The Impact of ESG Performance on Corporate Investment Efficiency: Evidence from Chinese Agribusiness Companies

Abstract

This study conducts an empirical examination of the impact of ESG (Environmental, Social, and Corporate Governance) performance on corporate investment efficiency, utilizing fixed-effects and mediation-effects models with a sample of 125 listed agribusiness companies in China from 2013 to 2022. The results of the fixed-effects regression indicate that superior ESG performance can effectively enhance corporate investment efficiency. Furthermore, the results of the mediation-effects analysis unveil the underlying mechanism through which ESG performance contributes to investment efficiency: by reducing agency costs and alleviating financing constraints. Moreover, the heterogeneity analysis suggests that ESG performance promotes investment efficiency more significantly in low-competition and moderately competitive market environments. By contrast, its effect may be somewhat muted in highly competitive markets. The findings of this study indicate that agribusiness companies should integrate ESG strategies, increase information transparency disclosure, and refine the allocation and management of resources in their operations.

1. Introduction

In the context of the progressive development of the globalized economic system, corporate social responsibility has emerged as a pivotal metric for evaluating corporate value and market influence. The evolution of the ESG strategy system, comprising environmental, social, and corporate governance, from a singularly moral constraint to a systematic value creation instrument, indicates a paradigm shift in corporate social responsibility (CSR) [1]. Integrating environmental protection, stakeholder rights and interests, and governance effectiveness into the decision-making framework has led to a new paradigm of synergistic development, balancing economic performance and social benefits, thereby contributing to sustainable development. This new paradigm offers a practical approach to achieving sustainable development.

Third-party ESG ratings have emerged as a key reference for evaluating corporate ESG performance in China [2,3]. Nevertheless, such ratings may exhibit bias toward specific dimensions, as rating agencies often apply differential weighting schemes to particularly align with the preferences of investor groups or market expectations. Notably, uniform rating standards do not reflect the differences between firm types, causing variability in the validity of ESG assessments. To address this issue, this study innovatively proposes an ESG scoring framework based on the characteristics of agribusiness companies, termed “Balanced ESG Performance” (B-ESG). This framework involves assigning equal weight to all three dimensions (E, S, and G), thereby allocating attention and resources in a balanced manner. Also, this framework can help agribusiness companies achieve a balanced approach in each area, thereby avoiding an overemphasis on one dimension to the detriment of ability to monitor other potential risks.

Using an empirical approach, this research investigates the impact of ESG performance on corporate investment efficiency, based on a sample of 125 domestically listed agricultural firms in China from 2013 to 2022. Based on the B-ESG framework, the analysis incorporates third-party ESG scores to capture structural differences across rating systems. Fixed-effects and mediation-effect models are constructed to comprehensively examine the influence of ESG performance on investment efficiency and to explore the underlying mechanisms. Our findings indicated that ESG performance enhances investment efficiency in agribusiness companies by mitigating agency costs and easing financing constraints. This study contributes to the literature by addressing the existing research gap concerning ESG implications within the unique context of agribusiness companies. These findings will provide valuable theoretical support for the formulation and implementation of ESG strategies in agribusiness companies.

The structure of the present paper is as follows. Section 2 reviews the theoretical development of ESG performance and presents the hypotheses of this study. The third section of the paper provides a comprehensive overview of the research methodology employed in the study. This section outlines the establishment of variables and the subsequent modeling techniques employed in the research. The fourth section of this text is dedicated to presenting the empirical results. In Section 5, the extant empirical results derived from the preceding section are discussed, the conclusions of the present paper are drawn, and the study’s practical implications are presented.

2. Literature Review

2.1. ESG Rating System and Methodological Controversies

Although third-party ESG rating systems have been widely utilized in existing studies, certain limitations remain. The prevailing ESG screening strategy is susceptible to “false positives”, and an overreliance on explicit indicators such as carbon emissions may result in the oversight of significant risks [4]. ESG ratings also have an impact on the Chinese stock market, and the integration of ESG factors is associated with improved financial performance [5]. Also, the impact of rating divergence on corporate innovation has been investigated [6]. The authors’ findings suggest that ESG rating differences have a positive impact on corporate green innovation, with the positive impact of ESG rating differences on green innovation being more pronounced in companies with more substantial independent director resources and higher media attention. Moreover, the approaches to ESG ratings by different organizations vary significantly, leading to the possibility that the same company’s ESG score could differ substantially. Approximately 72% of European companies do not have an external ESG rating, which could lead to these companies being excluded from investment by asset managers relying on third-party sustainability assessments [7]. The low correlation between different ESG ratings suggests that there are significant differences in ESG ratings even among European companies that follow similar sustainability disclosure regulations. This suggests that dynamic adjustment of ESG weight allocation is needed. The efficacy of an integration strategy depends on the granularity of ratings, and a model with segmentation-adjusted weights could boost annualized excess returns by 2.8 percentage points [8].

The validity of existing ESG rating systems has been questioned due to the risk of “false positives” and biased indicator weights. This has the potential to engender rating divergence, which may incentivize firms to increase green R&D investments.

2.2. ESG Rating System for Agribusiness Companies

To explore the impact of ESG performance on agribusiness investment efficiency, it is first necessary to clarify the connotation of ESG and its role in agribusiness operations [9]. Existing research has identified risk, information, and strategy perspectives as reflecting the key ways in which ESG practices play a role, directly or indirectly, in favoring and avoiding harm and creating value for firms [10,11]. The environmental governance dimension (E) controls negative environmental externalities in the production chain. It includes specific practices such as climate change management (e.g., social responsibility initiatives to promote inclusive growth in the industrial chain). The social responsibility dimension (S) extends beyond the traditional scope of employee rights and interests to include the inclusive growth of the industrial chain, such as the training of new professional farmers, community co-construction (e.g., the “enterprise–farmer” benefit linkage mechanism), food safety traceability, and other agriculture-specific responsibility issues. The corporate governance dimension (G) responds to the complexity of the property rights structure of agribusinesses. It reduces operational uncertainty by improving bio-asset disclosure, strengthening the transparency of cooperative decision-making, and establishing an anti-corruption mechanism in the agricultural sector. In summary, considering the critical roles played by the E, S, and G dimensions in agribusiness operations, it is evident that all three are equally important and indispensable in promoting investment efficiency in the agricultural sector. Therefore, the proposed B-ESG framework assigns equal weights to these dimensions to ensure a comprehensive and reasonable assessment of their impact on agribusiness investment efficiency.

Moreover, this study also demonstrates that the existing ESG performance studies focus on listed companies, with comparatively less attention paid to the agribusiness sector as a distinct industry. This paper focuses on agribusiness, filling gaps in the existing literature on industry segmentation and providing a more targeted empirical basis for relevant policy-making and industry practice. Most of the literature typically focuses on overall performance when exploring ESG strategies, often overlooking the potential heterogeneity effect among the three major ESG dimensions. In this study, the three indicators of environmental governance (E), social responsibility (S), and corporate governance (G) are analyzed separately and independently to reveal the distinct impact mechanisms of each dimension on corporate investment efficiency. This detailed sub-dimensional study can provide a more accurate reflection of the specific performance of agribusinesses in each dimension [12]. Furthermore, it has the potential to provide enterprises with a more scientific and precise basis for making informed decisions about ESG strategies.

2.3. Relationship Between ESG and Financial Performance

Earlier studies have revealed the relationship between environmental, social, and governance (ESG) factors and financial performance [13,14]. A nonlinear model reveals that corporate social responsibility (CSR) has a threshold effect on financial performance, and that overinvestment may lead to diminishing marginal returns [14,15]. A meta-analysis of more than 2000 empirical studies confirms that ESG performance has a positive relationship with financial performance; however, there are moderating variables, such as industry attributes [16]. This finding is further substantiated in Asian markets, where Budsaratragoon and Jitmaneeroj [17] ascertain that ESG factors exhibit a more pronounced synergistic value effect in emerging markets. Domestic scholars further introduce internal control mediating variables to reveal the transmission path by which ESG enhances firm value through governance optimization [18]. Cross-country studies show that there is an efficiency boundary for asymmetric optimization of the three dimensions of ESG, and that firms need to maintain the equilibrium of their E/S/G scores (dispersion coefficient <0.3) to maximize their performance, with imbalanced firms’ valuation discount rates amounting to 12–18% [19].

ESG exhibits a nonlinear relationship with financial performance, characterized by a significant threshold effect. Industry attributes moderate their positive association, generating a synergistic value amplification effect in emerging markets. Domestic studies further elucidate the role of ESG in enhancing corporate value by optimizing internal control mechanisms.

2.4. Mediation Effects Between ESG and Financial Performance

The argument that CSR promotes investment efficiency by reducing information asymmetry-induced overinvestment was first proposed by Benlemlih and Bitar [20]. High-quality ESG disclosure is also found to improve the accuracy of investment decisions and reduce capital mismatches [21]. In addition, a three-stage model of “ESG—Resource Allocation—Investment Efficiency” was proposed from the perspective of integrated management [22]. This model emphasizes the cooperative effect of cross-sectoral collaboration [22]. Domestic studies are more industry-specific. The efficacy of environmental, social, and governance (ESG) investment is enhanced by alleviating financing constraints [23]. Conversely, regulatory intensity moderates the effectiveness of ESG [24].

The present study investigates the impact of corporate social responsibility (CSR) and Environmental, Social, and Corporate Governance (ESG) performance on investment efficiency. It demonstrates that high performance significantly enhances firm investment efficiency by reducing information asymmetry, improving the quality of information disclosure, optimizing resource allocation, and mitigating financing constraints. However, the study also shows that the effects of CSR and ESG performance are moderated by factors such as regulatory intensity.

Agribusiness companies with excellent ESG performance have been shown to improve investment efficiency through a triple value transmission: environmental compliance reduces the political risk premium, social responsibility practices enhance supply chain resilience, and governance optimization reduces agency costs, ultimately forming a pattern of “ESG premium—lower cost of capital—improved investment efficiency.”

2.5. Research Hypotheses

In the agribusiness sector, ESG performance may play a particularly important role due to its exposure to regulatory scrutiny, supply chain vulnerabilities, and environmental externalities. Existing research also highlights that ESG performance can reduce information asymmetry and agency costs, improve internal governance structures, and alleviate financing constraints. These channels offer a theoretical foundation for understanding how ESG initiatives contribute to investment efficiency in firms operating within complex institutional environments. Accordingly, this study proposes the following hypotheses:

Hypothesis 1 (H1).

ESG performance is positively associated with corporate investment efficiency in agribusiness companies. ESG engagement promotes transparency, accountability, and long-term orientation, which together improve capital allocation and reduce investment inefficiencies.

Hypothesis 2a (H2a).

ESG performance improves investment efficiency by mitigating agency costs. ESG practices—particularly those related to governance—help align the interests of managers and shareholders, reduce opportunistic behaviors, and strengthen internal control mechanisms, thereby enhancing the quality of investment decisions.

Hypothesis 2b (H2b).

ESG performance improves investment efficiency by alleviating financing constraints. High ESG ratings reduce perceived firm risk among investors, thereby lowering the cost of capital and improving access to external financing, which facilitates more efficient capital allocation.

3. Research Design

3.1. Methodology

3.1.1. Correlation of ESG and Agribusiness Investment Efficiency

To test the ESG–investment efficiency linkage, we construct the following fixed-effects models.

Equation (1) examines the relationship between ESG (as measured by the Sino-Securities Index: SSI) and agribusiness investment efficiency.

Equation (2) examines the relationship between balanced ESG and the efficiency of agribusiness investment. Equation (3) investigates the relationship between E and agribusiness investment efficiency. Equation (4) examines the relationship between S and agribusiness investment efficiency, while Equation (5) explores the relationship between G and agribusiness investment efficiency.

$$\begin{gathered} {Inev}_{i,t}={\alpha }_0+{\alpha }_1{ESG}_{i,t}+{\alpha }_2{tang}_{i,t}+{\alpha }_3{fixed}_{i,t}+{\alpha }_4{lev}_{i,t}+{\alpha }_5{imma}_{i,t}+{\alpha }_6{fhold}_{i,t}+{\alpha }_7{cash}_{i,t}+{\alpha }_8{age}_{i,t} \\ +{\alpha }_9{invest}_{i,t}+{\alpha }_{10}{grow}_{i,t}+{\alpha }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(1)

$$\begin{gathered} {Inev}_{i,t}={\omega }_0+{\omega }_1{ESG1}_{i,t}+{\omega }_2{tang}_{i,t}+{\omega }_3{fixed}_{i,t}+{\omega }_4{lev}_{i,t}+{\omega }_5{imma}_{i,t}+{\omega }_6{fhold}_{i,t}+{\omega }_7{cash}_{i,t} \\ +{\omega }_8{age}_{i,t}+{\omega }_9{invest}_{i,t}+{\omega }_{10}{grow}_{i,t}+{\omega }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(2)

$$\begin{gathered} {Inev}_{i,t}={\beta }_0+{\beta }_1{E}_{i,t}+{\beta }_2{tang}_{i,t}+{\beta }_3{fixed}_{i,t}+{\beta }_4{lev}_{i,t}+{\beta }_5{imma}_{i,t}+{\beta }_6{fhold}_{i,t}+{+\beta }_7{cash}_{i,t}+{\beta }_8{age}_{i,t} \\ +{\beta }_9{invest}_{i,t}+{\beta }_{10}{grow}_{i,t}+{\beta }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(3)

$$\begin{gathered} {Inev}_{i,t}={\gamma }_0+{\gamma }_1{S}_{i,t}+{\gamma }_2{tang}_{i,t}+{\gamma }_3{fixed}_{i,t}+{\gamma }_4{lev}_{i,t}+{\gamma }_5{imma}_{i,t}+{\gamma }_6{fhold}_{i,t}+{\gamma }_7{cash}_{i,t}+{\gamma }_8{age}_{i,t} \\ +{\gamma }_9{invest}_{i,t}+{\gamma }_{10}{grow}_{i,t}+{\gamma }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(4)

$$\begin{gathered} {Inev}_{i,t}={\lambda }_0+{\lambda }_1{G}_{i,t}+{\lambda }_2{tang}_{i,t}+{\lambda }_3{fixed}_{i,t}+{\lambda }_4{lev}_{i,t}+{\lambda }_5{imma}_{i,t}+{\lambda }_6{fhold}_{i,t}+{\lambda }_7{cash}_{i,t}+{\lambda }_8{age}_{i,t} \\ +{\lambda }_9{invest}_{i,t}+{\lambda }_{10}{grow}_{i,t}+{\lambda }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(5)

The meaning of the variables in Equations (1)–(5) is elucidated in

Table 1. Variables connotation.

Variables	Name	Symbol	Meaning
Explained variable	Investment efficiency	${Inev}_{i,t}$	Residuals from Richardson’s investment efficiency model
Explanatory variables	ESG	${ESG}_{i,t}$	ESG (SSI)
	Balanced ESG	${ESG1}_{i,t}$	$ESG1=0.33\times \mathrm{E}+0.33\times S+0.33\times G$
	ESG: E	$E_{i,t}$	E
	ESG: S	$S_{i,t}$	S
	ESG: G	$G_{i,t}$	G
Control variables	quick ratio	${flow}_{i,t}$	(Cash + short-term investments + accounts payable)/current liabilities
	asset structure	${tang}_{i,t}$	(Net fixed assets + net deposits)/total assets
	fixed assets	${fixed}_{i,t}$	Fixed/total assets
	gearing	${lev}_{i,t}$	Total liabilities/total assets
	intangible assets	${imma}_{i,t}$	Net intangible assets/total assets
	equity concentration	${fhold}_{i,t}$	Ratio of shares held by the largest shareholder
	cash flow	${cash}_{i,t}$	Net cash flow/total assets
	listing age	${age}_{i,t}$	Natural logarithm of the number of years a firm has been listed
	investment expenditure	${invest}_{i,t}$	Cash paid for acquisition and construction of fixed assets, intangible assets, and other long-term assets/total assets
	growth	${grow}_{i,t}$	Growth rate of business revenue
	year	$\sum year$	Year dummy variable
	industry	$\sum indus$	Industry dummy variable
	random interference term (RIT)	${\epsilon }_{i,t}$

.

3.1.2. Mediating Models

• Mitigating agency problems

The separation of ownership and management is prevalent in contemporary corporate governance structures. This separation can result in information asymmetry between the principal (shareholders) and the agent (management), thereby precipitating conflicts between them. Such conflicts have the potential to exert a detrimental influence on the healthy development of the enterprise. In pursuit of corporate efficiency, management may engage in opportunistic behaviors, such as reducing expenditures through mass layoffs or reduced hiring, which can harm the firm’s long-term development [25,26].

However, companies with excellent ESG performance tend to demonstrate a strong commitment to environmental protection, a deep sense of social responsibility, and a robust corporate governance structure. These companies can mitigate conflicts between shareholders and management through effective governance mechanisms, which can help improve corporate investment efficiency. The positive effects of ESG are reflected in its contributions to the environment and society, as well as in its optimization of internal corporate governance, which is crucial for improving companies’ overall performance.

To explore the relationship between ESG performance and firms’ investment efficiency in greater depth, this study introduces the concept of agency costs (management) as a mediating variable for analysis. The term “agency cost” refers to the management fee rate of a firm, which reflects the costs borne by management in the firm’s operations. An elevated management fee rate can indicate a heightened agency cost, potentially intensifying the discord between shareholders and management. In this study, we utilize the methodology developed by Wen, et al. [27] to construct a model that will serve as a test of the mediating mechanism of agency cost:

$$\begin{gathered} {manage}_{i,t}={\delta }_0+{\delta }_1{ESG}_{i,t}+{\delta }_2{tang}_{i,t}+{\delta }_3{fixed}_{i,t}+{\delta }_4{lev}_{i,t}+{\delta }_5{imma}_{i,t}+{\delta }_6{fhold}_{i,t}+{\delta }_7{cash}_{i,t}+{\delta }_8{age}_{i,t} \\ +{\delta }_9{invest}_{i,t}+{\delta }_{10}{grow}_{i,t}+{\delta }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(6)

$$\begin{gathered} {Inev}_{i,t}={\rho }_0+{\rho }_1{ESG}_{i,t}+{\rho }_2{manage}_{i,t}+{\rho }_3{tang}_{i,t}+{\rho }_4{fixed}_{i,t}+{\rho }_5{lev}_{i,t}+{\rho }_6{imma}_{i,t}+{\rho }_7{fhold}_{i,t} \\ +{\rho }_8{cash}_{i,t}+{\rho }_9{age}_{i,t}+{\rho }_{10}{invest}_{i,t}+{\rho }_{11}{grow}_{i,t}+{\rho }_{12}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(7)

2

Reducing financing constraints

Enterprises invariably find themselves constrained by financial limitations when formulating investment strategies. In the capital market context, enterprises frequently encounter challenges in accessing financial resources, particularly those enterprises characterized by a lack of transparency in their information or a history of financial instability. Nevertheless, a firm’s environmental, social, and governance (ESG) performance has the potential to mitigate this issue to a certain extent.

The concept of “good ESG performance” is predicated on the premise that a company has met the requisite environmental protection, social responsibility, and corporate governance standards. Such companies typically prioritize the quality of information they disclose to the public, thereby enhancing the transparency of their corporate information. Enhancing transparency fosters increased investor confidence in the enterprise, while simultaneously drawing the attention of the media and the general public. This positive feedback loop has been shown to function as an effective monitoring mechanism.

The mediating variable designated is the financing constraint (SA). Following the methodology proposed by Hadlock and Pierce [28], the measurement of financial constraints and the corresponding parameter settings in this study are specified as follows:

$$SA=-0.737\mathit{size}+0.043{\mathit{size}}^2-0.04\mathit{age}$$

(8)

This study defines size as the natural logarithm of firm size, and age is measured as the year the firm went public. The present study employs the methodology of Wen, Chang, Hau, and Liu [27] to construct the following model:

$$\begin{gathered} {SA}_{i,t}={\eta }_0+{\eta }_1{ESG}_{i,t}+{\eta }_2{tang}_{i,t}+{\eta }_3{fixed}_{i,t}+{\eta }_4{lev}_{i,t}+{\eta }_5{imma}_{i,t}+{\eta }_6{fhold}_{i,t}+{\eta }_7{cash}_{i,t}+{\eta }_8{age}_{i,t} \\ +{\eta }_9{invest}_{i,t}+{\eta }_{10}{grow}_{i,t}+{\eta }_{11}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(9)

$$\begin{gathered} {Inev}_{i,t}={\phi }_0+{\phi }_1{ESG}_{i,t}+{\phi }_2{SA}_{i,t}+{\phi }_3{tang}_{i,t}+{\phi }_4{fixed}_{i,t}+{\phi }_5{lev}_{i,t}+{\phi }_6{imma}_{i,t}+{\phi }_7{fhold}_{i,t}+{\phi }_8{cash}_{i,t} \\ +{\phi }_9{age}_{i,t}+{\phi }_{10}{invest}_{i,t}+{\phi }_{11}{grow}_{i,t}+{\phi }_{12}{flow}_{i,t}+\sum year+\sum indus+{\epsilon }_{i,t} \end{gathered}$$

(10)

3.2. Variables and Data Sources

3.2.1. Variables

• Mitigating agency problems

The present study employs Richardson’s model as a methodological framework for assessing investment efficiency. This model measures investment efficiency by assessing the deviation of a firm’s actual investment from its “optimal investment level,” comparing actual investment to normal investment to identify underinvestment or overinvestment. Compared to traditional financial indicator methods, Richardson’s model controls multiple influencing factors to isolate non-optimal investment behaviors caused by agency problems, financing constraints, or information asymmetry. As a result, it has been widely adopted in investment efficiency research due to its solid theoretical foundation and strong empirical explanatory power [29,30].

$$\begin{gathered} {Inv}_{i,t}={\alpha }_0+{\alpha }_1{Inv}_{i,t-1}+{\alpha }_2{Lev}_{i,t-1}+{\alpha }_3{Growth}_{i,t-1}+{\alpha }_4{Size}_{i,t-1}+{\alpha }_5{Cash}_{i,t-1}+{\alpha }_6{R}_{i,t-1}+{\alpha }_7{Age}_{i,t-1} \\ +\sum Year+\sum Industry+{\epsilon }_{i,t} \end{gathered}$$

(11)

Inv is the new capital investment of enterprises. To enhance the rigor of model specification and more accurately identify the net effect of ESG performance on corporate investment efficiency, we include a set of control variables based on the relevant literature and firm growth theories [31,32]. This approach strengthens the robustness and external comparability of the results and helps mitigate potential biases caused by omitted variables. Variables considered include financial leverage (Lev), growth opportunity (Growth), asset size (Size), cash flow status (Cash), stock return (R), enterprise age (Age), year (Year), and industry (Industry) as dummy variables, and model residual (ε). The symbol ε is used to denote the residual of the regression. When ε > 0, it is indicated that the enterprise overinvests; when ε < 0, it is indicated that the enterprise underinvests. In summary, when ε > 0, enterprises overinvest; when ε < 0, enterprises underinvest. In this study, the absolute value of ε is taken as the enterprise’s investment efficiency, i.e., the larger the absolute value of ε is, the lower the enterprise’s investment efficiency is, and the smaller the absolute value of ε is, the higher the enterprise’s investment efficiency is. The definitions of specific variables are shown in

Table 2. Relevant variables of firms’ investment efficiency.

Variables	Symbol	Meaning
new capital investment	${Inv}_{i,t}$	Total investment less maintenance investment
new investment expenditures	${Inv}_{i,t-1}$	New investment expenditures
financial leverage	${Lev}_{i,t-1}$	Gearing
growth opportunities	${Growth}_{i,t-1}$	Tobin’s Q (Market Capitalization/Total Assets)
asset size	${Size}_{i,t-1}$	Natural logarithm of total assets
cash flow position	${Cash}_{i,t-1}$	Net cash flows from operating activities/total assets at the beginning of the year
stock yield	$R_{i,t-1}$	Annualized individual stock returns on reinvestment of cash dividends
age	${Age}_{i,t-1}$	Years on market = years of observation − years of IPO
year	$\sum Year$	Dummy variable for year
industry	$\sum Industry$	Industry dummy variable
residual	${\epsilon }_{i,t}$	Residuals of model estimation

.

2

Explanatory Variables

This study selects the ESG performance scores, E-indicator, S-indicator, and G-indicator, provided by CSI. The scores of balanced ESG performance are derived from the average weighting of the scores of E, S, and G, i.e.,

$$ESG1=0.33\times W+0.33\times S+0.33\times G$$

(12)

3

Control Variables

In the context of the extant literature, the present study employs a range of control variables, including asset structure (tang), fixed asset ratio (fixed), gearing ratio (lev), intangible asset weight (imma), equity concentration (fhold), cash flow (cash), age at listing (age), investment expenditure rate (invest), growth (grow), and quick ratio (flow). Dummy variables are utilized for year (year) and industry (indus), while ε is designated as a random disturbance term. The definitions of the specific variables are shown in Table 1.

3.2.2. Data Source and Description

The initial sample comprises Chinese-listed agribusinesses from 2013 to 2022, a period coinciding with the “Belt and Road” initiative’s proposal. Companies excluded from the data processing included Special Treatment (ST), Particular Transfer (PT), Particular Special Treatment (*ST), and those that lacked complete data. The final sample consisted of 1250 observations from 125 listed firms over 10 years. ESG data were sourced from CSI, while company financial data were obtained from the Cathay Pacific database (CSMAR).

The descriptive statistics for the primary variables of the 125 listed agricultural companies are presented in

Table 3. Descriptive statistics.

Variables	Obs.	Mean	SD	Min	Median	Max
Inev	1250	0.040	0.064	0.000	0.025	1.602
ESG	1250	73.524	5.412	48.980	73.732	88.980
ESG1	1250	71.395	5.319	49.890	71.535	88.110
E	1250	63.040	7.578	39.070	63.120	88.290
S	1250	73.349	8.922	31.410	74.249	94.520
G	1250	79.959	7.911	19.600	81.200	97.330
tang	1250	0.435	0.150	0.103	0.436	0.851
fixed	1250	0.253	0.130	0.023	0.238	0.695
lev	1250	0.377	0.163	0.028	0.357	0.935
imma	1250	0.048	0.046	0.000	0.038	0.424
fhold	1250	28.202	16.628	0.385	28.302	76.070
cash	1250	0.074	0.081	−0.261	0.069	0.652
age	1250	2.536	0.531	1.099	2.565	3.434
invest	1250	0.049	0.042	0.001	0.039	0.328
grow	1250	0.475	2.250	−1.419	0.092	30.399
flow	1250	1.608	1.590	0.066	1.150	17.390

Source: Authors’ calculations.

. The investment efficiency data of the listed agricultural companies demonstrate volatility, with a high standard deviation of 0.064, which exceeds the mean value of 0.040. This finding indicates significant variations in investment efficiency among different firms. The considerable disparity between the maximum value of 1.602 and the minimum value of 0.000 accentuates the pronounced divergence in investment efficiency among the listed agricultural companies. The study’s findings indicate a general lack of investment efficiency among listed agricultural companies in China. The mean value of CSI ESG performance is 73.524, a relatively low value, indicating that China’s agricultural listed companies have poor ESG performance. Its standard deviation is 5.412, indicating that the ESG performance among agricultural listed companies is uneven. The gap between companies is too large. A notable gap exists between the mean value of balanced ESG performance (71.395) and the ESG performance of CSI, thereby providing a significant foundation for the study’s findings. For multicollinearity tests, please refer to Appendix A.

4. Empirical Results

4.1. Benchmark Regression

Table 4. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)
	Inev	Inev	Inev	Inev	Inev
ESG	−0.001 ***
	(−2.989)
ESG1		−0.001 ***
		(−3.092)
E			−0.001 **
			(−2.563)
S				−0.000
				(−1.223)
G					−0.001 ***
					(−2.745)
tang	−0.055 ***	−0.055 ***	−0.053 ***	−0.051 **	−0.053 **
	(−2.811)	(−2.818)	(−2.624)	(−2.592)	(−2.579)
fixed	−0.018	−0.018	−0.018	−0.024	−0.022
	(−0.844)	(−0.814)	(−0.822)	(−1.059)	(−0.984)
lev	0.023	0.023	0.026	0.026	0.022
	(0.986)	(1.010)	(1.174)	(1.126)	(0.944)
imma	0.225	0.225	0.228	0.231	0.230
	(1.453)	(1.465)	(1.495)	(1.474)	(1.499)
fhold	0.000	0.000	0.000	0.000	0.000
	(0.452)	(0.437)	(0.404)	(0.390)	(0.446)
cash	0.008	0.008	0.008	0.006	0.006
	(0.317)	(0.308)	(0.301)	(0.233)	(0.245)
age	−0.003	−0.003	−0.003	−0.003	−0.001
	(−0.687)	(−0.736)	(−0.610)	(−0.692)	(−0.271)
invest	0.356 ***	0.355 ***	0.345 ***	0.354 ***	0.353 ***
	(4.028)	(4.018)	(3.852)	(3.930)	(3.969)
grow	0.000	0.000	0.000	0.000	0.000
	(0.282)	(0.301)	(0.350)	(0.336)	(0.202)
flow	0.001	0.001	0.001	0.001	0.001
	(0.616)	(0.584)	(0.626)	(0.655)	(0.768)
_cons	0.072 *	0.073 *	0.038	0.022	0.054 *
	(1.857)	(1.928)	(1.285)	(0.595)	(1.699)
N	1250	1250	1250	1250	1250
adj. R2	0.128	0.128	0.126	0.124	0.128

t-statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the impact of the variables on investment efficiency in the agribusiness sector. The regression analysis yielded an ESG coefficient of −0.001, which attained a statistical significance level of 1%. This finding suggests that the positive correlation between ESG performance and investment efficiency in agribusinesses indicates the pivotal role that ESG strategies play in enhancing economic efficiency.

In addition, the coefficient of ESG1 is found to be −0.001, which is also significant at the 1% level. This finding highlights the strong correlation between balanced ESG performance and a firm’s investment efficiency. The hypothesis is that achieving balanced development in all three dimensions of ESG will significantly improve investment efficiency. This balanced ESG strategy has been demonstrated to help firms achieve collaboration across all areas, while concurrently enhancing the efficiency with which they utilize their resources.

The regression results for ESG and ESG1 demonstrate a 1% significance level, thereby providing substantial evidence that using balanced ESG strategies as a criterion substantiates the rationality of the ESG ratings of extant third-party rating agencies.

During the regression analysis, it was determined that the social responsibility variable (S) was not significant in the regression results. The rationale behind this is that we hypothesize that this non-significance may be closely related to the specific sample of agribusinesses in this study. Agribusinesses often assume social responsibility in their production, primarily due to their close interaction with the natural environment. For instance, the conservation of soil, managing water resources, and maintaining biodiversity in agricultural production are all natural expressions of corporate social responsibility. It is essential to note that these behaviors, although considered routine in the operations of agribusinesses, encompass a wide range of social responsibility aspects.

However, it is essential to note that these behaviors may already be incorporated into the existing social responsibility evaluation system. This results in a certain degree of double-counting when quantitatively assessing an agribusiness’s social responsibility. In other words, the actual contribution of agribusinesses in terms of social responsibility may have been overestimated in the evaluation system, thus failing to reflect its proper impact in the regression analysis.

4.2. Robustness Testing

4.2.1. Variable Substitution Method

Existing studies typically employ two indicators to investigate the relationship between corporate growth and investment efficiency: the growth rate of corporate revenue and Tobin’s Q value. Each of these two indicators has its focus. The revenue growth rate directly reflects the increase in an enterprise’s sales revenue over a specified period. Conversely, Tobin’s Q value considers the ratio of an enterprise’s market value to its replacement cost of assets. This is a more comprehensive reflection of an enterprise’s market performance and growth potential.

In this study’s benchmark regression analysis, the growth rate of corporate revenue was selected as a measure of corporate growth. However, to verify the robustness of the conclusions, this part of the analysis will utilize an alternative measure, Tobin’s Q, to reassess the relationship between corporate growth and investment efficiency.

As shown in

Table 5. Variable substitution and adding missing variables.

	M1	M2
	Inev	Inev
ESG	−0.001 ***	−0.001 ***
	(−2.818)	(−3.168)
tang	−0.057 ***	−0.050 ***
	(−2.971)	(−2.729)
fixed	−0.009	−0.022
	(−0.419)	(−1.001)
lev	0.028	0.024
	(1.219)	(1.014)
imma	0.234	0.225
	(1.507)	(1.502)
fhold	0.000	−0.000
	(0.219)	(−0.230)
cash	−0.011	0.001
	(−0.393)	(0.018)
age	−0.004	0.001
	(−0.911)	(0.210)
invest	0.352 ***	0.357 ***
	(3.889)	(4.038)
flow	0.001	0.001
	(0.584)	(0.698)
tobin	0.005 ***
	(3.531)
grow		0.000
		(0.226)
st		0.000
		(1.405)
_cons	0.060	0.052
	(1.636)	(1.087)
N	1250	1250
adj. R2	0.137	0.130

t-statistics in parentheses; *** p < 0.01.

, despite implementing this novel measurement system, the coefficient of M1 on ESG remains substantially negative at the 1% level, aligning with the preceding benchmark regression analysis outcomes. This finding further corroborates the negative correlation between ESG performance and agribusiness investment efficiency, thereby suggesting that favorable ESG performance significantly enhances agribusiness investment efficiency, and that this enhancement is independent of specific measures of firm growth. This further supports the hypothesis that the correlation between ESG performance and agribusiness investment efficiency is robust.

4.2.2. Considering Omitted Variables

In the capital market context, institutional investors assume a pivotal role, characterized by their professional demeanor, capacity for comprehensive information acquisition, and substantial financial resources. Compared to small and medium-sized shareholders, institutional investors generally possess the capacity to comprehend internal and external information regarding enterprises with greater expediency and precision. In conjunction with their extensive investment experience and substantial financial resources, they render themselves an indispensable component within the enterprise’s external monitoring system. The involvement of institutional investors has been shown to enhance the effectiveness of corporate decision-making processes. Furthermore, the adoption of a professional approach by these investors has been shown to influence corporate investment efficiency positively.

In this study, the role of institutional investors in the investment efficiency of agribusiness is explored further by selecting the proportion of institutional investors’ shareholding (ST) as a potential omitted variable for analysis. This indicator is measured by calculating the sum of the shareholding proportions of the top 10 outstanding shareholders. This can serve as an indicator of the degree of institutional investor participation in the enterprise.

Following the introduction of the omitted variable of institutional investor shareholding, the regression analysis results are reported in M2 of Table 5. Despite considering the impact of institutional investor shareholding, the coefficient of ESG remains significantly negative at the 1% level, consistent with the results of the previous regression. This finding further validates the correlation between ESG performance and agribusiness investment efficiency, suggesting that even after controlling for the variable of institutional investor shareholding, the increase in ESG performance continues to make a significant contribution to the growth of agribusiness investment efficiency.

4.2.3. Endogeneity Test

This study employs the lagged values of ESG indicators as instrumental variables based on two key considerations. First, the lagged ESG variables are strongly correlated with the current ESG performance, satisfying the relevance criterion for instrumental variables. Second, as observations from prior periods, these lagged variables are not affected by the contemporaneous error term, thereby ensuring their exogeneity and effectively mitigating the estimation bias caused by endogeneity. Therefore, using lagged ESG indicators as instruments is both reasonable and feasible in this study. The results of the endogeneity test reported in

Table 6. Endogeneity test.

	First Stage	Second Stage
Variable	ESG	Inev
LESG	0.295 ***
	(0.000)
ESG		−0.004 **
		(0.002)
Observations	1105	1105
R-squared	0.068	0.068
id FE	YES	YES
Year FE	YES	YES
Control variables	YES	YES
Indiscernibility test		56.63
p-value		0
Weak instrumental variables test (WIVT)		68.98
Critical values: 10%		16.38

Robust standard error in parentheses; *** p < 0.01, ** p < 0.05,

demonstrate that all p-values for the non-identifiable test are less than 0.01, indicating a high degree of statistical significance. This result indicates a strong correlation between the selected instrumental variables and the explanatory variables, providing a solid basis for using the instrumental variables as proxies for the explanatory variables, thereby effectively eliminating the interference of endogeneity issues. Specifically, in instances where the instrumental variables exhibit a high degree of correlation with the explanatory variables, it can be posited that the instrumental variables possess the capacity to genuinely mirror the alterations in the explanatory variables, unencumbered by the influence of endogeneity bias. Consequently, this enhances the credibility and robustness of the model’s estimation outcomes. Instrumental Variables Generalised Method of Moments Regression Please refer to Appendix B.

4.3. Analysis of Impact Mechanisms

4.3.1. Mediation of Agency

The mediation mechanism test in

Table 7. Mediation mechanism test of agency.

	(1)	(2)
	Manage	Inev
ESG	−0.001 ***	−0.001 ***
	(−2.660)	(−2.813)
manage		−0.172 ***
		(2.715)
tang	−0.056 **	−0.047 **
	(−2.611)	(−2.530)
fixed	0.079 ***	−0.030
	(2.953)	(−1.351)
lev	−0.027 *	0.027
	(−1.742)	(1.146)
imma	−0.07	0.228
	(−0.768)	(1.479)
fhold	−0.001 ***	0.000
	(−3.987)	(1.031)
cash	−0.081 ***	0.023
	(−4.010)	(0.877)
age	0.002	−0.004
	(0.327)	(−0.906)
invest	−0.002	0.356 ***
	(−0.065)	(4.150)
grow	0.001 *	0.000
	(1.886)	(0.089)
flow	0.003 **	0.000
	(1.896)	(0.265)
_cons	0.144 ***	0.047
	(3.995)	(1.205)
N	1250	1250
adj. R2	0.329	0.135

t-statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

indicates that, after incorporating the mediating variables into the model, the ESG coefficient remains significant at the 1% level. This finding suggests a substantial negative relationship between ESG performance and firms’ investment efficiency. Concurrently, the agency cost is also substantial at the 1% level, indicating that ESG performance enhances firms’ investment efficiency by reducing agency costs.

4.3.2. Financing Constraints

The mediation mechanism test for financing constraints indicates that the ESG coefficient is significant at the 10% level and the SA at the 1% level following the incorporation of the mediator variable (

Table 8. Mediation mechanism test of financing constraints.

	(1)	(2)
	SA	Inev
ESG	0.056 ***	−0.001 *
	(4.755)	(−1.942)
SA		−0.005 ***
		(−2.901)
tang	−1.540 *	−0.063 ***
	(−1.886)	(−3.242)
fixed	0.562	−0.015
	(0.618)	(−0.744)
lev	2.362 ***	0.035
	(4.318)	(1.531)
imma	−0.544	0.222
	(−0.286)	(1.440)
fhold	0.007	0.000
	(1.207)	(0.702)
cash	2.690 ***	0.022
	(3.085)	(0.856)
age	−0.438 **	−0.006
	(−2.085)	(−1.199)
invest	2.418 **	0.369 ***
	(2.094)	(4.170)
grow	0.026	0.000
	(0.800)	(0.442)
flow	−0.057	0.001
	(−1.165)	(0.432)
_cons	−0.113	0.071 *
	(−0.094)	(1.831)
N	1250	1250
adj. R2	0.341	0.134

t-statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01.

). The decline in the significance level of ESG may be attributed to the possibility that firms may increase their capital investment in environmental protection, social responsibility, and governance structures to enhance their ESG performance. While such investment benefits firms’ sustainable development and social responsibility in the long run, it may increase the financial burden in the short term for listed firms already facing more significant financing constraints. This additional financial pressure may weaken the immediate effect of ESG in improving investment efficiency, leading to a decrease in the significance level of the ESG coefficients.

4.4. Heterogeneity Test

The degree of market competition is a pivotal factor that must be considered in an enterprise’s investment decision-making process. A moderately competitive environment has been shown to stimulate enterprises’ innovation spirit and market adaptability [33]. Furthermore, it has been demonstrated that such an environment can encourage enterprises to optimize their investment decisions and improve investment efficiency continually [34]. However, the intensity of market competition and its impact on enterprise investment efficiency vary. In this study, the Herfindahl Index (HHI) is employed as a metric to ascertain the degree of market competition [35]. The Herfindahl Index measures market concentration and, consequently, the intensity of market competition. It is calculated by determining the sum of the squares of the ratios of each company’s operating revenues to the total operating revenues of the industry. The Herfindahl–Hirschman Index (HHI) determines the market’s competitive intensity, categorizing markets as low, average, or high-competition based on the HHI value. Specifically, markets with a Herfindahl index greater than 0.25 are defined as low-competition markets, those with an index between 0.25 and 0.01 are considered moderately competitive, and those with an index less than 0.01 are deemed highly competitive.

The heterogeneity tests are presented in

Table 9. Heterogeneity test.

	(1)	(2)	(3)
	Inev	Inev	Inev
ESG	−0.001 *	−0.001 ***	0.002 *
	(−0.957)	(−2.653)	(.)
tang	0.054	−0.060 ***	0.067
	(1.059)	(−2.912)	(.)
fixed	−0.149 ***	−0.013	0.000
	(−2.937)	(−0.576)	(.)
lev	0.058 *	0.026	0.000
	(1.718)	(1.077)	(.)
imma	0.129	0.225	0.000
	(1.024)	(1.428)	(.)
fhold	0.000	0.000	0.000
	(0.865)	(0.428)	(.)
cash	0.091	−0.005	0.000
	(1.156)	(−0.176)	(.)
age	0.004	−0.003	0.000
	(0.302)	(−0.702)	(.)
invest	0.660 ***	0.336 ***	0.000
	(8.267)	(3.412)	(.)
grow	−0.002	0.000	0.000
	(−1.164)	(0.313)	(.)
flow	0.015 *	0.001	0.000
	(1.920)	(0.623)	(.)
HHI	−0.002	−0.004	0.000
	(−0.030)	(−0.109)	(.)
_cons	0.011	0.069	−0.131
	(0.197)	(1.551)	(.)
N	68	1179	3
adj. R2	0.426	0.122	.

t-statistics in parentheses; * p < 0.1, *** p < 0.01.

, with the analysis conducted based on varying degrees of market competition. The table illustrates that the regression results correspond to the low, moderate, and high-competition markets. The findings indicate that the regression results are significant for all degrees of market competition, with negative coefficients in the first and second columns. This suggests that firms’ investment efficiency is promoted in low and moderately competitive markets. This phenomenon may be attributed to the moderate competitive pressures firms face in these markets, which motivate them to optimize resource allocation and enhance the quality of their investment decisions. The positive coefficient in the third column indicates that firms’ investment efficiency is dampened in high-competition markets. This phenomenon may be attributed to the heightened uncertainty and risk associated with high-competition markets, which prompt firms to adopt a more cautious approach in their investment decision-making. In a highly competitive market, firms may avoid large-scale capital expenditures if they are at a competitive disadvantage, and this conservative investment strategy may impact their investment efficiency.

5. Discussion

The present study makes the following innovations and academic contributions to the exploration of the relationship between ESG and agribusiness investment efficiency.

• In-depth deconstruction of heterogeneity in the agricultural industry

By emphasizing the centrality of ESG practices in highly resource-dependent industries and revealing targeted implementation paths across industry contexts, this work advances existing theoretical frameworks. In contrast to the existing literature on ESG, which has primarily focused on comprehensive corporate entities or industries characterized by substantial asset-based manufacturing [16], there has been comparatively little attention paid to the distinctive attributes of agribusinesses. Such attributes include, but are not limited to, resource dependence, ecological vulnerability, and a high degree of symbiosis with rural communities [36]. The present study focuses on the agricultural sector and is the first to systematically verify the applicability and validity of third-party ESG ratings in this industry. The positive relationship between ESG and investment efficiency in agribusiness further corroborates the importance of ESG in emerging markets [17]. This provides new evidence for studying ESG value transmission mechanisms in this segment.

2

Endogenous paradox of social responsibility (S)

This study uncovers an endogenous contradiction within the industry by demonstrating that the social responsibility dimension does not meet the criteria for statistical significance in the agribusiness sample, contrary to the studies that emphasize the balanced resonance of the three ESG dimensions [19]. The discrepancy between this finding and those of Budsaratragoon and Jitmaneeroj [17] on the value of ESG collaboration in emerging markets may be attributable to the “invisibility” of social responsibility practices in agribusiness. Specific social responsibilities have been integrated into daily operations [18] in the context of food safety traceability. This has resulted in the occurrence of double-counting within the rating system.

3

Theoretical extension of the moderating effect of competitive context

Empirical results demonstrate a nonlinear moderating effect of market competition on ESG effectiveness within the agricultural sector. When market concentration is low (HHI > 0.25), it is found that agricultural firms can improve resource allocation and cost structure through sustained ESG inputs, which significantly enhances their investment efficiency. In highly competitive market environments (HHI < 0.01), however, firms are under short-term pressure to survive and tend to prioritize cutting long-term ESG inputs in order to compete for market share. This results in the suppression of ESG’s contribution to investment efficiency. This finding is at odds with the linear model of “ESG—resource allocation—efficiency” proposed by Harymawan, Nasih, Agustia, Putra, and Djajadikerta [22], which reveals the nonlinear moderating effect of the intensity of market competition on ESG effectiveness.

4

Industry suitability of mediation paths

Advancing mechanistic understanding, this research empirically validates ESG’s dual-mediation path for agribusiness investment efficiency while revealing sectoral applicability differences. According to the three-stage “ESG—resource allocation” model proposed by Harymawan, Nasih, Agustia, Putra, and Djajadikerta [22], this study highlights the crucial role of ESG in facilitating the effective allocation of resources through the optimization of internal control and information transparency. However, agribusiness firms demonstrate a marked weakness in their approach to financing constraints, primarily attributable to their overreliance on policy-based credit and subsidies, as opposed to the proactive utilization of market-based financing mechanisms. While policy credit has been demonstrated to alleviate short-term financial pressures, it is unable to reflect the intrinsic value of improved corporate governance through market-based interest rate signals, as commercial loans do. This has the effect of attenuating the marginal contribution of ESG to optimizing the financing structure.

Furthermore, the governance dimension (G) demonstrates a distinctive industry-specific trajectory in agriculture. By employing localized innovative mechanisms, such as biological asset rights, land transfer contracts, and cooperative governance, agribusinesses can further reduce agency costs. Unlike Al-Hiyari, Ismail, Kolsi, and Kehinde [37], this study finds that agribusinesses place greater emphasis on the clarity of property rights and grassroots governance innovations, rather than relying exclusively on the diversity of board structures to enhance governance effectiveness. This discrepancy can be attributed to the substantial heterogeneity inherent in the industry regarding the design of governance mechanisms.

In summary, this study contributes to the expansion of the applicable boundaries of ESG theory through industry-focused and sub-dimensional testing, while also providing empirical evidence for the differentiated implementation of ESG strategies in the agribusiness sector.

6. Conclusions and Recommendations

6.1. Conclusions

Empirical evidence demonstrates that third-party ESG ratings possess substantial economic explanatory power regarding listed agricultural companies. Furthermore, ESG performance exhibits a robust and positive correlation with investment efficiency, a finding that is substantiated by the instrumental variable method and multiple robustness tests. The environmental (E) and governance (G) dimensions are identified as the primary drivers of investment efficiency, while the social responsibility (S) dimension is determined to be the primary driver of investment efficiency. This is because agricultural companies inherently assume social responsibilities, such as ensuring food security and providing benefits to farmers. The (S) dimension has been identified as the core element driving investment efficiency. However, the social responsibility (S) dimension is weakened by the natural social obligations of agribusinesses, such as ensuring food security, linking farmers’ benefits, and other social obligations. This results in a weaker incremental scoring effect. Regarding the mechanism of action, ESG optimizes resource allocation efficiency by reducing agency costs and alleviating financing constraints through dual paths. However, the effectiveness of ESG is significantly regulated by the degree of market competition. Indeed, ESG plays a significant role in both low-competition and moderately competitive markets. In contrast, highly competitive markets tend to inhibit the role of ESG due to short-term survival pressures. This provides a theoretical basis for the differentiated implementation of ESG strategies in agribusiness and calls for rating agencies to optimize the social responsibility indicator system by incorporating agriculture-specific indicators, such as supply chain inclusiveness and food loss rate, to enhance the industry’s appropriateness of ESG evaluation.

6.2. Limitations and Future Perspectives

This study acknowledges several limitations. First, the empirical analysis assumes a linear relationship between ESG performance and corporate investment efficiency. However, it is possible that a nonlinear relationship exists, which should be explored in future research using more flexible modeling approaches. Second, due to data availability constraints, the set of control variables does not account for certain qualitative or unobservable factors such as corporate culture, which may also influence investment efficiency. Finally, this study focuses specifically on the effect of ESG performance on investment efficiency, without examining its potential impact on other aspects of corporate performance. In addition, ESG performance may exert spillover effects on the regions in which firms operate or be shaped by regional institutional and environmental conditions—an area that warrants further investigation.

6.3. Recommendations

Firstly, deepening the strategic integration of environmental, social, and governance (ESG) factors is essential. Agribusinesses must assimilate the ESG concept as a fundamental component of their corporate strategy, encompassing all dimensions of daily operations and decision-making processes. It is imperative for enterprises to not only verbally align with the ESG concept but also to meticulously integrate it into their strategic framework through system design, organizational structure adjustment, and cultural construction. To achieve this, agricultural enterprises must formulate ESG policies that are both forward-looking and practical, and refine specific objectives, such as environmental protection, social responsibility, and corporate governance, to each business level. Consequently, companies can ensure that ESG practices align with their overall strategic objectives, optimizing resources and enhancing efficiency while promoting sustainable development. To ensure the effective implementation of their ESG strategy, companies should establish a dedicated ESG management structure and define the allocation of responsibilities and workflow for each department. For instance, when formulating new projects or making major investment decisions, ESG risk assessment should be incorporated into the necessary procedures, and a corresponding performance assessment mechanism should be established. This approach mitigates potential risks and incentivizes each department to prioritize and operationalize ESG objectives in their daily practices.

Secondly, agribusinesses are advised to establish a reliable mechanism for information disclosure. This will ensure that their operational status, financial performance, and ESG practices are communicated transparently, accurately, and in a timely manner to investors and the public. Enterprises can disclose key data and strategic progress regularly through various channels, including annual reports, sustainability reports, and online interactive platforms. These disclosures should detail specific measures and achievements in environmental protection, fulfillment of social responsibility, and improvement of corporate governance. Such transparent communication enables enterprises to enhance their market credibility and brand image, thereby fostering investor confidence and facilitating external supervision and social co-governance. Moreover, it provides a foundation for feedback and continuous improvement, preventing inaccurate or delayed disclosure and thus averting potential misinformation for investors and the public.

Finally, the process of fine-tuning resource allocation and management is paramount. In pursuing enhanced ESG performance, enterprises must implement sophisticated management of resources, ensuring optimal input–output ratios in the domains of environmental protection, social responsibility, and corporate governance. Enterprises are advised to undertake a systematic assessment of the benefits and costs of various ESG investments to identify those projects that have the potential to deliver the most significant environmental and social benefits. By establishing a scientific resource allocation mechanism and prioritizing different areas, enterprises can ensure that resources are reasonably allocated to various ESG dimensions without compromising the enterprise’s financial health. Enterprises also need to establish a dynamic monitoring and evaluation system to regularly review the actual results of each investment and adjust the resource allocation strategy based on the market environment and internal operations.

Consequently, companies can effectively implement ESG objectives while enhancing their social and environmental values. This is achieved by maintaining financial soundness and competitiveness, thus achieving the dual goals of short-term performance and long-term sustainable development.