How Enterprise Resilience Affects Enterprise Sustainable Development—Empirical Evidence from Listed Companies in China

Abstract

With the frequent occurrence of various emergencies, the stable operation of enterprises has been seriously affected, and the research of resilience has received more and more attention in various fields. Enterprise resilience (En_RES) is not only related to corporate survival but is also the key to determining whether a company can realize long-term development. To explore the impact of En_RES on enterprise sustainable development (En_SD), this paper conducts an empirical test using panel regression models based on the data of A-share listed companies in China from 2004 to 2022. It is found that En_RES has a significant positive contribution to En_SD, which is more obvious when the degree of environmental uncertainty is lower, the degree of information sharing is higher, and the degree of business complexity is higher. The mechanism test analysis finds that En_RES can further contribute to En_SD by reducing the bankruptcy risk, improving credit availability, and optimizing resource allocation efficiency. This paper innovatively analyzes and verifies the impact of En_RES on En_SD and its functioning mechanism from the perspective of microenterprises, which not only enrich the theoretical relationship between En_RES and En_SD but also provide important references for enterprises to pay attention to and develop resilience in practice, which can help enterprises better cope with challenges, grasp opportunities, and make contributions to the sustainable development of enterprises.

1. Introduction

From the alternating effects of “black swans” and “gray rhinos”, to geopolitical tensions, to operational pressures brought about by climate change, resource depletion, and other ecological and environmental issues [1,2], from the introduction of policies and regulations by governments to promote sustainable economic and social development, to the growing concern of consumers and investors about the social and environmental performance of companies, to the green transformation of competitors, corporate operational practices and residents’ living and consumption habits are gradually focusing on the goal of sustainable development (SD). At the same time, the rapid iteration of technology and the in-depth advancement of economic globalization, so that enterprises not only have to face fierce international competition but also have to deal with increasingly severe ecological problems and complex business environments, these factors have a serious impact on the stable operation and long-term development of enterprises [3]. In this context, how enterprises can cope with the increasingly changing business environment and maintain stable development has gradually become a topic of concern for scholars, and a number of studies have carried out useful discussions and research on enterprise resilience (En_RES) [4,5,6,7,8,9,10] and enterprise sustainable development (En_SD) [11,12,13,14,15,16], but an overemphasis on resilience can have a negative impact on the financial, environmental, and social performance of enterprises [17]; the development and refinement of En_RES especially often requires a significant investment of resources—does this put pressure on the short-term financial position of an organization, and, in turn, does this potentially conflict with or hinder its pursuit of En_SD? Academics have not yet provided a systematic and in-depth analysis. In addition, most of the existing research focuses on the factors affecting En_RES [8,9] and En_SD [13,14,15], without combining the two. To answer this question, this paper will combine the existing research, through theoretical analysis and empirical testing, and scientifically and reasonably explore and demonstrate the direct and indirect impacts of En_RES on En_SD through empirical research, to provide the theoretical foundation and practical support for the realization of En_SD goals and the construction and enhancement of resilience capabilities. Compared with existing studies, the possible marginal contributions of this paper are as follows: (1) from the perspective of the economic consequences of En_RES, the direct and indirect relationship between En_RES and En_SD is analyzed and tested, which not only enriches the research on the economic consequences of En_RES, but also further complements the relevant research on En_SD, and provides new theoretical perspectives and empirical evidence for understanding and promoting En_SD; and, (2) from the perspective of mechanism testing, this paper innovatively unifies the corporate bankruptcy risk, corporate credit availability, and resource allocation efficiency into the research framework, and clearly elaborates the role mechanism of En_RES affecting En_SD through empirical testing, enriching the exploration and development of risk management theory, credit rationing theory, and resource base theory in the literature research and practical application.

The other parts of this paper show the following, respectively: the second part provides a comprehensive review of the existing literature; the third part presents some research hypotheses based on the theoretical analysis; the fourth part selects some variables and determines the model applied in this paper; the fifth part analyzes the results of the empirical test on the basis of the fourth part, including the baseline regression analysis and robustness test; the sixth part is a study of the study with a further expansion and analysis, including a heterogeneity analysis and mechanism test; and, finally, the conclusions and limitations of the study and possible future research directions are shown.

2. Literature Review

2.1. Related Research on Enterprise Resilience

The existing literature has comprehensively analyzed En_RES mainly from three aspects: the definition, measurement, and impact and consequences.

First, in terms of the definition of En_RES, most scholars have conceptualized En_RES from the perspectives of the process, capability, and outcome. From a process perspective, En_RES is defined as the process by which a firm actively responds to, struggles to digest, strives to recover from, and fuels growth in the face of external shocks [18], including adjusting its configurations to adapt to the impacts of negative events, as well as improving and optimizing over time. In the capability perspective, En_RES is the ability of a firm to perceive threats, respond to them, adapt to them, and grow against the odds after suffering an external shock [7], which emphasizes the firm’s adaptability and resilience in the face of challenges, including coping strategies for environmental changes, threats, or crisis events. In terms of outcomes, En_RES is the result of a firm’s ability to operate healthily, adapt positively, and continue to grow when it is hit by an unfavorable event [5], and this resilience is reflected not only in the firm’s continued operations but also in its ability to learn from and improve upon shocks and increase its ability to cope with similar events in the future. In general, there exists a large number of studies on the definition of En_RES, but there is no unified understanding of the definition of En_RES.

Second, in terms of the measurement of En_RES, some scholars have collected primary data to measure En_RES by designing questionnaires [19,20], such as the En_RES Scale proposed by Kantur and Say (2015) [21]. Some scholars select a single economic indicator [22] or construct a comprehensive indicator system [23] to measure En_RES; e.g., Feng and Xue (2023) use the return on net assets to measure En_RES [8], and Wu et al. (2024) selected the magnitude of the stock price decline and the duration of the stock price decline to measure En_RES based on the corporate stock price data [24]. Wang et al. (2024) selected a series of economic indicators to measure En_RES from the perspectives of production and operation, and survival and recovery [9]. Liang and Li (2023) selected a series of economic indicators to measure En_RES from the perspectives of the ability to withstand risk, ability to adapt and adjust, and ability to recover and outperform [17]. Finally, in terms of the impact and consequences of En_RES, most studies have analyzed the factors that influence resilience; e.g., Feng and Xue (2023) found that government subsidies enhance firms’ risk-coping capacity before and at the time of crisis events [8]. Zhang et al. (2024) conducted an empirical test using data from Chinese listed companies and found that managerial overconfidence contributes to En_RES [10]. Sajko et al. (2021) found that CEO greed is inversely related to En_RES and CSR is positively related to En_RES based on the financial crisis context using data from US S&P1500 listed companies [7]. Based on a sample of Chinese listed firms, Wang et al. (2024) empirically analyzed and found that, the better the ESG performance is, the more resilient the firm is [9].

Finally, as for the economic consequences of En_RES, Rai et al. (2021) applied structural equation modeling to analyze the impact of En_RES on sustainable economic and social development in terms of crisis expectations, organizational robustness, and recoverability [19]. Liang and Li (2023) argued that there is a double-edged effect of En_RES on ESG performance [17].

2.2. Related Research to Enterprise Sustainable Development

The research on En_SD mainly focuses on the definition, indicator measurement, and influence factors.

Firstly, under the definitional perspective, most of the existing discussions on the definition of En_SD are based on the concept of SD. SD was first defined as meeting the needs of the present without compromising the ability of future generations to meet their needs [25]. With the deepening of related theoretical research and practical exploration, En_SD has been increasingly emphasized and concerned by researchers and practitioners [16,26]. According to Álvarez Jaramillo et al. (2018), En_SD means that, to ensure the long-term development and survival of a company, the impacts of economic, social, and environmental factors should be taken into account in the process of corporate operations and decision-making [11]. Pieloch-Babiarz et al. (2020) argued that the core of En_SD lies in the implementation of eco-friendly initiatives that improve the quality of production and operations and contribute to the development of employees [12]. Although a large number of studies have been conducted in this area, the concept of En_SD has not formed a unified cognition [27], and each has its focus. In general, En_SD requires enterprises to focus on long-term survival and development, and, in the production and operation process, focus on ecological environmental protection, the effective use of resources, and other inclusive development.

Secondly, there are two main types of direct and indirect measurements in the measurement of indicators of En_SD. With regard to the direct measurement method, some scholars have drawn on the scale system proposed by Chang and Cheng (2019) to collect relevant questioning data and measure them [28]. With regard to the indirect measurement methods, (1) some scholars collect corporate economic data and use a sustainable growth model to calculate the degree of En_SD [29,30], and some scholars have also used ESG scores to measure En_SD [31]; and (2) some scholars construct an indicator system for En_SD—for example, Dong et al. (2022) constructed an indicator system to measure En_SD in three aspects: the value creation capability, value management capability, and social responsibility performance [27]. Alexopoulos et al. (2018) and Xie and Zhu (2021) measured the En_SD performance in terms of the corporate environmental performance and financial performance [32,33]. Liu and Guo (2023) used the economic performance, environmental performance, and social performance for the evaluation [34], and Wang et al. (2023) characterized En_SD in terms of the environmental performance, financial performance, and production performance [35].

Thirdly, about the factors influencing the En_SD, from an intra-firm perspective, Ye et al. (2022) used a questionnaire to collect data from the manufacturing industry and applied structural equation modeling to find that organizational strategy and corporate social responsibility have a positive impact on En_SD [13]. Inthavong et al. (2023) found that continuous learning has a significant impact on En_SD [14]. Folqué et al. (2023) conducted a systematic review of existing studies and found that the implementation of sustainable investment strategies can help firms achieve their sustainability goals [15]. Of course, some studies believe that the goals and implementation path of En_SD must be perfectly integrated into the organizational culture, and only in this way can En_SD be successful [36]. Similarly, from the external perspective of firms, Yang et al. (2023) and Ji et al. (2023) revealed the impact of the digital economy on En_SD [16,37], and Industry 4.0 [38,39,40] and Industry 5.0 [41] will also have a significant impact on En_SD.

Throughout the existing literature, it is found that existing scholars have discussed a great deal of research related to En_RES and En_SD, but research related to the economic consequences of En_RES has yet to be explored in depth—in particular, is the large number of resources consumed by En_RES in the process of cultivation, development, and improvement conflicting with En_SD goals? Does this impede the realization of En_SD goals? For this reason, this study empirically analyzes and tests the direct and indirect impacts of En_RES on En_SD from the perspective of micro-firms, using data from Chinese A-share listed companies between 2004–2022 and applying panel regression models.

3. Theoretical Analysis and Research Hypothesis

3.1. Direct Effects Analysis

The frequent occurrence of natural disasters, public health events, and social security incidents worldwide requires enterprises to continuously improve and develop their resilience to cope with internal and external uncertainties in the business environments [1,2]. Drawing on the definition of the capability perspective [7] and referring to the risk management theory, En_RES can be understood as the ability of enterprises to anticipate a crisis before it comes, to respond to it when it arrives, and to develop dynamically after the crisis is over.

A resilient enterprise can improve its ability to cope with risks, optimize resource allocation, improve productivity, adapt to market changes, release positive signals for external participants and stakeholders, and have better access to financial support, thus helping it to achieve its long-term economic, social, and environmental goals. Specifically, before a crisis occurs, a resilient enterprise has better risk prediction and identification capabilities, which can help the enterprise gain a sharper insight into changes in market trends, anticipate impending crises more quickly and with more foresight, and help the enterprise make early preparations for and implement programs to cope with the impacts of uncertain events on the enterprise, to achieve its economic, social, and environmental goals. In the event of a crisis, enterprises with strong resilience can communicate with stakeholders effectively and promptly, and, at the same time, such enterprises can allocate and optimize resources in a fast, agile, and flexible manner, which can help them to effectively resist, quickly buffer, and respond in a timely manner to the enormous pressure brought about by emergencies, and to achieve long-term sustainable and stable development. After the crisis, the enterprise with strong resilience can not only make a timely response after the crisis event and adjust to restore its operation but the enterprise can also capture new development opportunities from the crisis, turn the crisis into an opportunity to swim upstream, and help the enterprise shape a new competitive advantage in the new normal after the impact of the crisis event and further enhance the opportunity for enterprise development, which will help the enterprise to operate soundly and realize the long-term goals. Referring to the report on resilience published by the BCG Henderson Institute, a schematic diagram of the developmental state of a business in a critical situation is shown in Figure 1.

Just as a fire at one of Philips’ factories in the early 21st century affected the operations of two phone manufacturers, Nokia and Ericsson, the different response modes of the two phone manufacturers reflect the impact of the resilience possessed by different firms at the onset of a crisis on the firms’ production and operations and their SD. After a problem with a raw material supplier, the production of phones was affected. Nokia first assessed the existing inventory and communicated effectively with the company’s employees, customers, suppliers, and other stakeholders, and further sought an alternative supply with existing suppliers or factories in an attempt to restore the normal production pattern of the phone; in the case of Ericsson, after the problems with the raw material supplier, it did not actively seek solutions to deal with the crisis event, and there was no prevention of an alternative supply beforehand. This ultimately led to Ericsson’s exit from the phone production market, while Nokia realized an increase in phone market share.

Therefore, hypothesis H1 is proposed: there is a significant positive effect of En_RES on En_SD.

In addition, the external environment faced by different enterprises, their ability to share information, and the complexity of the business they own may vary.

First of all, the construction of En_RES needs to consider the external environment faced by the enterprise, and the stability of the external environment is likely to have an impact on the economic consequences of En_RES. The theory of environmental uncertainty states that the external environment faced by the enterprise has a profound impact on the operation and management of the enterprise [42,43]. The more unstable the external environment of the enterprise is, the more the enterprise needs to adopt a flexible strategy to enhance its adaptability, such as cultivating enterprise resilience, responding quickly to market changes, improving the enterprise’s ability to resist and adapt to changes in the external environment, guaranteeing the stable operation of the enterprise, and helping the realization of long-term goals.

Second, the development of En_RES requires information sharing between firms and their partners to cope with an increasingly changing market environment, and differences in the degree of information sharing can affect the impact of En_RES on En_SD. The information asymmetry theory mentions that there are differences in the information that different subjects in the market have and understand. Failure to share information among enterprises means that they cannot respond promptly to real-time changes in demand from downstream customers and dynamic fluctuations in supply from upstream enterprises, which makes them prone to market and operational risks and is not conducive to the realization of their economic, social, and environmental objectives. In this context, if the enterprise establishes an effective resilience management and risk response system, it will be able to cope with the uncertainty risk of the upstream and downstream enterprises, and further guarantee the realization of the En_SD goals.

Finally, the complexity of a firm’s business affects the organization’s operations, which further acts on the impact of En_RES on En_SD. Business process management theory suggests that, when the complexity of an enterprise’s business increases, the business process becomes more complex, which may bring more market risks, operational risks, and management risks, increase unnecessary costs, reduce decision-making efficiency, and is not conducive to the realization of En_SD. Therefore, the more complex the business process, the more important it is for enterprises to build a flexible and adaptable resilience system, establish an effective risk management framework, improve production and operation processes, reduce unnecessary problems and costs, and contribute to the En_SD.

Therefore, hypothesis H2 is proposed: the impact of En_RES on En_SD may vary with a different stability of the external environment, different degrees of information sharing, and different degrees of business complexity.

3.2. Indirect Effects Analysis

First of all, from the perspective of risk management, risk management theory holds that enterprises should establish an effective risk management framework, formulate reasonable risk management strategies and implement risk control measures, and assess, identify, and respond to the impact of various risks and crisis events on the enterprise, and reduce the likelihood of enterprise bankruptcy. The stronger the resilience of the enterprise, the better the risk prediction, management, and response mechanism established within the organization, which can help the enterprise identify the potential risks faster and more effectively, take effective measures to cope with them, reduce the problems of poor operation, financial loss, or credibility damage due to external shocks or internal management deficiencies [44], and reduce the probability of the emergence of a bankruptcy risk, and help the enterprise to achieve long-term stable development. Secondly, in terms of stakeholders, signaling theory suggests that various information disclosed externally by the enterprise (including but not limited to the financial data, market performance, etc.) will signal future developments to the external parties of the enterprise and its stakeholders. If the enterprise has the problem of a bankruptcy risk, it will transmit unfavorable signals in the market, directly affecting the attitude and decision-making of stakeholders, hindering the improvement of market competitiveness [45], and it is not conducive to the lasting development of the enterprise. Enterprises with strong resilience can demonstrate strong risk resistance and sound business strategies for stakeholders, which helps to enhance stakeholders’ confidence and attract more stakeholders’ attention, and also helps enterprises to establish a good image in the market and realize the stable development of the economy, society, and environment.

The credit rationing theory suggests that, when a bank grants a loan to a firm, it assesses the risk of the loan based on the firm’s own business conditions and credit history. In terms of financing costs, resilient enterprises usually have higher credit ratings and more robust financial conditions and are more likely to obtain loans or other financial services from banks or other financial institutions [46], and, if the assessment results suggest that the risk of granting a loan to the enterprise is lower, the bank will further reduce the loan interest rate and extend the term to reduce the cost of enterprise financing, increase the utilization rate of the funds, and safeguard the enterprise’s long-term business. From the perspective of financing channels, the theory of enterprise resilience and signaling theory point out that, the stronger the En_RES, the more it can show the stable business situation and good financial performance to the outside of the enterprise, which not only helps to enhance the ability of the enterprise to obtain high-quality credit, and, at the same time, attracts the attention of more investors and financiers when the enterprise needs funds to expand its scale, innovate its technology, and expand its market [46]. Therefore, enterprises with strong resilience can broaden their financing channels and diversify their financing methods to enhance their ability to obtain credit, optimize their financial flexibility, and help them respond to market changes promptly, seize development opportunities, and provide diversified options for future development.

Resource base theory suggests that the resources possessed by a firm shape the firm’s competitive advantage [47]; while taking into account the scarcity of resources, firms are required to allocate resources appropriately. In terms of decision-making, the more resilience an enterprise has, the more it can show that the enterprise has an efficient and reasonable decision-making mechanism [48], which helps the enterprise to make accurate judgments quickly in the complex and changing market environment, and to invest limited resources in the appropriate areas and benefits, and to realize the rational allocation of resources [49]. If an enterprise is facing bankruptcy and reorganization, a resilient enterprise will quickly make strategic adjustments and business restructuring, adjust its business strategy, and optimize resource allocation to restore operational vitality and avoid bankruptcy. In the process of resource utilization and allocation, the improvement in En_RES requires the establishment of an effective communication mechanism and collaboration platform between departments and departments, and between enterprises and enterprises, to facilitate the efficient flow of resources across departments and enterprises, to effectively enhance the utilization efficiency of resources in key links [4], to improve the product quality, to reduce the consumption of resources and the generation of waste, and to comply with the long-term goal of En_SD.

Therefore, hypothesis H3 is proposed: En_RES contributes to the achievement of the goal of En_SD by reducing the risk of enterprise bankruptcy, enhancing the availability of corporate credit, and optimizing the efficiency of resource allocation.

Based on the above direct and indirect effects analysis, the research hypothesis and its mechanism of action are diagrammed in Figure 2.

4. Research Design

4.1. Variable Selection

The explained variable is enterprise sustainable development (En_SD). Based on the dynamic development perspective, the business state and SD ability of enterprises are continuously improved with the development of the economy and society, and the sustainable growth model constructed by Van et al. (2004) based on the sustainable growth rate can better capture this dynamic change of enterprises [30]. Therefore, the sustainable growth model proposed by Van et al. (2004) was used to measure the level of En_SD [30]. The specific formula is as follows: En_SD = net sales margin × earnings retention ratio × (1 + equity ratio) ÷ [1 ÷ total asset turnover—net sales margin × earnings retention ratio × (1 + equity ratio)].

The explanatory variable is enterprise resilience (En_RES). In the process of measuring En_RES, most of the existing literature uses questionnaires, indicator system construction, and core variable methods to measure En_RES. For the threshold to the possible omissions in the construction and selection process of the indicator system, the core variable method proposed by Martin (2012) was borrowed in the benchmark regression model to measure En_RES [50]. The idea of the specific calculation is as follows: the sales revenue of enterprise i is compared with all enterprises, and, if the sales revenue of enterprise i is better than the average level of all enterprises, it indicates that the current En_RES ability is better; on the contrary, it indicates that its resilience level is lower.

The improvement in the En_SD level is influenced by other factors in addition to the possible influence of En_RES. Regarding the existing studies, the control variables are selected as company characteristic variables: listing time (LT); corporate financial characteristic variables: enterprise size (SIZE), gearing ratio (LEV), operating revenue (OR), growth (GN), earnings volatility (PV), and current ratio (CR); and corporate management characteristic variables: shares balance (THR), the proportion of management shareholding (MBP), etc. In addition to these, the model takes into account the impact of individual-fixed effects and time-fixed effects.

4.2. Model Building

To estimate the impact of En_RES on En_SD, the following model is constructed:

$$En\_SDit={\alpha }_{0}En\_RE{S}_{it}+{\alpha }_{1}Control{S}_{it}+I{D}_i+Yea{r}_t+{\epsilon }_{it}$$

(1)

where i and t represent the enterprise and year, respectively, En_SD is the explained variable, En_RES is the explanatory variable, Controls is a series of control variables selected in this paper, ID is the individual fixed effect, Year is the year fixed effect, and ℇ is the random perturbation term. If the coefficient α₀ is greater than 0, it implies that En_RES will contribute to the En_SD, and vice versa, in which case it will have a dampening effect.

4.3. Source of Data

For the threshold for the continuity and availability of data, this paper selects the data of A-share listed companies in China from 2004 to 2022, and treats the original sample as follows: financial sector data are excluded; STPT firms are excluded; firms listed for less than one year are excluded; and a large number of missing data values in the sample are excluded. To avoid the effect of extreme values in the sample, all variables are subjected to a 1% bilateral shrinkage. The final result is 4373 firms with 36,619 sample observations. The required data are obtained from the CSMAR database, Wind database, etc.

5. Empirical Analysis

5.1. Descriptive Statistics

Descriptive statistics of the main variables are shown in

Table 1. Descriptive statistics.

Variables	Observations	Mean	Standard Deviation	Minimum	Median	Maximum
En_SD	36,619	0.074	0.073	−0.022	0.055	0.420
En_RES	36,619	2.070	6.407	−9.038	0.845	45.994
LT	36,619	10.032	7.057	1.000	9.000	27.000
OR	36,619	21.473	1.434	18.322	21.329	25.568
SIZE	36,619	8.308	1.279	5.900	8.130	12.264
LEV	36,619	0.429	0.198	0.057	0.427	0.864
THR	36,619	58.391	14.905	23.360	59.300	90.190
MBP	36,619	11.508	18.649	0.000	0.113	67.349
GN	36,619	0.157	0.243	−0.234	0.099	1.407
PV	36,619	0.030	0.038	0.001	0.017	0.240
CR	36,619	2.328	2.271	0.319	1.611	14.655

. The mean value of En_SD is 0.074, the minimum value is −0.022 and the maximum value is 0.420; and the mean value of En_RES is 2.070, the minimum value is −9.038, and the maximum value is 45.994, implying that the resilience capacity and sustainability levels vary from one firm to another. In addition, the data for both En_SD and En_RES are right-skewed, indicating that some firms have higher levels of resilience capacity and sustainability and that there is a significant gap in the resilience and sustainability between different firms.

5.2. Regression to Baseline

The results of the empirical test of En_RES on En_SD are shown in

Table 2. Regression to baseline.

Variables	(1)	(2)	(3)	(4)
	En_SD	En_SD	En_SD	En_SD
En_RES	0.002 ***	0.002 ***	0.001 ***	0.001 ***
	(32.80)	(32.77)	(15.25)	(12.96)
LT			0.000 ***	0.007 ***
			(2.79)	(3.32)
OR			0.032 ***	0.045 ***
			(46.28)	(47.02)
SIZE			−0.029 ***	−0.045 ***
			(−36.37)	(−39.59)
LEV			0.042 ***	0.049 ***
			(14.40)	(13.39)
THR			0.000 ***	0.000 ***
			(6.69)	(9.11)
MBP			−0.000	−0.000
			(−0.68)	(−0.76)
GN			0.047 ***	0.043 ***
			(32.41)	(28.31)
PV			0.459 ***	0.428 ***
			(49.97)	(43.47)
CR			0.001 ***	0.000
			(4.11)	(1.25)
Constant	0.070 ***	0.053 ***	−0.433 ***	−0.603 ***
	(108.38)	(26.13)	(−42.52)	(−40.35)
Observations	36,619	36,619	36,619	36,619
R-squared		0.054		0.178
ID	NO	YES	NO	YES
Year	NO	YES	NO	YES

Note: Values in parentheses indicate T-values; *** indicate significance at the 1% levels.

, where column (1) is the regression result without the inclusion of control variables and fixed effects; column (2) is the regression result after the inclusion of individual- and time-fixed effects without adding control variables; column (3) is the regression result after the inclusion of the control variables but not controlling for time and individual effects; and column (4) is the regression result after adding a series of control variables and individual- and year-fixed effects. The above results indicate that En_RES has a significant positive contribution to En_SD and hypothesis H1 is confirmed.

5.3. Robustness Checks

5.3.1. Replacement of Variables

To further validate the robustness of the baseline regression results, the regression was re-run with replacement variables, drawing on existing research practices.

First, based on the univariate measure proposed by Martin (2012) [50], a dummy variable is used to measure En_RES (En_Res1), drawing on the DesJardine et al. (2019) study [51]. This is calculated as follows: compare the sales revenue of firm i with all firms, and assign a value of 1 to En_RES if the sales revenue of firm i is better than the average of all firms, and vice versa, in which case the value will be 0. The results of the regression are shown in column (1) of

Table 3. Robustness checks.

Variables	(1)	(2)	(3)	(4)	(5)
	Substitution of Explanatory Variables		Replacement of Explained Variables	Instrumental Variable Approach	System GMM
	En_SD	En_SD	S	En_SD	En_SD
En_RES			0.000 ***	0.001 ***	0.001 ***
			(6.74)	(8.63)	(6.22)
En_Res1	0.014 ***
	(16.76)
Stock_P		0.000 ***
		(3.47)
LT	0.007 ***	0.007 ***	0.000	0.001 ***	0.000
	(3.31)	(3.31)	(0.15)	(4.37)	(0.21)
OR	0.044 ***	0.048 ***	−0.001 ***	0.021 ***	0.042 ***
	(45.83)	(50.85)	(−6.38)	(20.59)	(8.54)
SIZE	−0.044 ***	−0.047 ***	−0.002 ***	−0.015 ***	−0.039 ***
	(−38.48)	(−41.60)	(−7.54)	(−12.62)	(−6.48)
LEV	0.048 ***	0.049 ***	0.009 ***	0.030 ***	0.063 ***
	(13.33)	(13.47)	(13.61)	(5.99)	(2.73)
THR	0.000 ***	0.000 ***	0.000	0.000 ***	0.001 ***
	(9.48)	(10.19)	(1.10)	(5.40)	(2.64)
MBP	−0.000	−0.000	−0.000 ***	0.000 *	0.000
	(−1.07)	(−0.73)	(−9.42)	(1.90)	(0.10)
GN	0.044 ***	0.049 ***	−0.002 ***	0.052 ***	0.052 ***
	(29.52)	(33.43)	(−5.60)	(20.13)	(5.59)
PV	0.446 ***	0.435 ***	0.029 ***	0.474 ***	0.282 ***
	(45.51)	(44.12)	(16.64)	(24.72)	(5.95)
CR	0.000	0.000	−0.000 ***	0.001 ***	0.006 ***
	(1.01)	(0.85)	(−3.58)	(3.22)	(2.83)
Constant	−0.602 ***	−0.646 ***	0.133 ***	−0.319 ***	−0.629 ***
	(−40.75)	(−44.25)	(43.82)	(−21.05)	(−9.01)
Observations	36,619	36,599	30,834	36,619	29,351
AR (1)					0.000 ***
AR (2)					0.712
Hansen					0.189
R-squared	0.181	0.174	0.058	0.188
ID	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES

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

, and the coefficient of En_RES is significantly positive.

Second, in order to better determine the changes in firms before and after suffering a crisis event, firms’ stock price volatility was chosen as a proxy variable for En_RES (Stock_P), drawing on the Levine et al. (2016) and Albuquerque et al. (2020) studies [52,53]. Column (2) of Table 3 demonstrates the results from the regression, where the effect of En_RES on En_SD is significantly positive.

Finally, drawing on Xie and Zhu’s (2021) study, data were collected from both corporate financial performance and environmental social responsibility performance to measure En_SD [33]. Among them, financial performance is measured by the profitability of total assets (ROA); environmental social responsibility performance is measured using the total score of ratings disclosed by third-party agencies, and the data of the two dimensions are standardized, and the standardized data are used to calculate the level of En_SD (S), as shown in the following formula [32]. Replacing the original variables with the recalculated En_SD indicators, the regression results are shown in column (3) of Table 3. After replacing the measure of the explained variables, the coefficient of En_RES is significant at the 1% level, i.e., En_RES significantly improves En_SD, and the regression results are robust.

$$S_{it}=[(1-\left|RO{A}_{it}-HES{G}_{it}\right|)\ast \sqrt{RO{A}_{it}\ast HES{G}_{it}}]/1$$

(2)

5.3.2. Endogeneity Test

Given the potential for a bidirectional causality between En_RES and En_SD, that is to say, enterprises with strong resilience are less affected by external uncertainties, have strong adaptability to environmental changes, and a high ability to effectively prevent and cope with uncertainty risks, which contributes to the En_SD, the higher level of En_SD, to a certain extent, requires enterprises to engage in prudent investments and decision-making, build a scientific, reasonable, and flexible resource allocation and utilization system, avoid a single resource dependence and utilization, reduce the probability of financial risk, enhance the enterprise’s risk-resistant ability, and contribute to the resilience of the development. Therefore, to alleviate the possible endogeneity problem between En_SD and En_RES, firstly, drawing on Fisman and Svensson’s (2007) study, the province–industry–year means of En_RES were chosen as instrumental variables and regressed using the least squares estimate [54], and the results of the second stage are shown in column (4) of Table 3. The Underidentification test is significant at the 1% level, the Cragg–Donald Wald F statistic and the Kleibergen–Paap Wald rk F statistic are significant at the 10% level, and there is no weak identification problem. The results in column (4) of Table 3 show that the impact of En_RES on En_SD is significantly positive. Secondly, the regression is conducted using the system GMM model and the regression results are shown in Table 3 column (5); the results indicate that there is no second-order serial correlation and weak instrumental variable problem, and the model is set up reasonably and the coefficient of En_RES is significantly positive at 1% level. The above test results show that the results of the benchmark regression remain significant after considering the endogeneity problem.

5.3.3. Other Robustness Tests

(1) Excluding the effect of some extreme samples: In the process of business operation, the occurrence of unexpected events may affect the business and development environment, such as COVID-19 in 2020, the stock market crash in 2015, the financial crisis in 2008, etc.; such events will lead to the enterprise’s years of data bias, which will affect the results of the benchmark regression if it is not excluded. To further verify the robustness of the baseline regression results, the 2020, 2015, and 2008 samples were excluded from the regression analysis and re-run, and the results are shown in the first three columns of

Table 4. Other robustness tests.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Excluding the 2020 Sample	Excluding the 2015 Sample	Excluding the 2008 Sample	Retention of Samples with More Than Five Consecutive Years of Occurrence	Control Industry	Control Province
	En_SD	En_SD	En_SD	En_SD	En_SD	En_SD
En_RES	0.001 ***	0.001 ***	0.001 ***	0.001 ***	0.001 ***	0.001 ***
	(11.61)	(14.20)	(12.96)	(11.81)	(12.76)	(12.94)
LT	0.008 ***	0.007 ***	0.007 ***	0.005 **	0.008 ***	0.008 ***
	(3.56)	(3.34)	(3.33)	(2.30)	(3.43)	(3.49)
OR	0.045 ***	0.044 ***	0.045 ***	0.046 ***	0.047 ***	0.045 ***
	(44.76)	(44.64)	(46.41)	(45.15)	(47.96)	(46.83)
SIZE	−0.045 ***	−0.044 ***	−0.045 ***	−0.043 ***	−0.048 ***	−0.045 ***
	(−38.07)	(−37.74)	(−38.98)	(−36.29)	(−40.97)	(−39.38)
LEV	0.048 ***	0.047 ***	0.048 ***	0.039 ***	0.049 ***	0.050 ***
	(12.57)	(12.52)	(12.98)	(10.17)	(13.33)	(13.61)
THR	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
	(9.67)	(8.91)	(8.20)	(8.35)	(8.55)	(9.07)
MBP	−0.000	−0.000	−0.000	−0.000	−0.000	−0.000
	(−0.80)	(−1.01)	(−0.75)	(−0.66)	(−0.47)	(−0.84)
GN	0.044 ***	0.046 ***	0.042 ***	0.045 ***	0.044 ***	0.043 ***
	(27.54)	(28.31)	(27.51)	(27.50)	(28.65)	(28.20)
PV	0.438 ***	0.421 ***	0.420 ***	0.436 ***	0.421 ***	0.430 ***
	(41.54)	(41.49)	(42.18)	(39.83)	(42.68)	(43.57)
CR	0.000	0.000	0.000	0.000	0.000	0.000
	(0.91)	(1.00)	(1.07)	(0.27)	(1.02)	(1.29)
Constant	−0.603 ***	−0.591 ***	−0.599 ***	−0.626 ***	−0.679 ***	−0.668 ***
	(−38.80)	(−38.25)	(−39.44)	(−40.28)	(−25.55)	(−25.12)
Observations	33,819	34,615	35,552	31,376	36,619	36,614
R-squared	0.178	0.180	0.177	0.178	0.453	0.448
ID	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
IND					YES
PROVINCE						YES

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

.

(2) Retention of consecutive samples: Considering that some of the companies have been in operation for a shorter period, the economic indicators may be significantly different from other companies, affecting the regression estimation results. Therefore, the regression estimation is re-run by retaining the firms that have been present continuously for five years or more from the original sample, and the estimation results are shown in column (4) of Table 4.

(3) Replacement fixed effects: Enterprises are located in different industries, and there may be differences in the development situation and operational capacity; enterprises are located in different regions, so the business environment, policy support, and other capabilities will also be different. Therefore, the regression test is re-run based on the baseline regression by adding industry and province fixed effects, respectively. The results are shown in columns (5) and (6) of Table 4.

The above regression results show that the coefficient on En_RES is significantly positive at the 1% level and the conclusion is robust.

6. Heterogeneity Analysis and Mechanism Testing

6.1. Heterogeneity Analysis

Referring to Ghosh and Olsen’s (2009) study, the coefficient of variation of operating income is used to calculate the environmental uncertainty faced by the firm (AIEU) [55]. The larger the index, the greater the environmental uncertainty faced by the firm. The interaction terms of AIEU and En_RES are added to the baseline regression model to test whether the impact of En_RES on En_SD varies according to the stability of the firm’s external environment. The results in column (1) of

Table 5. Heterogeneity analysis.

Variables	(1)	(2)	(3)
	En_SD	En_SD	En_SD
En_RES	0.001 ***	0.001 ***	0.001 ***
	(12.50)	(12.52)	(12.73)
AIEU	0.004 ***
	(9.56)
AIEUEn_RES	−0.000 ***
	(−8.97)
ASY		−0.056 ***
		(−31.75)
ASYEn_RES		−0.000 ***
		(−3.00)
SEG			−0.002 ***
			(−3.15)
SEGEn_RES			0.000 *
			(1.83)
LT	0.007 ***	0.007 ***	0.007 ***
	(3.09)	(3.07)	(3.27)
OR	0.046 ***	0.044 ***	0.046 ***
	(46.78)	(41.34)	(47.78)
SIZE	−0.045 ***	−0.056 ***	−0.043 ***
	(−39.42)	(−44.63)	(−37.21)
LEV	0.047 ***	0.074 ***	0.036 ***
	(12.70)	(18.86)	(9.63)
THR	0.000 ***	0.001 ***	0.000 ***
	(7.99)	(17.60)	(7.74)
MBP	−0.000	−0.000 **	−0.000
	(−0.80)	(−2.20)	(−0.85)
GN	0.043 ***	0.038***	0.046 ***
	(27.50)	(24.51)	(30.00)
PV	0.409 ***	0.414 ***	0.415 ***
	(40.02)	(40.82)	(39.05)
CR	0.000 *	0.000 *	0.000
	(1.73)	(1.76)	(0.26)
Constant	−0.614 ***	−0.496 ***	−0.629 ***
	(−39.93)	(−29.33)	(−40.62)
Observations	35,313	33,607	35,455
R-squared	0.181	0.211	0.177
ID	YES	YES	YES
Year	YES	YES	YES

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

show that the interaction term between environmental uncertainty and En_RES is significantly negative at the 1% level, suggesting that the building of En_RES contributes to the achievement of sustainable growth when the external environment faced by the firm is more stable. On the one hand, the reduction in environmental uncertainty means that the external environment in which the enterprise is located is stable and predictable, but it does not mean that the enterprise can rest on its laurels. Especially in the context of the frequent occurrence of emergencies in recent years, enterprises should pay more attention to the dynamics and unpredictability of the market environment, realize the importance of resilience for future development, and ensure stable business operations by strengthening resilience capabilities. On the other hand, resilience not only helps companies survive better in times of adversity but also creates a unique competitive advantage for them in good times [56], such as in a stable environment through innovation in the organizational structure, adjusting staffing, optimizing resource allocation, and deepening market insights and other strategies to further enhance the core competitiveness, for En_SD to build up momentum.

Drawing on Amihud and Mendelson’s (1986) study, liquidity ratios, illiquidity ratios, and reversal indicators were extracted based on company stock trading data, and the first principal component was extracted using principal component analysis to be recorded as a proxy variable for the degree of information sharing (ASY) [57]. A larger ASY implies that firms face a more severe degree of information asymmetry and a poorer degree of information sharing among firms. Based on model (1), the interaction term between ASY and En_RES was included in the model to test the effect of heterogeneity. In the regression results presented in column (2) of Table 5, the interaction term between the degree of information sharing and En_RES is significantly negative at the 1% level, implying that, when the degree of information sharing faced by a firm is higher, the En_RES capability contributes to the firm’s ability to achieve sustainable growth. This may be because, in enterprises with a better degree of information sharing, the barriers between departments and enterprises no longer exist, and enterprises can more accurately obtain the operating status of upstream enterprises and the demand status of downstream customers, which can greatly alleviate the impact of potential risks on business operations, make more realistic decisions, reduce the impact of internal and external conflicts on the stable development of enterprises, and help to realize sustainable goals.

Referring to the Gong et al. (2016) approach, the natural logarithm of the number of subsidiaries held by a firm plus one and adjusted for industry averages is used to characterize the complexity of a firm’s operations (SEG) [58]. The larger the value, the more complex the business operations of the firm. The regression results in column (3) of Table 5 show that the interaction term between the business complexity and En_RES is significantly positive at the 10% level and hypothesis H2 is further supported. With high business complexity, enterprises face frequent changes in the market environment and customer demand, the organization’s internal management or structure, the external risk environment, and other factors on the development of the enterprise’s impact on the sharp increase, while, in the highly complex business environment, there is more need for enterprises to efficiently and reasonably allocate resources, to achieve inter-departmental efficient communication and collaboration. Resilience enables enterprises to establish an effective risk response, prevention, and management system, adjust the operation strategy, efficiently allocate limited resources, respond to customer needs promptly, quickly adapt to market changes, and help enterprises develop in the long term. Therefore, when a company possesses a higher level of business complexity, the more the building of En_RES helps the company to achieve sustainable growth.

6.2. Mechanism Testing

Based on the previous theoretical analysis, En_RES plays a key role in building an efficient risk prediction, response, and treatment system, while enterprises with strong resilience can send positive signals to the market to help enterprises achieve economic, social, and environmental goals, and ultimately realize En_SD. To verify whether En_RES can promote En_SD by reducing the bankruptcy risk, drawing on Ohlson’s (1980) study, the OScore model was chosen to represent the risk of corporate bankruptcy (OScore) [59]. The larger the value, the higher the bankruptcy risk faced by the enterprise. The regression results are shown in column (1) of

Table 6. Mechanism testing.

Variables	(1)	(2)	(3)
	Bankruptcy Risk	Credit Availability	Resource Allocation Efficiency
	OScore	Flex	TFP
En_RES	−0.014 ***	0.003 ***	0.002 ***
	(−13.50)	(23.91)	(8.85)
LT	0.036	0.000	−0.004
	(0.94)	(0.07)	(−0.53)
OR	−0.587 ***	−0.036 ***	0.939 ***
	(−32.84)	(−16.08)	(307.02)
SIZE	0.047 **	0.031 ***	−0.273 ***
	(2.22)	(11.79)	(−75.85)
LEV	8.087 ***	0.336 ***	0.097 ***
	(116.97)	(39.11)	(8.49)
THR	−0.000	0.001 ***	0.001 ***
	(−0.22)	(6.76)	(6.08)
MBP	−0.002 *	0.001 ***	0.000
	(−1.73)	(6.70)	(0.90)
GN	−0.310 ***	0.310 ***	0.102 ***
	(−11.03)	(88.20)	(21.50)
PV	−4.314 ***	−0.397 ***	0.205 ***
	(−21.15)	(−16.15)	(6.63)
CR	−0.329 ***	0.002 ***	0.026 ***
	(−61.60)	(3.30)	(30.68)
Constant	0.700 **	0.353 ***	−9.903 ***
	(2.52)	(9.97)	(−208.65)
Observations	33,077	35,745	35,037
R-squared	0.543	0.343	0.913
ID	YES	YES	YES
Year	YES	YES	YES

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

. The results show that En_RES helps to reduce the risk of corporate bankruptcy, probably because a higher level of En_RES means a better ability to cope with uncertain risks and environments, which, to a certain extent, avoids the emergence of corporate bankruptcy due to external shocks or internal management deficiencies, and it can also send useful signals to the outside of the enterprise and its stakeholders based on the disclosure of the enterprise’s operating information and financial information to enhance the confidence of the stakeholders and establish a good image, and help the enterprise realize SD.

Based on the previous discussion, to further validate whether En_RES can contribute to En_SD by enhancing credit availability, drawing on Huang et al.’s (2022) study, the ratio of firms’ new liabilities to their total assets is chosen as a proxy for firms’ credit availability (Flex) [60].The larger the Flex, the higher the probability of firms being able to access funds. The regression results, as shown in column (2) of Table 6, indicate that En_RES contributes to the availability of credit to firms. The possible reason is that a resilient enterprise can better identify, assess, and deal with various risks, which not only conveys to the outside world the enterprise’s ability to operate stably and its ability to cope with risks, which makes banks or other financial institutions willing to provide credit support, but also helps to reduce the risk of corporate default, and enhances the confidence of credit institutions in the repayment of the enterprise, which, in turn, contributes to the broadening of the enterprise’s financing channels and diversification of its financing methods, and helps the enterprise establish a long-term competitive advantage and realize En_SD.

Theoretical analysis shows that, for the enterprise with a strong resilience ability, the more efficient the resource utilization mechanism, the more it can put resources into reasonable scenarios and goals, enhance its utilization efficiency, and realize En_SD. Based on this analysis, one of the important paths through which En_RES influences En_SD is the resource allocation efficiency, and, to validate this mechanism, the total factor productivity (TFP) is added to the benchmark model for regression as a measure of the corporate resource allocation efficiency. The regression results, as shown in column (3) of Table 6, indicate that En_RES contributes to resource allocation efficiency. The possible reasons for this are as follows: the resource base view proposes that the resources possessed by an enterprise are established, and, in the face of frequently changing markets and environments, resilient enterprises are more willing to upgrade existing technologies and processes, and can rationally allocate limited resources, improve resource utilization, cope with the occurrence of uncertainty risks, reduce unnecessary costs, and help them achieve En_SD.

7. Discussions and Conclusions

7.1. Discussions

Against the background of the complex and changing internal and external environments and the urgent goal of ecological protection and green development, the stable operation and long-term healthy development of enterprises have been seriously affected, and many scholars have already made a lot of efforts in the research field of En_RES and En_SD. However, previous studies have simply examined the influencing factors of En_RES and En_SD, and have not included En_RES and En_SD into a unified research category. We should consider that the development and improvement of En_RES usually requires a large amount of resource investment, which may put pressure on the short-term financial performance of enterprises, and thus potentially conflict with or hinder their pursuit of long-term SD. To answer this question, this paper starts from the perspective of the economic consequences of En_RES, and, for the first time, integrates En_RES and En_SD into a unified research framework. Using the data of China’s A-share listed companies from 2004 to 2022, we analyze and test the relationship between En_RES and En_SD from the perspectives of direct and indirect impacts by using a panel regression model.

7.1.1. Theoretical Contributions

Existing studies have only analyzed and tested the impact of certain factors on En_RES and En_SD, and few studies have examined the impact of En_RES and En_SD from the perspective of En_RES. To make up for this deficiency, this paper empirically analyzes and tests the impact of En_RES and En_SD based on the perspective of En_RES, using the data of China’s A-share listed companies, which not only theoretically expands the study of the economic consequences of En_RES, but also enriches the study of the factors influencing the En_SD.

Additionally, based on the theory of risk management, credit rationing, and foundation of resources, this paper analyzes and tests the impact of En_RES and En_SD from the perspective of the enterprise bankruptcy risk, credit availability, resource allocation efficiency, and other aspects of the selection of economic indicators; the empirical testing of the role of the above three factors in the En_RES affects the role of the relationship between En_SD, and not only enriches the exploration and application of the three theories in the literature research and empirical testing, but also opens up the black-box framework of En_RES affecting En_SD, and provides new perspectives and evidence for the study of the relationship between En_RES affecting En_SD.

7.1.2. Practical Contributions

The findings of this paper reveal the positive impact of En_RES on En_SD; therefore, (1) from the perspective of enterprises, listed companies should pay attention to the role of En_RES in promoting En_SD if they want to realize SD, and the development and improvement of resilience should be taken as the strategic core of the long-term development of enterprises. Through continuous technological innovation and investment, optimize the product production process and business structure, improve the efficiency of resource allocation, and reduce the waste and unreasonable use of resources. Establish and improve the information sharing platform, break the information silo, promote the effective degree of information exchange between enterprise departments, enterprises, and enterprises, and improve the accuracy and efficiency of strategic decision-making. It establishes a sound risk management and prevention and control system, monitors in real time the operating conditions of upstream enterprises and changes in the demand of downstream enterprises, adapts to changes in the market environment in a better and more timely manner, and endeavors to enhance the ability to cope with changes in the internal and external environments, adapt to changes in the internal and external environments, and recover and develop dynamically, to assist enterprises in achieving SD; and, (2) from the government’s point of view, it should encourage, guide, cultivate, and select some typical enterprises to actively explore the pathways, and means of constructing and perfecting En_RES, continuously explore the advantages of En_RES to the En_SD, form a demonstration effect, and continue to deepen the benefits of En_RES to the development of enterprises. At the same time, enterprises that actively carry out resilience work and continuously improve their resilience ability are given appropriate policy subsidies and tilts, or financial institutions are guided to give credit preferences to reduce their resources or economic burdens, to avoid bankruptcy or other problems due to the unstable internal and external environments, and to encourage more enterprises to continuously improve their resilience ability, and, eventually, a favorable atmosphere will be formed with enterprises as the main body and the industry participating together, responding to market changes through collective actions and enhancing the En_RES, so as to promote stable growth and En_SD.

7.2. Conclusions

This paper analyzes and tests the impact of En_RES on En_SD from direct and indirect perspectives using panel regression models with data from A-share listed companies in China from 2004 to 2022. The results show that the enhancement and improvement in En_RES capacity will promote enterprises to achieve SD; the heterogeneity results show that the support capacity of En_RES for En_SD is more significant in enterprises with a lower degree of environmental uncertainty, a higher degree of information sharing, and a higher degree of business complexity; and the mechanism test finds that En_RES helps enterprises to achieve SD by reducing the degree of enterprise bankruptcy risk, improving credit availability, and optimizing the efficiency of resource allocation.

7.3. Research Limitations and Future Research Directions

In this study, we analyzed and examined the relationship between En_RES and En_SD by using the data of Chinese A-share listed companies. However, due to the limitations inherent in the data sample, specifically its confinement to the Chinese market context, the generalizability of our conclusions may be restricted. Looking ahead, future research could use the data of S&P 500 listed companies in the United States to further validate this relationship.

In addition, this paper uses the core variable method to measure En_RES. However, it has limitations. The indicator selection is relatively single, making it hard to comprehensively and dynamically capture enterprises’ true resilience, leading to an inaccurate assessment. Although the indicator system is used for the robustness test, considering the diverse operational characteristics of different enterprises, the current indicator system may not be reasonable enough, affecting the test reliability. Therefore, future research can focus on the three key dimensions of the pre-crisis prevention ability, in-crisis response ability, and post-crisis dynamic development ability to select the appropriate indicators to accurately portray En_RES.