Greening Emerging Economies: Enhancing Environmental, Social, and Governance Performance through Environmental Management Accounting and Green Financing

Abstract

Given the rising interest in sustainability globally, this paper investigates whether the environmental management accounting (EMA) and green financing of a firm are associated with superior environmental, social, and governance (ESG) performance, considering manufacturing firms from emerging economies like Bangladesh to address a gap in relevant research. Drawing on the perspective of contingency theory, this study is one of the first to analyze how EMA and green financing enable sustainable production to enhance ESG performance, as well as the mediation that sustainable production exerts on this relationship. This study entails an analysis of ESG performance in sensitive industries, i.e., those that are more likely to cause social and environmental damage. To test our hypotheses, we applied partial least squares path modeling to analyze data from 467 responses. Further, we used fuzzy set qualitative comparative analysis (fsQCA) to check the robustness. The results suggest that sensitive industries present superior ESG performance through integrating EMA and green financing. Further, empirical evidence demonstrates that sustainable production fully mediates the relationship between EMA and ESG performance. Meanwhile, sustainable production does not moderate green financing and ESG performance. For managers, this study demonstrates how embedding green financing and EMA into the organizational process for transitioning to a sustainable production model can present superior ESG performance. Our study contributes to research on both the impact of EMA and green financing on ESG performance, mediation effects of sustainable production, and integrated analysis using PLS-SEM and fsQCA, and the practice of sustainability management in firms in developing countries.

1. Introduction

Amidst recent global crises and a turbulent business landscape marked by supply chain disruptions, sustainable development, market fluctuations, and evolving consumer behavior, managers are increasingly prioritizing sustainability practices [1,2,3,4,5]. These encompass environmental management accounting (EMA), green financing, energy development, renewable energy sources, and sustainable production [2,4,6,7,8,9], serving as focal points for managers seeking to address sustainability challenges across environmental, social, and governance (ESG) dimensions [10,11].

EMA facilitates the systematic tracking and analysis of environmental costs and performance metrics, aiding in identifying areas for improvement and mitigating environmental risks [12,13]. Green financing initiatives offer innovative financial mechanisms to fund sustainability projects, while sustainable production practices aim to minimize environmental impact and promote social responsibility throughout the production process [14,15]. Sustainable production and ESG performance are pivotal components of sustainability efforts, offering significant competitive advantages to organizations [16,17,18,19]. By adopting sustainable production methods like resource efficiency, waste reduction, and renewable energy utilization, organizations can reduce their carbon footprint, conserve natural resources, and enhance their reputation among environmentally conscious stakeholders [17,20]. Similarly, a strong ESG performance signals a company’s commitment to operating ethically, responsibly, and sustainably, leading to increased investor confidence, improved brand reputation, and enhanced access to capital [18].

However, despite the growing emphasis on sustainability, there remains a need for further research to explore sustainability issues in diverse geographical regions beyond traditional Western contexts [2,7,20,21,22,23]. Contingency theory offers a relevant framework for examining how contextual factors influence sustainability practices and outcomes across different regions [24]. Manufacturing industries play a crucial role in sustainability research due to their significant environmental and social impacts [25]. A particularly specific measurement of EMA is required to make actual predictions of EMA practices. Further, the integration of green finance and sustainable production with EMA and ESG is a highly demanded phenomenon for the current world which seeks sustainability. Additionally, the mediating effects of sustainable production are another requirement to be visualized through this study.

This study makes the following key contributions to the field of sustainability research. Firstly, it provides a precise measurement of the integral factors of EMA, enhancing the accuracy of predicting EMA practices. Secondly, it integrates green finance and sustainable production with EMA and ESG performance, offering a comprehensive framework for understanding their synergistic effects on sustainability. Thirdly, this study investigates the mediating role of sustainable production, revealing how these practices can enhance overall ESG performance. Fourthly, this study uses contingency theory to interpret the conceptual model and employ relationships. Lastly, it employs both asymmetric modeling (fsQCA) and symmetric modeling (PLS-SEM) to predict synergistic effects on ESG performance, allowing for a more robust analysis of greening emerging economies.

2. Materials and Methods

2.1. Theoretical Background

Research within the realm of firm ESG performance encompasses a variety of theoretical frameworks, such as institutional theory, stakeholder theory, social exchange theory, social network theory, resource-based theory, and organizational learning theory, to uncover the factors influencing ESG performance [6,26,27,28,29,30]. This paper diverges from current research by embracing contingency theory as its theoretical underpinning. Through the lens of contingency theory, this study investigates the potential integration of EMA and green financing while considering mediating factors. This integrated approach is proposed to be more intricate and potentially more impactful than any singular component, ultimately leading to improved ESG performance [24,31]. Consequently, this research contributes to advancing the comprehension of sustainability management practices within the framework of contingency theory, offering fresh insights into the intricate relationships between organizational strategies and ESG outcomes.

Contingency theory, a management theory, suggests that there is not a singular optimal approach to organizing or managing a company, as the best course of action is contingent upon the specific circumstances encountered by the organization [31]. Originating in the 1960s through the work of scholars like Joan Woodward and Paul Lawrence, contingency theory underscores the importance for organizations to tailor their structures, strategies, and practices to align with the unique demands of their environment [24,31]. In the context of this study, contingency theory is relevant and useful as it provides a theoretical framework for understanding how EMA and green financing practices can be adapted and tailored to different organizational contexts to enhance environmental, social, and governance (ESG) performance [24,32]. Contingency theory suggests that organizations must align their management practices with the demands of their external environment, such as regulatory requirements, market conditions, and stakeholder expectations. By applying contingency theory, this study can explore how organizations can strategically integrate EMA and green financing practices in response to these external contingencies, thereby maximizing their effectiveness in driving ESG performance.

Furthermore, contingency theory emphasizes the importance of considering mediating factors and contextual variables when designing management interventions [32]. In the context of this study, mediating factors may include organizational culture, leadership style, and resource availability, which can influence the effectiveness of EMA and green financing practices in enhancing ESG performance. By adopting a contingency-based approach, this study can identify the specific conditions under which EMA and green financing are most likely to lead to improved ESG outcomes, providing valuable insights for organizations seeking to implement sustainable management practices.

2.2. Environmental Management Accounting and ESG Performance

EMA is a strategic approach that integrates environmental considerations into an organization’s management accounting systems [13,14,28]. It involves identifying, analyzing, and managing environmental costs, as well as tracking environmental performance metrics to guide decision-making and improve overall environmental sustainability [33]. EMA enables companies to assess the financial implications of their environmental initiatives, enhance resource efficiency, comply with regulations, and achieve sustainable growth. In this research, the selection of EEI, ECT, CEM, and ERT as proxies for EMA is anchored in their alignment with the fundamental principles of environmental accounting [12,32,34]. EEI encapsulates the core tenet of EMA by measuring the effectiveness of operational improvements in minimizing resource consumption and waste generation [35]. ECT mirrors EMA’s emphasis on identifying, categorizing, and monitoring environmental costs, thereby enabling the integration of these costs into the decision-making process [36,37]. CEM is intertwined with EMA’s objective of mitigating environmental impacts, specifically focusing on carbon emission reduction [38]. Lastly, ERT resonates with EMA’s commitment to transparency and stakeholder engagement by disclosing the outcomes of environmental initiatives [16,39]. Consequently, this integration collectively captures the essence of EMA, reinforcing its role as a robust indicator of sustainable and environmentally conscious business practices.

Caiado, de Freitas Dias, Mattos, Quelhas, and Leal Filho [14] underscore how EMA, aligned with eco-efficiency, aids the transition to sustainable development by enhancing financial, environmental, and social performance. Zhang et al. [40] emphasize the significance of EMA in boosting eco-efficiency, technology innovation, and well-being performance for global sustainable development. Moreover, Liu et al. [41] propose a data-driven approach for eco-efficiency evaluation in industrial production, resulting in enhanced eco-efficiency and production benefits. Deng and Gibson [15] delve into improving eco-efficiency in agricultural production in Shandong, China, revealing trade-offs between agricultural production and urbanization. Additionally, de Araújo et al. [42] exemplify the use of EMA in evaluating eco-efficiency and determinants in Brazilian municipalities, identifying sustainable balances between environmental and economic variables. Shah et al. [43] exemplify the application of eco-efficiency in tracking urban sustainability transitions through an analysis of eco-industrial development in Ulsan, Korea. Meanwhile, Magarey et al. [44] spotlight eco-efficiency as a strategy for optimizing pest management sustainability, emphasizing the need to balance production and ecological impact. Lin et al. [45] adopt a dynamic approach to evaluate eco-efficiency in the semiconductor industry, highlighting the interplay between economic development and environmental protection. Deng and Gibson [46] tackle land use management’s relationship with eco-efficiency, investigating its role in enhancing land sustainability. Czyżewski, Matuszczak, Grzelak, Guth, and Majchrzak [22] delve into the complexities of agricultural sustainability, particularly concerning the Common Agricultural Policy’s contribution to eco-efficiency. Lastly, Long [35] presents a comprehensive assessment of eco-efficiency and effectiveness in sustainable urban development in China, emphasizing the importance of both factors in achieving well-rounded sustainability goals.

Shan et al. [47] examine the intricate relationship between energy prices, non-linear fiscal decentralization, and carbon emissions, highlighting the potential of fiscal policies and energy shifts to influence environmental quality. Raut et al. [48] delve into the fusion of big data analytics and sustainable operational practices, revealing predictors of sustainable business performance in the context of manufacturing industries. Carvalho [49] draws attention to the mining industry’s dual role as a provider of essential resources and a source of environmental disruption, underscoring the urgency for transformative changes in mining practices to align with sustainable development objectives. Bibri [33] outlines an analytical framework that links the Internet of Things (IoT) and big data analytics to enhance environmental sustainability in the context of smart sustainable cities, emphasizing the potential of advanced technologies in shaping future urban development. Lastly, Helleno, de Moraes, and Simon [34] propose an integrative approach that combines Lean Manufacturing principles with sustainability indicators to evaluate manufacturing processes, showcasing a method to concurrently assess economic, social, and environmental aspects.

Lee, Hashim, Ho, Fan, and Klemeš [23] emphasize the significance of sustainable energy, water management, transportation, and low-carbon emission technology in promoting holistic low-carbon development in Asia and beyond. Jiang et al. [50] delve into the implications of Bitcoin mining on carbon emissions, highlighting the need for effective policies to mitigate the environmental impact of blockchain operations. Tiwari, Daryanto, and Wee [36] present an integrated inventory management model that incorporates carbon emission considerations, offering insights into the reduction of both costs and carbon footprint in supply chain operations. Khan [51] examines the intricate nexus between carbon emissions, poverty, economic growth, and logistics operations in Southeast Asian countries, providing insights into potential strategies for environmental improvement. Qian, Hörisch, and Schaltegger [20] explore the relationship between environmental management accounting and carbon management, shedding light on the effectiveness of different tools in enhancing corporate sustainability practices. Lastly, Wu et al. [52] investigate the decoupling relationship between economic growth and carbon emissions in China’s construction industry, shedding light on the role of different driving forces in achieving sustainable development.

Gardner et al. [53] emphasize the significance of transparency in global commodity supply chains for effective sustainability governance. Orazalin and Mahmood [54] reveal that transparent reporting practices within the Russian oil and gas industry impact economic, environmental, and social performance indicators. Kahlenborn [55] highlights how transparency drives the green investment market, promoting environmentally conscious investment choices. Buallay [16] and Kuzey and Uyar [39] demonstrate that transparency through sustainability reporting positively influences operational, financial, and market performance in the European banking sector and the Turkish market, respectively. Silvestre and Ţîrcă [56] delve into innovation as a driver for sustainable development, indicating that the transparent dissemination of innovative solutions can address pressing environmental and social challenges. Lastly, Diouf and Boiral [57] shed light on stakeholder perceptions, emphasizing the need for authentic transparency in sustainability reporting to foster trust and accountability.

The scholarly landscape reveals a conspicuous void in the extant literature concerning the substantiation of relationships delineated in the hypotheses below (H1a–H1d). Notably, a discernible research lacuna exists regarding the unexplored intersections between EEI, ECT, CEM, and ERT, and their intricate interconnections with the broader domain of ESG performance within the organizational milieu. Although the imperatives of environmental management accounting are widely acknowledged as pivotal for the propagation of sustainable operational paradigms, extant investigations have predominantly been circumscribed to the examination of isolated facets of environmental management, exhibiting a conspicuous dearth in inquiries investigating the intricate synergies these accounting practices engender with the broader tapestry of ESG performance outcomes. Consequently, this study undertakes a pioneering endeavor to bridge this scholarly gap employing a meticulously orchestrated inquiry, aimed at unraveling the hitherto uncharted associations between the designated variables and ESG performance, thereby elucidating the intricate interplay between environmental management modalities and the overarching panorama of corporate sustainability, all within the compass of a comprehensive analytical framework.

H1a. 

Eco-efficiency improvement has an association with ESG performance.

H1b. 

Environmental cost tracking has an association with ESG performance.

H1c. 

Carbon emission management has an association with ESG performance.

H1d. 

Environmental reporting transparency has an association with ESG performance.

2.3. Green Financing and ESG Performance

The existing body of literature presents a multifaceted perspective on the relationship between green financing and ESG performance [58,59]. Wang et al. [60] take a global approach to establish a causal link between green finance and sustainable development, elucidating the positive impacts of green finance on sustainable outcomes. Ronaldo and Suryanto [59] focus on Indonesia and emphasize the pivotal role of green finance in achieving environmental and economic sustainability. Similarly, Zhang and Wang [61] construct an evaluation system for green finance development and analyze its relationship with sustainable energy development in China. Nguyen, Do, Hoang, and Nguyen [4] delve into the efforts of Vietnamese commercial banks in advancing green business initiatives during the COVID-19 pandemic, highlighting the role of private sector players in facilitating green finance.

Exploring sustainable practices in G7 countries, Yang, Du, Razzaq, and Shang [19] underscore the importance of clean energy, green financing, and green economic development in fostering sustainable practices across various industries. Zhang [11] provides an examination of green finance development’s influence on firms’ ESG performance, focusing on mitigating the risk of hypocritical ESG disclosures. Sun, Zhou, and Gan [58] investigate the impact of local green finance policies on corporate ESG performance in China, revealing mechanisms by which green finance can enhance sustainability outcomes. Furthermore, Xue, Wang and Bai [18] shed light on how local green finance policies enhance corporate ESG performance, particularly in non-state-owned companies and those with high executive social capital. Ng [62] contributes to the discourse by exploring the emergence of green investing and financial services, tracing their development in response to stakeholder expectations and the global governance framework for green finance. These studies collectively illustrate the complex interplay between green financing and various dimensions of sustainability, offering insights into its potential to drive ESG performance, support sustainable development, and shape corporate behaviors and practices. However, a notable gap exists in the literature concerning the direct link between green financing and specific ESG indicators. This study aims to address this gap by examining the relationship between green financing and ESG performance indicators, contributing a novel perspective to the ongoing discourse on sustainable finance.

H2. 

Green financing has an association with ESG performance.

2.4. Environmental Management Accounting, Green Financing, and Sustainable Production

In this section, we delve into the intricate relationships between EMA, green financing, and their impact on sustainable production. Ronalter, Bernardo, and Romaní [10] present a cross-regional empirical study showcasing the positive impact of quality and environmental management systems (QMSs and EMSs) on enhanced ESG performance in Europe, East Asia, and North America. Ali et al. [63] contribute to the ongoing discussion by delving into the effects of environmental management practices (EMPs) on both environmental and financial performance within Malaysia, uncovering the mediating role played by ESG disclosure. Johnstone [12] conducts a thorough analysis of environmental management systems within Small and Medium Enterprises (SMEs), outlining avenues for further research into the interplay between environmental strategy, management accounting, and control mechanisms. Jiao, Zhang, He, and Li [7] delve into business sustainability and competitive advantage within the manufacturing sector, underscoring the importance of green intellectual capital, environmental management accounting, and energy efficiency in attaining these goals. Deb, Rahman, and Rahman [25] expand their investigation to the context of Bangladesh, scrutinizing the impact of environmental management accounting on both the environmental and financial performance of manufacturing firms. Rahman and Rahman [64] shed light on green reporting as a mechanism for environmental sustainability, emphasizing its role in mitigating environmental costs and advancing corporate social responsibility.

Furthermore, Rahman and Islam [65] investigate the relationship between green accounting, energy efficiency, and environmental performance within Bangladeshi pharmaceutical and chemical companies, underscoring the mediating influence of energy efficiency. Solovida and Latan [66] establish a link between environmental strategy, environmental management accounting, and environmental performance, illustrating the mediating role of EMA within this nexus. Latan et al. [67] take a deeper dive by investigating the combined impacts of corporate environmental strategy, top management commitment, and environmental uncertainty, emphasizing the significance of environmental management accounting as a mechanism for enhancing corporate environmental performance. Balashova, Gorbacheva, Tokareva, Chernovanova, and Yagupova [21] propose a new approach to forming universally applicable information for socially oriented environmental accounting reports, emphasizing the importance of accurately recording and analyzing the environmental activities of enterprises. Despite the growing body of research examining the relationships between EMA, green financing, and their impacts on sustainable production, a significant literature gap persists in the integration and comprehensive understanding of these interconnected factors.

H3a. 

Eco-efficiency improvement has an association with sustainable production.

H3b. 

Environmental cost tracking has an association with sustainable production.

H3c. 

Carbon emission management has an association with sustainable production.

H3d. 

Environmental reporting transparency has an association with sustainable production.

H4. 

Green financing has an association with sustainable production.

2.5. Mediating Effects of Sustainable Production

Sustainable production involves integrating environmental considerations into all stages of the production process [14]. This integration ensures that environmental impacts are minimized, resources are used efficiently, and waste generation is reduced [40]. By incorporating sustainability principles into production practices, companies can mitigate environmental degradation and promote long-term ecological balance [41]. Sustainable production emphasizes resource efficiency, including the responsible use of raw materials, energy, and water [7]. By optimizing resource utilization and minimizing waste generation, sustainable production practices contribute to reducing environmental footprints and enhancing overall sustainability performance [63]. This efficiency not only benefits the environment but also improves cost-effectiveness for companies in the long run [17].

Sustainable production is often synonymous with increased economic viability [33]. By adopting sustainable practices, companies can lower production costs through efficiency gains, reduce regulatory compliance costs, and enhance their reputation and market competitiveness [60]. This economic sustainability is crucial for the long-term success and resilience of businesses in a rapidly changing global landscape. Sustainable production practices also consider social impacts, including labor conditions, community engagement, and stakeholder welfare [43]. By prioritizing social responsibility, companies can improve employee satisfaction, enhance community relations, and foster inclusive growth. This social dimension of sustainability is essential for building trust and maintaining a positive reputation among stakeholders [68]. Sustainable production helps companies comply with environmental regulations and manage risks associated with environmental and social issues [69]. By proactively addressing sustainability concerns, companies can mitigate legal and reputational risks, avoid costly penalties, and adapt to evolving regulatory requirements. This proactive approach to risk management strengthens the resilience of businesses in the face of environmental and social uncertainties. Given these reasons, sustainable production acts as a crucial mediator in the relationship between various environmental management practices (such as eco-efficiency improvement, environmental cost tracking, carbon emission management, and environmental reporting transparency), green financing, and ESG performance. Sustainable production serves as the mechanism through which these practices translate into tangible environmental, social, and governance outcomes, thereby enhancing overall sustainability performance. Therefore, the following hypotheses were proposed and subsequently evaluated:

H5a. 

Sustainable production mediates the relationship between eco-efficiency improvement and ESG performance.

H5b. 

Sustainable production mediates the relationship between environmental cost tracking and ESG performance.

H5c. 

Sustainable production mediates the relationship between carbon emission management and ESG performance.

H5d. 

Sustainable production mediates the relationship between environmental reporting transparency and ESG performance.

H6. 

Sustainable production mediates the relationship between green financing and ESG performance.

3. Methods

3.1. Data

In this study, the focus was on examining the combined effect of environmental management accounting and green financing, alongside sustainable production, on ESG performance within manufacturing firms in Bangladesh. Data were randomly gathered from managerial-level individuals across various manufacturing industries in the country. These industries were chosen based on the argument by some scholars that manufacturing companies typically face high-velocity environmental issues, making them suitable for our research focus [25]. The chosen participants occupied key strategic roles within their organizations, enabling them to offer valuable insights into social, environmental, economic, governance, and inter-organizational matters. Independent online surveys were conducted over two months in 2023, yielding 467 valid responses. Respondents were reached via email and furnished with a survey link, with stringent measures implemented to safeguard the confidentiality and anonymity of their feedback. Before commencing data collection, ethical clearance for the questionnaire items was secured, and requisite adjustments and enhancements to the survey tools were implemented.

3.2. Measures and Conceptual Model

EMA, conceptualized as a second-order construct, was evaluated using a 16-item scale compiled from Qian, Hörisch, and Schaltegger [20], Magarey, Klammer, Chappell, Trexler, Pallipparambil, and Hain [44], Khan [51], and Kuzey and Uyar [39]. This overarching construct encompassed four primary constructs: eco-efficiency improvement (EEI) with 4 items, environmental cost tracking (ECT) with 4 items, carbon emission management (CEM) with 4 items, and environmental reporting transparency (ERT) with 4 items. ESG performance was assessed using a 5-item scale adapted from Xue, Wang, and Bai [18], while sustainable production was measured through a 4-item scale adapted from Bradley [70]. Furthermore, green financing was evaluated via a 4-item scale adapted from Yang, Du, Razzaq, and Shang [19]. Respondents rated each item on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Drawing upon the findings of the literature review, this study developed a conceptual model as depicted in Figure 1.

3.3. Data Analysis Techniques

A range of theoretical perspectives has been utilized to investigate the relationships among the variables. For instance, it has been posited that the low R2 values derived from regression and PLS-SEM analysis may lead to erroneous conclusions regarding the capacity of multiple measures to significantly explain variance in dependent variables [71]. While results from PLS-SEM analysis offer a broad overview of trends, fsQCA provides deeper insights into the intricacies of achieving desired ESG performance states [13]. Rooted in configuration theory, fsQCA analysis enables a comprehensive examination of complex and nonlinear interactions among various elements [72]. This study chose fsQCA analysis due to its unique capability to accommodate both outcome and predictor variables on a fuzzy scale (continuous), in contrast with other QCA methods relying solely on a dichotomous scale (binary) [8,73]. Essentially, the adoption of the configurational approach offered by fuzzy set theory represents a novel and valuable contribution to this research field.

Within the overall dataset, managerial positions were distributed as follows: 38% were held by line managers, 42% by middle managers, and the remaining participants occupied top managerial positions. Concerning the size of surveyed firms categorized by the number of employees, 18% had fewer than 50 employees, 42% had between 51 and 250 employees, and the remaining firms had over 250 employees. Regarding the age distribution of the surveyed firms, 22% were less than 5 years old, 38% were between 6 and 10 years old, 27% were between 11 and 15 years old, and the remainder had been in operation for over 15 years.

3.4. Common Method Bias

During the research design phase, an a priori approach was followed based on the recommendations of Hair, Risher, Sarstedt, and Ringle [71] to address common method bias (CMB), as advocated by Deb, Rahman, and Rahman [25]. Additionally, an a posteriori approach was implemented. To identify potential sources of CMB, the full lateral collinearity test was conducted. The results, presented in

Table 1. Common method bias test. Full collinearity VIFs.

CEM	ECT	EEI	ERT	ESG	GF	SP
1.308	2.280	1.733	1.760	1.308	1.881	2.487

Note: VIF: variance inflation factor; CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental; social, and governance performance; GF: green financing; SP: sustainable production.

, indicate that the model is not affected by CMB, as the highest variance inflation factor (VIF) observed was 2.487, which is below the threshold of 3.3.

4. Results

4.1. Findings from Symmetric Modeling

In this study, variance-based structural equation modeling (PLS-SEM) was employed for symmetric modeling. The assessment of construct validity, encompassing convergent and discriminant validity, as well as reliability, involved several indicators including item loadings, weights, Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE), as presented in

Table 2. Measurement model evaluation.

Constructs	Sign and Scale	Loadings	Weights	CA	CR	AVE
Carbon emission management	CEM1: Our organization regularly measures and monitors its carbon emissions.	0.898	0.221	0.928	0.948	0.821
	CEM2: We have set specific targets for reducing carbon emissions.	0.951	0.290
	CEM3: Our company invests in technologies to reduce carbon emissions.	0.890	0.316
	CEM4: We publicly report our carbon emission data.	0.885	0.277
Environmental cost tracking	ECT1: We track all costs associated with environmental compliance.	0.819	0.458	0.786	0.808	0.514
	ECT2: Environmental costs are integrated into our financial reporting.	0.739	0.320
	ECT3: Our organization uses environmental cost data to make business decisions.	0.641	0.305
	ECT4: We have a dedicated budget for managing environmental costs.	0.656	0.294
Eco-efficiency improvement	EEI1: Our processes are designed to minimize waste and resource use.	0.778	0.317	0.785	0.860	0.606
	EEI2: We regularly invest in eco-efficiency technologies.	0.798	0.322
	EEI3: Our company sets targets for improving eco-efficiency.	0.711	0.245
	EEI4: We train employees on practices that improve eco-efficiency.	0.823	0.391
Environmental reporting transparency	ERT1: Our environmental reports are available to the public.	0.762	0.270	0.779	0.856	0.598
	ERT2: We provide detailed information on our environmental impact.	0.761	0.317
	ERT3: Our environmental reporting meets or exceeds industry standards.	0.781	0.298
	ERT4: Stakeholders trust the accuracy of our environmental reports.	0.789	0.405
Environmental, social, and governance performance	ESG1: Our ESG policies are well documented and accessible.	0.740	0.236	0.797	0.860	0.552
	ESG2: We have a dedicated team responsible for ESG initiatives.	0.776	0.264
	ESG3: Our company actively engages with stakeholders on ESG matters.	0.779	0.326
	ESG4: We regularly review and update our ESG practices.	0.738	0.262
	ESG5: Our ESG performance is integrated into our overall business strategy.	0.677	0.255
Green financing	GF1: Our company utilizes green financing options for projects.	0.904	0.318	0.835	0.896	0.692
	GF2: We have clear criteria for selecting green finance investments.	0.938	0.341
	GF3: Our green financing initiatives are aligned with sustainability goals.	0.885	0.307
	GF4: We publicly disclose information about our green finance activities.	0.536	0.227
Sustainable production	SP1: We prioritize sustainability in our production processes.	0.746	0.290	0.825	0.884	0.657
	SP2: Our products are designed with sustainability in mind.	0.819	0.293
	SP3: We use sustainable materials in our production.	0.847	0.316
	SP4: Our production methods reduce environmental impact.	0.826	0.333

Note: CA: Cronbach’s alpha; CR: composite reliability; AVE: average variance extracted; CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing; SP: sustainable production.

. Notably, all retained item loadings exhibited significant t-values. To ensure convergent validity, CA, CR, and AVE estimates for each construct were examined, surpassing the conventional thresholds of 0.70, 0.70, and 0.5, respectively, as recommended by Hair Jr. et al. [74].

In

Table 3. Discriminant validity testing using the Fornell–Larcker criterion and HTMT matrix.

Fornell–Larcker Criterion	CEM	ECT	EEI	ERT	ESG	GF	SP
CEM	0.906
ECT	0.067	0.717
EEI	0.085	0.578	0.779
ERT	0.392	−0.036	−0.015	0.774
ESG	0.453	−0.022	0.028	0.458	0.743
GF	0.458	−0.029	−0.013	0.647	0.512	0.832
SP	0.039	0.732	0.623	−0.062	0.061	−0.042	0.810
HTMT Matrix	CEM	ECT	EEI	ERT	ESG	GF	SP
CEM	-
ECT	0.096	-
EEI	0.094	0.754	-
ERT	0.453	0.117	0.046	-
ESG	0.519	0.105	0.119	0.557	-
GF	0.509	0.086	0.065	0.842	0.622	-
SP	0.067	0.949	0.756	0.074	0.105	0.062	-

Note: CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental social, and governance performance; GF: green financing; SP: sustainable production.

, discriminant validity was assessed employing Fornell and Larcker’s criterion in conjunction with the heterotrait–monotrait (HTMT) ratio. Following the Fornell–Larcker criterion, it was observed that the square root of the average variance extracted (AVE) for each construct surpassed its correlation coefficients with other constructs [75]. Moreover, all HTMT ratio values were below 0.90 [71]. Consequently, the findings of this study affirm both convergent and discriminant validity. For a comprehensive breakdown, refer to Table 3.

In

Table 4. Direct effects of EMA and green financing on ESG.

	Coefficients	2.5% CI	97.5% CI	T Statistics	Decisions
H1a: EEI → ESG	0.024	0.125	0.077	0.461	No
H1b: ECT → ESG	0.165 ***	0.287	0.037	2.611	Yes
H1c: CEM → ESG	0.259 ***	0.175	0.347	5.934	Yes
H1d: ERT → ESG	0.182 ***	0.093	0.280	3.821	Yes
H2: GF → ESG	0.280 ***	0.170	0.383	5.104	Yes

Note: *** = p < 0.01; CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing.

, the direct effects of EMA and green financing on ESG performance are thoroughly examined. The results offer valuable insights into the nuanced relationships between these variables. Environmental cost tracking (ECT) demonstrates a statistically significant positive impact on ESG performance, as indicated by a coefficient (β = 0.165, p < 0.01). This suggests that organizations that effectively track environmental costs tend to exhibit higher levels of ESG performance. Similarly, CEM and environmental reporting transparency (ERT) both show significant positive effects on ESG performance, with coefficients of β = 0.259 and β = 0.182, respectively (both p < 0.01). These findings highlight the importance of actively managing carbon emissions and maintaining transparent environmental reporting practices to enhance overall ESG performance (see Figure 2).

Conversely, eco-efficiency improvement (EEI) is found to have an insignificant but positive impact on ESG performance, with a coefficient of β = 0.024 and p > 0.10. While the effect is not statistically significant in this analysis, the trend suggests that efforts to improve eco-efficiency may still contribute positively to ESG performance outcomes. Furthermore, green financing (GF) emerges as a significant driver of ESG performance, with a positive coefficient of β = 0.280 (p < 0.01). This indicates that organizations leveraging green financing initiatives tend to achieve higher levels of ESG performance, underscoring the importance of sustainable financing practices in driving overall environmental, social, and governance objectives.

Table 5. Direct effects of EMA and green financing on sustainable production.

	Coefficients	2.5% CI	97.5% CI	T Statistics	Decisions
H3a: EEI → SP	0.301 ***	0.225	0.375	7.846	Yes
H3b: ECT → SP	0.558 ***	0.487	0.631	15.179	Yes
H3c: CEM → SP	0.013	0.076	0.053	0.377	No
H3d: ERT → SP	0.037	0.116	0.040	0.937	No
H4: GF → SP	0.008	0.072	0.088	0.192	No

Note: *** = p < 0.01; CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; GF: green financing; SP: sustainable production.

presents a detailed examination of the direct effects of EMA and green financing on sustainable production. The analysis reveals intricate insights into the various components’ contributions to sustainable production outcomes. Notably, eco-efficiency improvement (EEI) emerges as a significant driver, with a coefficient (β = 0.301, p < 0.01) signifying a substantial positive impact on sustainable production. This underscores the importance of enhancing eco-efficiency within organizational processes to achieve tangible improvements in sustainable production outcomes. Similarly, ECT demonstrates a robust positive influence, with a coefficient (β = 0.558, p < 0.01) indicating a significant impact on sustainable production. This highlights the critical role of accurately monitoring environmental costs in driving sustainable production initiatives within organizations. However, CEM and ERT exhibit positive coefficients (β = 0.013 and β = 0.037, respectively), albeit statistically insignificant (p > 0.10), suggesting potential contributions to sustainable production that lack statistical robustness in this analysis. Additionally, while GF shows a positive coefficient (β = 0.008) on sustainable production, it is statistically insignificant (p > 0.10), indicating a potential but unconfirmed role in supporting sustainable production practices.

In

Table 6. Mediating effects of sustainable production.

	Coefficients	2.5% CI	97.5% CI	T Statistics	Decisions
H5a: EEI → SP → ESG	0.063 ***	0.025	0.103	3.232	Yes
H5b: ECT → SP → ESG	0.117 ***	0.047	0.188	3.270	Yes
H5c: CEM → SP → ESG	0.003	0.017	0.012	0.363	No
H5d: ERT → SP → ESG	0.008	0.026	0.010	0.911	No
H6: GF → SP → ESG	0.002	−0.018	0.017	0.187	No

Note: *** = p < 0.01; CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing; SP: sustainable production.

, the mediating effects of sustainable production on the relationship between EMA and ESG performance, as well as between GF and ESG performance, are explored. The findings illuminate the nuanced dynamics within these relationships. Sustainable production emerges as a significant mediator between eco-efficiency improvement and ESG performance, with a coefficient of β = 0.063 (p < 0.01). This implies that enhancements in eco-efficiency lead to improved sustainable production practices, subsequently positively influencing ESG performance outcomes. Similarly, sustainable production acts as a mediator between environmental cost tracking and ESG performance, with a coefficient of β = 0.117 (p < 0.01). This suggests that the effective monitoring of environmental costs contributes to enhanced sustainable production practices, consequently leading to heightened levels of ESG performance.

Conversely, sustainable production does not act as a mediator in the relationship between carbon emission management and ESG performance, as indicated by a coefficient of β = 0.003 and p > 0.10. This suggests that while the management of carbon emissions may directly impact ESG performance, its effect is not significantly influenced by sustainable production practices within this context. Similarly, sustainable production does not mediate the relationship between environmental reporting transparency and ESG performance, with a coefficient of β = 0.008 and p > 0.10. This implies that although transparent environmental reporting may positively contribute to ESG performance, its impact is not mediated through sustainable production practices. Furthermore, the analysis reveals that the relationship between green financing and ESG performance is not mediated by sustainable production, with a coefficient of β = 0.002 and p > 0.10. This suggests that the influence of green financing initiatives on ESG performance is not significantly affected by sustainable production practices within this study’s framework.

4.2. Findings from Asymmetric Modeling

Asymmetric modeling using fuzzy sets (fsQCA) integrates fuzzy sets and fuzzy logic, offering a nuanced approach to understanding complex relationships. This methodology holds significance for several reasons. Firstly, traditional methods like correlation and beta coefficients may inadequately capture the association between variables, particularly when the relationship is nonlinear. The application of fuzzy sets, as advocated by scholars such as Rahman et al. [76], provides a framework that allows for multiple solutions, all leading to the same outcome. This flexibility is valuable in situations where traditional regression analysis may fail to identify independent measures that impact the outcome in only a subset of cases, as highlighted by Olya and Altinay [77]. Therefore, asymmetric modeling with complexity theory, facilitated by fuzzy sets, offers a more comprehensive understanding of complex relationships that may not be fully captured by conventional statistical methods [72].

Secondly, the reliability of symmetric approaches like multiple regression analysis (MRA) and structural equation modeling (SEM) is questioned when testing models featuring numerous independent variables with high correlation among them. This skepticism stems from concerns regarding the confounding impact of collinearity, as emphasized by Olya and Altinay [77]. In MRA and SEM, the presence of collinearity complicates result interpretation, potentially leading to inaccurate conclusions. Furthermore, despite the utilization of large sample sizes, these methods may not effectively control for the effects of confounding variables such as gender, age, and education, as noted by Olya and Altinay [77]. Hence, the limitations of symmetric approaches highlight the necessity for alternative methodologies like asymmetric modeling with fuzzy sets to offer more robust and accurate analyses, especially in complex scenarios with multiple interrelated variables.

Thirdly, in real-world scenarios, achieving a favorable outcome often relies on various antecedents collectively forming what is termed an algorithm within the asymmetric method, as discussed by Woodside [78]. Unlike symmetric relationships, where high values of an independent variable (X) are both sufficient and necessary for predicting high values of a dependent variable (Y), in asymmetric approaches, high levels of X are sufficient but not necessarily required for predicting high levels of Y. This distinction underscores the nuanced nature of asymmetric modeling, where outcomes are determined by combinations of antecedents rather than strict linear relationships [72]. Therefore, asymmetric modeling offers a more flexible and realistic framework for comprehending complex relationships and predicting outcomes in real-world contexts.

Fourthly, the asymmetric approach incorporates both positive and negative logic, recognizing that relying solely on one logic can lead to fallacious conclusions. Advocates, as highlighted by Woodside [78], argue against the notion of “net effects” in asymmetric methods, contending that observed net effects may overlook cases where contradictory outcomes occur. This underscores the importance of examining all combinatory conditions where an independent variable (X) may have either a positive or negative influence on the outcome variable (Y), as emphasized by Olya and Altinay [77]. In our study, we explore the various combinatory conditions through which CEM, ECT, EEI, ERT, GF, and SP interact to predict ESG performance, as depicted in Figure 3. This approach allows for a comprehensive understanding of how different factors collectively influence the outcome variable, contributing to a more nuanced analysis of complex relationships.

The initial step in the fsQCA analysis involved calibration, given that the variables in our study were measured on a 7-point Likert scale, necessitating rescaling [8,72]. We utilized the latent variable scores as inputs for the fsQCA analysis. To ensure the suitability of our uncalibrated data, we evaluated skewness and kurtosis, confirming that they fell within acceptable ranges (skewness less than ±1 and kurtosis less than ±2), indicative of a normal distribution. Following recommendations from prior research [8,72,73], the measures were calibrated into fuzzy sets with values ranging from 0 to 1. Specifically, 0.95 represented full set membership, 0.5 denoted the crossover point, and 0.05 indicated no set membership. The calibration process involved transforming variables into calibrated sets using the fsQCA program, where the maximum value represented full membership, the average value denoted the crossover point, and the minimum value indicated full non-membership. The results of this transformation, alongside other descriptive statistics of the causal conditions under investigation, are presented in

Table 7. Descriptive statistics along with calibrated variables.

Variable	Fully In	Cross-Over	Fully Out	Mean	Std. Dev.	Minimum	Maximum	N Cases
CEM	1.55	0.00	−2.37	0.535	0.289	0.05	0.95	467
ECT	2.10	0.00	−3.42	0.538	0.228	0.05	0.95	467
EEI	2.45	0.00	−2.40	0.496	0.245	0.05	0.95	467
ERT	2.44	0.00	−2.86	0.514	0.229	0.05	0.95	467
ESG	2.13	0.00	−2.91	0.525	0.242	0.05	0.95	467
GF	2.46	0.00	−2.93	0.512	0.225	0.05	0.95	467
SP	1.69	0.00	−3.48	0.562	0.246	0.05	0.95	467

Note: Calibration is conducted on the latent variable scores. Thus, fully in, cross-over, and fully out are designed through maximum, average, and minimum values of the latent scores. CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing; SP: sustainable production.

. This comprehensive approach ensures that variables are appropriately prepared for subsequent fsQCA analysis, facilitating a rigorous examination of causal relationships.

The second step of the analysis involved necessity analysis, also referred to as configurational element assessment. A condition is deemed necessary when its consistency score exceeds 0.9, as outlined by Ragin [79]. Necessity analysis entails evaluating the proportion of fuzzy set scores within a condition (across all cases) that are equal to or lower than the corresponding scores in the outcome [73]. The consistency scores presented in

Table 8. Results of necessary conditions for predicting ESG performance.

Configurational Conditions	High		Low (Negation)
	Consistency	Coverage	Consistency	Coverage
Carbon emission management	0.903	0.788	0.654	0.580
~ Carbon emission management	0.572	0.647	0.761	0.777
Environmental cost tracking	0.750	0.732	0.774	0.683
~ Environmental cost tracking	0.675	0.768	0.697	0.716
Eco-efficiency improvement	0.702	0.743	0.717	0.685
~ Eco-efficiency improvement	0.703	0.733	0.731	0.689
Environmental reporting transparency	0.910	0.826	0.676	0.623
~ Environmental reporting transparency	0.631	0.683	0.812	0.794
Green financing	0.914	0.833	0.683	0.632
~ Green financing	0.641	0.691	0.820	0.799
Sustainable production	0.781	0.729	0.775	0.653
~ Sustainable production	0.628	0.755	0.678	0.737

Note: Shaded rows indicate necessary conditions for predicting ESG performance. ~ indicates negation (lower level) of the condition.

indicate that carbon emission management, environmental reporting transparency, and green financing are all necessary factors for achieving a high level of ESG performance. However, it is important to note that they are not individually sufficient. This assertion was corroborated by negation analysis, which revealed that the absence of these conditions resulted in lower scores for ESG performance (consistency score < 0.90). This comprehensive examination of configurational elements provides valuable insights into the complex interplay between various factors and their impact on ESG performance outcomes.

The third step in the fsQCA analysis involves applying the fsQCA truth table algorithm to generate a truth table comprising 2^k rows, where k represents the number of outcomes considered in the analysis [79]. Each row in the truth table represents every possible combination among the causal conditions. For instance, in a truth table involving two causal conditions, there would be four logical combinations between them. In our study, the truth table was evaluated based on frequency and consistency values, following the guidelines established by Ragin [79]. Frequency refers to the number of observations for each possible combination, with a suggested threshold of 3 for samples exceeding 150 [72]. Meanwhile, consistency measures the degree to which cases correspond to the set-theoretic relationships expressed in a solution [72], with a recommended threshold of 0.75 [79]. This rigorous evaluation process ensures that the truth table accurately captures the interplay between causal conditions and outcomes, facilitating a comprehensive analysis of complex relationships. In fsQCA software (v4.1), three sets of solutions are typically generated: complex, parsimonious, and intermediate. These sets are distinguished based on the presence of “easy” and “difficult” counterfactuals. “Easy counterfactuals” occur when an unnecessary causal condition is added to a set of causal conditions that already predict the focal outcome. Conversely, “difficult counterfactuals” arise when a condition is removed from a set of causal conditions, resulting in an outcome under the assumption that this condition is unnecessary [72,73].

The complex solution encompasses all possible configurations of conditions or elements and includes neither easy nor difficult counterfactuals [77]. However, this solution tends to be excessively complex and impractical, offering limited insights into causal configurations. In contrast, the parsimonious solution identifies vital conditions that can be either easy or difficult counterfactuals [72]. It provides essential insights into the causal relationships by highlighting the key factors influencing the outcome. On the other hand, the intermediate solution focuses on vital conditions based on easy counterfactuals [77]. It represents a compromise between complexity and simplicity, incorporating essential elements from both the parsimonious and complex solutions [8]. By doing so, it offers a more balanced and manageable approach to understanding causal configurations, providing valuable insights while maintaining practicality.

These solutions are distinguished by necessary and sufficient conditions, categorized into core and peripheral conditions [77]. “Core” conditions, or essential elements, are indispensable and exhibit a strong causal relationship with the outcome, commonly found in parsimonious and intermediate solutions. Conversely, “peripheral” conditions are less critical and may be interchangeable, typically present only in the intermediate solution [72]. These conditions may manifest as present, absent, or deemed irrelevant (“do not care”), where their presence or absence does not significantly affect the outcome. Using ESG performance as the outcome variable, the fsQCA analysis comprehensively examines the relationships between causal conditions and their impact on the outcome, shedding light on factors influencing ESG performance. The principles of mediation and indirect effects elucidate the direct impact of independent and mediating variables on dependent variables. In our study, the measurement model identified a mediatory path from the symmetry analysis. Complementing the asymmetry analysis, tests assessed the direct effects of conditions on outcome variables. The results in

Table 9. Configurations for achieving high scores of ESG performance (sufficiency analysis).

Configurations	Coverage	Consistency
ESG = ƒ (CEM, ECT, EEI, ERT, GF, SP)
S1: ƒ (ECT*EEI*SP)	0.623	0.790
S2: ƒ (CEM*ECT*EEI*GF)	0.526	0.926
S3: ƒ (EEI*ERT*SP)	0.578	0.892
S4: ƒ (ERT*GF*SP)	0.627	0.913
Solution coverage	0.896
Solution consistency	0.711

Note: Frequency cutoff: 1; consistency cutoff: 0.816. CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing; SP: sustainable production.

detail how CEM, ECT, EEI, ERT, GF, and SP are all necessary conditions, yet individually insufficient for achieving higher ESG performance scores.

Moreover, our analysis reveals specific combinations of these conditions that are both necessary and sufficient for predicting higher scores of ESG performance. For instance, Solution S1 highlights that a combination of ECT, EEI, and SP is necessary and sufficient for predicting higher ESG performance. Similarly, Solution S2 indicates that CEM, ECT, EEI, and GF together constitute a necessary and sufficient combination for predicting higher ESG performance. Additionally, Solution S3 suggests that EEI, ERT, and SP in combination are necessary and sufficient for predicting higher ESG performance. Finally, Solution S4 demonstrates that ERT, GF, and SP collectively form a necessary and sufficient combination for predicting higher ESG performance. These findings underscore the complex interplay between various factors in influencing ESG performance outcomes and provide valuable insights for developing comprehensive sustainability strategies. All solutions demonstrated high consistency, indicating the reliability of the findings. Consistency measures the degree to which the observed cases correspond to the set-theoretic relationships expressed in the solution. Additionally, coverage signifies the extent to which a particular solution can explain variations in the outcome, like the concept of R-square in regression and structural equation modeling (SEM). The overall solution coverage suggests that the causal conditions (CEM, ECT, EEI, ERT, GF, and SP) collectively account for 89.60% of the membership in the solution associated with very high ESG performance. This high coverage indicates that the identified combinations of causal conditions offer a comprehensive explanation for the observed variations in ESG performance outcomes. It underscores the robustness of the solutions and the effectiveness of the selected variables in predicting and understanding ESG performance levels.

Table 10. Configurations for achieving low (~Negation) scores of ESG performance (sufficiency analysis).

Configurations	Coverage	Consistency
~ESG = ƒ (CEM, ECT, EEI, ERT, GF, SP)
S1: ƒ (~CEM*~ERT*~GF)	0.626	0.903
S2: ƒ (~EEI*ERT*GF)	0.530	0.745
S3: ƒ (~CEM*ECT*SP)	0.571	0.857
S4: ƒ (EEI*~GF*SP)	0.594	0.850
S5: ƒ (ECT*EEI*~ERT*SP)	0.568	0.853
Solution coverage	0.913
Solution consistency	0.710

Note: Frequency cutoff: 1; consistency cutoff: 0.822. CEM: carbon emission management; ECT: environmental cost tracking; EEI: eco-efficiency improvement; ERT: environmental reporting transparency; ESG: environmental, social, and governance performance; GF: green financing; SP: sustainable production. ~ indicates negation (lower level) of the condition.

presents the outcomes of negating the conditions, which further corroborate the findings from Table 9 and the SEM analysis. These results provide additional support for the hypotheses put forward in this study. Notably, the analysis reveals that specific causal conditions, including CEM, ECT, EEI, and GF, along with ERT, GF, and sustainable production (SP), are both necessary and sufficient for achieving higher scores of ESG performance. This implies that as the combination of CEM, ECT, EEI, and GF increases, a firm’s ESG performance also increases. Similarly, an increase in the combination of ERT, GF, and SP corresponds to an increase in a firm’s ESG performance. Additionally, a predictive validity test was conducted to assess the reliability and validity of the research model, affirming the robustness and accuracy of the findings. These results provide valuable insights into the factors influencing ESG performance and enhance our understanding of the predictive power of the research model.

5. General Discussion

This study draws upon contingency theory to investigate the determinants of ESG performance. The research model proposed in this study posits sustainable production as a key mediator in the relationship between EMA, GF, and ESG performance. Six hypotheses were formulated to explore this framework, encompassing both direct and indirect effects. The four direct effects hypothesize the impact of EMA on SP, the impact of GF on SP, the impact of EMA on ESG performance, and the impact of GF on ESG performance. Additionally, two indirect effects propose that EMA and GF influence ESG performance through their effects on sustainable production. In addition to employing partial least squares structural equation modeling (PLS-SEM) analysis, the study utilizes fuzzy set qualitative comparative analysis (fsQCA) to explore the combined effects of the antecedents of ESG performance (EMA and GF) along with sustainable production. By integrating these methodologies, this study aims to provide a comprehensive understanding of how EMA, GF, and sustainable production collectively influence ESG performance. This approach allows for a nuanced examination of the complex interrelationships among these factors, contributing to both theoretical knowledge and practical insights in the field of sustainability. The PLS-SEM results indicate significant direct and indirect effects of EMA and GF on SP and ultimately on ESG performance [9]. Specifically, the analysis reveals that EMA positively influences SP, which in turn positively impacts ESG performance. Similarly, GF has a positive direct effect on SP and, subsequently, on ESG performance. These findings highlight the importance of implementing environmentally sustainable practices and securing green financing to enhance both sustainable production processes and overall ESG performance outcomes. Moreover, fsQCA results offer additional depth by identifying specific configurations of conditions associated with high ESG performance [13]. The analysis reveals that certain combinations of antecedents, including EMA, GF, and SP, are necessary and sufficient for predicting higher ESG performance. This suggests that organizations must adopt a holistic approach that integrates environmental management practices, financial strategies, and sustainable production methods to achieve optimal ESG performance levels. More specifically, fsQCA suggests gauging the combinatory conditions by which EMA, that is environmental management accounting (CEM, ECT, EEI, and ERT), and green financing can predict ESG performance.

5.1. Theoretical Implications

The findings of this study make important contributions to the literature by documenting the impacts of EMA and green financing on sustainable production and ESG performance. First, by applying fsQCA, this study responded to the recent research call to examine how the EMA practices of firms combined with green financing contribute to improvements in sustainability processes [24,31]. Through this approach, this study offers insights into how the integration of EMA practices and green financing strategies influences sustainability outcomes within organizations. Specifically, it documents the combined effects of EMA practices, such as CEM, ECT, EEI, and ERT, with green financing initiatives on sustainable production and ultimately ESG performance.

Secondly, this study contributes to the enrichment of existing theoretical pathways regarding EMA practices, green financing, and sustainable production by adopting contingency theory as a novel theoretical foundation. This approach facilitates the development of hypotheses suggesting that EMA, green financing, and sustainable production collectively influence ESG performance. This theoretical framework is crucial as it underscores the significance of exploring various combinations and configurations of EMA, green financing, and sustainable production within the context of ESG performance, as advocated by Otley [31].

The findings of this study hold significant theoretical implications, particularly in the context of contingency theory. By adopting contingency theory as the theoretical foundation, this research extends the current research theme by elucidating how EMA and green financing can function as integrative mechanisms, incorporating mediating factors to enhance their effectiveness and ultimately culminate in improved ESG performance. This approach underscores the complexity of sustainability practices and emphasizes the importance of considering the interplay between multiple organizational factors in driving sustainability outcomes. Moreover, by demonstrating the integrative nature of EMA and green financing within the framework of contingency theory, this study contributes to advancing theoretical understanding of how organizations can strategically leverage these practices to achieve sustainable development goals.

5.2. Managerial Implications

In the face of increased competition, managers need strategic guidance to navigate the complexities of sustainability practices and drive organizational success. Based on the findings of this study, three key recommendations can be as follows. First, managers should prioritize the integration of EMA practices and green financing strategies within their organizations. This entails adopting a holistic approach that aligns financial decision-making with environmental sustainability goals. By leveraging EMA to track and analyze environmental performance metrics and strategically utilizing green financing options, managers can optimize resource allocation, minimize environmental impacts, and enhance overall ESG performance. Second, managers should focus on enhancing sustainable production processes within their organizations. This involves implementing eco-efficient technologies, reducing carbon emissions, and improving resource efficiency throughout the production lifecycle. By prioritizing sustainable production practices, managers can not only mitigate environmental risks but also enhance operational efficiency, reduce costs, and strengthen the organization’s competitive position in the market. Third, managers should adopt a contingency-based approach to sustainability management, recognizing that the effectiveness of EMA and green financing strategies may vary depending on contextual factors. This involves conducting a thorough analysis of organizational needs, capabilities, and external environmental factors to tailor sustainability initiatives accordingly. By embracing flexibility and adaptability in their approach, managers can effectively respond to changing market dynamics, regulatory requirements, and stakeholder expectations, ultimately driving sustainable growth and long-term success.

5.3. Methodological Implications

This study offers a significant methodological contribution by integrating both fsQCA and PLS-SEM methodologies, enhancing the understanding of sustainability management practices. By introducing novel combinations of EMA, green financing, and sustainable production, this research provides fresh perspectives on addressing sustainability challenges across ESG domains. Through the utilization of both symmetric (PLS-SEM) and asymmetric (fsQCA) approaches, this study broadens the methodological toolkit available to researchers and practitioners for analyzing complex causal relationships within sustainability contexts. These methodological advancements carry practical implications, as they furnish robust frameworks for evaluating and enhancing sustainability initiatives within organizations. By bolstering the rigor and validity of research in this field, these methodologies can catalyze tangible progress toward achieving sustainable development objectives.

6. Conclusions

In conclusion, this study advances the understanding of sustainability management practices and their impact on organizational performance. Through the application of both variance-based structural equation modeling (PLS-SEM) and fsQCA, this research elucidated the complex interplay between EMA, green financing, sustainable production, and ESG performance. This study revealed that integrating EMA practices and green financing strategies can positively influence sustainable production processes and ultimately enhance ESG performance. Additionally, the application of fsQCA provided valuable insights into the nonlinear and synergistic effects of sustainability factors on organizational outcomes. Overall, these findings underscore the importance of adopting a holistic approach to sustainability management and leveraging advanced analytical techniques to navigate the complexities of sustainable development. Moving forward, organizations can use these insights to inform strategic decision-making and drive positive environmental and social impact while simultaneously achieving financial success.

Several limitations should be acknowledged in this study. Firstly, the generalizability of the findings may be constrained as the research was primarily conducted within the manufacturing industries in Bangladesh. Extending the research model to encompass diverse sectors and geographical regions would enhance its applicability and robustness. Secondly, the measurement of ESG performance relied on input from managerial-level individuals, which could potentially introduce bias into the results. Utilizing alternative methods for assessing ESG performance, such as incorporating stakeholder perspectives or employing objective metrics, could mitigate this limitation. Additionally, while the current study employed both PLS-SEM and fsQCA methodologies, exploring additional methodologies with greater predictive power, such as artificial intelligence techniques, could offer deeper insights into the complex relationships between sustainability practices and organizational outcomes. Finally, this study is based on survey data that may not be sufficient to prove the hypotheses for the real world. Thus, future researchers are advised to use the actual firm-level data, such as operational and financial performance, for testing whether the performance is structurally changed before and after the EMA measures are implemented. Addressing these limitations in future research endeavors would contribute to a more comprehensive understanding of sustainability management and its implications for organizational performance.