the angular DEA efficiency measurement neglects one aspect of either input or output. Tone and Tsutsui (2010) [57] initially proposed a non-radial and non-angular efficiency measurement model based on slack.

In light of this, this study employs the annual SO₂ emissions of industrial enterprises listed companies as undesired outputs and calculates the Malmquist–Luenberger Productivity index based on the SBM (Slack-Based Measure) directional distance function using MATLAB R2019a. Additionally, because this study examines panel data across years, it opts for calculations under variable returns to scale. It is noteworthy that the Malmquist–Luenberger Productivity index reflects the growth rate of the GTFP, representing the change relative to the GTFP of the previous year, and cannot be utilized directly in regressions. Therefore, this study establishes the base period value at 1 and multiplies it by the calculated ML Productivity Index to obtain the actual values of GTFP, ultimately providing the adjusted actual values of GTFP for the years 2009–2019. Additionally, to address the potential bias in the results due to measurement errors, this study further employs GTFP calculated by the Slacks-Based Measure–Generalized Malmquist–Luenberger (SBM-GML) to replace the GTFP as a robustness test.

4.1.2. Independent Variable: Treat × Post

The independent variable is a binary indicator (${\mathit{Treat}}_c{\times \mathit{Post}}_t$) representing whether the city implements the AAQSs. The value of ${\mathit{Treat}}_c{\times \mathit{Post}}_t$ equals 1 if a city implemented the AAQSs in year $t$, and 0 otherwise.

4.1.3. Mechanism Variables

Cost: Referring to Chen et al. (2021) [38], we use the operating cost to measure firms’ total costs.

Financial Constraints: Referring to Mu et al. (2023) [58], we proxy corporate financial constraints using the SA, KZ, and WW indices. The higher the value (or the greater the absolute value) of these indices, the more severe are the financing constraints faced by the firm.

SA: We referred to Hadlock and Pierce (2010) [59] to calculate the SA index with the following model:

$${\mathit{SA}}_{\mathit{it}}=-0.737\times {\mathit{Size}}_{it}+0.043\times {\mathit{Size}}_{it}^2-0.040\times Ag{e}_{it} ,$$

(1)

Here, ${\mathit{Size}}_{it}$ is the natural logarithm of total assets, and $Ag{e}_{it}$ equals the number of years since the firm has been founded. A firm with a higher absolute value in the SA index could be given to be more financially constrained.

KZ: We employ the KZ index as another measurement of corporate financial constraints. We referred to Ding et al. (2022) [60] to calculate the KZ index with the following model:

$$\begin{gathered} K{Z}_{\mathit{it}}=-1.002\times {\displaystyle \frac{C{F}_{it}}{Asset{s}_{it-1}}}+0.283\times Tobi{n}_{it}+3.139\times 0.040\times {\displaystyle \frac{Deb{t}_{it}}{Asset{s}_{it}}}+ \\ 39.368\times {\displaystyle \frac{DI{V}_{it}}{Asset{s}_{it-1}}}-1.315\times {\displaystyle \frac{Cas{h}_{it}}{Asset{s}_{it-1}}}, \end{gathered}$$

(2)

Here, $C{F}_{it}$ represents cashflow, $Asset{s}_{it}$ represents total assets, $Tobi{n}_{it}$ denotes Tobin’s Q, $Deb{t}_{it}$ represents total debt, $DI{V}_{it}$ denotes dividends, $Cas{h}_{it}$ denotes cash and cash equivalents. If a firm has a higher value in the KZ index, the firm could be said to be more financially constrained.

WW: We employ the WW index as another measurement of firm’s financial constraints. We referred to Whited and Wu (2006) [61] calculate the WW index with the following model:

$$\begin{gathered} W{W}_{\mathit{it}}=-0.091\times CF{A}_{it}-0.062\times DivPa{y}_{it}+0.021\times Le{v}_{it}- \\ 0.044\times {\mathit{Size}}_{it}+0.102\times {\mathit{ISG}}_{it}-0.035\times {\mathrm{SG}}_{it}, \end{gathered}$$

(3)

Here, $CF{A}_{it}$ is the ratio of cashflow to total assets, $DivPa{y}_{it}$ is a dummy variable for cash dividend payment, which equals 1 if the dividend is positive, and 0 otherwise. $Le{v}_{it}$ is the ratio of long-term debt to total assets, and ${\mathit{Size}}_{it}$ is the natural logarithm of total assets. ${\mathit{ISG}}_{it}$ demotes the industrial sales growth. ${\mathit{SG}}_{it}$ refers to the sales growth. If a firm has a higher value in the WW index, the firm could be said to be more financially constrained.

Green technological innovation: referring to Lu et al. (2022) [62], we employ the number of green utility model patent grants plus one as a proxy for green technological innovation.

4.1.4. Control Variables

This study employed control variables at the firm, industry, and city levels. The firm-level control variables include the following: the type of enterprise ownership (soe) is categorized based on the nature of equity ownership, with state-owned enterprises assigned a value of 1 and others assigned 0; the logarithm of the number of employees (lnlabor); fixed assets per capita (capital); the logarithm of operating income (lnincome); return on assets (roa); the debt-to-asset ratio (lev), calculated as the total liabilities of the enterprise divided by total assets; government subsidies (gov), represented by the logarithm of the total amount of funding obtained from the government; the enterprise wage level (lnsalary), represented by the logarithm of total employee compensation; firms’ governance structure (sc10), indicated by the proportion of shares held by the top ten shareholders; management level (mer), represented by the ratio of administrative expenses to main business income; and enterprise value (tobin), indicated by the Tobin Q ratio. The industry-level control variable is the market concentration (hhi). The city-level control variables include: the level of urban economic development (lnPGDP), represented by the logarithm of per capita GDP in the city. The scale of a city is indicated by the logarithm of the urban population (lnpop). The state of urban healthcare is indicated by the number of beds per capita in healthcare institutions (bed). The urban industrial structure is represented by the proportion of secondary and tertiary sectors in the GDP (SI, TI).

The summary statistics of main variables used in the empirical part of this paper is shown in

Table 1. Summary statistics and variable description.

Variables		Count	Mean	Sd	Min	Max
Dependent variable	GTFP	28,417	0.494	0.490	0	1.965
Independent variable	Treat × Post	28,417	0.449	0.497	0	1
Enterprise level control variable	soe	28,417	0.365	0.481	0	1
	lnlabor	28,417	7.594	1.268	3.892	10.94
	capital	28,389	0.052	0.0485	0.000	0.236
	lnincome	28,417	21.30	1.530	9.044	28.72
	roa	28,417	0.0526	0.751	−48.31	108.4
	lev	28,417	0.441	0.575	−0.195	63.97
	gov	26,997	16.01	1.833	2.536	23.15
	lnsalary	28,417	16.89	1.716	4.783	23.31
	sc10	28,417	58.77	15.51	22.46	90.30
	mer	28,416	0.0997	0.0867	0.009	0.576
	tobin	24,353	2.095	1.339	0.869	8.758
Industry level control variable	hhi	24,272	0.198	0.181	0.038	1
City level control variable	lnPGDP	28,417	10.34	0.801	6.100	11.59
	lnpop	26,719	6.401	0.672	4.526	8.119
	bed	26,718	3.596	0.818	1.401	4.918
	SI	26,717	42.64	11.09	11.70	89.75
	TI	26,717	52.71	13.36	9.760	83.52

.

4.2. Model

4.2.1. Baseline Model

The difference-in-differences (DID) method is a commonly used analytical approach to policy evaluation. DID has emerged as the predominant approach [63] for evaluating the impact of environmental policy implementations. Variations in AAQSs implementation across different cities and times provide an opportunity for DID analysis. Due to the implementation of the AAQSs in different cities, the cities were divided into two groups: the treatment group and the control group. The group usually affected by the policy is called the “treatment group,” and the sample that is not affected is called the “control group.”

To examine the impact of environmental information disclosure on firms’ GTFP, this study refers to Li et al. (2023) [64] and sets the empirical model in Equation (4):

$${\mathit{GTFP}}_{\mathit{ict}}=\alpha {\mathit{Treat}}_c\times {\mathit{Post}}_t+\beta X_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{\mathit{ict}} ,$$

(4)

In the model, i represents the firm, c represents the city, and t represents the year. ${\mathit{GTFP}}_{\mathit{ict}}$ denotes GTFP. ${\mathit{Treat}}_c$ is a dummy variable for city grouping, which is assigned a value of 1 if the city where the firm is located has implemented the new standards policy (total of 74 pilot cities), and 0 otherwise. ${\mathit{Post}}_t$ is a dummy variable for time, which is assigned a value of 1 for the year when the new standards policy was implemented and onwards (2012, and later), and 0 otherwise. ${\mathit{Treat}}_c{ \times \mathit{Post}}_t$ represents the interaction term in the difference-in-differences estimation. $X_{it}$ is a vector of control variables, including controls at both the firm and city levels. ${\mu }_i$ represents firm fixed effects, controlling for factors at the firm level that do not change over time; ${\delta }_t$ represents time fixed effects; and ${\epsilon }_{\mathit{ict}}$ denotes the error term.

We are interested in $\alpha$, which represents the average effect of the environmental information disclosure on GTFP. In Equation (4), if $\alpha$ is significantly positive, it indicates that environmental disclosure can enhance firms’ GTFP. If $\alpha$ is significantly negative, it suggests that environmental disclosure can inhibit the improvement of firms’ GTFP. And if $\alpha$ does not significantly differ from 0, environmental information disclosure has no nexus with firms’ GTFP.

4.2.2. Mechanism Analysis Model

This section examines whether environmental information disclosure enhances firms’ GTFP through channels such as total firm costs, financing constraints, and green technological innovation, and establishes the following empirical model in Equation (5):

$${\mathit{Mechanism}}_{\mathit{ict}}=\alpha {\mathit{Treat}}_c\times {\mathit{Post}}_i+\beta X_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{\mathit{ict}},$$

(5)

Here, ${\mathit{Mechanism}}_{\mathit{ict}}$ represents the mechanism variable, with all other indicators being consistent with the baseline model.

4.2.3. Lagged Model

Given that environmental information disclosure may not have an immediate impact, this study lags the core explanatory variables by one period; meanwhile, to avoid simultaneous equations bias, we also lag all control variables by one period [24]. In this study, the empirical model is set as shown in Equation (6):

$${\mathit{GTFP}}_{\mathit{ict}+1}=\alpha {\mathit{Treat}}_c\times {\mathit{Post}}_t+\beta X_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{\mathit{ict}} ,$$

(6)

All the indicators were consistent with those of the baseline model.

5. Results

5.1. Baseline Results

In this study, we employed Equation (4) to investigate whether environmental information disclosure can promote the improvement of firms’ GTFP; the results are presented in

Table 2. Effects of the environmental information disclosure on GTFP: baseline results.

	(1)	(2)	(3)
	GTFP	GTFP	GTFP
Treat × Post	−0.005 (−0.67)	0.008 * (1.71)	0.013 ** (2.18)
lnlabor		−0.007 *** (−2.72)	−0.006 ** (−2.23)
roa		−0.011 (−0.80)	−0.022 (−0.85)
lev		0.008 (0.90)	0.011 (1.06)
sc10		0.000 *** (3.51)	0.000 *** (3.02)
soe		−0.009 (−1.19)	−0.009 (−0.89)
tobin		0.000 (0.37)	0.000 (0.21)
gov		0.002 *** (2.77)	0.002 *** (2.82)
capital		−0.009 (−0.43)	−0.004 (−0.16)
mer		−0.134 *** (−6.25)	−0.147 *** (−4.95)
lnsalary		0.000 (0.22)	0.000 (0.14)
lnincome		0.000 * (1.96)	0.000 * (1.94)
hhi			−0.011 (−1.28)
lnpGDP			0.060 * (1.93)
lnpop			−0.013 (−0.54)
SI			0.001 (0.80)
TI			0.002 (1.11)
bed			−0.033 ** (−2.39)
constant	0.500 *** (153.08)	0.536 *** (22.90)	0.009 (0.02)
N	28,194	23,058	18,134
R2	0.945	0.961	0.957
firm fixed effect	YES	YES	YES
Year fixed effect	YES	YES	YES

Notes: The t-values are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01, where N denotes the number of samples. soe is the type of enterprise ownership; lnlabor is the logarithm of the number of employees; capital is fixed assets per capita; lnincome is the logarithm of operating income; roa is return on assets; lev is debt-to-asset ratio; gov is government subsidies; lnsalary is enterprise wage level; sc10 is the proportion of shares held by the top ten shareholders; mer is management level; tobin is Tobin Q ratio; hhi is market concentration; lnPGDP is the logarithm of per capita GDP; lnpop is the logarithm of the urban population; bed is the number of beds per capita in healthcare institutions; SI and TI represent by the proportion of secondary and tertiary sectors in the GDP, respectively.

. Column (1) contains no control variables. Column (2) introduces control variables representing firm characteristics. Compared to the first column, the estimated coefficient is positive and statistically significant at the 10% level. After incorporating a series of control variables that characterize firm, industry, and city features, Column (3) shows that environmental information disclosure significantly improves firms’ GTFP. Firms’ governance structure (sc10), government subsidies (gov), operating income (lnincome), and the level of urban economic development (lnPGDP) are significantly positively correlated with firms’ GTFP. Internal corporate governance, external policy support, firm development, and urban environmental construction all enhance firms’ GTFP.

5.2. Parallel Trend Test

The application of the difference-in-differences method is contingent on the parallel trend assumption, to ascertain that it is the implementation of the new standards policy, rather than the systematic differences inherent to the treatment and control groups, which account for changes in firms’ GTFP. It is necessary to evaluate whether the treatment and control groups follow the same trend before policy implementation. To this end, this study followed Wang et al. (2019) [54] and compared the temporal trend of the annual means of the dependent variable for the treatment and control groups, as shown in Figure 1. The figure indicates that the GTFP of both groups followed a consistent trend from 2009 to 2012, suggesting that the GTFP of firms in the pilot and non-pilot areas exhibited similar trajectories before the policy was enacted. Post-2012, the GTFP of the treatment group increased significantly, surpassing the annual mean of the control group, further demonstrating the efficacy of environmental information disclosure in enhancing firms’ GTFP.

5.3. Robustness Checks

5.3.1. Add the Fixed Effect of Different Dimensions

The firm serves as the smallest unit of data used in this study. A firm’s fixed effects already account for all time-invariant factors associated with the firm, such as the industry to which it belongs and its geographical location. When variations in a firm’s GTFP are minimal within individual firms, but substantial across different firms, considering only firm’s fixed effects may lead to biased estimation results. To address this potential issue, this study verifies the results by incorporating additional fixed effects beyond firm fixed effects. Specifically, in the empirical tests, this study includes firm, industry, city, and year fixed effects to control for characteristics that remain constant over time at different levels, and the impact of common temporal trends. Column (1) in

Table 3. Effects of environmental information disclosure on GTFP: robustness checks.

	(1)	(2)	(3)	(4)	(5)
	GTFP	PSM-DID	SBM-GML	GTFP_W	GTFP
Treat × Post	0.014 **	0.013 **	0.010 *	0.008 *
	(2.22)	(2.13)	(1.89)	(1.87)
L. Treat × Post					0.005 *
					(1.73)
Constant	−0.032	−0.056	0.864 ***	0.834 ***	0.400 *
	(−0.08)	(−0.13)	(3.31)	(2.75)	(1.77)
N	18,133	15,898	9583	9583	17,467
R2	0.957	0.956	0.140	0.084	0.966
Controls	YES	YES	YES	YES	YES
Firm fixed effect	YES	YES	YES	YES	YES
Industry fixed effect	YES	NO	NO	NO	NO
City fixed effect	YES	NO	NO	NO	NO
Year fixed effect	YES	YES	YES	YES	YES

Notes: t values are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. N represents the number of samples. GTFP_W indicates that the GTFP was winsorized to handle extreme values. L. Treat × Post indicates the one-period lag in time.

presents the results for the multiple dimensions of the fixed effects. Comparing these results with the benchmark regression results in Column (3) of Table 2, it is evident that the magnitude and level of statistical significance of the Treat × Post coefficient do not change substantially.

5.3.2. PSM-DID

This study employed the propensity score matching–difference-in-differences (PSM-DID) method to address the selection bias issue between the treatment and control groups. The regression model uses a logit model to estimate the matching scores and adopts a 1:1 nearest-neighbor matching method. According to the balance test results of PSM, the average treatment effect of GTFP is 0.828 (ATT), which is significant at the 1% level. The matched samples were then regressed using Equation (4); the results are shown in Column (2) of Table 3, which are consistent with the basic regression results.

5.3.3. Alternative Dependent Variable

This study replaces the dependent variable with SBM-GML and regresses it with Equation (4), and the results are presented in Column (3) of Table 3. The magnitude and significance of the core explanatory variables coefficient are largely consistent with the basic regression results, further corroborating the robustness of the research findings in this study, indicating that substituting the dependent variable does not significantly affect the estimation outcomes.

5.3.4. Extreme Value Treatment

Meanwhile, to avoid potential bias in the results that might be introduced by extreme values, this study applies a two-sided trimming at the 1% level to the dependent variable GTFP and uses the trimmed variable as another alternative variable for GTFP, denoted as GTFP_W. Column (4) of Table 3 presents the results, where the coefficient of the core explanatory variable remains positive and significant. This indicates that after two-way trimming at the 1% significance level, the effect of the implementation of the new air quality standards on the enhancement of firms’ GTFP is consistent with the baseline regression.

5.3.5. Policy Time-Lag Effect

The enhancement of firms’ GTFP through environmental information disclosure requires a certain period of time. Additionally, we avoid the potential impact of policy lag on the regression results. In this study, we employed Equation (6) for regression analysis. The results, presented in Column (5) of Table 3, indicate that the estimated coefficient of the core explanatory variable remains significantly positive. This finding suggests that after accounting for the policy’s time lag, the results obtained in this study maintain robustness.

5.3.6. Exclude Other Policy Interference

AAQSs were implemented in 2012, and the research sample ranged from 2009 to 2019. During this period, several other environmental regulation policies were concurrently in effect, including the first phase of the low-carbon city pilot policy in 2010, the second phase of the low-carbon city pilot policy in 2012, the carbon emissions trading policy in 2012, the Environmental Protection Law in 2015, and the pollution tax reform policy in 2018. To mitigate the potential confounding effects of these policies on the identification of the impact of environmental information disclosure, this study systematically excludes the aforementioned pilot cities or incorporates the interaction terms between the pilot industries and the implementation years in Equation (4). The results are presented in

Table 4. Effects of the environmental information disclosure on GTFP: excluding other policy interference.

	(1)	(2)	(3)	(4)
	GTFP	GTFP	GTFP	GTFP
Treat × Post	0.016 **	0.017 **	0.013 **	0.013 **
	(2.18)	(1.67)	(2.15)	(2.19)
Constant	0.311	0.299	0.012	0.012
	(0.54)	(0.57)	(0.03)	(0.03)
N	12,128	2584	18,134	18,134
R2	0.951	0.945	0.950	0.950
Controls	YES	YES	YES	YES
Firm fixed effect	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES

Notes: t values are in parentheses. ** p < 0.05. N represents the number of samples.

. Column (1) illustrates the regression results after excluding low-carbon pilot cities from 2010. Column (2) presents the regression results after excluding the pilot cities involved in the 2012 carbon emissions trading policy. Column (3) displays the regression results after controlling for the interaction between the industries that were the focus of the Environmental Protection Law and the dummy variable for 2015. Column (4) shows the regression results after controlling for the interaction between heavily polluting industries that were the focus of the Environmental Protection Law and a dummy variable for 2018. The consistency of these results with the baseline regression results indicates that the impact of other policy interventions does not significantly affect the identification of the impact of environmental information disclosure on firms’ GTFP, thereby validating the research conclusions.

5.4. Placebo Test

The placebo test is a statistical method used to assess treatment effects in causal relationship studies [65]. It introduces one or more “placebo” variables with no actual therapeutic effect to simulate the treatment variable, thereby testing the robustness and reliability of the research results.

The findings of this study may have been influenced by unobservable factors at the city or industry level, potentially compromising the reliability of the conclusions. To address this concern, this study refers to Shi et al. (2021) [23], who conducted a placebo test. This test involved the use of a fictional policy implementation time or a treatment group. If similar conclusions were drawn from this test, it would suggest that the identified policy effects may be attributable to other unobservable factors, thus undermining the reliability of the study’s findings. The placebo test involved randomly selecting the policy implementation time for each city and constructing interaction terms with dummy variables based on the fictional implementation years. The study included 1000 samplings and the conducting of a baseline regression. Figure 2 presents the kernel density plot of the coefficients of the dummy variable interaction terms after 500 regressions along with the associated p-values. The results indicate that the coefficients of the dummy variable interaction terms are predominantly concentrated around zero, with most p-values exceeding 0.1, which aligns with the expectations of the placebo test in both the economic and statistical terms. These findings further substantiate the reliability of the policy effects examined in this study, suggesting that the results of the baseline regression are unlikely to be attributable to other unobservable factors.

5.5. Heterogeneity Analysis

The preceding analysis evaluates the effect of environmental information disclosure on firms’ GTFP. However, the potential heterogeneity of the impact of environmental information disclosure shocks across firms with varying characteristics warrants further investigation. Consequently, this study conducts empirical analyses based on distinct firm attributes; the results are presented in

Table 5. Effects of the environmental information disclosure on GTFP: Heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	Ownership		Firm Size		Age
	SOE	Non-SOE	Small	Large	Younger	Older
Treat × Post	0.006	0.015 *	0.022 *	0.007	0.015 **	0.009
	(0.92)	(1.77)	(1.90)	(1.46)	(2.28)	(1.22)
constant	0.244	0.071	−0.496	0.164	0.404	0.037
	(0.47)	(0.14)	(−0.62)	(0.58)	(0.83)	(0.07)
N	7644	10,432	7908	9941	9660	9910
R2	0.896	0.967	0.960	0.952	0.971	0.931
Controls	YES	YES	YES	YES	YES	YES
firm fixed effect	YES	YES	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES	YES	YES

Notes: t values are in parentheses. * p < 0.10, ** p < 0.05. N represents the number of samples.

.

5.5.1. Enterprises Ownership

The theoretical section above indicates that the new air quality standards policy primarily affects firms’ behavior or performance by influencing environmental information disclosure and mitigating principal–agent problems within firms. Given that there are certain differences in principal–agent issues between state-owned enterprises (SOEs) and non-state-owned enterprises (Non-SOEs), their responses to environmental information disclosure may also differ. This study categorizes the sample into SOEs and Non-SOEs based on the nature of ownership and conducts basic regressions. The results, presented in Columns (1) and (2) of Table 5, indicate that in the Non-SOE group, the estimated coefficient for Treat × Post is 0.015, which is statistically significant at the 10% level. In the SOEs group, the estimated coefficient for Treat × Post was 0.006, but it did not achieve statistical significance at the 10% level. These findings suggest that the impact of environmental information disclosure on GTFP varies across enterprises with different ownership structures, with a more pronounced effect on the GTFP of Non-SOEs.

The factors contributing to this outcome may include: First, SOEs tend to exhibit more pronounced principal-agent problems. Executives in SOEs often prioritize the maximization of short-term profits or political achievements, and there is a lack of incentives for long-term projects, such as innovative investments, resulting in lower efficiency in green innovation. By contrast, Non-SOEs demonstrate a better alignment of rights and responsibilities, with relatively less severe principal–agent conflicts and more comprehensive supervisory and incentive mechanisms, leading to higher efficiency in green innovation. Second, compared to Non-SOEs, SOEs may experience reduced environmental regulatory pressure due to potential preferential treatment in environmental enforcement by local authorities. SOEs may have access to fiscal subsidies or credit and financial more support from local governments, which enables them to more readily absorb the additional costs associated with environmental policies. Thus, the impact of the compliance costs associated with environmental information disclosure is relatively less significant. Non-SOEs lacking these advantages are subject to greater pressure from increased regulatory compliance costs, which compels them to engage in green technological innovation to enhance their GTFP. Third, compared to Non-SOEs, SOEs possess advantages in terms of technology, scale, and innovation funding, and tend to exhibit higher levels of productivity and environmental performance, which could also contribute to the non-significant impact of environmental information disclosure on GTFP.

5.5.2. Enterprises Scale

The capacity to adapt to policy changes may differ among companies of various sizes. Larger corporations with robust adaptive capabilities might not be significantly impacted by environmental information disclosure. In contrast, smaller businesses may need to adjust swiftly to meet policy requirements for survival. This study used operating income as a measure of company size. The operating income of larger companies is above the median, and that of smaller enterprises is below the median. As illustrated in Columns (3) and (4) of Table 5, the regression results for smaller firms show significant positive effects, whereas larger firms exhibit a positive but statistically insignificant coefficient at the 10% level. This finding implies that environmental information disclosure has a more pronounced impact on the GTFP of smaller companies. Several factors may contribute to this outcome: larger firms might face greater difficulties in coordinating multiple departments for green innovation, making implementation more challenging than smaller firms. Additionally, they possess a greater ability to withstand external pressures, analogous to a large vessel’s capacity to endure adverse conditions. Conversely, smaller firms benefit from increased organizational agility, allowing for more flexible adjustments. When confronted with stricter environmental regulations, they can respond rapidly, driving them to engage in green innovation and ultimately enhancing their GTFP.

5.5.3. Enterprise Age

To a certain extent, the duration of a firm’s establishment reflects its developmental stages. Firms with shorter establishment periods may be in the growth phase, whereas those with longer histories may be in a stable development phase. Consequently, their responses to environmental information disclosure may differ. Regressions were conducted separately for firms with an age greater than or equal to the median of the sample year, and for those younger than the median. The results, as shown in Columns (5) and (6) of Table 5, indicate that, in the group of younger firms, the estimated coefficient for Treat × Post is 0.015, which is statistically significant at the 5% level. In the group of older firms, the estimated coefficient for Treat × Post is 0.009, which fails to achieve statistical significance at the 10% level. This suggests that environmental information disclosure has a differential impact on the GTFP of firms of different ages, specifically promoting the enhancement of GTFP in firms with a shorter establishment period while having no significant effect on firms with a longer establishment period. The reasons for this outcome may be as follows: firms with a longer history are more mature and their green technology levels have already reached a relatively high standard. By contrast, firms with a shorter history, being relatively new to the market, may have a stronger motivation for green innovation when faced with the pressure of environmental information disclosure. Therefore, the external shock of environmental information disclosure serves as an opportunity for them to improve green technology, thereby enhancing their GTFP.

5.6. Mechanism Analysis

The aforementioned arguments demonstrate that environmental information disclosure has a statistically significant positive impact on the GTFP. Additionally, policy effects exhibit a degree of heterogeneity across firm characteristics. In light of these findings, this study further analyzes the mechanisms through which environmental information disclosure affects firms’ GTFP by specifically examining the channels or pathways of influence. The theoretical analysis above indicates that environmental information disclosure may affect firms’ GTFP through financing constraints, total costs, and green technological innovation channels. This part uses Equation (5), for empirical testing.

5.6.1. Cost Effect

This section examines the impact of environmental information disclosure on firms’ GTFP by increasing their overall expenses. Column (1) of

Table 6. Effects of the environmental information disclosure on GTFP: mechanism analysis.

	Cost	Financing Constraint			Green Technology Innovation
	(1)	(2)	(3)	(4)	(5)	(6)
	Cost	SA	KZ	WW	Lngreen	Green
Treat × Post	0.023 **	−0.005 ***	−0.095 *	−0.005 ***	0.018 *	0.324 *
	(2.19)	(−3.06)	(−1.70)	(−3.73)	(1.65)	(1.86)
constant	8.208 ***	−3.016 ***	−0.455	−0.612 ***	−1.163 **	−22.244 ***
	(13.25)	(−29.81)	(−0.16)	(−7.56)	(−2.06)	(−3.35)
N	18,134	18,134	21,748	16,766	26,475	10,157
R2	0.963	0.972	0.700	0.819	0.641	0.807
Controls	YES	YES	YES	YES	YES	YES
firm fixed effect	YES	YES	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES	YES	YES

Notes: t values are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. N represents the number of samples.

shows the estimation results. The findings reveal that, after accounting for other factors, environmental information disclosure significantly increases firms’ total costs (non-environmental expenses). This cost increase drives companies to find ways to boost efficiency, encourage green innovation, and enhance the GTFP. These results suggest that environmental information disclosure can influence a firm’s GTFP through increased total costs, thus supporting H1.

5.6.2. Financing Constraint Effect

This section examines the impact of environmental information disclosure on firms’ GTFPs by alleviating their financing constraints. Columns (2) to (4) of Table 6 present the regression results. After controlling for other factors, the results consistently demonstrated that the estimated coefficient for Treat × Post was significantly negative. This indicates that, regardless of the financing constraint indicator employed, environmental information disclosure reduces firms’ financing constraints. Therefore, firms have more substantial financial support for green innovation, which promotes the improvement of the GTFP and confirms H2.

5.6.3. Green Technology Innovation Effect

This section examines the impact of environmental information disclosure on firms’ GTFPs by promoting or necessitating green technological innovation. Furthermore, considering that the number of patents is a discrete integer variable, linear regression may yield biased estimates. Therefore, this study employs a count model for robustness checks [66]. Columns (5) and (6) of Table 6 present the regression results of environmental information disclosure on firms’ green technological innovation. The results indicate that, after controlling for the effects of other factors, environmental information disclosure encourages firms to engage in green technological innovation. Hence, environmental information disclosure can improve firms’ GTFP through green technological innovation, confirming H3.

6. Discussion

6.1. GTFP Welfare Analysis

GTFP is an important indicator for measuring the coordinated development of resources, the environment, and the economy in a country or region. It not only considers the efficiency of traditional production factors, such as labor and capital, but also includes non-traditional factors, such as energy consumption and environmental pollution. An increase in GTFP indicates a higher economic output within resource and environmental limits. This enhances economic, environmental, and social welfare.

In terms of economic welfare, from a corporate perspective, our study finds that environmental information disclosure significantly improves firms’ GTFP through three pathways: increasing total costs, alleviating financing constraints, and promoting green technological innovation. This indicates that through environmental information disclosure, firms can optimize resource allocation and reduce investment in highly polluting and inefficient projects, thereby improving production efficiency and product quality. In the long run, such optimization not only enhances firms’ market competitiveness, but also attracts more investor attention, providing them with more financial support and further promoting their sustainable development. From a macroeconomic standpoint, the improvement of GTFP helps optimize resource allocation and reduce resource waste and environmental costs in economic activities. As more firms respond to the requirements of environmental information disclosure, the production efficiency and resource utilization efficiency of the entire industry will be significantly enhanced. This enhancement not only helps to adjust and upgrade the economic structure but also promotes the transformation of the economy towards high-quality development, injecting new momentum into economic growth.

Regarding environmental welfare, our mechanism analysis shows that environmental information disclosure, by increasing firms’ total costs, compels them to optimize production processes and reduce pollutant emissions. This reduction in emissions not only helps to improve local environmental quality but also promotes the sustainable use of resources and has a positive impact on ecosystem health. In the long term, the improvement of the GTFP helps achieve coordinated development between economic activities and the ecological environment. By reducing over-reliance on natural resources and lowering environmental pollution, firms can maximize economic benefits while protecting the ecological environment and creating a more sustainable development environment for future generations. This sustainability not only helps protect natural resources but also promotes the recovery and protection of ecosystems, laying a solid foundation for future economic development.

In terms of social welfare, our study found that environmental information disclosure, by promoting green technological innovation, reduces firms’ pollutant emissions, thereby lowering public health risks and improving overall health. This not only reduces medical costs but also enhances social welfare and promotes social equity and harmony. Our heterogeneity analysis shows that the impact of environmental information disclosure is more significant for Non-SOEs, small firms, and younger firms. These firms are usually weaker in terms of their resources and technological innovation capabilities. Through the external pressure of environmental information disclosure, they can enhance their green innovation capabilities, thereby achieving coordinated development of the economy and the environment, narrowing the gap between different firms, and promoting social equity.

6.2. Implications

Based on the conclusions of this study, the following insights were drawn to better leverage the effects of environmental information disclosure.

First, the government should enhance the design of environmental information disclosure systems, thereby encouraging enterprises to improve the quality and quantity of their environmental reports. Furthermore, authorities should establish a standardized environmental reporting system with appropriate incentives and sanctions. Concurrently, the government should implement differentiated environmental regulations, impose more stringent penalties on polluters, and provide subsidies and support for environmentally responsible enterprises. These measures will create a favorable climate to achieve both economic growth and environmental conservation.

Second, for enterprises, organizational leaders should establish a strategic vision and accurately assess the relationship between fulfilling environmental responsibilities and their own development. It is particularly crucial to focus on enhancing managerial competencies, utilizing capability as the primary criterion for selecting managers, and actively promoting the development of management skills through initiatives, such as professional development training. Additionally, enterprises should collaborate proactively with long-term institutional investors by leveraging their capital, reputation, and information networks to introduce high-quality resources for firms’ advanced green research and development efforts.

Third, for public media, it is essential to establish and improve a supervisory mechanism to fully leverage media’s role in overseeing and governing firms’ environmental and social responsibilities. Simultaneously, the media should enhance its professionalism and independence, ensuring that it reports facts accurately and correctly guides public opinion. It should strive to be a sharp sword in monitoring heavily polluting enterprises rather than a tool for covering up, distorting, or diverting attention.

Fourth, when evaluating investment opportunities, investors should prioritize the quality of a firm’s environmental data disclosure. It is essential for investors to adopt a long-term investment strategy that focuses on value-based investing and identifying businesses committed to high-quality green innovation initiatives. Furthermore, investors should cultivate a heightened sense of responsibility, acknowledging the significant role that long-term institutional investors play in enhancing corporate governance.

6.3. Limitations and Future Research

This study has several limitations. First, cost, financing constraints, and green technological innovation were tested in the analysis of the influencing mechanisms. Therefore, other influencing factors may need to be explored. Further research is required to conduct theoretical analyses and empirical tests of the underlying mechanisms to generate practical insights. Second, due to data limitations, this study relies on data from A-share-listed companies, which may not fully represent the entire spectrum of enterprises in China. Future research should extend to unlisted companies, especially small and medium-sized enterprises.

7. Conclusions

Green innovation is fundamental to the establishment of an ecological civilization and the promotion of sustainable development. GTFP functions as a critical mechanism in the construction of an ecological civilization, playing an essential role in enhancing environmental stewardship and facilitating high-quality economic and social advancement. Based on this, this study explores whether environmental information disclosure can enhance a firm’s GTFP and its underlying transmission pathways. This study uses data from Chinese companies listed on stock exchanges between 2009 and 2019. It considers the annual sulfur dioxide (SO₂) emissions of these listed firms as undesirable outputs. Subsequently, the study determines the actual GTFP value by multiplying the Malmquist–Luenberger (ML) productivity index, which is derived from the SBM directional distance function. Using the difference-in-differences method and Ambient Air Quality Standards (2012) as a quasi-natural experiment, this study empirically examines the impact of environmental information disclosure on firms’ GTFP and its mechanisms. The study finds that: (1) environmental information disclosure significantly enhances firms’ GTFP, and this conclusion still holds after a series of robustness tests. (2) Mechanism analysis reveals that environmental information disclosure mainly affects the GTFP by increasing the total costs of firms, alleviating firms’ financing constraints, and promoting firms’ green technological innovation. (3) Heterogeneity tests show that the enhancing effect of environmental information disclosure on firms’ GTFP is mainly present in non-state-owned enterprises, small-scale enterprises, and enterprises with shorter existence periods.