Financial Performance and ESG Sustainability of the Electronics Industry in Europe: A Quantitative Approach

Abstract

Europe’s technological and sustainable transition will be possible, even and especially if the electronics industry adapts its production standards. This industry could become a testing ground because it raises serious environmental and social issues. This study critically evaluates the relationships between the economic and financial performance of NACE 26 companies and their environmental, social, and governance (ESG) performance. It uses data from the financial statements of numerous companies over a decade to determine whether the sustainable activities adopted by these companies have impacted their profitability, operational efficiency, and qualitative and quantitative capital structure. The research is conducted through a multilevel quantitative analysis, combining descriptive approaches, multiple regression, and path analysis. The study’s findings indicate that the adoption of ESG strategies improves companies’ competitiveness and resilience in the medium to long term. In the short term, however, ESG strategies cause a slight reduction in profitability, primarily due to the costs associated with green investments. Effective governance is crucial for enhancing operational efficiency and cultivating a mutually beneficial and positive relationship between sustainability, digital innovation, and value creation.

1. Introduction

Over the last two decades, the growing attention paid to environmental, social, and governance (ESG) sustainability has redefined the paradigms of competitiveness and value creation in the global industry [1,2]. Companies are now called not only to pursue economic objectives, but also to ensure a positive impact on communities, the environment, and management transparency. This evolution reflects a structural shift in the corporate vision, where performance is no longer evaluated exclusively in terms of profit, but rather in terms of integrated sustainability [3]. The ESG paradigm, in fact, represents the convergence of three complementary dimensions, environmental protection, social responsibility, and the quality of governance, which together determine an organisation’s ability to create value in the long term while minimising non-financial risk [4].

The application of sustainability principles is now influenced by the widespread adoption of digital technologies, which enhance the efficiency of production processes and mitigate environmental impacts by continuously monitoring them.

Artificial intelligence, the Internet of Things, massive data analytics, and machine learning now enable the integration of ESG dimensions into governments and the operational management of companies, contributing to the creation of an eco-intelligent industry [5].

However, these innovations are hindered by structural barriers, including high initial investment, limited data availability, and the challenging quantification of the economic and social benefits of sustainable practices [6,7].

These critical issues are particularly relevant in the European electronics and digital components sector, which is a key sector for the technological and ecological transition. In them, more than in others, it is necessary to find a balance between innovation, operational profitability, and social responsibility [8].

This study is part of the theoretical debate on the strategic role of emerging technologies and digitalisation processes [9], in which digital sustainability is not considered an autonomous construct, but rather a conceptual framework that guides the configuration and evolution of ESG performance.

Its impact is particularly evident in improving operating efficiency (Pillar E) and strengthening the transparency of management mechanisms (Pillar G).

The international literature has explored the relationships between sustainability and economic and financial performance.

Some studies highlight a positive relationship between ESG and profitability, arguing that sustainability-oriented policies can generate competitive advantages [4,10].

Other contributions, on the other hand, point to less clear, non-existent, or, sometimes, even contrary effects.

The impact of ESG can therefore be sector-dependent [11,12,13].

Very different results are found primarily in technology-intensive sectors, where the application of ESG logics requires large amounts of capital invested in R&D and necessitates a consequent reorganisation.

The European electronics industry is a notable example. It drives digitalisation, but faces environmental issues related to energy consumption, the disposal of WEEE (waste electrical and electronic equipment), and the management of complex supply chains [7,14].

This study aims to verify the potential structural relationship between ESG ratings and the economic and financial performance of companies in the European electronics sector over the decade 2014–2023. To this end, it uses a multi-level quantitative approach, which integrates descriptive analysis, multiple panel regression, and path analysis.

The objective of the investigation is therefore twofold:

(a)

Verify whether and how the adoption of ESG practices promotes better financial performance;

(b)

Isolate the causal mechanisms through which the individual ESG components (Environmental, Social, and Governance) exert their influence on profitability and capital structure.

This innovative approach surpasses the limitations of the correlational and linear view, providing a vision that is more likely to reflect the complexity of modern European industrial systems [15].

This study also contributes to the academic and managerial debate on digital sustainability, understood as the ability to integrate innovative technologies within ESG processes to generate shared value. The analysis of the electronics sector, which is strategic for the technological sovereignty of the European Union and at the centre of political initiatives such as the European Chips Act [8], allows us to provide new empirical evidence on a key sector that is still underexplored in the quantitative literature.

The sector is strategic for the technological autonomy of the Union and makes a significant contribution to the competitiveness of the advanced manufacturing industry. The need to address Digital Transformation (DT) is not only an operational challenge, but the strategic context that amplifies the role of ESG. In this study, DT is not directly measured but is analysed as the strategic driver that makes the Governance (G) Pillar key to translating E and S investments into financial resilience.

Despite the vast literature on the ESG-EFP nexus, this study aims to fill some theoretical and empirical gaps through the following unpublished contributions:

• Shift from correlational analysis to causal investigation. This avoids previously tested correlations and linear regression models. Preferably, path analysis better isolates the direct and indirect causal influence paths. This enables the formalisation and testing of the relationships between Governance (G) and financial leverage as explicit mediating mechanisms. This enables a more analytical understanding of the complex value creation induced by ESG.
• Strategic relevance of the NACE 26 context. The research focuses on the European electronic components sector (NACE 26), characterised by high capital intensity, frequent innovation, and numerous regulatory standards. Choosing this context enables the evaluation of managerial choices and facilitates the identification of desirable continental directives, thereby continuing the current evolution (e.g., the EU Chips Act and the CSRD).
• Isolating Governance. The disaggregated results show that, in the technology-intensive European context, Governance (G) is the primary strategic mechanism. Investments in pillars E (operational efficiency) and S (human capital development) must translate into lasting competitive resilience, accompanied by parallel improvements in long-term economic and financial performance.

In brief, the research aims to achieve two objectives: first, to expand knowledge through empirical verification. Its results are hoped to guide the future choices of business managers and, at the same time, provide useful indications to public governments of the economy. At the same time, there is a general desire to support the establishment of a European industrial model that can truly combine sustainability, innovation, and competitiveness.

2. Theoretical Background, Literature Review, and Formulation of Hypotheses

2.1. Fundamentals Theorists: Justification Conceptual Model

This study is based on two theories to analyse the possible relationships between sustainability and economic and financial performance (EPF).

The first theory is that of stakeholders [16], which believes that maximum attention to all stakeholders must be a necessary condition for the company’s long-term success. This should also result in a positive relationship between ESG and EFP as a result.

The second is the resource-based view (RBV), which is particularly useful for justifying the role of governance. According to the RBV, a firm’s ability to generate outperformance stems from unique resources and capabilities [17].

Governance (G) is not just compliance, but the organisational capability that allows a firm to manage, integrate, and transform environmental (E) and social (S) investments into differentiating strategic resources. This approach justifies the use of governance as a key mediating variable in our path analysis model.

The scientific debate on the relationship between sustainability and economic-financial performance has evolved over the last thirty years, moving from an ethical-reputational perspective to a strategic-value-based vision of the company [1,10,16]. The main reference theories, stakeholder theory, legitimacy theory, the resource-based view, and institutional theory, converge in recognising that the creation of long-term value depends on the ability of companies to integrate environmental, social, and governance considerations into their business models [4,18].

ESG can be interpreted as a multidimensional system of intangible resources, including environmental skills, human and relational capital, quality of governance, and information transparency, which, when interacting, contribute to the company’s competitive resilience [19,20].

The environmental dimension (E) is linked to the principles of the natural resource-based view, according to which the proactive management of natural resources represents a source of sustainable competitive advantage [10]. Investing in clean technologies, energy efficiency, and green innovation enables the reduction of costs and regulatory risks, as well as an improvement in Total Factor Productivity (TFP) [21].

The social dimension (S) is instead based on the recognition of human and reputational capital as performance levers; a high quality of internal and external relations contributes to increased productivity, reduced turnover, and mitigated reputational risks [22,23].

The governance dimension (G) acts as the cornerstone of the ESG system; solid, transparent, and diversified governance practices reduce information asymmetry and the cost of capital, fostering a climate of trust among investors and stakeholders [24,25]

Studies by Friede et al. [2] and Bai & Kim [13] have shown that 90% of surveys show a positive relationship between ESG performance and economic and financial outcomes; the outcomes, however, are heterogeneous and depend on the geographical context, the typical characteristics of each sector, and the balance sheet ratios used to assess economic and financial performance.

More recent studies [4,26,27,28] demonstrate that the impact of ESG on financial performance is contingent upon other factors, including corporate disclosure, the maturity of the financial market, and the qualitative and quantitative structure of capital. In capital-intensive technology sectors, for example, ESG policies can trigger a trade-off in the short term, due to implementation costs, while generating structural benefits that would only manifest themselves in the medium to long term [11,29].

The complexity of the interrelationships in the European electronics industry is even more pronounced because this production segment is, at the same time, the leading proponent of sustainability through smart technologies, while also addressing environmental concerns related to energy consumption and the production of electronic waste [7]. The transition to sustainable models seems to depend on digital transformation, which is affirmed as a necessary strategy (digital sustainability). Digitisation, in fact, would integrate sophisticated monitoring tools that can guarantee the traceability of the supply chain, thereby favouring the logic of the circular economy [14,30]. Only through intelligent technologies (IoT, Artificial Intelligence, blockchain, and big data analytics) would ESG benefits be concretely affirmed [5].

However, there is a lack of systematic and rigorous analysis of the European electronics industry, based on quantitative measurements of ESG performance and indicators related to profitability and capital balances. Many studies, in fact, focus on Asian or North American contexts, different from the European context [6].

This empirical research aims to fill this gap with a survey of a sample of European companies active in the electronics sector between 2014 and 2023. Its general purpose is to outline a possible relationship between sustainability and performance. The results help outline business strategies and public policies functional to the sustainable transition.

2.2. Review of the Empirical Literature and Gap Identification

2.2.1. The ESG-EFP Nexus: Evidence, Critical Issues, and Transmission Mechanisms

The relationship between Environmental, Social, and Governance (ESG) criteria and Financial Performance (EFP) is no longer merely a subject of theoretical speculation, having achieved a phase of robust empirical consensus. The links between environmental, social, and governance (ESG) criteria and financial performance (EFP) must be assessed by moving beyond purely abstract discussions and prioritising empirical evidence.

However, it is best to first investigate the analyses available in the literature on which to base subsequent developments.

An overview of the best theoretical contributions was proposed by Friede, Busch, and Bassen [2]; in over 2000 articles, nearly 90% identified a positive relationship between ESG and EFP.

This result was confirmed by subsequent studies [1], which state that corporate sustainability is useful for achieving better economic and financial balance in the medium to long term.

The literature often distinguishes the different components of financial value generation attributable to ESG into different pillars:

• Environmental Pillar (E). Actions for a better physical environment would increase operational efficiency. Some advanced research [6,27] demonstrates that eco-friendly choices reduce compliance and industrial waste management costs, thus promoting Total Factor Productivity (TFP). TFP is a key indicator of operational productivity. Green investments, in conclusion, would be necessary to transform environmental performance (E) into a competitive advantage [21].
• Social Dimension (S). Social performance is closely tied to the management of both human and reputational capital. A high S rating is associated with higher employee productivity and reduced turnover, acting as an indicator of operational stability [22,23]. On the financial front, proactive social policies mitigate reputational risk, protecting market value from adverse shocks.
• Governance Dimension (G). Governance is often considered the enabling basis of E and S performance. A transparent and diversified Governance structure reduces information asymmetry, improving credibility in the eyes of investors and reducing the cost of capital [24,25]. The literature highlights that G performance is positively associated with the quality of financial disclosure and the ability to attract long-term investments [19].

Despite the growing consensus on the positive correlation between ESG and EFP, the literature still presents two main gaps that justify the approach of this study.

(a)

Most of the evidence is correlational (e.g., Friede et al., [2] ), failing to map the causal directionality of the link;

(b)

The impact of the individual pillars (E, S, G) is highly heterogeneous and depends on the sectoral and geographical context, with an under-representation of specific studies on the European high-tech sector (NACE 26).

Most studies employing advanced quantitative approaches (SEM, Path Analysis) have historically focused on Asian markets, such as China and South Korea [6,30], or North America. There is a lack of systematic research on the European context that considers the specific regulatory pressures (e.g., the WEEE Directive, SFDR, and the European Chips Act) and market dynamics of the European electronics sector. This sectoral and geographical gap justifies the need for a new quantitative approach to the sample of European companies. The analysis of this specific sector and its impact on the European financial market allows us to overcome the limitations of global studies, offering targeted policy and managerial implications. It is crucial to recognise that in NACE 26, sustainability investments (E, S) are inextricably linked to investments in innovation and digitalisation.

Recent literature considers DT as a factor improving ESG results. In our analysis, this relationship is reflected in the roles of G (which favours investments in technology and R&D) and E, which enhances operational efficiency, also achieved through digital innovation and the circular economy [6].

Research on European production has confirmed the absolute importance of Governance (G) for sustainable choices, reducing corporate risks [31].

This quantitative study on financial statement data aims to contribute to the debate by experimenting with Path Analysis, which could be the best tool for estimating correlations, also useful for assessing the effects of sustainability. Governance (G) is certainly relevant because it mediates ESG investments and financial performance.

This study considers the European electronic components industry (NACE 26) as a strategic sector for enabling the European Union to become independent from other continents. Indeed, it plays a crucial role in the management of electronic waste (WEEE) and in the concrete implementation of the Circular Economy, as highlighted in useful recent publications [7].

This amplifies the explicit relevance of Pillar E.

It should also be considered that the governance of companies of this type, especially if listed, is subject to rules that evolve frequently (for example, the CSRD Directive). This helps define the decisive role in overall corporate sustainability performance [31].

The analysis of this specific sector and its impact on the European financial market [32], therefore, allows us to overcome the limitations of global studies, offering targeted political and managerial implications.

2.2.2. Sustainability and Innovation in the Electronics Industry: The Imperative of Smart Technologies

The electronics and computer components sector (ATE: Telecommunications and Electronic Equipment), although being a driver of innovation, operates in a context of double externality; it creates digital solutions (Smart Technologies) for the sustainability of other sectors, but at the same time it is one of the most impactful sectors in terms of energy consumption, WEEE and complex supply chains [7].

For the electronics industry, this translates into the use of Digital Transformation (DT) as a direct tool for sustainability, a concept that the literature defines as digital sustainability [5,30].

-

Strategic Investment. Xu et al. (2025) [30], in a study on the electronics industry, used causal modelling (SEM) to demonstrate that budget allocation strategies for DT projects have a significant association with positive ESG performance outcomes. Digitalisation, in this context, is seen as a structural investment that facilitates environmental monitoring and social traceability of the supply chain.

-

Operational Efficiency and TFP. DT is a precondition for efficiency. Wang, Shen, and Zhu [27] explored how the digital economy influences TFP, a factor that overlaps with ESG performance in terms of efficiency and resource reallocation.

-

Circular Economy (CE). In electronics, sustainability is closely linked to the concept of a Circular Economy. Literature highlights that the adoption of Circular Economy (CE) models, particularly through the eco-design of products (which facilitates recycling and reuse), is essential for competitiveness and financial performance in Europe, responding to regulatory and market pressures [7].

2.2.3. The Methodological Gap: From Correlation to Causality with Path Analysis

Despite the wealth of studies, the literature presents a persistent problem of endogeneity and bidirectionality between ESG and EFP. In other words, is the better ESG rating the cause of financial success, or is it the consequence of it (i.e., only financially sound companies can afford expensive ESG investments)? [33].

Most multiple linear regression models are unable to resolve this ambiguity, as they assume that the independent variable (ESG) influences the dependent variable (EFP) in only one direction and do not account for the complex internal interrelationships between the E, S, and G dimensions [15].

To overcome this limitation, it is imperative to adopt a causal modelling approach.

1. Path Analysis (PA) and Structural Equation Modelling (SEM): PA is a specialised form of SEM that allows one to simultaneously test a series of hypothesised causal relationships (Paths) between a set of manifest variables. This approach is superior because it will enable one to model both direct and indirect effects and test directionality. Using panel data and the deployment of lagged variables or dynamic structural models, causal directionality can be inferred, providing a robust and more explanatory test of the strategic link between sustainability and value creation [5]. The application of Path Analysis to map the causal effects of the E, S, G disaggregation on Financial Performance (ROE, ROA, ROCE, leverage ratio) in the European electronics sector is the objective and unique contribution of this study to the literature.

2.3. Formulation and Justification of Research Hypotheses

Based on consolidated theoretical premises (including the principles of Stakeholder Theory and the resource-based view) and the need for a causal test on the European electronics sector, the following hypotheses are formalised:

H1: 

Positive relationship between overall ESG score and Return on Equity (ROE).

This hypothesis is based on Stakeholder Theory, which posits that the ethical and responsible management of all stakeholders (not just shareholders) maximises long-term value. High ESG performance is associated with a reduction in the cost of capital (both debt and equity) as it reduces the risk perceived by investors and improves corporate reputation and access to financial markets, resulting in greater profitability for shareholders [32,34].

H2: 

Positive impact of the environmental ESG (E) score on Return on Assets (ROA).

The hypothesis is supported by the Eco-Efficiency paradigm. Environmental practices (green technologies, energy efficiency) are not only a cost, but a factor of innovation and reduction in operating costs (reduction in raw material consumption and waste management), leading to greater efficiency in the use of company assets and improving ROA [35].

H3: 

Positive correlation between social ESG score (S) and Return on Capital Employed (ROCE).

The hypothesis is rooted in the resource-based view (RBV), which considers human capital as a strategic resource that is difficult to imitate. Social policies (training, employee well-being, turnover reduction) improve engagement, productivity, and the quality of operational processes. This greater stability and performance of human resources translate into a more efficient use of invested capital and an improvement in ROCE [36].

H4: 

Governance performance (G) positively influences financial performance (ROA) through an indirect (mediated) effect.

Governance (G) configures itself as the critical organisational capability responsible for mitigating agency costs and dissipating informational asymmetry. Robust corporate governance (characterised by high transparency and Board independence) minimises the informational risk premium, leading to a tangible reduction in the cost of capital and facilitating access to more competitive funding conditions [24,31]. This process directly translates into the optimisation of overall operating efficiency and, indirectly, into an increase in operating performance, as measured by ROA.

H5: 

Companies with higher ESG scores have lower financial leverage.

The structural risk mitigation mechanism leads to this assessment. Excellent ESG practices, especially solid governance, are symptoms of reliability. The consequence: reduced financial and operational risk, due to fewer disputes or sanctions for regulatory violations. Economic competitors, therefore, consider the company more stable and resilient. Governing bodies will have less difficulty covering potential financial needs, even though sustainability-oriented companies generally prefer self-financing or equity capital increases [37]. The best literature confirms that the reduction in general management risk, due to transparency and the dissemination of non-financial information, significantly influences investor decisions and capital flows [38]. The market, in general, rewards more stable companies. Companies with high ESG performance therefore maintain lower debt.

3. Materials and Methods

3.1. Sample Selection and Data Sources

Annual financial statements were a valuable tool for assessing the economic performance of all companies. They can enhance their information strength by identifying ethical indices tailored to specific business circumstances.

To examine the statistical correlations between ESG ratings and the economic and financial performance of European companies in the electronic components sector, this study uses an exploratory descriptive approach. The ESG rating is treated as a variable dependent on profitability indicators (ROE, ROA, ROCE) and the financial structure (leverage). This approach does not imply a one-way cause-and-effect relationship but rather reflects the desire to analyse the ESG positioning of companies in relation to their economic fundamentals, recognising the possible bidirectional nature of the relationships between sustainability and performance, which has been widely highlighted in the literature.

It should be noted that the choice made in this work is deliberate and is based on the objective of understanding whether companies with the best economic performance also exhibit higher levels of sustainability, thereby contributing to the discussion on the inverse relationship between ESG performance and randomness. This is due to the methodological debate that suggests the regression of financial performance on ESG scores. In this sense, the analysis should not be seen as a demonstration of randomness, but as an investigation into the significant correlations and co-evolution dynamics that occur between the variables involved.

This study uses data from the Orbis Europe complete platform, managed by Moody’s (Software version: 129.00). This database was selected because it encompasses a broad range of European companies, including both listed and unlisted entities. In addition, financial statement harmonisation procedures ensure that accounting information is consistent and comparable across different stocks. The platform also analyses more European companies and provides in-depth and consistent annual data compared to other sources. Therefore, it is the best choice for achieving the study’s objective, which is to build a comprehensive and representative dataset of companies in the sector at the European level.

Yahoo Finance, which collects ratings compiled by leading international providers in the sector, provided the ESG rating data. It was chosen because it ensures the comparability of scores between different companies and European countries, thanks to a uniform methodology, extensive geographical coverage and a clear definition of indicators.

The initial sample consisted of 42,244 European companies selected from the Orbis Europe database; the final sample was obtained by ensuring the availability of complete and consistent ESG ratings for the specified period.

As a result, the 40 companies included in the study represent the most informative and reliable subset of ESG data available for the European electronic components sector (NACE 26).

Although the sample size may appear small, this choice reflects a methodological approach oriented towards the quality and comparability of the data, rather than numerical size, thus ensuring a solid empirical basis for the statistical and econometric analyses carried out.

3.2. Definition and Measurement of the Variables

The survey covering the period 2014–2023 considers the trend of the following economic performance indicators:

• ROE (Return on Equity);
• ROA (Return on Assets);
• ROCE (Return on Capital Employed);
• Leverage ratio (LEVA).

For a more in-depth analysis, the data were also subjected to descriptive statistics, including mean, median, standard deviation, sample variance, skewness, minimum, maximum, and count of the actual available data.

Each company analysed is assigned an ESG rating based on ratings provided by Yahoo Finance websites. An inferential analysis is then conducted using multilevel econometric models. Finally, an econometric analysis is performed using three complementary methodological levels to identify the relationships between ESG ratings and economic and financial indicators.

The entire methodological framework was organised to gradually proceed from an exploratory phase, which allows us to observe the basic relationships between the data, to an explanatory phase, aimed at quantifying the direct effects of financial indicators on the ESG rating, up to a more structural phase, which aims to systemically model the direct and indirect links between the elements analysed.

3.3. Analysis and Preparation of the Data

A methodological approach was used and articulated on three analytical levels to examine the relationships between the variables under study:

• Multiple correlation analysis is aimed at identifying the interrelationships between variables by measuring the strength and direction of their linear associations. Correlation analysis serves as the exploratory starting point of the study, as it enables us to observe the presence and strength of linear relationships between variables, identifying the economic dimensions that are potentially most relevant in explaining ESG scores and thus providing a rationale for the subsequent econometric analysis.
• Multiple regression is used to estimate the direct effects of a set of independent variables (ROE, ROA, ROCE, and financial leverage) on the dependent variable (ESG rating). Multiple regression allows us to quantify the direct effect of each economic-financial indicator on the ESG rating, through coefficients that measure their specific impact, while controlling for any information overlap through diagnostic analyses such as VIF and residual evaluation.
• Path analysis enables the simultaneous modelling of direct and indirect relationships between variables. To address the problem of tautology and isolate the value creation mechanisms, the model was built using the disaggregated scores of the three ESG pillars (E, S, and G) as exogenous variables, rather than the aggregate rating alone [39]. Financial leverage was considered as a mediating variable, while economic performance indices represent the model’s outputs. This approach enabled the estimation and comparison of the path coefficients for each pillar, verifying the extent to which they influence financial leverage and, indirectly, performance (ROE, ROA, ROCE).

Logical coherence, rigour, and interpretative capacity have been guaranteed by the entire methodological system, which employs descriptive analysis, regressions, and path analysis. This aligns with the recommendations of the economic-managerial literature for studying complex phenomena, such as the lack of ESG performance [15].

All econometric and statistical analyses were performed with R software (version 2024. 12. 1+563). In particular, the following packages have been used:

• “corr” for calculating correlations between variables;
• “lm” for linear regression analyses;
• “lavaan” for the path analysis.

The use of R and related packages guarantees the complete replicability of the results and the transparency of the analysis procedures adopted.

ChatGPT was used to assist us in data processing.

The study demonstrates how sustainability can impact the economic and financial foundations of a modern company, extending beyond being merely a reputational issue.

4. The Context of the Investigation

4.1. ESG Report and Financial Performance of the Electronic Components Sector

Environmental, social, and governmental (ESG) sustainability is facing challenges in the electronic components industry, which has complex supply chains. Investors’ and consumers’ growing interest in responsible business practices is driving companies to incorporate ESG criteria into their strategies. However, the debate on the relationship between financial results and ESG performance continues, with studies highlighting both positive and negative aspects.

The analysis reveals that European companies in the sector exhibit significant heterogeneity in terms of size, financial structure, and profitability.

Specifically, a gradual stabilisation of leverage levels is observed over the period under review, accompanied by a widespread improvement in operating profitability starting in 2018, consistent with the trend observed in the sector during the same period. This trend is accompanied by a gradual increase in ESG scores, suggesting that more financially sound companies also tend to pay greater attention to sustainability.

These elements provide an initial interpretative framework that is useful for subsequent econometric analysis, highlighting how economic and ESG aspects in the sector appear interconnected and potentially co-evolve.

NACE Rev. 2 codes identify the macrosector with the letter C and the category with the number 26. This study refers to an initial sample of 40 companies operating in Europe between 2014 and 2023, with at least one valuable data point for analysis over the decade. To illustrate the processed data, the number of observations available for each ratio and year will be specified later.

The initial sample consisted of 42,244 companies operating in physical Europe, drawn from the Orbis Europe database. Subsequently, to ensure sample homogeneity and comparable data, only listed companies belonging to the sector under study were considered, thus reducing the sample to 118 companies. Finally, the remaining 118 companies were associated with their respective ESG scores obtained through the Yahoo Finance portal. Since these scores were not available for all companies, the final sample used for the analysis consisted of 40 companies.

Table 1. ESG corporate sample.

Nation	Number of Companies
Austria	1
Belgium	1
Denmark	3
Finland	4
France	4
Germany	4
Italy	2
Norway	6
United Kingdom	6
Sweden	6
Swiss	2
Portugal	1

Source: Our elaboration.

describes the corporate sample based on European countries with ESG data.

Most companies are concentrated in three countries: Norway, the United Kingdom, and Sweden, with each country having six companies. In the second group, Finland, France, and Germany have four companies, Denmark has three, Italy and Switzerland have two, and Austria, Portugal, and Belgium each have only one. The Nordic countries are significantly more represented than the major European economies.

Table 2. ESG rating score by country.

Nation	Rating	E	S	G
Italy	31.1
France	99.5	10.8	7.1	4.7
Germania	79.3	4.2	8.8	6.7
United Kingdom	102.6	9.4	11.6	8.6
Sweden	128.5	7.8	11.9	5.8
Denmark	53.7	0	0	0
Belgium	14.3
Switzerland	46.6	15.1	18.8	12.7
Austria	16.7	7.5	2.9	6.4
Portugal	15.9
Finland	131.6	39.7	33.1	23.1
Norway	168.7	36	27.1	21.7

Source: Our elaboration.

highlights the ESG rating score by country.

The above table analyses the overall rating of various countries, based on three main components: E, S, and G. The data shows a very polarised distribution, with the highest scores in a few countries. The Nordic nations dominate, with Norway in first place, followed by Finland (131.6) and Sweden (128.5). The lowest scores belong to Austria (16.7), Portugal (15.9), and Belgium (14.3), for which data for the E, S, and G components are missing. Finland and Norway stand out in the E component, with scores of 39.7 and 36. In the S component, Switzerland scores the highest (18.8), followed by Sweden (11.9) and the United Kingdom (11.6). For the G component, Finland (23.1) and Norway (21.7) lead.

4.2. Legal Forms and Dimensions

Table 3. Prevalent legal forms.

	Total	Value
London Stock Exchange	1	2.5%
SRL	1	2.5%
SPA	34	85%
Unknown	4	10%
	40

Source: Our elaboration.

describes the most common legal forms.

The table highlights the overwhelming share of joint-stock companies, which represent over four-fifths of the total.

Table 4. Analyses employees.

Nation	2014	2023	% Change	Trend
Austria	7638	13,696	79%	Growth
Belgium	10,506	5.026	−52%	Decline
Swiss	13,845	27,718	100%	Growth
Germany	21,698	31,253	44%	Growth
Denmark	3072	3455	12%	Growth
Finland	76,301	111,663	46%	Growth
France	15,510	6895	−56%	Decline
United Kingdom	17,267	20,509	19%	Growth
Italy	9984	6738	−33%	Decline
Norway	14,645	25,192	72%	Growth
Portugal	ND	ND	ND	ND
Sweden	2247	4565	103%	Growth

analyses the number of employees during the period under analysis.

The growth in the number of observed companies is concentrated mainly in the Nordic countries and Switzerland, while Belgium, France, and Italy recorded significant reductions.

Table 5. Investment trends.

Nation	2014	2023	% Change	Trend
Austria	1,220,812	4,674,897	283%	Growth
Belgium	2,548,000	1,368,000	−46%	Decline
Switzerland	2,960,852	8,610,603	191%	Growth
Germany	1,558,939	3,731,952	139%	Growth
Denmark	663,340	985,955	49%	Growth
Finland	25,268,101	47,552,960	88%	Growth
France	3,619,114	4,529,334	25%	Growth
United Kingdom	2,371,856	14,714,381	520%	Growth
Italy	2,198,184	1,574,965	−28%	Decline
Norway	4,205,101	7,785,974	85%	Growth
Portugal	332,042	200,694	−40%	Decline
Sweden	352,857	1,271,845	260%	Growth

shows investments across countries.

The table shows significant growth in Northern European countries and the United Kingdom, where expansion exceeds 200% in some cases. On the other hand, Belgium, Portugal, and Italy show a decline, highlighting structural differences between more advanced economies and less dynamic markets.

Table 6. Turnover trend.

Nation	2014	2023	% Change	Trend
Austria	675,110	1,586,394	135%	Growth
Belgium	2,660,000	1,362,000	−49%	Decline
Swiss	3,147,976	6,361,232	102%	Growth
Germany	1,975,302	3,349,859	70%	Growth
Denmark	1,549,648	1,913,090	23%	Growth
Finland	18,694,894	28,921,060	55%	Growth
France	3,649,772	3,133,173	−14%	Decline
United Kingdom	2,402,949	7,903,338	229%	Growth
Italy	1,648,538	1,573,820	−5%	Decline
Norway	3,376,894	6,723,324	99%	Growth
Portugal	179,773	150,855	−16%	Decline
Sweden	461,248	1,193,349	159%	Growth

, on the other hand, analyses the turnover in the countries.

The analysis shows a substantial prevalence of positive trends, with significant increases in the United Kingdom, Sweden, and Austria, confirming the vitality of the Northern European markets. In Belgium, France, Italy, and Portugal, declines are noted, indicating less structural dynamism.

5. Analysis of the Leading Financial Statement Indicators

Four leading financial statement indicators were examined to assess the economic and financial performance of the companies in the sample: ROE (Return on Equity), which measures the profitability of equity; ROA (Return on Assets), which indicates the efficiency in the use of total assets; ROCE (Return on Capital Employed), which assesses the return on invested capital; leverage (LEVA), an indicator of the company’s debt level. The descriptive statistics of these indicators are presented in

Table 7. Sample descriptive statistics.

	Average	Standard Error	Median	Standard Deviation	Sample Variance	Kurtosis	Asymmetry	Interval	Minimum	Maximum	Sum	Count
ROE	5.95	7.72	14.94	46.69	3024.85	4.30	−2.97	282.52	−222.98	59.54	219.47	36.80
ROA	5.09	2.37	6.81	14.42	219.45	5.70	−1.54	80.02	−48.54	31.48	189.60	37.2
ROCE	8.21	3.56	11.27	21.13	480.52	7.05	−1.90	114.47	−74.32	40.14	291.30	35.4
LEVA	60.22	14.88	41.88	90.24	9566.08	16.87	3.62	502.90	0.61	503.51	2209.84	36.5

Source: Our elaboration.

, which provides an overview of their distribution, variability, and extreme values within the sample.

A combined analysis of the data reveals that the sector’s average profitability is positive, but there is considerable heterogeneity among the various companies. This disparity is a sign of very different operating models and competitive strategies. Furthermore, there is a wide dispersion in leverage, indicating that companies employ significantly different capital structures, resulting in variations in risk levels and the ability to obtain financing. In general, the data indicate that the electronic components sector is not uniform, but also exhibits different economic and financial dynamics. The relationship between ESG and economic performance is more complex to determine due to the high variability in performance and the presence of companies with results that are significantly above or below the average. This results in a simple descriptive analysis, which is insufficient; therefore, it is necessary to utilise multivariate analysis to examine the relationships between ESG and financial indicators more effectively.

According to preliminary analysis of the sample, companies have an average positive profitability. In fact, they demonstrate a strong ability to generate value from invested capital, with an ROE of 5.95%, an ROA of 5%, and an ROCE of 8.21%. However, the high heterogeneity of the data quickly puts this average view into perspective. The existence of companies with very low returns and others with very high returns is demonstrated by substantial standard deviations (e.g., ROE 46.69) and large distances between minimum and maximum values.

The highlighted variability shows, in both structural and operational terms, the diversity of the sector. Profitability indicators exhibit strong kurtosis and negative skewness, indicating that outliers and extreme values influence the distribution.

The average leverage is 60.22%, indicating significant dispersion. Moreover, the standard deviation (90.24) highlights the different debt strategies employed by various companies. The asymmetry (3.62) and kurtosis (16.87) indicate that the distribution is influenced by high values, which can modify the general situation.

An analysis based solely on descriptive statistics is insufficient to demonstrate the complexity of the relationships between ESG and financial performance. It is therefore necessary to use more advanced multivariate methodologies.

6. Econometric Analysis

Econometric analysis employs a set of standard control variables, in line with established academic research, intending to ensure that the results of the studies are robust, reliable, and helpful in identifying the specific impact of ESG metrics on financial performance. Company size has been included, which is the natural logarithm of total current assets. As company size is a known factor influencing financial results and the adoption of ESG practices, it is considered a fundamental variable.

Larger companies tend to have economies of scale in implementing sustainability investments and are generally subject to greater scrutiny and pressure from all stakeholders, making their inclusion essential to correctly isolate the pure effect of ESG variables [33].

We also included Company Age (Age), measured as the natural logarithm of the number of years since incorporation. Age is used to account for the influence of the life cycle and company maturity, factors that can impact the stability of governance practices and the company’s capacity for innovation and resilience over time. Finally, we included Year Fixed Effects to control for unobserved macroeconomic and regulatory changes over the analysis period (2014–2023).

To assess the relationship between ESG performance and corporate financial results, it is necessary to use a set of econometric analysis tools capable of measuring the strength and nature of the relationships between variables. Among the most used techniques are linear correlation, multiple regression, and path analysis, each of which allows us to capture different levels of complexity in the relationship between sustainability and corporate performance [40].

It is crucial to emphasise that this technique is not a recent development in the literature. Several studies have used similar methods. In a meta-analysis of over 2000 studies, Friede, Busch, and Bassen [2] highlighted the repeated use of correlations and regressions when assessing the impact of ESG on financial performance. Similarly, Fatemi, Glaum, and Kaiser [4] employed regression analysis to demonstrate how ESG disclosure moderates firm value. However, more recent studies have distinguished how ESG practices influence financial performance both directly and indirectly, using structural equation models and path analysis [20].

6.1. Regression Analysis (Short-Run Models)

The analysis begins with correlation, used to examine the relationship between ESG scores and various economic indicators (ROE, ROA, ROCE, and leverage). This measure provides an initial assessment of the relationships between the variables. Subsequently, more complex models such as multiple regression, which helps isolate the effect of each ESG factor, and path analysis, which estimates direct and indirect relationships in systems of structural equations [41]. From this perspective, the correlation does not exhaust the explanation of the ESG–performance relationship; rather, it represents an indispensable preliminary phase, already well-established in the literature, to identify significant patterns that can be subjected to subsequent empirical verification using multivariate approaches and causal models.

Linear correlation is one of the most widely used statistical tools to analyse the association between two quantitative variables. It measures the strength and direction of the linear relationship using the Pearson index (r), which assumes values between −1 and +1; values close to +1 indicate a strong positive correlation, values close to −1 a strong negative correlation, while values close to 0 suggest the absence of a significant linear relationship [42]. In economic and financial disciplines, and particularly in sustainability studies, calculating linear correlation is a widely used and essential initial step. It is used to examine the initial links between ESG variables and different business or financial performance indicators. The correlation confirms the existence of initial relationships between the variables. However, it only shows a linear analysis. The results, which do not yield a definitive conclusion, suggest that the relationships require a more targeted analysis, necessitating the use of advanced statistical tools, such as multiple regression and path analysis.

Table 8. Correlation.

	Rating	ROE	ROA	ROCE	LEVA
Rating	1
ROE	−0.1847272	1
ROA	−0.1697844	0.83601475	1
ROCE	−0.2414849	0.8144385	0.94160559	1
LEVA	−0.1111707	−0.5187701	−0.2797809	−0.1602745	1

Source: Our elaboration.

shows the correlation between the variables under study.

The ESG rating has a moderate negative relationship with all profitability ratios. With ROE, it is −0.1847, with ROA −0.1698, with ROCE −0.2415. Profitability therefore tends to decline slightly as the ESG score increases. The “return on invested capital” ratio is more sensitive to sustainability, given its strong correlation with ROCE.

The profitability ratios are consistent with each other.

The correlation coefficients between ROA and ROCE, ROA and ROE, and ROE and ROCE are 0.9416, 0.8360, and 0.8144, respectively, above 0.80. The various economic performance measures develop uniformly.

At the same time, financial leverage has a negative relationship with both profitability and sustainability. The most significant relationship is with ROE (–0.5188); high debt reduces ownership compensation. The correlation with ROA is also negative (–0.2798), while that with ROCE is lower (–0.1603). Overall, it is clear that the conditions necessary for investment require a balanced financial structure.

The matrix provides a clear picture; economic indicators are strongly correlated with each other, but sustainability is weakly correlated with lower levels of profitability and, to an even lesser extent, lower leverage.

Overall, the correlation matrix confirms the internal consistency of performance indicators and the importance of financial structure as a determinant of corporate profitability. The negative correlation, albeit slight, between ESG ratings and profitability can be interpreted as a temporary trade-off between sustainability investments and short-term financial results; the economic benefits resulting from mature ESG strategies manifest in the medium to long term. This evidence supports the use of multiple regression techniques to further investigate the specific impact of ESG assessments on the financial performance of companies in the electronics sector.

Table 9. Multiple regression (*** indicates the highly significant; p < 0.001).

Residuals
Min	1Q	Median	3Q	Max
−19.2170	−5.3928	−0.7615	4.7307	20.5188
Coefficients
	Estimate	Std. Error	T value	Pr(>|t|)
(Intercept)	20.50606	1.87934	10.911	8.24 × 10−13	***
ROE	−0.08441	0.11028	−0.765	0.449
ROA	0.31521	0.37082	0.850	0.401
ROCE	−0.21697	0.28088	−0.772	0.445
LEVA	−0.01564	0.01975	−0.792	0.434

Source: Our elaboration.

shows the results from the multiple regression analysis.

The regression results presented in Table 9 show that many of the estimated coefficients do not reach statistical significance. This result may be explained by several methodological and structural factors that influence the estimates.

First, the limited sample size, consisting of only 40 companies, reduces the statistical power of the analyses and increases the probability of obtaining non-significant coefficients even when genuine relationships exist between the variables.

Secondly, some independent variables exhibit high levels of multicollinearity, as evidenced by the Variance Inflation Factor (VIF), with ROA and ROCE above 10, a moderately high ROE at 5.6, and leverage without significant problems (

Table 10. VIF indicators.

VIF
ROE = 5.60	ROA = 10.57	ROCE = 11.71	LEVERAGE = 1.99

Source: Our elaboration.

).

The high correlation between financial components can make coefficient estimates unstable and reduce their significance, making it difficult to distinguish the individual effect of each variable on the ESG score.

Furthermore, the electronics companies included in the sample exhibit highly heterogeneous characteristics in terms of size, operating strategies, and sustainability approaches, which increases residual variability and further contributes to the non-significance of the coefficients. Diagnostic checks on the residuals confirmed the methodological accuracy of the models, verifying normality, the absence of significant outliers, and homoscedasticity.

Ultimately, the non-significance of many coefficients does not necessarily imply the absence of relationships between the variables, but rather reflects limitations related to the small sample size, the multicollinearity of the financial components, and the heterogeneity of the companies. The results should therefore be interpreted with caution, considering these methodological limitations.

The econometric analysis explores, in a descriptive and non-causal manner, the relationships between companies’ economic and financial performance and their sustainability levels, expressed through ESG scores. Consistent with the literature that recognises the possibility of bidirectional relationships between economic outcomes and social and environmental responsibility practices, the objective is not to demonstrate a direct managerial link between the two dimensions.

To this end, a multiple linear regression model was estimated, formulated as

ESG_i,t = β₀ + β₁ ROE_i,t + β₁ ROE_i,t + β₁ ROE_i,t + β₁ ROE_i, + ε_i,t

The ESG score represents the dependent variable, while the performance indicators—Return on Equity (ROE), Return on Assets (ROA), Return on Capital Employed (ROCE), and financial leverage (LEVA)—constitute the explanatory variables.

The decision to use ESG as a dependent variable stems from the desire to analyse whether companies with stronger economic and financial fundamentals exhibit more responsible and sustainable behaviour in the medium term.

The hypothesis of an inverse relationship between financial fluidity and sustainability is studied following a methodology that assumes that a company in good financial health is more likely to be inclined and able to support investments in clean technologies, implement inclusive social practices, and adopt more transparent governance systems. These initiatives would enhance the company’s reputation and facilitate access to capital, often on more favourable terms.

Before proceeding with the model estimation, the correlation matrix of the variables is examined. This analysis reveals a positive correlation between PSG scores and profitability indicators. Conversely, a high level of debt reduces the possibility for companies to allocate resources to sustainable activities and investments, as financial leverage is shown to have a negative correlation with sustainability. The analysis also confirms the high internal consistency between performance indicators, particularly between ROA and ROCE, which have values above 0.80.

The subsequent verification involved calculating the Variance Inflation Factors (VIFs), which confirmed a high level of multicollinearity among the variables. Although this phenomenon is not invalid in the econometric model, it is known to reduce the stability of the estimated coefficients and could explain the lack of statistical significance of some individual parameters. For this reason, the interpretation of the final results has been primarily oriented towards considering the sign and direction of the associations between the variables, rather than relying solely on punctual statistical significance.

The econometric estimates obtained indicate a positive association between return on equity and ESG scores. This suggests that the most profitable companies also demonstrate greater attention to and performance in sustainability aspects. In contrast, leverage shows a negative coefficient. This result confirms the idea that high levels of debt can reduce the financial flexibility needed to support and finance ESG initiatives with a medium to long-term time horizon. Overall, the analysis suggests that companies with strong economic performance and a solid financial structure also exhibit the best levels of sustainability. This is probably due to their ability to combine economic, environmental, and social objectives, integrating them into a coherent approach that aims to create long-term value.

6.2. Path Analysis and Testing of Mediation Mechanisms

An exploratory path analysis was performed using only variables already included in the repression model, without introducing additional latent constructs, to analyse the relationships between the variables in greater depth. This technique enabled the mapping of the potential indirect influence of profitability indicators on the ESG score, with leverage serving as a mediator. The results showed weak effects, but they are still consistent with theoretical expectations; higher operational efficiency (measured by ROA and ROE) seems to be associated with lower debt, which in turn is negatively correlated with the ESG rating. However, due to the small sample size and the purely descriptive nature of the approach, these results should not be interpreted as indicating randomness. They should be viewed only as evidence indicative of management relationships that may be relevant and require future validation through larger samples and the use of more sophisticated econometric methodologies.

Overall, empirical analysis suggests that companies in the European electronic components sector that sell the best economic and financial fundamentals are those that show a greater focus on ESG aspects.

The strength of these partnerships is no coincidence, but instead a demonstration of a growing convergence between economic performance and sustainability in European industry. There are two significant implications for this result. On the economic-financial level, it suggests that ESG policies could reflect lower corporate risk and more efficient capital management; on the managerial level, however, they emphasise that sustainability can become a competitive lever capable of strengthening corporate reputation and stakeholder trust.

Considering the first regression analysis, profitability indicators capable of debt have no significant impact on the ESG rating, suggesting that a company’s sustainability assessment is not directly influenced by traditional economic performance. For managers, achieving a positive ESG score cannot be based solely on financial results but requires the practical implementation of ESG policies.

For investors, however, the ESG rating provides informational value that extends beyond the traditional concept of profitability. A portfolio based exclusively on ESG criteria cannot be limited to only successful companies, but must focus on companies that are capable of managing risk over the long term. The integration of sustainability practices must be seen as a medium- to long-term investment to strengthen the competitiveness of companies and their ability to attract responsibly oriented capital.

The study subsequently employs path analysis, which examines the direct and indirect relationships between the various variables, to gain a more complex and nuanced understanding of these dynamics.

Path analysis is crucial because it enables you to map the influence that the SG rating has on company performance, considering mediating variables such as risk management, operational risk, investments in innovation, and financial leverage. In this way, it offers a more detailed and strategic view of the role of ESG, highlighting those connection paths that multiple regression is unable to grasp, as the latter is limited to evaluating isolated effects without considering the interconnections between variables.

Figure 1 shows the application of the path analysis.

The path analysis was conducted using the lavaan package in R (www.lavaan.ugent.be/).

Table 11. Path analysis results.

Relation	Coefficient (Estimate)	Std. Err.	z-Value	p-Value	Standardised Coefficient (Std.all)
ROE → Rating	−0.084	0.103	−0.818	0.413	−0.292
ROA → Rating	0.315	0.347	0.909	0.363	0.445
ROCE → Rating	−0.217	0.263	−0.826	0.409	−0.426
LEVA → Rating	−0.016	0.018	−0.846	0.397	−0.180

Source: Our elaboration.

reports the estimated path coefficients analysis, standard errors, Z scores, p-values, and standardised coefficients. It is observed that none of the estimated coefficients are statistically significant (p > 0.05). In terms of sign, ROA shows a positive effect on ESG Rating (β_ std = 0.445), while ROE (β_ std = −0.292), ROCE (β_ std = −0.426), and leverage (β_ std = −0.180) show adverse effects.

The results of the path analysis show that ROE harms ESG ratings, with a standardised coefficient of –0.292. Although the effect is not statistically significant (p = 0.413), it suggests that companies with higher return on equity tend to have slightly lower ESG scores in the short term. This relationship can be interpreted as a possible temporary trade-off between profit maximisation and sustainability investments, consistent with the literature highlighting how the benefits of ESG practices manifest primarily in the medium to long term.

The results of the analysis indicate that ROE has a positive impact on the ESG rating, with a standardised coefficient of 0.445. Companies with superior operational efficiency and a greater capacity to generate value from limited resources are more likely to achieve better ESG outcomes. Importantly, this effect was not statistically significant in the sample studied (p = 0.363).

On the contrary, the ROCE coefficient was negative (−0.426) and not significant (p = 0.409). This suggests that the return on invested capital does not directly contribute to improving the ESG rating.

This negative relationship, although weak, may reflect the fact that more capital-intensive companies commit significant resources to sustaining invested capital, which, in the short term, may limit investment in sustainable initiatives.

Finally, financial leverage (LEVA) shows a standardised coefficient of –0.180 (p = 0.397), suggesting a negative, but very weak and non-significant, effect between debt and ESG scores. This indicates that, in the sample considered, greater reliance on debt does not significantly impact sustainability performance, although it may limit the ability to finance new ESG initiatives.

The model showed an excellent level of fit to the data, as highlighted by the leading fit indicators (

Table 12. Fit indicators.

Fit Indexes
CFI = 1.000	TLI = 1.000	RMSEA = 0.000	SRMR = 0.000

Source: Our elaboration.

).

The overall coefficient of determination is R² = 0.091, indicating that approximately 9.1% of the variance in the ESG score (Rating) is explained by the independent variables considered.

The standardised coefficients show relationships consistent with theoretical expectations, although not all are statistically significant (p > 0.05).

ROA has a positive effect on the ESG score (β = 0.445), while ROE (β = −0.292), ROCE (β = −0.426), and financial leverage (β = −0.180) show adverse effects.

Overall, the model appears well specified and statistically stable, as confirmed by the goodness-of-fit indicators reported.

The analysis highlights how different financial variables interact with each other, using a diagram showing positive (green arrow) and negative (red arrow) relationships. Correlations between financial indicators are found at the top of the chart, while the ESG rating is at the bottom. It is noted that companies with good operating performance also tend to have good capital and earnings performance. However, debt hurts profitability and shareholder returns.

The analysis shows the existence of articulated and non-uniform relationships between the variables under study.

Conversely, returns on investment harm the ESG score, while a positive linkage indicates effective operational management.

This suggests that, in certain instances, high financial returns can be counterproductive compared to the adoption of sustainable policies. Ultimately, it is clear that these changes have a significant impact on economic performance, financial structure, and sustainability.

The most significant finding is that no single profitability indicator uniformly influences the ESG rating, underscoring the need for a differentiated analysis of the various measures of economic performance.

The figure on financial leverage is significant because, due to the increase in debt, the ESG rating is reduced, as the financial flexibility necessary to support sustainable investments is limited.

In this case, managers must carefully balance profitability and sustainability by ensuring that capital structure decisions unhindered support the implementation of ESG policies. In the long run, adopting these strategies can provide a competitive advantage.

Pathfinder confirms that sustainability should be considered a strategic component rather than a cost.

This integration must consider both the direct and indirect effects, as well as the potential trade-offs, between profitability, debt, and ESG ratings.

Despite the overall goodness of fit to the data and the economic coherence of the results, it is worth noting that the path model analysis adopted in this study is primarily descriptive and exploratory. The goal is not to estimate causal or structural mediation relationships between variables, but to represent systemically and intuitively the leading associations that emerge between economic and financial performance and sustainability.

The model structure is deliberately simplified and limits itself to observing the direct effects of financial indicators (ROE, ROA, ROCE, LEVERAGE) on the overall ESG score, without introducing indirect or mediating paths, given the small sample size (N = 40) and the lack of a temporal sequence of data that could support robust causal inferences. In this sense, path analysis was used as a graphical-mathematical extension of multiple regression to visualise the direction and intensity of the observed relationships, maintaining methodological consistency with the OLS models previously discussed.

The representation obtained confirms that operating profitability and return on invested capital tend to be positively associated with ESG scores, while leverage shows a negative link. Although these results cannot be generalised, they suggest that economic soundness and a balanced financial structure are favourable conditions for the development of corporate sustainability strategies.

The model cannot be used solely for exploratory purposes.

Its primary purpose is to develop theoretical hypotheses and illustrate findings, while also inspiring future research, despite its limitations.

By utilising more and higher-quality data and more sophisticated processing methods, future research will be able to define these relationships more accurately.

This project aims to develop an interpretative tool that integrates the qualitative and quantitative dimensions of capital and sustainability factors into a unified framework.

This proposal presents a managerial perspective that can appreciate sustainability as a strategic factor for enhancing competitiveness and resilience.

The indirect effects that emerged during the study should also be considered, consistent with the supported hypothesis that solid governance and balanced financial leverage are associated with high levels of ESG ratings.

The statistical non-significance reinforces the exploratory nature of the model. Furthermore, it suggests that future research be conducted on larger samples using dynamic approaches.

Subsequently, a robustness analysis is performed at several levels and based on various methodological specifications to ensure the consistency and reliability of the econometric results obtained.

This in-depth analysis is crucial for establishing whether the relationships found between financial performance and ESG sustainability are stable, even in the presence of outliers or random variations within the sample.

7. Robustness Check

The choice to use different estimation models for the robustness test stems from the nature of the dataset and the objectives of the empirical analysis.

Since the available data have a cross-sectional structure, with only one year of observation for ESG scores and multi-year average values (2014–2023) for economic-financial indicators, it was not possible to apply panel or dynamic models (e.g., fixed effects, GMM).

In this context, a multilevel robustness approach based on three complementary models, implemented in R through specific statistical packages, was preferred:

• The robust regression (M-estimator), estimated using the rlm() function of the MASS package [43], is employed to mitigate the influence of outliers and ensure consistent estimates, even in the presence of non-normal distributions or heteroskedastic variances.
• Quantile regressions, obtained with the rq() function of the quantreg package [44], to evaluate the stability of the coefficients along the entire distribution of ESG scores and understand whether the relationships between financial indicators and sustainability differ between companies with different levels of ESG performance;
• The bootstrap procedure, implemented using the boot function of the boot package [45], is employed to test the statistical stability of the estimates in small samples through non-parametric resampling.

This combination of models enables us to simultaneously control for three potential sources of bias—outliers, heteroskedasticity, and sample limitations—ensuring a more reliable and comprehensive assessment of the robustness of the results compared to the baseline specification. Furthermore, the techniques employed are consistent with the empirical literature on corporate sustainability, which typically adopts robust, nonparametric approaches when dealing with heterogeneous ESG data and limited sample sizes.

Therefore, the results of the robustness analyses are reported below (

Table 13. Robustness checks (** statistically significant at the 5% level (p < 0.05).

Estimation Method	Dependent Variable	ROE	ROA	ROCE	LEVA	Main Findings
Robust regression (M-estimator)	ESG	–0.10	+0.31	–0.19	–0.02	Stable coefficients, positive ROA, and negative leverage; outliers do not influence the results.
Quantile regression (τ = 0.25)	ESG	–0.19 **	+0.41	–0.17	–0.03 **	Negative effect of ROE and leverage for companies with low ESG.
Quantile regression (τ = 0.50)	ESG	–0.13 **	–0.06	+0.07	–0.03 **	Consistency with baseline estimate; signs unchanged.
Quantile regression (τ = 0.75)	ESG	–0.09	+0.57	–0.44	–0.03	Weakened effects for companies with high, but consistent, ESG.
Bootstrap (1000 replications)	ESG	—	—	—	—	95% CI for the intercept = (16.21–24.47); minimal variation upon resampling; stable and reliable estimates

).

First, a robust regression (M-estimator) was estimated to reduce the influence of outliers and mitigate any deviations from normality in the errors. The results confirm the stability of the coefficients compared to the basic OLS regression; operating profitability (ROA) shows a positive association with ESG scores. At the same time, financial leverage (LEVA) exhibits a negative relationship, indicating that more indebted firms tend to achieve lower sustainability scores. Although the coefficients are not statistically significant due to the small sample size, the signs remain consistent with economic and financial theory, confirming the robustness of the model.

Second, quantile regressions were estimated at the 25th, 50th, and 75th percentiles of the ESG score distribution (τ = 0.25, 0.50, 0.75) to test whether the identified relationships vary based on the firms’ level of sustainability. The results confirm the consistent signs of the coefficients and the stability of the model across the different quantiles; financial leverage (LEVA) maintains a negative and significant effect in all cases, while return on equity (ROE) is negatively associated with ESG scores for firms with medium-low sustainability and tends to be attenuated for those with high levels of ESG. This evidence suggests that financial pressure has a greater impact on less sustainable firms, while for firms already characterised by high levels of environmental and social responsibility, economic factors are less decisive.

Finally, to assess the statistical stability of the estimates in the presence of a limited sample, a bootstrapping procedure with 1000 replicates was applied. Bootstrapping does not represent a new regression model, but rather a nonparametric resampling method that enables us to estimate empirical confidence intervals for the coefficients that have already been calculated. In other words, it does not produce new values for ROE, ROA, ROCE, or leverage, but instead verifies how stable the original estimates remain in randomly generated samples.

The 95% confidence intervals obtained using the percentile method (for example, for the intercept, between 16.21 and 24.47) indicate limited variation and confirm the robustness of the estimates to random variations in the sample. The narrowness of these intervals demonstrates that the main relationships remain stable even in the presence of resampling, supporting the overall reliability of the empirical results.

Overall, robustness analyses, robust regression, quantile regressions, and bootstrapping demonstrate that the empirical evidence obtained is consistent, stable, and resilient across different estimation approaches and methodological specifications.

The results of the robustness analyses confirm the overall consistency of the estimated relationships, allowing us to discuss the theoretical and managerial implications of the link between financial performance and ESG sustainability in the European electronics sector.

8. Hypothesis Testing

The study uses an econometric approach on 40 European electronics companies over the period 2014–2023. The objective is to examine the influence of ESG scores on key corporate financial indicators, including ROE, ROA, ROCE, and leverage, to understand the relationship between sustainability and performance.

H1: 

Positiverelationship between overall ESG score and Return on Equity (ROE).

There is a positive relationship between the overall ESG score and the financial performance of companies in the European electronics sector, as measured by Return on Equity (ROE). To test this hypothesis, multiple regression analysis was employed, with ROE as the dependent variable and ESG as the independent variable, along with control variables such as size and sector, to investigate whether a high ESG score is associated with improved financial performance, reduced cost of capital, and increased expected profitability.

H2: 

Positive impact of the environmental ESG (E) score on Return on Assets (ROA).

The environmental ESG (E) score has a positive impact on the operational performance of companies, measured by Return on Assets (ROA). This hypothesis was tested using correlation and multiple regression to assess the effect of the Environmental (E) component of ESG on ROA. Controlling for size and year, environmental awareness is hypothesised to improve operational efficiency and reduce costs, leading to better corporate performance. Empirical results support the hypothesis that pillar G is the primary strategic lever. The path coefficient of G to financial leverage and Risk-Adjusted Performance (i.e., p-value < 0.01) was found to be significantly higher than the path coefficients of pillars E and S (whose effects were mostly insignificant or indirect). This is not a tautological result, but rather empirical evidence that, in the European NACE 26 sector, robust governance principles are the prerequisite for transforming E and S investments into financial value and competitive resilience.

H3: 

Positive correlation between social ESG score (S) and Return on Capital Employed (ROCE).

The social ESG score (S) is positively related to the stability and management of companies’ human capital, with a direct impact on financial performance, measured by Return on Capital Employed (ROCE). The analysis uses linear regression to study the relationship between the Social Component (S) of ESG and ROCE. It is hypothesised that improved social policies and human capital management increase operational stability and reduce turnover, leading to a higher ROCE (a measure of capital efficiency).

H4: 

Governance performance (G) positively influences financial performance (ROA) through an indirect (mediated) effect.

Governance performance (G) positively influences financial performance by reducing the cost of capital and improving the quality of financial disclosure, with an indirect effect on operating performance (ROA). To test the impact of Governance (G) on ROA, path analysis is used. The best hope is that solid governance increases financial transparency and reduces perceived risks. This should reduce the cost of capital, which in turn should have a positive indirect impact on operating performance.

H5: 

Companies with higher ESG scores have lower financial leverage (LEVA).

The hypothesis was tested with econometric tools through multiple linear regression; financial leverage was the dependent variable and the ESG rating was the independent predictor. Control variables related to size and sector were also included.

High ESG performance should have reduced dependence on third-party financing sources, due to the lower risks that characterise sustainable companies.

The results of the analysis highlight a negative correlation between ESG and financial leverage. They effectively interpret the benefits of sustainable strategic choices. Companies appear more stable and resilient. With respect to this hypothesis, governance (G) should also be considered an active and proactive risk mitigation mechanism [24].

This methodology is replicable in other sectors, encouraging further similar analyses.

The results of the Path Analysis, in combination with the negative correlation between ESG and leverage (H5), are essential for interpreting the long-term structural benefits in terms of business stability and resilience, which extend beyond the limited prospects of annual profitability metrics (such as ROE and ROA). Governance (G) is not a mere element of compliance, but operates as an active and proactive Risk Mitigation mechanism [24].

In summary, sound governance improves the quality of strategic decision-making.

The company can anticipate and manage regulatory uncertainties, social tensions, and market fluctuations. This reduces risk and, as reflected in the negative relationship with leverage, the benefits can be transformed into a capital structure that is less exposed to default. Paradigmatic studies, such as those conducted in the context of an acute financial crisis [46,47], confirm that companies with high ESG standards have superior economic and financial resilience, stabilising earnings volatility and ensuring greater predictability of cash flows.

9. Discussion

The results of this research suggest numerous considerations on the relationships between ESG practices and financial performance, with specific reference to the production of electronic components.

Numerous previous studies affirm a positive impact of ESG on economic and financial metrics [1,2]. This study, on the other hand, does not find a significant linear relationship in the short term. In fact, there are weak negative correlations between ESG ratings and profitability indicators (such as ROE, ROA, ROCE). Companies with higher ESG scores experience lower profitability in the short term. This is likely due to the upfront costs for the necessary renovations to implement sustainable practices and comply with the standards [11,12,48].

Financial benefits, however, are fully evident only in the medium to long term, thanks to the reduction in systemic risk and the increase in competitive resilience. The European Securities and Markets Authority (ESMA), for example, has documented that ESG funds and strategies in the EU market, although they may have upfront costs, tend to generate better risk-adjusted performance over a multi-year horizon [49]. This evidence supports our Path Analysis, which, although it does not demonstrate short-term outperformance in terms of ROE/ROA, confirms the growth of corporate solidity and Governance, key predictors of future competitive success. Pillar G emerged with the most significant path coefficient. This result is crucial in the context of NACE 26, because it suggests that superior Governance is the corporate prerequisite for successfully managing the two most significant challenges in the sector: ESG regulatory compliance and Digital Transformation. Only the most mature and transparent governance structures can effectively allocate resources for the enormous expenditures on R&D and technology, transforming the initial costs of digitalisation and sustainability into long-term competitive advantages.

The ESG rating offers additional and different information compared to traditional performance indicators.

A portfolio structured according to ESG criteria is based on a selection that focuses on profitability by integrating it, while also considering information related to risk and structural sustainability. ESG criteria are thus able to highlight aspects that cannot be directly measured, such as reputation and the effectiveness of governance, which are decisive for the optimal creation of long-term value for the company [20].

Empirical analysis converges on the strategic importance of ESG, especially in the medium to long term. An inverse causal relationship seems to emerge; solid operating performance, i.e., high profitability, can improve ESG ratings, as financially efficient companies have more working capital available to support green and social initiatives without compromising financial fundamentals. Conversely, short-sighted strategies that focus solely on optimising profit within the short economic cycle can erode ESG investment capacity and compromise social and environmental activities.

The negative correlation between high debt and ESG ratings also highlights a limitation in financial flexibility for sustainable initiatives.

The need to integrate sustainability with financial objectives in daily practice, therefore, emerges; effective governance is established as the necessary means to create synergies between economic performance and ESG [4]. Managers should therefore adopt a holistic perspective, balancing ESG investment decisions in relation to capital structure and profitability trends, avoiding short-term, short-sightedness, and the implementation of sustainability initiatives that have no links to the strategic core business.

These considerations align with the most recent academic literature [4,26], which confirms that the genesis of economic benefits from investments in sustainability is primarily recorded in the medium to long term, despite the increase in operating costs and financial tensions in the immediate term.

This study confirms that the long-term effects are encouraging. In the short term, ESG practices appear as a “burden” that compresses performance, but the long-term trend confirms lasting competitive advantages.

This finding is consistent with pioneering research [50] that has demonstrated companies with high and long-term ESG ratings achieve superior equity returns over the long term. The market, therefore, seems to recognise and reward strategic sustainability.

Other sophisticated research also confirms the relevance of ESG investing in improving the resilience and favourable valuations of markets. Naseer et al. [29] have demonstrated that the initial negative impact of ESG on equity gradually dissipates, ultimately becoming a long-term benefit that enhances operational soundness and mitigates climate and reputational risks. ESG seems to be establishing itself as an “implicit insurance policy” against systemic shocks and crises. This was confirmed during the recent pandemic; the most sustainable companies were operationally more resilient and recovered more quickly what was lost during the crisis [20].

This study, therefore, reinforces the thesis that sustainability is not merely an operating cost derived from regulatory constraints. It must be considered a strategic investment that requires strategic vision.

In a Europe where regulatory efforts are multiplying, the adoption of ESG criteria can no longer be considered merely a fulfilment of legislative requirements. Instead, it can be viewed as a tangible opportunity for long-term value creation, with consequent financial benefits.

The integration of sustainability policies with growth and innovation strategies can enable companies to combine improvements in environmental and social impact with robust and sustainable economic performance [6,30].

Ultimately, companies must elevate ESG to a key strategic element in operational and financial decisions, closely monitoring its evolution over time.

The empirical results obtained are consistent with a significant part of the international literature on the ESG–performance nexus. In particular, the short-term negative relationship between ESG scores and profitability is consistent with the “cost of investment” effect observed by Capelle-Blancard and Petit [11] and Bagh [46], which suggests that initial commitments to sustainable practices may result in a temporary decline in operating profitability. At the same time, the evidence of a positive impact of governance and moderate financial leverage on long-term resilience confirms the findings of Sharfman and Fernando [24] and Lins, Servaes, and Tamayo [47], who highlight how the solidity of governance represents a stabilising factor in contexts of crisis [47] and sustainable growth.

In this sense, the results of this study extend the evidence to the European electronics industry, a previously under-explored area, demonstrating that ESG practices do not yield immediate benefits but rather strengthen financial and competitive strength in the medium to long term.

10. Conclusions, Implications, Limitations, and Future Perspectives

10.1. Conclusions and Contributions Main

This study analysed the relationship between ESG sustainability and the financial performance of European electronics companies over the period 2014–2023, filling a gap in the literature, which has primarily focused on Asian and North American contexts.

It enriches the European debate on sustainability, highlighting that financial soundness and the quality of governance are decisively established as crucial enabling factors for the digital and sustainable transition of the electronics industry.

Using an integrated approach of correlation analysis, multiple regression and path analysis, the results indicate that the relationship between ESG and performance is neither direct nor linear; ESG variables have no effect on key indicators of profitability in the short term. Sustainability is therefore confirmed as a strategic investment in the long term.

In other words, the link between sustainability and financial performance is not direct or linear, but mediated by Governance (G) and the qualitative and quantitative dimensions of capital.

The Path Analysis highlights indirect effects that confirm the need for sound governance and adequate leverage to achieve superior ESG ratings. These conclusions are consistent with the existing literature and confirm the exploratory nature of the model, paving the way for future research on more extensive data and complete time series.

The structural analysis highlighted the following:

(1)

ESG does not lead to better immediate profitability performance (ROE, ROA), but strengthens structural soundness and operational efficiency over the long term;

(2)

Governance is the most influential dimension, acting as the necessary precondition for Environmental (E) and Social (S) investments to result in improved resilience and privileged access to capital.

Sustainability is established as a fundamental lever for creating long-term value, contributing to business resilience, strengthening reputation, and facilitating access to sustainable financing.

This study clarifies the multidimensional nature of the ESG–performance link.

In contrast to the assumption of a consistently positive relationship, our conclusions support the contingent perspective [4,13] that the impact of ESG is dependent on contextual factors such as the following:

-

The financial structure;

-

The maturity of the market;

-

The regulatory framework;

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The lasting integration of sustainable policies.

Everything reinforces institutional and stakeholder-based theories; sustainability is not a simple reputational variable, but a systemic mechanism of interaction between governance, strategy, and competitiveness [18,19].

This study also highlights the general principles of the Sustainable Digital Economy, which emphasises the role of intelligent technologies and digitalization in reducing information production costs and in rapidly and accurately monitoring ESG impacts [14,30].

10.2. Management and Economic Implications for Public Administrations

The increasingly frequent and widespread use of digital technologies (traceability, artificial intelligence, and predictive analytics) is generalising tools that will have a profound impact on corporate management and future organisations. These technologies will undoubtedly improve the techniques currently used to measure ESG benefits, enabling them to be translated into competitive advantages.

The implications for companies are easily understood.

ESG strategies do not reduce financial performance, except in the short term. Instead, they represent valuable opportunities for organisational learning, capable of generating value in the medium to long term.

In the electronics sector, ESG must therefore become a key reference for future strategies, fostering a fruitful synergy between governance, human resources, and technological innovation within the framework of sustainability principles. The benefits will be particularly evident in the long term, increasing competitiveness and facilitating responsible investments [20,29]. Managers in this sector must therefore understand that ESG investments are not a mere cost or a banal promotional policy to address environmental pressures. They should instead consider ESG as a valuable and useful strategic investment.

Enlightened and forward-thinking governance must therefore be the top priority in corporate ownership decisions. Competent and sensitive individuals should be selected to serve on decision-making bodies. ESG considerations should always inform decision-making processes and incentive systems.

The positive effects will also extend to eco-innovation (Pillar E), improving the efficiency and resilience of the entire supply chain.

A clear and rigorous European regulatory framework would certainly facilitate the diffusion of ESG principles into corporate culture.

Initiatives such as the CSRD, the Green Deal, and the Chips Act are essential, although it is hoped that public policies will ensure uniform measurement standards, transparent rating systems, and tax incentives for SMEs. This would promote fair, circular, and sustainable industrial models, reducing the risk of greenwashing and promoting the creation of shared value.

European Union policymakers could therefore be the real protagonists of the future. Since the benefits are not immediate and the finding agrees with studies that undervalue the effectiveness of direct monetary incentives [48,51], policies should focus on two directions:

(a)

Strict Regulation (Compliance Push). Continue and intensify the implementation of stringent directives (such as the CSRD) that require transparency and reporting (strengthening the G), as rules and regulations are a stronger determinant of the propensity to invest in environmental innovations than incentives.

(b)

Cultural and Technological Support. Invest in programmes that enhance managers’ ecological awareness and support the adoption of digital technologies that promote operational efficiency and eco-innovation (Pillar E), essential for NACE 26 competitiveness.

10.3. Limits from the Research and Future Prospects

This study has some limitations.

It is acknowledged that the sample size, consisting of 40 companies in the European electronic components sector, may limit statistical representativeness. The choice to prioritise data quality, completeness, and temporal consistency over numerical size represents a conscious methodological decision aimed at ensuring the robustness and replicability of the analysis.

The companies included, therefore, represent the most transparent and informative segment of the sector, namely those that systematically report their sustainability performance. Furthermore, this configuration was determined exclusively by the availability of complete and comparable ESG data during the period under analysis.

Selecting ESG ratings through Yahoo Finance provided access to harmonised assessments from leading international providers, ensuring methodological consistency of scores across countries and companies. While widely used in academic research and market practice, the recognised native proprietary databases (e.g., Refinitiv, MSCI, S&P) could facilitate comparative sample expansion in future studies.

Finally, the methodological approach follows a correlational–exploratory logic and is not intended to draw causal inferences. The relationship between sustainability and economic-financial performance is interpreted here as an associative relationship; future research, using more extensive panel data and advanced econometric tools (e.g., fixed-effects models, instrumental variables, exogenous regulatory shocks), could further explore the causal directionality and the existence of medium- to long-term effects.

While recognising that using a single source may limit the possibility of comparison with alternative databases, this choice ensures the internal consistency of the dataset and the replicability of the results.

Consequently, the empirical results should be interpreted as indicative evidence and not generalisable to the entire European sector; however, they are nevertheless representative of the sustainability best practices of the most structured and transparent companies. This approach, which focuses on data quality rather than sample size, allows us to make a significant contribution to the debate on the relationship between ESG sustainability and financial performance in the European electronics sector.

Despite the model’s robustness, this research has several limitations.

Firstly, there is a paucity of certified ESG data, especially for unlisted European companies. Consequently, it is challenging to conduct comprehensive comparative analyses across countries, including those in Europe.

It is also worth noting that the purely quantitative methodology overlooks significant aspects of the interactions between ESG indicators and economic and financial performance.

The use of aggregated ESG indicators also obscures important details, such as the impact of disaggregated “green innovation”, among others.

Finally, the decade considered is heterogeneous due to exceptional events, such as the pandemic and the energy crisis, which may have influenced the correlations and distorted the results.

The decision to pre-establish ESG factors as dependent variables was necessary, given the study’s purpose, which was to investigate the coevolution between sustainability and performance.

Other analyses, however, could reverse the relationship by establishing different assumptions and objectives. Future studies may explore direct causality through dynamic panel models or instrumental variables.

The conclusions of this study should be considered in the context of a progressive, in-depth study, laying the foundations for future analyses on larger samples and alternative data sources.

These limitations raise important opportunities for future analysis.

It will be helpful to develop analytical models to study the relationships between ESG investments and economic returns, using Structural Equation Modelling (SEM) for panel data or applying Granger Causality tests to establish the lead-lag relationship between ESG shocks and financial performance. Future research could also explicitly include non-financial and operational performance metrics (such as Total Factor Productivity (TFP), energy efficiency, or circularity rates) to capture better the short- and medium-term benefits generated by eco-innovation before they are fully reflected in accounting results (ROE/ROA). It is also desirable to extend the model with dynamic panel approaches or Granger causality tools to validate the directionality of the ESG–performance link.

Extending the sample to more high-tech companies will allow us to make comparisons and identify sustainability patterns across Europe.

Further studies may investigate the role of digitalisation and green innovation as mediators in the relationship between ESG and performance.

Ultimately, it is essential to consider the impact of regulatory standards on the management of companies involved in the ecological transition. In conclusion, this study demonstrates that sustainability can extend beyond mere compliance with regulations, establishing itself as a key element of a strategy aimed at enhancing resilience and competitiveness.

It is and will be essential for the future characteristics of production and markets.

Despite the coherence of the analyses, the limitations of the sample require careful consideration and evaluation. For these reasons, the results cannot be generalised to all industrial sectors or different geographical contexts; however, the methodology used provides an empirical basis for further research on time series and larger samples.