Managerial Myopia and Enterprise Green Total Factor Productivity: Perspectives on the Supervisory Effect and Incentive Effect

Abstract

Enhancing green total factor productivity is essential for the development of a green economy. However, from a micro perspective, it remains unclear how management, as the primary decision-maker, influences enterprise green total factor productivity. Drawing on the perspectives of supervisory and incentive effects, this study examines the impact of managerial myopia on enterprise green total factor productivity, using data from Chinese industrial companies listed on the A-share markets of Shanghai and Shenzhen from 2007 to 2022. The findings indicate that managerial myopia significantly reduces enterprise green total factor productivity. Tests for moderation effects reveal that green investors and low-carbon city pilot policies, through supervisory effects, as well as equity incentives and environmental protection subsidies, through incentive effects, can effectively mitigate the negative impact of managerial myopia on enterprise green total factor productivity. Further analysis suggests that managerial myopia hinders green innovation activities within enterprises by reducing both the quality and the quantity of green innovation, thereby inhibiting green total factor productivity. This study contributes to the understanding of the impact of managerial myopia on enterprise green total factor productivity, offering empirical evidence and policy implications for addressing managerial myopia to enhance enterprise green total factor productivity.

1. Introduction

In recent years, the consequences of environmental pollution have become increasingly evident. As environmental issues grow more severe, global attention has turned toward reducing greenhouse gas emissions, mitigating pollution, and optimizing waste management [1,2]. For example, while China’s extensive economic development model has driven rapid economic growth, it has often overlooked environmental considerations, leading to significant problems such as high energy consumption, pollution, and emissions. These environmental challenges have notably impeded the potential for sustained economic progress. As a result, the impact of the ecological environment on economic growth has gradually attracted greater attention. Traditional total factor productivity measures, which exclude environmental factors, have led to discrepancies between calculated results and actual production efficiency, resulting in estimates that fail to fully and accurately assess societal economic performance [3]. In response, scholars have begun incorporating environmental factors into the evaluation of total factor productivity, leading to the development of the concept of green total factor productivity (GTFP) [4,5].

GTFP encompasses both qualitative and quantitative analyses of green development, making it an accurate indicator of the integration between economic performance and ecological sustainability [5]. GTFP refers to the optimization of resource allocation and the improvement of production efficiency, prioritizing resource and energy conservation during the process [6] and minimizing pollution and environmental damage as much as possible [7]. Summarizing the current scholarly research, this paper posits that GTFP is a composite indicator that builds on total factor productivity (TFP) by incorporating environmental factors into both input and output dimensions. It merges economic performance with ecological considerations to evaluate green and sustainable development. Currently, scholars measure GTFP in a manner similar to TFP, primarily using data envelopment analysis (DEA) as the basis for the measurement. Chung et al. estimated GTFP using the directional distance function (DDF), incorporating pollution emissions as “undesirable outputs” in the model [8]. Tone proposed a DEA model based on the slack-based measure (SBM) without orientation to enhance the measurement accuracy [9]. Building on this, Cooper et al. introduced a new SBM model that includes undesirable outputs to measure green economic efficiency [10]. However, the SBM method is limited to static studies and cannot capture the dynamic changes of a decision-making unit (DMU) over different years [11]. To address this, Oh proposed the global Malmquist–Luenberger (GML) index, which can capture dynamic characteristics and compare changes in GTFP over time [12].

The modern corporate agency relationship makes conflicts of interest between owners and management difficult to avoid. Management often adopts defensive behaviors to protect their own interests and reduce potential risks, which frequently leads to short-sighted actions such as reducing research and development (R&D) investment and suppressing broader investment activities [13]. Managerial myopia can cause management to prioritize short-term profits at the expense of the company’s long-term interests [14]. With a short decision-making horizon, management may overlook the future development of the enterprise, focusing instead on immediate gains [15,16,17]. Consequently, this paper defines managerial myopia as the tendency of management to adopt a narrow decision-making perspective driven by self-interest, as characterized by a short-term orientation and personal traits (such as language style), which leads to decision-making that emphasizes short-term profits and immediate development goals. Managerial myopia distorts company behavior; while it may partially fulfill management’s short-term pursuit of private interests, it violates the principle of maximizing shareholder value [18] and often neglects environmental and social responsibilities, potentially affecting GTFP. The existing literature primarily examines managerial myopia through two lenses: the reasons for its emergence and the characteristics of its manifestation. Regarding the reasons, most studies measure myopic behavior from perspectives such as the institutional investor shareholding ratios [19,20], stock turnover rates [19], and analyst coverage [16,21]. In terms of the characteristics, scholars explore it through investment behavior [22], R&D expenditure [23], and text analysis [24,25].

This study uses the supervisory and incentive effects as entry points to explore in depth whether managerial myopia affects enterprise GTFP. It focuses on analyzing how the supervisory effect and incentive effect regulate the impact of managerial myopia on enterprise GTFP and further investigates the mechanisms through which managerial myopia influences enterprise GTFP. The significance of this research is as follows. First, the study employs the Super-SBM-GML method to measure GTFP at the enterprise level, combining the static Super-SBM model with the dynamic GML index to expand the research perspective. Second, the study conducts empirical research by incorporating managerial myopia and enterprise GTFP into the same framework, focusing on factors that can inhibit the negative impact of managerial myopia on GTFP. The research findings indicate that managerial myopia significantly reduces enterprise GTFP; however, green investors and low-carbon city pilot policies under the supervisory effect, as well as equity incentives and environmental protection subsidies under the incentive effect, can effectively mitigate this negative impact. Third, this study further explores the pathways through which managerial myopia affects enterprise GTFP, finding that managerial myopia can inhibit enterprise GTFP by reducing both the quality and the quantity of green innovation.

The remainder of this paper is organized as follows. Section 2 reviews the existing literature, highlighting the research content and gaps. Section 3 provides theoretical analysis and formulates hypotheses. Section 4 constructs the research variables and regression models. Section 5 presents the results of the baseline regression, moderation effects, and robustness tests. Section 6 further explores the potential mediation effects. Finally, Section 7 concludes the paper with policy recommendations.

2. Literature Review

Research closely related to this study mainly addresses two aspects: the factors influencing GTFP and the economic consequences of managerial myopia. In terms of the factors influencing GTFP, existing academic research primarily focuses on macro external factors, with fewer studies examining micro internal factors. Regarding macro external factors, fiscal decentralization [26] and the digital economy [27] both exhibit a “U-shaped” relationship with GTFP. Additionally, the digital economy has positive spillover effects. The enactment of relevant policies, such as green finance policies [28,29], smart city construction [30], and urban environmental legislation [31], is significantly positively correlated with enterprise GTFP, indicating that these policies can substantially improve GTFP. Therefore, integrating economic development with mandatory environmental protection policies during periods of growth can effectively reduce environmental damage. From the perspective of micro internal factors, green innovation [32,33], digital transformation [34], and other related high-tech innovations, transformations, and developments are critical measures for enhancing enterprise GTFP.

From the perspective of the economic consequences of managerial myopia, existing research mainly focuses on internal investment behavior, innovation capability, earnings management, and corporate performance. Regarding investment behavior, managerial myopia leads to a preference for short-term profits over long-term benefits in terms of enterprise investment decisions. Myopic management tends to prioritize short-term investments, which exerts a crowding-out effect on long-term asset investments, such as research and development (R&D) and physical capital expenditures [16,17,22]. Concerning innovation capability, since technological innovation is a long-term process fraught with uncertainties, managerial myopia often results in conservative decision-making, which hinders innovation [35]. In terms of earnings management, managerial myopia can lead to practices such as earnings manipulation or even financial fraud, including the manipulation of costs and related expenses to artificially enhance or reduce earnings [36]. Regarding corporate performance, management may exploit the intangibility, ambiguity, and complexity of R&D activities to translate myopic motives into tangible actions, such as reducing R&D expenditures [13,23], thereby inhibiting improvements in enterprise total factor productivity [18].

Overall, research on managerial myopia primarily explores its effects on economic behavior [16,35,36] and corporate performance [18], with less attention paid to corporate green development, often neglecting the environmental consequences of managerial myopia. Additionally, research on GTFP predominantly focuses on the macro and meso levels, such as the national [8], regional [26], and city levels [30], with few studies addressing the micro-level measurement of enterprise GTFP or analyzing the factors that influence enterprise decision-making and, consequently, affect GTFP. This study examines the impact of managerial behavior on enterprise decision-making as a starting point to explore the effect of managerial myopia on enterprise GTFP. It also elaborates on the reasons for the emergence of managerial myopia, based on the supervisory effect and incentive effect, and analyzes how these effects regulate and mitigate the negative impact of managerial myopia on enterprise GTFP. This study provides specific pathways for enterprises to enhance their GTFP.

3. Theoretical Analysis and Hypotheses Development

3.1. Managerial Myopia and Enterprise GTFP

To enhance enterprise GTFP, it is essential to optimize resource allocation and improve production efficiency through transformation and reform. At the same time, efforts should be made to conserve resources and energy [37], as well as to minimize pollution and environmental damage [7]. According to Zhang et al. [31], investing in preventive measures is more effective in improving pollution control than relying on post-treatment emissions reduction. Wang et al. [34] argued that achieving GTFP requires substantial capital investment in green innovation to reduce the negative impacts on the environment to an acceptable level. Therefore, whether through improving efficiency or reducing pollution, increasing enterprise GTFP necessitates significant capital investment. However, myopic management tends to favor short-term investments and strategies [25], which are detrimental to long-term development. The supervisory and incentive effects can effectively curb the selfish tendencies of management, which often exacerbate managerial myopia.

From the perspective of lacking a supervisory effect, the principal–agent relationship makes it difficult for shareholders to effectively oversee management behavior, thereby fostering the emergence of managerial myopia. Firstly, shareholders do not directly manage the company; instead, it is managed by the executives, leading to potential conflicts of interest and increasing the likelihood of opportunistic behavior by management. Secondly, due to their informational and professional advantages, management benefits from information asymmetry with shareholders. Managers can deliberately withhold key information or obscure decision-making processes to gain personal benefits, providing them with opportunities for opportunistic investments. Management with both the motive and the opportunity is more prone to exhibit myopic tendencies. The upper echelons theory suggests that the behavioral decisions of a company’s management can significantly impact its governance practices and operational performance. Management with myopic tendencies, when driven by self-interest, often prioritizes short-term profits in decision-making [16,22], while neglecting long-term environmental and social responsibilities, as well as sustainable development goals. This short-sighted approach leads to a lack of long-term planning for green innovation in corporate investments, hinders the company’s transformation into a green entity, and hampers the improvement of enterprise GTFP [38].

From the perspective of lacking an incentive effect, management is not the long-term owner of the company. Without long-term incentives, and due to the inconsistency between their short-term interests and the company’s long-term goals, they often exhibit myopic behavior driven by self-interest during their tenure. Based on the theory of short-term orientation, myopic management tends to prioritize short-term economic gains at the expense of long-term sustainability. Specifically, such management focuses on improving short-term performance during their tenure. Without adequate internal and external incentives, management typically disregards the company’s long-term interests, leading to a reduction in investments that offer high returns but have slow and uncertain outcomes [39,40]. However, improving GTFP requires sustained investment of significant capital, characterized by long cycles and high risks. Managers with a short-term orientation may use their power to influence corporate investment behavior, affecting the scale and direction of investments, reducing the capital allocated to green initiatives, decreasing green innovation output, and ultimately inhibiting the improvement of the enterprise GTFP [13].

Based on the above analysis, this study proposes the following hypothesis:

Hypothesis 1.

Managerial myopia significantly inhibits the improvement of enterprise GTFP.

3.2. Analysis of the Supervisory Effect

Based on the internal supervisory effect, investors, as the internal stakeholders of the company, can effectively supervise and influence its management activities. With the rise of ESG investment and green finance concepts in the domestic capitalist market in recent years, social and environmental issues have gained more attention from investors, leading to the emergence of green investors. Firstly, the limited attention theory suggests that attention is a scarce resource [41]. When faced with a large amount of information, investors tend to focus only on areas of interest and overlook other details [42]. Compared to ordinary investors, green investors prioritize environmental information about companies [41] and incorporate their environmental beliefs into investment decisions. Management’s short-sighted behavior often disregards the environmental consequences of the company’s production and operational activities, which sends a strong negative signal to green investors who are particularly concerned with environmental issues. Green investors actively gather environment-related information about companies [43], thereby reducing management’s short-sighted behavior by mitigating information asymmetry regarding environmental issues. Secondly, green investors are a special category of institutional investors. Individual institutional investors often hold relatively small and dispersed shareholdings, with limited voting power in the companies they own. When they identify management behavior that conflicts with their investment preferences, they often increase their voting power through proxy solicitation or shareholder proposals [44]. For green investors, the environmental issues neglected by management’s short-sighted behavior are inconsistent with their own environmental investment beliefs. Compared to other investors, green investors are more likely to exercise their proxy rights and submit shareholder proposals to curb the impact of management’s short-sighted behavior.

From the perspective of the external supervisory effect, as urbanization and industrialization have accelerated, the Chinese government has issued a series of environmental regulations aimed at reducing greenhouse gas emissions and tightening restrictions on the carbon emission intensity. One of the earliest and largest-scale environmental regulations, the low-carbon city pilot policies, has effectively reduced the carbon emission intensity in the areas where it has been implemented [45]. The low-carbon city pilot policies are market-oriented environmental regulatory tools enforced by the government and characterized by mandatory supervision, which can mitigate the negative impact of managerial myopia on enterprise GTFP. Firstly, the low-carbon city pilot policies establish mandatory measures such as the creation of carbon emission trading and registration platforms. These platforms effectively supervise companies’ carbon emission behaviors and provide them with more transparent market information for technological learning and innovation imitation [46]. By learning from and imitating the technological advancements of other companies, firms can reduce the R&D costs of green technology innovation, lessen management’s concerns about the uncertainties of technological innovation, and encourage greater investment in green innovation capabilities [47], thereby curbing the short-sighted behavior of company management. Secondly, myopic management tends to focus more on short-term performance. The low-carbon city pilot policies impose mandatory legal restrictions on companies in pilot areas, significantly increasing the cost of carbon emissions and raising expectations of higher production costs [48]. This, in turn, reduces the short-term performance of companies. To maximize short-term profits, management with a short-term orientation may shift decision-making toward investments in long-term green assets, such as fixed assets that are difficult to monetize in the short term. This shift reduces the company’s pollution emission costs [31] and improves enterprise GTFP.

Accordingly, this paper proposes the following hypotheses:

Hypothesis 2a.

Green investors can significantly mitigate the negative impact of managerial myopia on enterprise GTFP.

Hypothesis 2b.

The low-carbon city pilot policies can significantly curb the negative impact of managerial myopia on enterprise GTFP.

3.3. Analysis of the Incentive Effect

From the perspective of the internal incentive effect, equity incentive schemes are a long-term effective incentive. By granting a certain amount of equity to management, these schemes align the interests of management with those of shareholders to some extent, encouraging management to make strategic decisions that enhance the long-term interests of the company [48]. Firstly, equity incentives can weaken the opportunistic motives of management. A well-structured equity incentive scheme can unify the interests of management and shareholders, largely mitigating the principal–agent problem [49]. Equity incentives can shift management’s decision-making away from conservative behavior, extend their strategic goals [50], and help them move beyond the short-term profit-seeking mindset, thereby reducing myopic behavior by diminishing their opportunistic motives, such as short-term investments. Secondly, equity incentives can encourage management to consciously invest in research and development (R&D). These incentives enable management to adopt a long-term perspective, fostering a sense of “ownership” that motivates them to choose projects that benefit the company’s long-term development. Continuous innovation is essential for a company’s growth, as it is through innovation that long-term competitive advantages are established and sustained [51]. As a result, management is more likely to actively invest in R&D, enhancing the company’s capacity for green innovation [48] and improving enterprise GTFP.

From the perspective of the external incentive effect, environmental protection subsidies, as market-oriented environmental regulatory tools, provide economic and material support for companies’ green innovation [52], which can help reduce opportunistic behavior by management. From the stakeholder perspective, environmental protection subsidies, as a government initiative, send positive signals to stakeholders, thereby enhancing the company’s external reputation [53]. In a society that advocates green development, companies receiving environmental protection subsidies are more favored by stakeholders. Due to stakeholder demands and pressures [54], management is motivated to obtain these subsidies and invest more in green-related activities, thereby crowding out myopic investment behavior driven by opportunistic motives. From the resource constraint perspective, a company’s green development requires substantial resource input, which can strain the operating cash flow for normal production activities. As a result, myopic management may be less willing to invest significant resources in green initiatives. This misallocation of resources reduces the efficiency of natural resource utilization and the ecological efficiency, making it difficult to effectively improve the enterprise GTFP [33]. Environmental protection subsidies, however, provide additional external resources, offering partial financial support for implementing green-related strategic decisions. This alleviates the burden on working capital caused by green activities [55] and reduces the inhibitory effect of opportunistic motives on the company’s green development. Therefore, by gaining favor with external stakeholders and acquiring additional resources, environmental protection subsidies can effectively curb management’s opportunistic motives, reduce myopic behavior, and ultimately enhance the enterprise GTFP.

Based on the analysis above, this paper proposes the following hypotheses:

Hypothesis 3a.

Equity incentives can significantly mitigate the negative impact of managerial myopia on enterprise GTFP.

Hypothesis 3b.

Environmental protection subsidies can significantly curb the negative impact of managerial myopia on enterprise GTFP.

4. Research Methods

4.1. Variable Measurement

4.1.1. Dependent Variable: GTFP

Drawing on the theories provided by Tone [9] and Oh [12], this paper utilizes the static Super-SBM model to measure GTFP at a cross-sectional time point and the dynamic GML index to measure the rate of change in GTFP over different time periods. Combining these two methods (Super-SBM-GML) allows for the calculation of a dynamic index of GTFP over a continuous time period. Calculating GTFP requires separately obtaining input and output indicators. Input factors include capital input, fixed asset input, labor input, and energy input. Capital input is measured using the perpetual inventory method of capital expenditure; fixed asset input is measured by the net value of fixed assets; labor input is measured by the number of employees; and energy input is measured by the enterprise’s industrial energy consumption. Output factors are divided into expected output and unexpected output. Expected output is measured by the enterprise’s main business income, while unexpected output is measured by the emissions of pollutants such as sulfur dioxide, dust, or wastewater from the enterprise.

Based on the studies by Wu et al. [33], due to the unavailability of data on sulfur dioxide, dust, and wastewater emissions at the enterprise level, this study uses the emissions of these three pollutants (SO₂, dust, and wastewater) at the city level as proxies for measuring enterprise pollutant emissions. The calculation of SO₂, dust, and wastewater emissions is performed as follows: First, the adjustment coefficient for each pollutant j (j = 1, 2, 3) in each city i (i = 1, 2, 3…n) is calculated as follows:

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{\frac{{\mathrm{P}}_{\mathrm{i}\mathrm{j}}}{\sum {\mathrm{P}}_{\mathrm{i}\mathrm{j}}}}{\frac{{\mathrm{O}}_{\mathrm{i}}}{\sum {\mathrm{O}}_{\mathrm{i}}}}}$$

(1)

where P_ij is the emission of pollutant j in city i; ∑P_ij is the total emission of pollutant j nationwide, O_i is the industrial output value of city i, and ∑O_i is the total industrial output value nationwide. After calculating the adjusted weighting coefficient, the emission of pollutant j in city i is calculated as:

$${\mathrm{e}\mathrm{m}}_{\mathrm{i}\mathrm{j}}={\mathrm{W}}_{\mathrm{i}}\times {\mathrm{Y}}_{\mathrm{i}\mathrm{j}}$$

(2)

where Y_ij is the original emission of pollutant j in city i. Finally, the emission of pollutant j for enterprise k (k = 1, 2, 3…n) located in city i is calculated as:

$${\mathrm{e}\mathrm{m}}_{\mathrm{k}\mathrm{j}}={\mathrm{e}\mathrm{m}}_{\mathrm{i}\mathrm{j}}\times {\displaystyle \frac{{\mathrm{O}}_{\mathrm{k}}}{{\mathrm{O}}_{\mathrm{i}}}}$$

(3)

where O_i is the industrial output value of city i; O_k is the industrial output value of enterprise k. Due to the lack of data on the industrial energy input for enterprises, this study uses city-level data again for the calculation:

$${\mathrm{I}\mathrm{P}}_{\mathrm{k}\mathrm{j}}={\mathrm{I}\mathrm{P}}_{\mathrm{i}\mathrm{j}}\times {\displaystyle \frac{{\mathrm{O}}_{\mathrm{k}}}{{\mathrm{O}}_{\mathrm{j}}}}$$

(4)

where IP_ij is the total energy consumption in the city where the enterprise is located.

This article first defines an environmental technical model, including expected output and non-expected output. It is assumed that the model includes n enterprises, each of which is a decision-making unit (DMUj, j = 1, 2, …n). Each DMU has three types of input–output indicators, including m types of inputs x^t (${\mathrm{x}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}\in {\mathrm{R}}_{\mathrm{n}\mathrm{m}}^{+}$), s₁ types of expected outputs y^t (${\mathrm{y}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}\in {\mathrm{R}}_{{\mathrm{s}}_1\mathrm{n}}^{+}$), and s₂ types of non-expected outputs b^t (${\mathrm{b}}_{\mathrm{i},\mathrm{j}}^{\mathrm{t}}\in {\mathrm{R}}_{{\mathrm{s}}_2\mathrm{n}}^{+}$). Therefore, the environmental technical function is as follows:

$$\mathrm{P}\mathrm{x}=\left\{\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)|{\mathrm{x}}^{\mathrm{t}}\ge \sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{k}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\le \sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{k}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}}\le \sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{b}}_{\mathrm{k}}^{\mathrm{t}},\mathsf{\lambda }\ge 0\right\}$$

(5)

where λ is a non-negative intensity vector of weights assigned to sectional input–output factors, and returns to scale are variable.

The construction of the Super-SBM model considering non-expected output is as follows:

$$\mathrm{m}\mathrm{i}\mathrm{n}\mathsf{\rho }={\displaystyle \frac{1+\frac{1}{\mathrm{m}}{\sum }_{\mathrm{k}=1}^{\mathrm{m}}\frac{{\mathrm{s}}_{\mathrm{i}}^{-}}{{\mathrm{x}}_{\mathrm{i}\mathrm{k}}^{\mathrm{t}}}}{1-\frac{1}{{\mathrm{s}}_1+{\mathrm{s}}_2}\left({\sum }_{\mathrm{a}=1}^{{\mathrm{s}}_1}\frac{{\mathrm{s}}_{\mathrm{a}}^{+}}{{\mathrm{y}}_{\mathrm{a}\mathrm{k}}^{\mathrm{t}}}+{\sum }_{\mathrm{c}=1}^{{\mathrm{s}}_2}\frac{{\mathrm{s}}_{\mathrm{c}}^{-}}{{\mathrm{b}}_{\mathrm{c}\mathrm{k}}^{\mathrm{t}}}\right)}}$$

(6)

$$\mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}{\mathrm{x}}_{\mathrm{i}\mathrm{k}}^{\mathrm{t}}\ge {\displaystyle \sum _{\mathrm{t}=1}^{\mathrm{T}}}{\displaystyle \sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}}{\mathsf{\lambda }}_{\mathrm{j}}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\mathrm{t}}-{\mathrm{s}}_{\mathrm{i}}^{-},\mathrm{i}=1,2,\dots ,m\\ {\mathrm{y}}_{\mathrm{a}\mathrm{k}}^{\mathrm{t}}\le {\displaystyle \sum _{\mathrm{t}=1}^{\mathrm{T}}}{\displaystyle \sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}}{\mathsf{\lambda }}_{\mathrm{j}}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{a}\mathrm{j}}^{\mathrm{t}}+{\mathrm{s}}_{\mathrm{a}}^{+},\mathrm{a}=1,2,\dots ,{\mathrm{s}}_1\\ {\mathrm{b}}_{\mathrm{c}\mathrm{k}}^{\mathrm{t}}\ge {\displaystyle \sum _{\mathrm{t}=1}^{\mathrm{T}}}{\displaystyle \sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}}{\mathsf{\lambda }}_{\mathrm{j}}^{\mathrm{t}}{\mathrm{b}}_{\mathrm{c}\mathrm{j}}^{\mathrm{t}}-{\mathrm{s}}_{\mathrm{c}}^{-},,\mathrm{c}=1,2,\dots ,{\mathrm{s}}_2\\ {\mathsf{\lambda }}_{\mathrm{j}}^{\mathrm{t}}\ge 0\left(\forall \mathrm{j}\right),{\mathrm{s}}_{\mathrm{i}}^{-}\ge 0\left(\forall \mathrm{i}\right),{\mathrm{s}}_{\mathrm{a}}^{+}\ge 0\left(\forall \mathrm{a}\right),{\mathrm{s}}_{\mathrm{c}}^{-}\ge 0\left(\forall \mathrm{c}\right)\end{array}\right.$$

(7)

where ρ is the target efficiency value, λ is the weight vector, and the subscript k denotes the decision-making unit being measured. The subscripts i, a, c represent the number of input variables, expected output variables, and non-expected output variables, respectively. The variables ${\mathrm{s}}_{\mathrm{i}}^{-}$, ${\mathrm{s}}_{\mathrm{a}}^{+}$, ${\mathrm{s}}_{\mathrm{c}}^{-}$ denote the slack variables for inputs, expected outputs, and non-expected outputs, respectively. By solving the Super-SBM index under the production possibility set for the appropriate period, the directional distance function is obtained:

$$\overline{{\mathrm{D}}_0^{\mathrm{G}}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{y}}^{\mathrm{t}},-{\mathrm{b}}^{\mathrm{t}}\right)$$

(8)

Based on the directional distance function solved by the Super-SBM, the GML index from period t to period t + 1 can be obtained:

$${\mathrm{G}\mathrm{M}\mathrm{L}}_{\mathrm{t}}^{\mathrm{t}+1}={\displaystyle \frac{1+\overline{{\mathrm{D}}_0^{\mathrm{G}}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{y}}^{\mathrm{t}},-{\mathrm{b}}^{\mathrm{t}}\right)}{1+\overline{{\mathrm{D}}_0^{\mathrm{G}}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{y}}^{\mathrm{t}},-{\mathrm{b}}^{\mathrm{t}+1}\right)}}$$

(9)

$${{\mathrm{G}\mathrm{M}\mathrm{L}}_{\mathrm{t}}^{\mathrm{t}+1}=\mathrm{G}\mathrm{T}\mathrm{E}\mathrm{C}}_{\mathrm{t}}^{\mathrm{t}+1}\times {\mathrm{G}\mathrm{T}\mathrm{C}}_{\mathrm{t}}^{\mathrm{t}+1}$$

(10)

Through linear programming, the measurement of the GML index can be decomposed into green technology efficiency (GTEC) and green technology progress (GTC). GTEC reflects efficiency changes resulting from improvements in the production system, economies of scale, and accumulated experience, while GTC arises from efficiency changes due to advancements in production technology and process innovation. The GML index represents the annual growth rate of enterprise GTFP. This study assumes a base period GTFP of 1. By multiplying this base coefficient by the GTFP index for each subsequent period, the GTFP for enterprises from 2007 to 2022 is obtained. The input and output indicators used for the GTFP measurement are detailed in

Table 1. GTFP input–output indicators.

Variable Types	Indicators	Calculation Method	Data Source
Factor Inputs	Capital Input	Perpetual Inventory Method	CSMAR
	Fixed Assets Input	Net Fixed Assets	CSMAR
	Labor Input	Number of Employees	CSMAR
	Energy Input	${\mathrm{IP}}_{\mathrm{kj}}={\mathrm{IP}}_{\mathrm{ij}}\times {\displaystyle \frac{{\mathrm{O}}_{\mathrm{k}}}{{\mathrm{O}}_{\mathrm{j}}}}$	CCSY
Expected Output	Industrial Output	Main Business Income	CSMAR
Unexpected Output	SO2 Emissions	${\mathrm{em}}_{\mathrm{kj}}={\mathrm{em}}_{\mathrm{ij}}\times {\displaystyle \frac{{\mathrm{O}}_{\mathrm{k}}}{{\mathrm{O}}_{\mathrm{i}}}}$	CCSY
	Dust Emissions		CCSY
	Wastewater Emissions		CCSY

.

4.1.2. Independent Variable: Managerial Myopia

This study follows the methodology of Brochet et al. [24] and Chen et al. [25], using the frequency of words related to a “short-term perspective” in the Management Discussion and Analysis (MD&A) section of annual reports, multiplied by 100, as a measure of managerial myopia (Ms). A higher Ms value indicates a greater degree of managerial myopia. Firstly, the set of words indicative of managerial myopia was determined by analyzing the MD&A corpus. This set includes terms related to time constraints, such as “within days”, “within months”, “within years”; immediate commands like “immediately”, “right away”, “at once”; and implicit features such as “opportunity”, “moment”, “pressure”, “challenge”, among others, totaling 10 words (see Appendix A for the detailed term selection). Secondly, Word2Vec machine-learning technology was used to expand this set, adding 33 additional words, including “latest”, “last”, “critical moment”, “coincide with”, among others. Finally, the frequency of these 43 words in the text was standardized by the total vocabulary of the MD&A.

4.1.3. Control Variables

Drawing from Chen et al. [25] and Wang et al. [34], this study selects the firm size (Size), leverage (Lev), total asset turnover (Tat), Tobin’s Q (TobinQ), cash holdings (Cfo), growth opportunities (Growth), firm age (Age), ownership nature (Soe), CEO–chair duality (Pos), and the proportion of independent directors (Idr) as control variables. Additionally, to account for errors generated by unobservable factors, this study also controls for firm (Firm) and year (Year) fixed effects. All variable definitions are shown in

Table 2. Definition of research variables.

Types	Variables	Definition
Dependent Variable	GTFP	Super-SBM and GML index calculation
Independent Variable	Myopia	Ratio of short-sightedness-related term frequency to total text vocabulary in MD&A
Moderating variables	Gi	The existence of green funds in listed companies is denoted as 1, otherwise it is 0
	Lc	Located in the policy implementation area and the period after policy implementation is denoted as 1, otherwise it is 0
	Ms	Equity incentive, the proportion of shares held by management in the total share capital of the company
	Es	The ratio of environmental protection subsidies to total assets
Control variables	Size	The natural logarithm of total assets
	Lev	Total liabilities divided by total assets
	Tat	The ratio of operating income to total assets
	TobinQ	The ratio of market value to total assets
	Cfo	The ratio of net operating cash flow to total assets
	Growth	The growth rate of operating income
	Age	The logarithm of the difference between the current year and the year of listing
	Soe	State-owned listed companies are assigned a value of 1, otherwise 0
	Pos	Chairman also serving as general manager is assigned a value of 1, otherwise 0
	Idr	The proportion of independent directors to the total number of directors on the board

.

4.2. Sample Selection and Data Source

To achieve continued convergence with the International Financial Reporting Standards (IFRS), China issued new Corporate Accounting Standards on 15 February 2006, which came into effect on 1 January 2007. Consequently, 2007 marks a significant milestone in aligning Chinese accounting standards with international standards. To ensure that the entire study period remains unaffected by changes in accounting standards policies, this study uses Chinese industrial listed companies in the A-share market of Shanghai and Shenzhen from 2007 to 2022 as the initial sample. After data collection, the following screening criteria were applied: (1) exclusion of *ST and ST listed companies; (2) exclusion of companies with severe data deficiencies; and (3) exclusion of companies in the financial industry. Finally, this study compiles data from 2692 enterprises, with a total of 20,760 observations. The financial information of listed companies comes from the China Stock Market and Accounting Research (CSMAR) database. Additionally, environmental subsidy data are sourced from annual environmental reports and notes from the financial statements of companies, while data on managerial myopia are drawn from the annual reports of companies. Provincial- and municipal-level economic and environmental statistical data are obtained from the China City Statistical Yearbook (CCSY).

4.3. Models Construction

Because the fixed effects (FEs) model can control for unobservable effects [56], its estimation results are unbiased and consistent. Therefore, this study constructs a two-way fixed effects Model (11) to examine the impact of managerial myopia on enterprise GTFP. Additionally, Models (12) and (13) are constructed to examine the moderating effect of supervisory factors on the impact of managerial myopia on GTFP. Similarly, Models (14) and (15) are constructed to examine the moderating effect of incentive factors on the impact of managerial myopia on GTFP.

$${\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\alpha }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(11)

$$\begin{array}{c}{\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{G}\mathrm{i}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\beta }}_3{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}\ast {\mathrm{G}\mathrm{i}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\beta }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\\ +\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\end{array}$$

(12)

$$\begin{array}{c}{\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{L}\mathrm{c}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\gamma }}_3{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}\ast {\mathrm{L}\mathrm{c}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\gamma }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\\ +\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\end{array}$$

(13)

$$\begin{array}{c}{\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\phi }}_0+{\mathsf{\phi }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\phi }}_2{\mathrm{M}\mathrm{s}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\phi }}_3{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}\ast {\mathrm{M}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\phi }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\\ +\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\end{array}$$

(14)

$$\begin{array}{c}{\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\omega }}_0+{\mathsf{\omega }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\omega }}_2{\mathrm{E}\mathrm{s}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\omega }}_3{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}\ast {\mathrm{E}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\omega }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\\ +\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\end{array}$$

(15)

5. Regression Results

5.1. Sample Descriptive Statistics

The descriptive statistics of the variables are shown in

Table 3. Descriptive statistics of variables.

Variables	N	Mean	Sd	Min	Median	Max
GTFP	20,760	1.003	0.022	0.828	1.001	1.217
GTEC	20,760	0.998	0.020	0.875	1.000	1.133
GTC	20,760	1.005	0.025	0.828	1.002	1.264
Myopia	20,760	0.094	0.087	0.000	0.077	1.439
Size	20,760	22.209	1.266	19.916	22.032	26.064
Lev	20,760	0.427	0.193	0.063	0.424	0.886
Tat	20,760	0.698	0.409	0.103	0.615	2.445
TobinQ	20,760	2.052	1.296	0.858	1.632	8.366
Cfo	20,760	0.054	0.068	−0.137	0.052	0.251
Growth	20,760	0.159	0.333	−0.479	0.110	1.928
Age	20,760	2.160	0.804	0.000	2.303	3.332
Soe	20,760	0.403	0.490	0.000	0.000	1.000
Pos	20,760	0.264	0.441	0.000	0.000	1.000
Idr	20,760	0.372	0.051	0.308	0.333	0.571

. The maximum and minimum values of GTFP are 1.217 and 0.828, respectively, with a standard deviation of 0.022, indicating that there is not much variation in the GTFP levels among different companies. The mean is 1.003, suggesting that the overall level of GTFP among industrial listed companies in China is relatively low, indicating significant room for improvement. Additionally, when comparing the indicators of GTEC and GTC, it is observed that GTC closely aligns with the trend and range of GTFP, while GTEC exhibits smaller fluctuations and smoother changes. Therefore, GTC can better represent GTFP, which is consistent with the findings of Wu et al. [33] and Wang et al. [34], indicating that the GTFP measurement in this study is reasonable. The maximum and minimum values of managerial myopia are 1.439 and 0.000, respectively, with a standard deviation of 0.087, indicating significant differences in the degree of managerial myopia among Chinese enterprises. The selected textual indicator of managerial myopia exhibits reasonable variability.

5.2. Benchmark Regression Results

Table 4. Benchmark regression analysis.

Variables	(1)	(2)	(3)
	GTFP	GTEC	GTC
Myopia	−0.008 ***	−0.002	−0.006 **
	(0.002)	(0.002)	(0.002)
Size	0.003 ***	0.001 ***	0.001 **
	(0.001)	(0.000)	(0.000)
Lev	0.001	0.004 ***	−0.003 *
	(0.002)	(0.001)	(0.002)
Tat	0.007 ***	0.003 ***	0.004 ***
	(0.001)	(0.001)	(0.001)
TobinQ	0.000	−0.000	0.000
	(0.000)	(0.000)	(0.000)
Cfo	0.003	0.005 **	−0.002
	(0.003)	(0.002)	(0.003)
Growth	0.000 ***	−0.000	0.000 ***
	(0.000)	(0.000)	(0.000)
Age	0.004 ***	−0.000	0.005 ***
	(0.001)	(0.001)	(0.001)
Soe	−0.003 **	−0.001	−0.002
	(0.001)	(0.001)	(0.001)
Pos	0.000	−0.001	0.001 *
	(0.001)	(0.000)	(0.001)
Idr	0.001	−0.001	0.002
	(0.004)	(0.004)	(0.005)
Constant	0.934 ***	0.967 ***	0.969 ***
	(0.011)	(0.007)	(0.010)
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
N	20,760	20,760	20,760
R-squared	0.124	0.024	0.122

*** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are reported in parentheses.

reports the results of the impact of managerial myopia on enterprise GTFP, as shown in column (1), which presents the regression results of Model (11). The coefficient of Myopia on GTFP is −0.008, significant at the 1% level, indicating that managerial myopia inhibits the improvement of GTFP, thus confirming Hypothesis 1. Furthermore, columns (2) and (3) report the effects of managerial myopia on GTEC and GTC, respectively. The coefficient of Myopia on GTEC is −0.002, which is not significant, while the coefficient on GTC is −0.006, which is significant at the 5% level, indicating a significant negative impact of Myopia on GTC. The similar impact of Myopia on GTFP and GTC suggests that GTC is a better representative of GTFP, indirectly confirming the view that variations in GTFP are mainly influenced by GTC [33,34].

5.3. Regression Results of the Supervisory Effect

5.3.1. Green Investors

Drawing on the measurement method for green funds proposed by Jin and Han [57], this study identifies funds invested in environmental protection, ecology, green energy, and other related fields as green investors (Gi). If such investment funds are present in listed companies, Gi is assigned a value of 1; otherwise, it is 0.

Table 5. Moderation effects test.

Variables	(1)	(2)	(3)	(4)
	GTFP	GTFP	GTFP	GTFP
Myopia	−0.007 **	−0.007 ***	−0.008 ***	−0.008 ***
	(0.003)	(0.003)	(0.002)	(0.002)
Gi	0.004 ***
	(0.001)
Myopia × Gi	0.059 ***
	(0.006)
Lc		0.002 ***
		(0.001)
Myopia × Lc		0.050 ***
		(0.006)
Ms			0.006 *
			(0.003)
Myopia × Ms			0.127 ***
			(0.016)
Es				−0.002 *
				(0.001)
Myopia × Es				0.018 ***
				(0.006)
Controls	YES	YES	YES	YES
Constant	0.930 ***	0.958 ***	0.938 ***	0.924 ***
	(0.013)	(0.013)	(0.011)	(0.011)
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	16,135	15,065	18,842	20,082
R-squared	0.155	0.161	0.116	0.127

*** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are reported in parentheses.

reports the moderating effect of green investors, i.e., the regression results of Model (12). The regression coefficient of the interaction term Myopia × Gi on GTFP is 0.059, which is significant at the 1% level. This result indicates that green investors mitigate the negative impact of managerial myopia on the GTFP of enterprises, thus confirming Hypothesis 2a.

5.3.2. The Low-Carbon City Pilot Policies

The low-carbon city pilot policies were implemented in three batches. Following the approach of Yu and Zhang [45], this study generated time dummy variables and policy dummy variables. The time dummy variable takes a value of 1 for the year of policy implementation and subsequent years, and 0 otherwise. The policy dummy variable takes a value of 1 for locations where the policy is implemented, and 0 otherwise. The product of these two variables measures the low-carbon city pilot policies (Lc). Additionally, due to overlaps in the pilot cities across the three stages, this study selected the earliest year of policy implementation as the policy implementation time. Table 5 reports the moderating effect of the low-carbon city pilot policies, i.e., the regression results of Model (13). The regression coefficient of the interaction term Myopia×Lc on GTFP is 0.050, which is significant at the 1% level. This result indicates that the low-carbon city pilot policies mitigate the negative impact of managerial myopia on the GTFP of enterprises, thus confirming Hypothesis 2b.

5.4. Regression Results of the Incentive Effect

5.4.1. Equity Incentives

Shareholders often grant shares to management to provide long-term incentives. Following the measurement method of Hao et al. [58], this study measures equity incentives by the proportion of shares held by management in the company’s total share capital. Column (3) of Table 5 reports the moderating effect of equity incentives, i.e., the regression results of Model (14). The regression coefficient of the interaction term Myopia × Ms on GTFP is 0.127, which is significant at the 1% level, indicating that equity incentives mitigate the negative impact of managerial myopia on enterprise GTFP, thus confirming Hypothesis 3a.

5.4.2. Environmental Protection Subsidies

Listed companies in heavily polluting industries are required to prepare annual environmental reports in accordance with the “Guidelines for the Environmental Information Disclosure of Listed Companies”, while other companies’ financial statement notes also disclose data related to environmental protection subsidies. Following the approach of Jiang et al. [52], this study collects and compiles the amount of environmental protection subsidies using keywords related to environmental protection, such as “green”, “emissions reduction”, “environment”, “sustainable”, “clean”, and “energy-saving”. The standardized amount of environmental protection subsidies relative to the total assets is used as a measure. Table 5 reports the moderating effect of environmental protection subsidies, i.e., the regression results of Model (15). The interaction term Myopia × Es has a regression coefficient of 0.018, which is significant at the 1% level, indicating that environmental protection subsidies mitigate the negative impact of managerial myopia on the GTFP of enterprises, thus confirming Hypothesis 3b.

5.5. Robustness Tests

5.5.1. Lagged Explanatory Variable

The myopic behavior of management, as characterized by a focus on short-term gains such as reduced research and development expenditures and short-term investments, may not immediately harm the company. However, such managerial myopia can negatively affect both the current and future GTFP of the enterprise. To account for this, the present study includes a lagged explanatory variable, Myopia_Lag, in the regression analysis. The results, reported in

Table 6. Robustness tests.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GTFP	Myopia	GTFP	GTFP	TFP_LP	TFP_OP	GTFP	GTFP
Myopia_Lag	−0.008 ***
	(0.002)
Ticket		0.009 **
		(0.004)
Myopia_Ins			−0.725 **
			(0.346)
Myopia				−0.007 ***	−0.089 ***	−0.080 **
				(0.002)	(0.030)	(0.032)
Mp1							−0.065 ***
							(0.022)
Mp2								−0.003 ***
								(0.001)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Constant	0.944 ***	0.165 ***	1.088 ***	0.948 ***	−5.828 ***	−4.904 ***	0.934 ***	0.937 ***
	(0.012)	(0.031)	(0.064)	(0.013)	(0.239)	(0.257)	(0.011)	(0.011)
Firm FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
N	17,034	18,467	18,467	15,490	15,924	15,924	20,760	20,760
PSM	No	No	No	Yes	No	No	No	No
R-squared	0.134	0.088	0.024	0.130	0.824	0.763	0.124	0.125

*** p < 0.01, ** p < 0.05. Standard errors are reported in parentheses.

, column (1), show that the coefficient of Myopia_Lag on GTFP is −0.008, which is significant at the 1% level, thereby confirming our hypothesis.

5.5.2. Instrumental Variable Method

To address the significant endogeneity issues stemming from bidirectional causality, this study employs the instrumental variable method for endogeneity testing. Gambling behavior, being a form of short-term speculation, can drive managers to prioritize short-term decisions over long-term value considerations [14], reflecting, to some extent, the opportunistic tendencies of management [59]. In this study, welfare lottery sales in the province where the company is located (Ticket) are used as an instrumental variable for managerial myopia. The results of the instrumental variable method are presented in columns (2) and (3) of Table 6. Column (2) reports the first-stage regression, where the coefficient of Ticket is significantly positive. Column (3) demonstrates that the coefficient of managerial myopia (Myopia_Ins) is significantly negative, thus corroborating the regression results.

5.5.3. Propensity Score Matching

To address the potential sample selection bias and endogeneity, this study re-examines the relationship between managerial myopia and enterprise GTFP using propensity score matching (PSM). Following the methodology of Sheng et al. [18], this study designates firms with managerial myopia above their own median as the treatment group. The main regression control variables serve as covariates, and a one-to-one nearest neighbor matching method is applied to match the treatment group with a control group.

Table A1. Covariate balance test for PSM samples.

Variables	Match	Mean		Mean Difference Test
		Treatment	Control	T-Value	p-Value
Size	Unmatched	22.330	22.092	13.310	0.000
	Matched	22.323	22.349	−1.430	0.154
Lev	Unmatched	0.457	0.398	22.020	0.000
	Matched	0.455	0.454	0.100	0.919
Tat	Unmatched	0.707	0.705	0.350	0.730
	Matched	0.707	0.709	−0.420	0.678
TobinQ	Unmatched	2.027	2.191	−5.600	0.000
	Matched	2.024	2.043	−0.870	0.384
Cfo	Unmatched	0.052	0.056	−3.500	0.000
	Matched	0.052	0.052	0.310	0.757
Growth	Unmatched	0.388	0.211	1.640	0.102
	Matched	0.178	0.198	−1.580	0.114
Age	Unmatched	2.281	2.041	21.770	0.000
	Matched	2.278	2.282	−0.350	0.723
Soe	Unmatched	0.480	0.325	23.000	0.000
	Matched	0.478	0.488	−1.510	0.132
Pos	Unmatched	0.224	0.303	−13.080	0.000
	Matched	0.224	0.225	−0.030	0.973
Idr	Unmatched	0.371	0.374	−3.710	0.000
	Matched	0.371	0.371	0.440	0.660

presents the results of the covariate balance test, indicating no significant differences in firm characteristics between the matched treatment and control groups. Column (4) of Table 6 reports the regression results after the propensity score matching, showing that the coefficient of Myopia on GTFP is −0.007, which is significantly negative at the 1% level, consistent with the findings from the full sample regression.

5.5.4. Substitution Variable Test

• Substitution Dependent Variable

This study uses TFP as an alternative measure of GTFP for companies. To address the potential biases associated with ordinary least squares estimation, this study adopts the methodologies proposed by Olley and Pakes [60] and Petrin et al. [61], using a semi-parametric approach to calculate TFP (TFP_LP and TFP_OP) for companies. Columns (5) and (6) of Table 6 present the regression results incorporating these TFP measures into the model. The regression coefficients of Myopia on both TFP_LP and TFP_OP are significantly negative, confirming the robustness of the findings.

2.

Substitution Independent Variable

Following the approach of Lai et al. [16], this study selects two alternative variables to measure managerial myopia. The first variable, Mp1, is the reduction in research and development (R&D) expenditure, which serves as a substitute indicator of managerial myopic behavior. Specifically, Mp1 is calculated as the difference between a company’s R&D expenditure in year t and that in year t − 1, divided by the total assets at the end of year t − 1. The second variable, Mp2, is a binary measure that indicates whether a company has reduced its R&D expenditure in the current year, providing an additional metric for assessing managerial myopia. Specifically, if a company’s R&D expenditure in year t is less than that in year t − 1, Mp2 is set to 1; otherwise, Mp2 is set to 0. Columns (7) and (8) in Table 6 present the regression results of the replaced explanatory variables. Both Mp1 and Mp2 have significantly negative regression coefficients on GTFP at the 1% level, supporting the hypothesis of the main regression.

Additionally, this study replaces the explanatory variable with the reduction in R&D expenditure (Mp1) and incorporates it into Models (11)–(14) to test the robustness of the moderation effect.

Table A2. Substitution variables for the moderating effects.

Variables	(1)	(2)	(3)	(4)
	GTFP	GTFP	GTFP	GTFP
Mp1	−0.115 ***	−0.081 *	−0.061 **	−0.051 **
	(0.033)	(0.044)	(0.027)	(0.023)
Gi	0.008 ***
	(0.001)
Mp1 × Gi	0.131 ***
	(0.034)
Lc		0.007 ***
		(0.001)
Mp1 × Lc		0.090 **
		(0.043)
Ms			0.014 ***
			(0.003)
Mp1 × Ms			0.169 **
			(0.070)
Es				−0.002 *
				(0.001)
Mp1 × Es				2.208 ***
				(0.668)
Controls	YES	YES	YES	YES
Constant	0.930 ***	0.961 ***	0.938 ***	0.927 ***
	(0.013)	(0.013)	(0.011)	(0.011)
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
N	16,135	15,065	18,842	19,456
R-squared	0.148	0.153	0.113	0.114

*** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are reported in parentheses.

presents the results of the moderation effect regression using this alternative explanatory variable. Columns (1) and (2) report the moderation of the supervisory effect, showing that the regression coefficients of the interaction terms Mp1 × Gi and Mp1 × Lc are both significantly positive. This further confirms that green investors and low-carbon city pilot policies can serve as moderating factors, thereby mitigating the inhibitory effect of managerial myopia on enterprise GTFP. Columns (3) and (4) present the moderation of the incentive effect, where the regression coefficients of the interaction terms Mp1 × Ms and Mp1 × Es are also significantly positive. This validates that equity incentives and environmental protection subsidies can act as moderating influences, thereby reducing the negative impact of managerial myopia on enterprise GTFP.

6. Further Analysis

This paper aims to further explore the mechanisms through which managerial myopia affects enterprise GTFP, clarifying the pathways through which managerial myopia influences enterprise GTFP. As previously analyzed, green innovation often requires a long time to yield economic benefits. Due to their short-term orientation, myopic managers may prioritize immediate economic gains, leading to insufficient support for green innovation activities. Additionally, managerial risk aversion may hinder enterprise exploration in the realm of green innovation. Given that green innovation projects typically involve new technologies, new markets, and uncertain environmental impacts, these projects often carry higher risks. Myopic managers may prefer conservative business decisions, showing less tolerance for the operational risks and performance pressures associated with green innovation. Consequently, managerial myopia can suppress enterprise GTFP by reducing investment in green innovation.

The innovativeness of patents decreases in the order of invention patents, utility model patents, and design patents. Zhang et al. [62] argue that green patent applications are more challenging and better reflect a company’s level of green innovation compared to granted green patents. Therefore, this study first measures green innovation by the proportion of total green patent applications (Gp). Additionally, it uses the proportion of green invention patent applications (Gq) and the proportion of green utility model patent applications (Gn) to measure the quality and quantity of green innovation, respectively. Following the mediation test method proposed by Jiang [63], this study constructs the two-way fixed effects Models (16)–(18) to examine the impact of managerial myopia on green innovation.

$${\mathrm{G}\mathrm{p}}_{\mathrm{i},\mathrm{t}}={\mathsf{\theta }}_0+{\mathsf{\theta }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\theta }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(16)

$${\mathrm{G}\mathrm{q}}_{\mathrm{i},\mathrm{t}}={\mathsf{\vartheta }}_0+{\mathsf{\vartheta }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\vartheta }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(17)

$${\mathrm{G}\mathrm{n}}_{\mathrm{i},\mathrm{t}}={\mathsf{\mu }}_0+{\mathsf{\mu }}_1{\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}+\sum {\mathsf{\mu }}_{\mathrm{i}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\sum {\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(18)

Columns (1)–(3) in

Table 7. Mediation effects test.

Variables	(1)	(2)	(3)
	Gp	Gq	Gn
Myopia	−0.105 ***	−0.059 ***	−0.045 ***
	(0.009)	(0.006)	(0.005)
Controls	YES	YES	YES
Constant	0.004	−0.002	0.006
	(0.047)	(0.035)	(0.027)
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
N	18,878	18,878	18,878
R-squared	0.010	0.007	0.006

*** p < 0.01. Standard errors are reported in parentheses.

report the regression results of managerial myopia on green innovation, the quality of green innovation, and the quantity of green innovation, respectively. The regression coefficients of Myopia on Gp, Gq, and Gn are all significantly negative at the 1% level, indicating that managerial myopia significantly reduces a company’s green innovation efforts and exerts a suppressive effect on both the quality and the quantity of green innovation. Consequently, managerial myopia can diminish enterprise GTFP by inhibiting green innovation behavior.

7. Conclusions and Policy Implications

This paper uses a sample of industrial companies listed on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2022 and employs a two-way fixed effects model to empirically test how managerial myopia impacts enterprise GTFP. In this way, it enriches the research on the consequences of managerial myopia and the determinants of GTFP, offering a new perspective for future studies. Furthermore, this study explores how to effectively mitigate the negative impact of managerial myopia on GTFP through both supervisory and incentive effects. It also investigates the specific mechanisms by which managerial myopia affects GTFP, providing theoretical significance and policy implications for curbing the adverse effects of managerial myopia and enhancing GTFP. Our empirical findings reveal that managerial myopia significantly inhibits enterprise GTFP. The analysis of the moderating effects shows that green investors and low-carbon city pilot policies can provide internal and external supervision, thereby reducing the negative impact of managerial myopia on GTFP. Additionally, equity incentives and environmental protection subsidies can act as internal and external incentives, mitigating the inhibitory effect of managerial myopia on enterprise GTFP. Further mechanism tests suggest that managerial myopia reduces enterprise GTFP by suppressing green innovation.

This study makes several theoretical contributions. First, unlike traditional research that primarily examines the direct green factors influencing GTFP, this paper adopts a managerial perspective, empirically demonstrating that managerial myopia—an often overlooked potential factor—indeed affects GTFP, thereby opening up new avenues for research. Second, while existing studies predominantly focus on how managerial myopia inhibits long-term-oriented factors, the critical role of GTFP in long-term corporate development has been underemphasized. This study integrates GTFP into the category of long-term orientation, confirming its partial restriction by managerial myopia and enriching the discourse on this topic. Third, this paper not only explores the negative impacts of managerial myopia but also constructs a theoretical framework based on the internal and external supervisory and incentive effects. It critically analyzes how these factors can mitigate the adverse effects of managerial myopia, particularly examining their moderating role in the relationship between managerial myopia and GTFP. This provides profound insights and valuable references for future research. Finally, this study further investigates the specific mechanisms through which managerial myopia affects GTFP, revealing that green innovation plays a crucial role in this process. This finding sheds light on the green crowding-out effect of managerial myopia, offering valuable insights for future scholars.

The research findings of this study have several policy implications. Managerial myopia plays a crucial role in shaping strategic decisions within enterprises, especially as the attention paid to and regulation of pollution emissions increase. Proactive preventive measures based on supervision and incentives are more effective in mitigating the negative effects of managerial myopia than remedial actions taken after the fact. From a supervisory perspective, the introduction of green investors and the implementation of government-led low-carbon city pilot policies can effectively reduce the adverse effects of managerial myopia on both enterprises and society. On the incentive side, actively promoting equity incentives within enterprises, alongside government enhancements of environmental protection subsidies, can encourage management to prioritize long-term strategic decisions. The synergistic effect of these internal and external supervisory and incentive measures can create a robust governance system, fostering the sustainable and healthy development of enterprises, the economy, and society. Therefore, both enterprises and governments must move beyond superficial endorsements of green development. They must delve deeper into corporate structures, focusing on the supervision and incentivization of managerial behavior. By establishing green-related corporate charters and policy frameworks, these entities can ensure that green governance measures are effectively implemented and truly impactful.