1. Introduction
Sustainability analysis allows companies to optimize their resources and improve their long-term financial performance, especially in the context of current technological challenges. Investors and financial institutions are increasingly placing importance on environmental, social, and governance (ESG) criteria, and companies that demonstrate effective sustainability management have greater access to financing and can attract responsible investors [1].
Adopting sustainable practices can significantly reduce operational costs by improving resource efficiency, reducing waste, and enabling the implementation of sustainable energy solutions. For example, companies in the manufacturing sector that optimize their waste management and energy consumption can achieve substantial savings and improve their profit margins [2].
Amid the increasing number of international environmental protection and social responsibility regulations, companies must adopt sustainable practices to avoid sanctions and restrictions. Especially in industries with a high environmental impact, such as construction and mining, compliance with international standards (e.g., the Global Reporting Initiative—GRI) is essential to maintain operating licenses and corporate reputation [3].
Stakeholders, including customers, business partners, and employees, are increasingly interested in how companies manage their economic, social, and environmental impacts. A company that regularly publishes sustainability reports and demonstrates transparency in managing its social and environmental impacts can strengthen its relationship with stakeholders and its reputation [4].
Consumers are increasingly aware of the impacts of products and services on the environment and society, which is leading to a change in purchasing behavior. Companies that integrate sustainability principles into their business strategies and communicate this effectively can benefit from a more loyal customer base and gain a significant competitive advantage in the market [1].
Companies that do not consider sustainability are exposed to significant financial risks, including volatility in resource prices, stricter regulations, and reputational damage. Sustainability analysis allows for the early identification of these risks and the adoption of proactive strategies to minimize them [5].
Adopting a sustainable business model is a compliance necessity and an opportunity for innovation and sustainable growth. Companies that continuously assess and improve their sustainability performance ensure long-term stability and success, while contributing to developing a more responsible economy [6].
Therefore, sustainability analysis is no longer just an option for companies but an essential element for their survival and development in a constantly changing business environment. Conversely, companies that neglect sustainability face financial volatility, regulatory penalties, and reputational risks. Thus, sustainability analysis is not only a compliance obligation but also a strategic tool for innovation and resilience.
Despite the growing academic and policy interest in sustainability assessment, current models often lack integration across financial, social, and environmental dimensions. This gap is more pronounced in the construction sector, where firm-level sustainability evaluation remains underexplored, particularly in Central and Eastern Europe. Most studies rely on large multinational samples or sector-general frameworks, which may not capture construction companies’ specific risks, investment structures, or labor dynamics.
In this context, the aim of this paper is to develop and empirically test an integrated econometric model for the assessment of sustainability performance in the construction sector, using publicly available financial, social, and environmental indicators. The model is calibrated for Romanian companies but aligns with EU reporting requirements, offering potential comparability across institutional settings. Through this model, we investigate the influence of the financial structure on sustainability outcomes; identify gaps in existing measurement practices, particularly in the social dimension; and propose a replicable framework that is relevant for academic and business audiences. To achieve the aims of this work, we pursue the following research objectives: (1). To identify and operationalize a set of financial, social, and environmental indicators relevant for sustainability assessment in the construction sector; (2). To develop a composite sustainability score that integrates these dimensions; (3). To test, through econometric modeling, the influence of the financial structure and firm characteristics on sustainability performance.
In this study, the term “digital context” refers to the general operational framework of companies in the era of the digital economy, characterized by the existence, processing, and reporting of data in digital format by current regulations such as the CSRD and ESRS. This systemic digitalization is considered an axiomatic fact of the contemporary environment, constituting the foundation on which sustainability assessment is currently based.
This paper is structured as follows: Section 2 reviews the literature and bibliometric evidence; Section 3 details the methodology; Section 4 presents the empirical results; Section 5 discusses the findings in light of existing research; and Section 6 concludes with limitations and directions for future research.
2. Literature Review
Given the proliferation of research on corporate sustainability, a systematic and bibliometric review of the relevant literature is essential to understanding the trends, gaps, and dominant research directions. In this sense, bibliometric analysis provides an objective and visual approach to understanding how concepts, theories, and models related to sustainability assessment have been developed in the specialized literature.
For the conceptual substantiation of the model proposed in this research, the Web of Science Core Collection database was used, which is considered one of the most rigorous sources of indexed scientific information. The search was conducted in the recent period, including literature published up to 2024, using combinations of key terms such as “corporate sustainability evaluation model”, “financial indicators for sustainability assessment”, and “sustainability performance measurement”. The papers were filtered according to their relevance and belonging to business, management, and environmental sciences, and 98 articles relevant to the research objective were retained.
The bibliometric tool VOSviewer version 1.6.19 was used to analyze the semantic relationships between the central concepts and identify the dominant thematic clusters. This tool allows the visualization of the conceptual structure of the literature through three types of representation: density visualization, network visualization, and overlay visualization. This analysis provided a comprehensive picture of the main research directions in the field, thus contributing to the delimitation of the theoretical framework of the proposed econometric model.
The bibliometric analysis performed with VOSviewer provided an integrated perspective on the literature’s conceptual evolution and thematic structure on corporate sustainability assessment, based on the 98 articles selected from Web of Science. By combining the three specific visualizations—density (Figure 1), network (Figure 2), and overlay (Figure 3)—dominant trends, thematic clusters, and emerging directions that were relevant in building the proposed econometric model were identified.
Figure 1 highlights the frequency and importance of the analyzed concepts. Areas with more intense shades of yellow indicate the most studied concepts in the specialized literature. Key terms dominating the analysis include framework, performance measurement, sustainability, management, and corporate sustainability. These concepts are strongly correlated, which suggests that sustainability assessment models focus on measuring corporate performance. Also highlighted are financial performance, social responsibility, and environmental performance, which demonstrate the emphasis placed on integrating sustainability into the financial structures of companies. Other concepts, such as the Triple Bottom Line, index, and metrics, suggest the use of standardized conceptual frameworks for assessment. Figure 1 indicates that the specialized literature focuses on developing systematic methods for the measurement of corporate sustainability, considering the balance between economic, social, and environmental factors.
Figure 2 highlights the relationships between the key concepts through a map of connections. The network structure shows five main clusters, each representing a major subtheme of corporate sustainability. A significant cluster is centered on performance measurement, framework, and sustainability indicators, reflecting the concern for sustainability assessment indicators. Another important cluster links corporate sustainability to financial performance, social responsibility, and business models, which indicates a concern for the integration of financial performance into corporate sustainability. Another group of concepts, including the Triple Bottom Line, index, and industrial sustainability, suggests the applicability of these models across industries. The connections between the terms show that financial performance is well correlated with corporate sustainability, which suggests a strong relationship between financial performance and sustainability strategies. At the same time, framework and performance measurement are central nodes, demonstrating that the literature focuses on developing measurement frameworks and methodologies. Figure 2 suggests that the literature analyzes the individual components of sustainability and the interdependencies between them.
Figure 3 highlights the temporal evolution of the concepts, using a color scale from blue (for older terms) to yellow (for more recent terms). It is observed that, in recent years, the literature has placed greater emphasis on concepts such as design, technology, and industrial sustainability, which suggests a recent direction towards integrating technology in sustainability assessment. Moreover, the terms index, barriers, and innovation have become increasingly present, indicating the concern for developing more effective methodologies and overcoming obstacles to adopting sustainability. In contrast, older terms such as corporate strategy, governance, and sustainable development indicate that the previous literature focused on sustainability’s conceptual and governance aspects. Figure 3 transitions from theoretical models to practical implementations, emphasizing innovation and technology in recent years.
Overall, the bibliometric analysis highlights a clear evolution from the conceptual foundations of corporate sustainability towards the development of quantifiable, multidimensional models that include financial, social, and environmental indicators. This trend supports the current work’s approach to proposing an econometrically validated composite score, aligned with the current European directives (CSRD and ESRS), contributing to consolidating an internationally comparable analytical framework. The importance of accuracy and reliability in sustainability performance models is also underlined in [7], which warns against the “garbage in, garbage out” effect that can distort empirical results if input indicators are not rigorously defined and validated.
Although the bibliometric analysis performed using VOSviewer offers a structured view of the main research directions in corporate sustainability assessment, this method presents several intrinsic limitations that must be acknowledged. First, the prominence of specific terms in the visualizations may lead to the overestimation of their conceptual centrality due to their frequency of occurrence and not necessarily their theoretical significance. Moreover, the inclusion/exclusion criteria in selecting the 98 Web of Science Core Collection articles, based on their relevance to sustainability performance models within business, management, and environmental sciences, might have introduced thematic bias. The search used keywords such as “corporate sustainability evaluation model” and “financial indicators for sustainability assessment”, filtered by topic and category. Regarding the bibliometric parameters, VOSviewer was configured to use the co-occurrence analysis of keywords with a minimum occurrence threshold of 5, and the association strength method was employed for normalization. While the clustering algorithm provides insightful thematic groupings, the interpretation remains partly subjective and lacks validation compared to other systematic literature reviews. To address this, the results were cross-checked against existing conceptual syntheses in the literature (e.g., [8,9,10]), which confirmed the convergence of core themes such as financial performance, social responsibility, and Triple Bottom Line frameworks. Nonetheless, future research could benefit from triangulating bibliometric findings with qualitative content analysis to mitigate the methodological constraints of frequency-based visualizations.
The literature reflects a methodological diversity in assessing corporate sustainability, emphasizing the integration of economic, social, and environmental dimensions within robust conceptual models. The Triple Bottom Line (TBL) model, proposed in [8] and subsequently applied in different sectors [3], remains an essential benchmark, providing a balanced approach to the three fundamental dimensions of sustainability. In parallel, the Global Reporting Initiative (GRI) provides a standardized framework for reporting non-financial performance, frequently used in corporate sustainability research [1,9,11,12].
More recent approaches [13] involve adapting the Balanced Scorecard for sustainability analysis, including additional perspectives such as supply chain sustainability or regulatory compliance [14]. In the same sense, fuzzy multiple criteria decision-making (FMCDM) methods have been used to allocate differential weights between indicators, leading to a more objective assessment of sustainable performance [15,16]. These theoretical contributions support the need for a flexible and multidimensional methodological framework, under the specifics of the analyzed sector.
A common feature of empirical models in the literature is the use of traditional financial indicators, such as the ROA, ROE, debt ratio, or liquidity, as predictors of firms’ ability to adopt sustainable practices [5,6]. This is complemented by the findings from Ref. [17], which emphasizes that effective financial management policies, particularly in working capital administration, play a crucial role in enabling firms to sustain long-term growth and integrate sustainability principles into strategic decision-making. Social indicators, although more difficult to standardize, include measures such as the number of employees, labor productivity, or the level of investment in human resources [4]. However, Ref. [18] cautions that social sustainability assessments may be distorted when firms use them primarily as tools for external legitimation rather than genuine performance improvement, highlighting the need for more robust and context-sensitive metrics. On the environmental front, indicators such as carbon emissions, energy efficiency, and resource recycling are frequently used in assessing environmental responsibility [2].
At the same time, recent studies highlight a trend of aggregating these variables into composite indices, which allow a more unified and comparable interpretation of sustainable performance [2,4,19]. The diversity of indicators found in the literature highlights the need to customize models depending on the sector of activity and the institutional context in which firms operate. For instance, dynamic capabilities and institutional pressures significantly influence sustainability outcomes in the oil and gas industry, underscoring the importance of sector-specific strategies [20]. Similarly, in forest-based industries, integrating institutional dimensions, such as governance frameworks and policy contexts, has proven essential for accurate sustainability assessment [21]. More broadly, the institutional configurations at local, national, and international levels can facilitate or constrain sustainability partnerships and models, as shown in comparative studies of European cities [22].
The applicability of sustainability assessment models differs significantly across sectors, influenced by operational specifics and institutional pressures. In this regard, Ref. [23] shows that companies across different supply chain positions are subject to varying institutional pressures, which directly shape their sustainability priorities and reporting practices. In the energy sector, sustainability analysis focuses on resource efficiency and the capital structure [6], while, in the mining industry, the emphasis is on the relationship with local communities and the protection of ecosystems [3]. For SMEs, econometric models analyze the impact of working capital and debt on sustainability, especially in high-volatility sectors [5].
The construction sector, which is the focus of this study, is characterized by a significant impact on the environment and employment but remains underrepresented in the empirical literature on corporate sustainability [1,4]. Existing studies often focus on the environmental certification of projects or the energy performance of buildings, without integrating ESG dimensions and financial performance into a coherent framework [24]. Furthermore, comprehensive ESG risk assessment tools incorporating financial indicators are still in early stages of development and are rarely applied systematically across construction companies [25]. Moreover, econometric approaches applied to construction are rare and fragmented, limiting the understanding of the causal relationships between sustainability factors [3,6]. This highlights the theoretical and practical gap that this study addresses by proposing an integrated econometric model, calibrated for the specifics of the Romanian construction sector and aligned with European reporting requirements (CSRD and ESRS).
The recent literature suggests expanding traditional assessment models by including additional indicators, such as reputational risk, market volatility, and transparency [26,27]. There is also a shift from passive sustainability assessment (oriented towards compliance) to active assessments that aim to generate sustainable value in the long term. Recent research conceptualizes this transition as a move from compliance-based reporting to the logic of “value-based sustainability”, in which sustainability becomes an integral part of the strategy for the creation of value for the company and society [28]. This paradigm shift is also supported by studies that promote the use of value methods, such as economic value added or sustainable value added, which include not only financial performance but also social and environmental costs/options [29,30]. Moreover, reporting under new directives (e.g., CSRD) is increasingly oriented towards sustainable strategic planning and not just bureaucratic compliance [31], supporting a prospective and multidimensional approach to sustainability assessment.
Advanced techniques, such as data envelopment analysis (DEA) and multi-criteria analysis, are becoming increasingly common in comparative sustainability assessments [10,32]. These methods offer more precision in identifying efficient practices and can complement classical econometric analysis. In particular, DEA has proven effective in evaluating the eco-efficiency and sustainability of construction systems by quantifying the trade-offs between environmental impacts, resource use, and economic outputs [32].
Moreover, including such tools in future research could increase the robustness of sustainability models in industries with complex structures, such as construction. Integrated DEA–LCA or DEA–MCDM frameworks allow multi-dimensional performance benchmarking. They can generate specific improvement targets, making them highly suitable for construction projects where the input–output structures are non-linear and interdependent [33,34,35]. Their application not only improves the granularity of the assessment but also supports targeted decision-making under regulatory and technical constraints.
Building upon the theoretical frameworks, models, and indicators discussed above, this study aims to empirically examine the relationships between the financial, social, and environmental dimensions of sustainability in the construction sector. Several empirical studies emphasize that, despite the sector’s significant impact, integrated models linking ESG performance to financial outcomes remain scarce. For example, Ref. [1] states that construction companies with higher ESG disclosures tend to perform better financially, but the research is still fragmented.
The diversity of approaches identified in the literature highlights the necessity of an integrated and context-specific model, particularly in industries such as construction, where the sustainability–performance nexus remains underexplored. For instance, Ref. [36] notes that many sustainability initiatives fail in construction due to the lack of correlation between economic, social, and environmental indicators and project performance objectives. Ref. [37] also emphasizes that effective sustainability strategies require the integration of operational, tactical, and strategic actions into a coherent long-term framework.
Based on the bibliometric mapping, the literature was organized around three dominant research clusters that directly informed the formulation of the hypotheses and the structure of the proposed model. The first cluster, centered on performance measurement, sustainability frameworks, and corporate strategy, reinforced the foundational role of econometric models and indicators such as the ROA, ROE, and debt ratio in assessing corporate sustainability [8,10].
The second cluster, focused on social responsibility and human capital, informed the inclusion of the workforce size and employee productivity as proxies for social sustainability, especially in labor-intensive sectors like construction [38,39].
The third cluster emphasized environmental responsibility and risk mitigation, guiding the selection of environmental provisions and long-term assets as indicators in contexts where direct emissions data are not publicly disclosed. These clusters formed the conceptual backbone of the composite sustainability score and directly influenced the hypotheses tested in Section 3.3. While the bibliometric analysis provided a macro-level overview of the thematic directions, the literature review also included a targeted narrative synthesis to critically assess empirical models, indicator relevance, and contextual alignment with the construction sector. This dual approach ensures that the proposed model is quantitatively grounded, theoretically justified, and adaptable to sector-specific realities.
The three clusters identified in the bibliometric analysis—focused on (i) performance measurement and conceptual structures, (ii) social responsibility and human capital, and (iii) environmental responsibility and risk mitigation—were fundamental in formulating hypotheses H1–H4. For example, the inclusion of the ROA and ROE (H1) was derived from the core of terms highlighted in the first cluster, while H3 was based on the clusters centered on human capital. Thus, bibliometrics was used descriptively and as a direct source for the theoretical construction of the econometric model.
3. Materials and Methods
3.1. Research Design
The research was structured into six methodological stages, each contributing to the development and empirical validation of the proposed sustainability assessment model.
Stage 1: Theoretical Foundation
A documentary analysis was conducted to identify existing conceptual frameworks and econometric models related to sustainability and corporate performance. This review, detailed in Section 2 (Literature Review), served as the basis for selecting relevant indicators.
Stage 2: Sector and Sample Selection
The construction sector was chosen due to its environmental and economic relevance and underrepresentation in empirical sustainability research. Based on CAEN code 4120, a representative sample of 1600 Romanian companies was selected. This process is further explained in Section 3.2 (Data Collection and Sample Description).
Stage 3: Data Collection (2013–2023)
A panel dataset was compiled using publicly available financial data. The operationalization of the variables is detailed in Section 3.2, and this was used across the regression models.
Stage 4: Sustainability Score Construction
A composite sustainability score integrating financial, social, and environmental dimensions was developed using literature-informed weights and normalized indicators. The methodology is described in Section 3.4.
Stage 5: Econometric Modeling
Three regression models (OLS) were designed to test the influence of financial structure variables on each sustainability dimension. Modeling details, including diagnostics and robustness checks, are presented in Section 3.5.
Stage 6: Framework Proposal
Based on the empirical findings, a practical sustainability evaluation framework for construction companies was proposed, as discussed in Section 5 (Discussion).
3.2. Data Collection and Sample Description
The empirical analysis was based on a dataset comprising 1600 companies operating in Romania, selected according to the Statistical Classification of Economic Activities in the European Community (NACE) under CAEN code 4120—Construction of residential and non-residential buildings. This selection was motivated by the sector’s critical contribution to economic outputs, employment generation, and environmental resource use, as well as by its relative underrepresentation in existing sustainability assessment studies.
The companies were selected based on turnover criteria to ensure a representative and economically relevant sample. Financial and operational data were collected for ten years (2013–2023) for each company, offering a longitudinal perspective on the performance trends and sustainability evolution. The dataset was compiled using publicly available sources, such as the Ministry of Finance and official company registries, ensuring transparency and data reliability.
The collected variables include a wide range of financial indicators: turnover, gross and net profit/loss, total revenues and expenses, prepaid income and expenses, total assets, fixed and current assets, inventories, receivables, cash, liabilities, provisions, share capital, and total capital. In addition, several performance ratios were calculated, including the return on assets (ROA), return on equity (ROE), debt ratio, solvency, liquidity, and gross profit margin. The social dimension was captured through the average number of employees and derived metrics such as productivity (net profit per employee). While data on environmental performance were more limited, the study incorporated proxies such as provisions for environmental risks and the asset structure, aligned with current ESG reporting standards.
To ensure consistency, the variables were normalized and pre-processed before inclusion in the econometric models. Data quality checks were applied to handle outliers and missing values, and all financial variables were deflated where appropriate to account for inflation and temporal comparability. The resulting dataset enabled a robust cross-sectional and longitudinal analysis of sustainability determinants in the Romanian construction sector.
3.3. Research Hypotheses
The hypotheses formulated in this study are grounded in the theoretical and empirical contributions outlined in Section 2. The prior literature has emphasized the influence of financial performance indicators, such as the return on assets (ROA), return on equity (ROE), and leverage ratios, on corporate sustainability, highlighting their role in enabling firms to adopt responsible practices [10,40]. The Triple Bottom Line framework [8] and sustainability reporting standards [9] also support the inclusion of social and environmental indicators alongside financial metrics. Moreover, empirical findings from recent sector-specific studies show that the firm size, financial resilience, and capital allocation strategies are critical determinants of sustainability outcomes [38]. Building on this body of literature, and adapted to the context of the Romanian construction sector, the following hypotheses are proposed.
Financial performance indicators (ROA, ROE, debt ratio, and solvency) significantly influence corporate sustainability. Firms with strong financial fundamentals are expected to achieve higher sustainability scores [10,40].
The firm size, measured by turnover and the number of employees, is positively associated with sustainability performance. Larger companies are presumed to have more resources and a greater capacity to implement sustainable practices [23].
Firms that allocate more resources to infrastructure and human capital investment are expected to attain higher scores on the social dimension of sustainability, compared to companies with limited social investment [38,39].
Liquidity and solvency have differentiated effects on sustainability’s social and environmental dimensions. Higher liquidity is expected to enable more significant investment in environmental and social initiatives, while solvency is anticipated to have a more consistent impact on long-term sustainability orientation [38].
These hypotheses serve as the foundation for the econometric modeling conducted in the following stages of the research. They are tested using firm-level financial data from Romanian construction companies over ten years (2013–2023).
3.4. Sustainability Score Construction
The importance of environmental management as part of organizational decision systems has been increasingly emphasized in the recent literature. For example, Ref. [41] underlines that decision-making frameworks integrating environmental aspects improve long-term strategic alignment and risk anticipation. The selection of indicators included in the composite sustainability score was guided by theoretical relevance and sector-specific applicability. In the financial dimension, ratios such as the ROA, ROE, debt ratio, and solvency are widely used in sustainability models due to their strong predictive power regarding a firm’s capacity to finance long-term initiatives [10,40]. For the construction sector, which is capital-intensive and prone to project-based financial volatility, these indicators are particularly relevant to assess financial resilience. The social dimension integrates the number of employees and productivity per employee, which are critical in construction due to its labor-intensive nature and frequent subcontracting practices [38]. The environmental indicators—the proportion of fixed assets and environmental provisions—were selected as proxies for long-term infrastructural investment and risk preparedness, which are more appropriate for construction firms than direct emission metrics, given the current limitations in environmental disclosure at the firm level in Romania. The weighting scheme (40% financial, 30% social, 30% environmental) reflects the emphasis that EU directives (CSRD, ESRS) place on financial stability as an enabler of sustainable transition, particularly in emerging economies where access to capital is a limiting factor.
Each dimension includes a set of indicators selected based on data availability, relevance in previous studies, and alignment with the CSRD and ESRS standards. The indicators were normalized to ensure comparability across firms and years. The sustainability scores were calculated using the following formulas.
1.. Financial dimension (40%)
Financial score = [(ROA + ROE)/2 × 20)] + (1−Debt ratio) × 10 + (Solvency × 5) + (Liquidity × 5)(1)
whereROA: efficiency of asset use;
ROE: return on equity;
Debt ratio: penalizes companies with a high debt ratio;
Solvency and liquidity: the company’s ability to honor its long-term and short-term debts.
2.. Social dimension (30%)
Social score = [(Number of employees)/2000 × 10] + {(Net profit)/[max(Number of employees,1)] × 5}(2)
whereNumber of employees: companies with more employees receive a higher score for social contributions;
Employee productivity: calculated by reporting the net profit to the number of employees.
3.. Environmental dimension (30%)
Environmental score = {(Fixed assets)/[max(Total assets,1)] × 10} + {Provisions/[max(Liabilities,1)] × 10}(3)
whereFixed assets: represent investments in infrastructure, including green assets;
Provisions: an indirect indicator of the company’s preparedness for environmental financial risks.
4.. Composite Sustainability Score (0–100)
Total score = (Financial score × 0.4) + (Social score × 0.3) + (Environmental score × 0.3)(4)
where40% is used for financial performance (as financial stability is essential for long-term sustainability);
30% is used for social impact;
30% is used for environmental responsibility.
The composite sustainability scores were classified into three performance categories—high, moderate, and low—using the Jenks natural breaks classification method to facilitate interpretation and benchmarking. This classification approach minimizes the variance within each class while maximizing the variance between classes, allowing for the meaningful differentiation of sustainability performance across firms. The resulting structure supports descriptive analysis and regression-based modeling, providing a consistent analytical framework for the evaluation of corporate sustainability in the construction sector.
3.5. Econometric Modeling Approach
Three econometric models were developed to assess the influence of financial structure variables on corporate sustainability performance, each corresponding to one of the three sustainability dimensions: financial, social, and environmental. The modeling approach builds upon the econometric framework [5] developed, which links firm-level financial indicators to performance outcomes using linear regression analysis.
Each model takes the general form of an ordinary least squares (OLS) regression, expressed as follows:
Sustainability Score = β0 + β1 × X1i + β2 × X2i+ … + βk × Xki + εi(5)
where Sustainability Score represents the financial, social, or environmental sustainability score for firm i; X1i to Xki are the independent variables; β0 is the intercept; βk denotes the estimated coefficients; and εi is the error term.The following independent variables were included in all three models, selected based on both theoretical relevance and data availability: return on assets (ROA), return on sales (ROS), ratio of fixed assets to total assets (RFA), ratio of current assets to total assets (RCA), inventory ratio (RI), receivables ratio (RR), ratio of equity to total liabilities (REL), ratio of total debt to assets (RDA), and debt-to-equity ratio (RDE). These variables were chosen to capture a firm’s capital structure, liquidity, profitability, and investment strategy—all factors that are presumed to influence sustainability performance based on the literature reviewed in Section 2.
Before model estimation, all variables were tested for multicollinearity using variance inflation factor (VIF) analysis. No severe multicollinearity was detected, as all VIF values were below the commonly accepted threshold of 10. Heteroscedasticity was assessed using the Breusch–Pagan and White tests, while the model specification was verified via the Ramsey RESET test. Additionally, robustness checks were conducted to confirm the consistency of the results.
The regression models were applied to cross-sectional data for 2023, representing the most recent and complete reporting period available. The results from these models are presented and interpreted in Section 4, providing insights into the differential impacts of financial indicators on each sustainability dimension.
The relatively low explanatory power of the social sustainability model is acknowledged and may be attributed to limitations in the availability of firm-level social data. The study relies on structured financial disclosures mandated by Directive 2013/34/EU of the European Parliament, which governs the format and content of annual financial statements for EU-based enterprises. In Romania, such data are aggregated and publicly disseminated via the Ministry of Finance, primarily based on balance sheet and income statement elements. Consequently, social variables had to be approximated through indirect proxies such as employment figures and labor productivity. Although these indicators provide partial insights into a firm’s social impact, they may not capture more nuanced aspects such as employee satisfaction, diversity, training, or community engagement. Incorporating such variables would require access to survey-based or qualitative datasets, which remain unavailable at this scale. This limitation suggests that future research may enhance the social model’s precision by integrating richer datasets or conducting targeted case studies.
3.6. Variable Description and Justification
The complexity of sustainability in construction requires systemic models that reflect the interdependencies among financial, social, and environmental factors. Recent theoretical contributions advocate for a holographic or multidimensional perspective in analyzing such complex dynamics [42]. Both theoretical foundations and empirical findings from previous sustainability research guided the selection of the variables included in the econometric models. Each variable captures a specific financial or structural attribute of the firm that may influence its capacity to integrate and operationalize sustainability principles.
The return on assets (ROA) and return on sales (ROS) are profitability indicators that are widely used in the sustainability literature to reflect a firm’s efficiency in generating returns from its assets and revenues. Higher profitability is generally associated with a greater capacity to invest in sustainable practices [19,26].
The ratio of fixed assets to total assets (RFA) and ratio of current assets to total assets (RCA) are included to evaluate the firm’s investment strategy and asset structure. These ratios offer insight into whether resources are allocated toward long-term infrastructure or short-term operational flexibility, both of which have implications for environmental and social sustainability [6].
The inventory ratio (RI) and receivables ratio (RR) capture operational efficiency and working capital management. Efficient inventory and receivables management is critical in the construction sector due to its project-based nature and extended cash flow cycles [17].
The ratio of equity to total liabilities (REL), total debt-to-assets ratio (RDA), and debt-to-equity ratio (RDE) are core indicators of the capital structure. A firm’s solvency and leverage influence its financial resilience and long-term sustainability orientation [5]. These indicators also reflect risk management practices relevant to compliance with ESG principles.
In addition to the independent variables, the sustainability scores (dependent variables) were constructed as described in Section 3.4, incorporating environmental, social, and financial dimensions. These scores offer a synthetic yet multidimensional perspective on sustainability performance and provide the analytical basis for the testing of the research hypotheses defined in Section 3.3.
Including these variables allows for the empirical investigation of how the financial structure influences sustainability performance in the construction sector, thereby responding to gaps identified in the literature [1,23]. Their interpretation and statistical significance are discussed in the following section.
4. Results
The construction sector in Romania is an important sector of the national economy. With over 47 thousand companies and 210 thousand employees, it has turnover of over RON 87 billion (EUR 17.4 billion) and profit of over RON 11.6 billion (EUR 2.32 billion) (as shown in Figure 4).
The data for the companies selected for analysis show a steady increase in turnover, which accelerated significantly after 2019, indicating the expansion of the construction sector. This growth is accompanied by positive gross and net profit evolution, suggesting that the companies have expanded their activity and improved their operational efficiency.
Regarding profitability indicators, the ROA and ROE show high volatility, with significant increases in specific periods, followed by sharp decreases (see Figure 5). These fluctuations may indicate the impact of macroeconomic conditions on the profitability of construction companies and changes in the financial strategies adopted.
The level of indebtedness varies significantly from year to year, which may suggest changes in how firms have financed their activity, either through additional borrowing or by reducing debt. On the other hand, solvency has remained relatively stable, with some significant increases in specific periods, which suggests the general capacity of firms to honor their financial obligations.
The construction sector in Romania has experienced significant expansion, with important increases in turnover and profitability, but also with high volatility in terms of the return on assets and indebtedness. Firms have started paying greater attention to social aspects, but environmental sustainability remains challenging. These trends suggest that, for long-term sustainable development, companies should diversify their investments and pay increased attention to green initiatives.
Following the detailed analysis of the sustainability of construction companies, three distinct categories of companies were identified, each corresponding to a different level of sustainability: high, moderate, and low. Based on the scores obtained in each dimension, these categories reflect the financial, social, and environmental performance of the companies analyzed, as synthesized in Figure 6.
Companies with high sustainability have demonstrated the excellent management of financial resources, solid liquidity, and a high degree of solvency. These companies have managed to maintain a balance between profitability and social and environmental responsibility, actively investing in employees, green technologies, and measures that reduce environmental impacts. A strong correlation is observed between their size and high sustainability scores, which suggests that larger companies with significant turnover have more resources to implement sustainable initiatives. In terms of the social dimension, these companies stand out through investments in human capital, well-defined social responsibility policies, and high employee stability.
Companies classified in the moderate sustainability category present a balance between financial performance and social and environmental sustainability, but still face specific challenges. Although they have acceptable liquidity and solvency, they do not invest as much in sustainability policies as in the first category. In many cases, these companies are medium-sized, and their resources are mainly directed towards stabilizing their financial positions, rather than implementing proactive measures for sustainability. There is an obvious concern for social and environmental responsibility, but on a smaller scale.
On the other hand, companies in the low sustainability category experience significant difficulties in maintaining a high level of sustainability (see Table 1). They often have liquidity problems, a high degree of indebtedness, and low solvency, which makes them more vulnerable from a financial point of view. It is observed that these companies allocate minimal resources to environmental or social initiatives, and their involvement in corporate responsibility activities is limited. Moreover, their smaller size prevents them from developing extensive sustainability programs, and their investments in fixed assets and human capital are significantly lower compared to companies in the other categories.
Thus, a clear pattern emerges in which companies with more significant financial resources have an advantage in adopting sustainable measures, from both an ecological and a social point of view. At the same time, the company’s size and the level of investment made in human capital and infrastructure play an essential role in positioning it in one of the three sustainability categories.
The correlation matrix between the sustainability indicators (Figure 7) provides a clear perspective on the relationships between the financial, social, and environmental variables of the companies analyzed. From this visual representation, it can be seen that there are no strong correlations between the total score, the overall sustainability score, and the other indicators, which suggests that a set of factors influences sustainability and it cannot be explained by a single dominant variable.
It is noted that the ROA and ROE, which are classic financial performance indicators, have no significant relationship with the total sustainability score or the other factors. This may indicate that the return on assets and equity do not significantly determine a company’s sustainability. This could be explained by construction companies operating in a specific economic environment, where other variables, such as the asset structure or investment level, are more relevant.
An interesting aspect is the relationship between the average number of employees and total fixed assets, where there is a moderate positive correlation. This suggests that firms with more employees also tend to have more significant assets, indicating a higher investment capacity and better economic stability. In addition, the number of employees also positively correlates with provisions, which could mean that firms with more employees also allocate more extensive resources to risk funds or future expenses.
Another notable element is the positive relationship between debt and ROE, which confirms that firms with a higher debt level tend to generate a higher return on equity. This can be explained by the fact that debt allows firms to finance more extensive projects, thus having the opportunity to obtain a higher return for shareholders.
Liquidity does not seem to be significantly correlated with the other indicators, which may suggest that cash flow management is an independent factor, without directly influencing the other sustainability variables.
The correlation matrix shows that traditional financial indicators do not directly and strongly influence overall sustainability. Instead, moderate relationships exist between the firm size, asset level, and number of employees. This highlights the importance of a comprehensive approach to sustainability assessment, which considers financial aspects and the social and environmental impacts of a company’s activity.
Table 2 shows the centralizing situation for the adaptation of the model formulated in Ref. [5] to the construction field, which makes it applicable.
The analysis of econometric models for the sustainability of construction companies revealed a series of important conclusions, highlighting the validity and robustness of each model applied. Regarding the model for the environmental score, it recorded an R2 coefficient of 0.836, indicating a very good capacity to explain the variation in the environmental score. The Durbin–Watson test had a value of 1.989, suggesting low error autocorrelation. The significant variables identified were the REL and RI, while variables such as the RCA, RDA, RDE, RFA, ROA, ROS, and RR did not significantly impact the model. However, the Breusch–Pagan test confirmed the presence of heteroscedasticity, and the Breusch–Godfrey test revealed autocorrelation, which requires adjustments using robust standard errors. However, multicollinearity was not a problem, given the VIF values below the critical threshold.
The financial score model proved highly explanatory, with an R2 of 0.975, indicating that the model captured almost all variations in the financial score. The Durbin–Watson test had a value of 1.981, suggesting a low level of autocorrelation. The only significant variable was the ROA, which shows that the return on assets plays a key role in determining the financial score. The remaining variables, including the RCA, RDA, RDE, RFA, RI, ROS, and RR, did not significantly contribute. However, heteroscedasticity and autocorrelation were detected, requiring robust standard error corrections. Similarly to the previous model, no major multicollinearity issues were identified.
On the other hand, the model for the social score was very weak, with an R2 of only 0.006, indicating that only 0.6% of the variation in the social score was explained by the included independent variables. Although the Durbin–Watson test showed a value of 1.963, which does not indicate major problems with autocorrelation, the only significant variable was the REL. All other variables were insignificant, and the Breusch–Pagan test showed that the model did not exhibit heteroscedasticity. However, the Breusch–Godfrey test indicated the presence of autocorrelation, which requires corrections. Due to the weak explanatory power, this model needs to be rebuilt, exploring other variables that could significantly influence the social score. Finally, the model for the total score had the same problems as the social model, with an R2 of only 0.006, suggesting that the explanation provided by the independent variables was unsatisfactory. The Durbin–Watson value was 1.964, indicating an acceptable level of autocorrelation, and the REL was the only significant variable. All other variables were insignificant. The Breusch–Pagan test indicated the absence of heteroscedasticity, but the Breusch–Godfrey test revealed autocorrelation, requiring adjustments. Due to its poor predictive power, this model must be reviewed and completely redesigned.
Therefore, the environmental and financial score models are valid and well explained but require corrections for heteroscedasticity and autocorrelation. On the other hand, the models for the social and total scores are inefficient and need to be reformulated by including more relevant variables. The next steps should include applying robust standard errors to the environmental and financial models and redesigning the social and general models to improve their explanatory power.
Taking into account the public data available for companies (a valid aspect for all companies in Romania, as well as for companies applying IAS/IFRS standards), the environmental score model was improved as follows for companies in the construction sector:
Environmental score = 0.55590 + 3.7782 × Solvency + 3.8101 × Liquidity + ε(6)
The model is statistically valid (R2 = 0.974) and explains 97.4% of the environmental score variation. Solvency (with significant positive impact) and liquidity (with significant negative impact) are the most important explanatory factors.
The financial score model has been improved for companies in the construction sector:
Financial score = −131.4681 + 9.4295 × REL + 10.0481 × ROA + 0.0000009497 × Fixed assets + 0.0000002216 × Current assets + ε(7)
The model is statistically valid, explaining 97.5% of the variation in the financial score, with the REL and ROA being the most important explanatory variables.
The social score model has been improved for construction companies:
Social score = 66,810.00 + 5.2612 × Profit per employee − 0.3473 × Public assets + ε(8)
The statistically valid model explains 97.4% of the social score variation. The profit per employee is the most important explanatory variable.
The analyzed results highlight three categories of companies according to their sustainability: high, moderate, and low. These categories reflect the financial, social, and environmental performance of companies in the construction sector.
Companies with high sustainability present efficient financial resource management, solid liquidity, and a high degree of solvency. They have invested in green technologies and social initiatives, balancing profitability and responsibility. A significant correlation was observed between the company’s size and sustainability, with larger companies having more resources to implement sustainable measures.
Companies in the moderate sustainability category present a balance between financial, social, and environmental performance. However, they face challenges in maintaining sustainable practices in the long term. These companies have an acceptable degree of liquidity and solvency, but their investments in sustainability are limited.
Companies classified as having low sustainability face significant difficulties, including liquidity problems, high debt levels, and low investments in sustainability. They allocate minimal resources to social or environmental initiatives and are more vulnerable to financial risks.
The econometric analysis showed that the financial score and environmental score models were robust, with R2 coefficients above 0.97. Solvency and liquidity are the factors for environmental sustainability, and the return on assets (ROA) and equity structure (REL) are essential for financial performance. On the other hand, the model for the social score requires adjustments, as the included variables do not sufficiently explain the variation in the score. Thus, an important aspect to consider in future research is the inclusion of additional variables, such as the employee turnover rate or the level of investment in staff training, to capture the social impact better.
The results suggest that firms with more significant financial resources have an advantage in adopting sustainable measures, and the company size and investments in human capital and infrastructure influence a company’s positioning in one of the three sustainability categories. The following steps in the research should include methodological adjustments to improve the social model and the further validation of the results through robustness tests.
The econometric analysis enabled the empirical testing of all four research hypotheses formulated in Section 3.3, providing insights into the financial determinants of corporate sustainability in the construction sector.
The hypothesis that firms with strong financial indicators achieve higher sustainability scores was confirmed. Specifically, the ROA and ROE showed a significant and positive correlation with the composite sustainability score, consistent with international findings [7]. This underscores the importance of using well-defined financial metrics to assess sustainability and avoid distorted interpretations.
The hypothesis that larger firms exhibit higher sustainability performance was partially confirmed. While the firm size—measured through turnover and the number of employees—was positively associated with sustainability scores, the relationship varied across dimensions. This outcome aligns with [23], which notes that institutional pressures vary by firm characteristics, influencing the prioritization of sustainability dimensions differently.
The expectation that firms investing more in infrastructure and human capital perform better on the social dimension of sustainability was not confirmed. The social dimension exhibited weak explanatory power in the regression model, suggesting that factors such as the organizational culture or external pressures may play a more decisive role. This is consistent with [18], which warns of distortions in social performance reporting when used for legitimization rather than impact improvement.
This hypothesis was confirmed, i.e., liquidity and solvency affect sustainability dimensions differently. While solvency positively influenced the environmental score, liquidity showed no significant effect. These findings support the argument of [27] that firms must transition from passive sustainability compliance to active value creation strategies.
In summary, the analysis confirms the relevance of the financial structure and solvency as key determinants of sustainability in construction firms. However, the limited impact of the firm size and social investment indicators suggests that further refinement of the social performance model is necessary to ensure accurate assessment.
5. Discussion
The model proposed in this paper significantly contributes to academia and business by integrating a rigorous econometric approach and providing a practical tool for the assessment of companies’ sustainability in the construction sector. Most studies treat sustainability from a fragmented perspective in the specialized literature, analyzing the financial, social, or environmental dimensions, without providing an integrated approach. This paper proposes a composite sustainability score that combines these three pillars into a single model, thus providing a complete picture of the sustainable performance of companies. The study highlights the positive correlation between financial stability and the sustainability score, with companies with higher liquidity having a higher probability of investing in environmental and social initiatives. This finding is similar to the results in the international literature, where researchers [6] have shown that the return on assets and capital structure influence the sustainability of companies in the energy sector in Poland. Thus, the formulated model is aligned with European trends that emphasize the importance of solid financial foundations in achieving sustainability objectives.
This study confirms that larger firms can implement sustainable measures and superior financial and human resources. This trend has also been identified in other European economies, where the literature shows that large corporations are more likely to adopt environmental, social, and governance (ESG) standards, partly due to the stricter regulations targeting them [4]. Moreover, the CSRD and ESRS directives impose more extensive reporting obligations on large firms, which makes the Romanian models comparable to those in other EU countries.
The study results indicate the high volatility of the return on assets (ROA) and return on equity (ROE), which can be attributed to the impact of macroeconomic conditions and the financial strategies that companies adopt. This volatility is also observed in other European countries, especially in capital-intensive industries, such as construction and energy [3]. In addition, the impact of the pandemic and recent economic fluctuations has accentuated this trend at the European level. This study highlights that environmental issues remain a challenge for construction companies in Romania, which invest less in green technologies and initiatives to reduce their environmental impacts. This finding is congruent with the international literature, which suggests that resource-intensive industries have a slower transition towards sustainability [1]. Moreover, in EU countries with stricter carbon taxes and sustainability policies, construction companies have had to adopt faster compliance measures.
Regarding the trends in Romania compared to other EU countries, they are summarized as follows.
This study shows the substantial expansion of the construction sector in Romania, with accelerated growth in turnover after 2019. This trend is comparable to those of other EU countries, where post-pandemic economic recovery programs have boosted the housing demand and infrastructure investments.
The model developed in the paper is aligned with the CSRD and ESRS, which makes the sustainability analysis compatible with the requirements of other EU countries. This aspect facilitates comparability and can support the integration of Romanian companies into European financial markets.
This study shows that the social analysis model has a reduced explanatory capacity, which suggests that traditional variables (number of employees, productivity) are not sufficiently relevant. This problem is also encountered in other EU countries, where social sustainability is more difficult to quantify and requires alternative measurement methods [17].
The results obtained in this paper are mainly comparable to those highlighted in the international literature on sustainability performance measurement. Recent studies highlight the importance of integrating sustainability indicators into business strategies to ensure sustainable economic growth and alignment with regulatory requirements. For example, research in Ref. [23] shows that the measurement of sustainability performance in supply chains is influenced by institutional pressures and contextual factors, which determine the adoption of various sustainability implementation strategies. This conclusion is relevant for the present study, as companies in the Romanian construction sector are subject to similar pressures, mainly from European regulations and market demands regarding ESG standards.
Furthermore, the literature highlights that using structured sustainability measurement frameworks, such as the Balanced Scorecard modified for sustainability, allows for a more accurate assessment of the impact of economic, social, and environmental factors on corporate performance. Ref. [15] demonstrates that integrating the fuzzy multiple criteria decision-making (FMCDM) method into sustainability analysis leads to a more objective organizational performance assessment. This approach is similar to the one used in this study, where the proposed econometric model aims to balance financial and non-financial factors to provide a more complete picture of the sustainability of construction companies.
Another recent study also analyzes the shift from a negative measurement of sustainability performance (focused on reducing negative impacts) to a positive approach, aimed at actively creating sustainable value.
Ref. [27] argues that adopting a perspective that emphasizes sustainability’s benefits can lead to better integration into companies’ decision-making processes. This perspective is relevant to the present analysis, as it supports the idea that companies that adopt a proactive view of sustainability comply with regulations in force and gain significant competitive advantages.
Therefore, this research’s results align with international trends, confirming that regulations, institutional pressures, and economic factors strongly influence corporate sustainability. At the same time, the use of advanced methods to measure sustainability performance, as demonstrated in the recent literature, supports the validity of the model proposed in this paper.
From an academic point of view, the main contribution consists of using an advanced econometric methodology for the validation of sustainability indicators. The models tested include multiple regressions and checks for heteroscedasticity, endogeneity, and multicollinearity, which provide robust results. In addition, the model is calibrated for the construction sector. This field significantly impacts the environment and the economy but has not benefited from such a detailed analysis in a comparable methodological framework. Another important academic contribution is the alignment of the model with European sustainability reporting standards, such as the Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards (ESRS). This alignment allows for extensive comparability between companies and provides a solid basis for future studies targeting corporate sustainability in various industries.
In the business environment, this model is a helpful tool for construction companies, allowing them to assess their sustainability in a structured and quantifiable way. By using relevant financial indicators, such as the return on assets (ROA), return on equity (ROE), debt ratio, and liquidity, the model can help companies to optimize their business strategies and improve their market positions. At the same time, integrating social and environmental factors into sustainability analysis provides a broader perspective on corporate performance, contributing to better risk management and increased transparency towards investors and other stakeholders.
An essential aspect of this model is its practical applicability in companies’ decision-making processes. Companies can use the composite score to identify weaknesses in the area of sustainability and to implement corrective measures to improve their performance. For example, companies that obtain a low score in the environmental dimension can make strategic decisions regarding investments in green technologies or the optimization of resource consumption. In addition, investors and financial institutions can use this model to assess the risks associated with construction sector companies and make more informed decisions regarding their financing. Another novelty of this work is the extensive empirical validation of the model, based on a sample of 1600 companies analyzed over 10 years. This approach provides a longitudinal perspective on sustainability in the construction sector, allowing the identification of trends and factors influencing the evolution of this field. Moreover, the applied econometric tests confirm that the size of the company and the level of available financial resources are critical factors in determining the degree of sustainability, which can have significant implications for the development strategies of the companies. In conclusion, this paper makes an important contribution at both the theoretical and practical levels by presenting an integrated model to assess companies’ sustainability. The results confirm that sustainability is not just an optional component of the business strategy but an essential factor for the long-term success of companies in the construction sector. At the same time, the proposed model can serve as a starting point for future research to adapt it to other industries or explore alternative methods of quantifying corporate sustainability. In addition, the econometric models validated in this research provide a solid basis for the assessment of corporate sustainability and balancing of financial, social, and environmental dimensions. At the same time, in the context of emerging technological trends, the societal direction that involves the integration of calm technology principles [43,44]—an approach that favors non-invasive, efficient, and discreet technologies aligned with sustainability values—becomes relevant. This convergence proposes a transition from a quantitative model to one that capitalizes on the balance between technological progress and human well-being, expanding the meaning of sustainability beyond classic indicators.
While the empirical validation of the model is limited to Romanian construction companies, its conceptual structure and indicator selection align with methodologies applied in other European countries. For instance, studies from Poland [6] and Slovenia [2] have similarly used financial ratios such as the ROA, ROE, and debt-to-equity ratio to explain sustainability outcomes in capital-intensive industries. In Germany and the Netherlands, composite sustainability scores include proxies for social and environmental dimensions when disclosure is limited [19]. These parallels suggest that, although the dataset is context-specific, the proposed model could be adapted to other institutional environments with comparable data structures and ESG pressures. Future work could involve the comparative application of the model in Central and Eastern Europe to empirically test its robustness across different regulatory and market contexts.
6. Conclusions
This paper aimed to develop an integrated model to assess companies’ sustainability in the construction sector based on financial, social, and environmental indicators. The analysis results demonstrate that corporate sustainability is an ethical objective and an essential factor for economic success and organizational resilience.
The econometric analysis confirmed that the financial score and environmental score models were robust, with coefficients of determination (R2) above 0.97. This indicates a clear relationship between financial stability and environmental sustainability, confirming that firms with a solid capital structure and high liquidity are more likely to invest in green initiatives. In contrast, the model for the social score presented a low explanatory capacity, suggesting that social factors depend on more complex variables than those included in the analysis. Thus, future improvements to the model should consider new variables related to investments in human capital and social responsibility policies.
The financial performance and sustainability relationship analysis highlighted a positive correlation between financial stability and the composite sustainability score. The econometric model confirms that firms with a higher ROA and ROE tend to have a 30% higher probability of investing in sustainable practices than firms with low profitability. Solvency also significantly impacted environmental sustainability, with firms with solvency above 50% having 18% higher environmental scores than firms with low solvency. These results confirm that financial stability is critical in adopting sustainable long-term strategies.
Regarding the impact of social indicators, this study shows that the social dimension of sustainability is more difficult to quantify, since the variables used (number of employees, employee productivity) did not sufficiently explain the variation in the social score. However, it is observed that companies that allocate additional resources to human capital development have a 12% higher employee retention rate and an 8% increase in productivity, which indicates an indirect link between social sustainability and a company’s economic success.
The positive impact of solvency on the environmental score confirms the link between environmental performance and sustainability. This study shows that companies that allocate provisions for environmental risks have 22% higher environmental scores than companies that do not have such reserves. Moreover, companies that have invested in green technologies have recorded a 15% reduction in long-term operational costs, which underlines the economic benefits of environmental sustainability.
The classification of companies according to sustainability highlighted three main categories: high sustainability, moderate sustainability, and low sustainability. Companies with a high sustainability score are characterized by sound financial management, investments in green technologies, and developed social policies. However, low-score companies have difficulty balancing economic performance and social and environmental responsibility. This differentiation suggests that large and well-capitalized companies have a significant advantage in adopting sustainable strategies. In contrast, smaller or highly leveraged companies are more vulnerable to financial and regulatory risks. The correlation of the financial, social, and environmental indicators indicates that companies that score highly in one dimension tend to perform better in the other two, suggesting that sustainability is an integrated process. This study confirms that 80% of companies with a high sustainability score have a balanced financial structure, invest in their employees, and implement environmentally friendly practices. This finding supports the notion that sustainable success requires a holistic approach and not just isolated improvements in one dimension.
From a practical point of view, the proposed model represents a valuable tool for managers, investors, and financial institutions. It provides a structured framework for the analysis of the sustainability of construction companies. Integrating the Corporate Sustainability Reporting Directive (CSRD) requirements and the European Sustainability Reporting Standards (ESRS) makes the model applicable internationally, facilitating comparisons between companies and alignment with new reporting requirements.
Based on these findings, a series of measures is recommended to improve the sustainability of construction companies. Investments in innovation and green technology should be a priority, given that companies that adopted green solutions benefited from a 10–15% reduction in operational costs and a 20% increase in energy efficiency. Moreover, improving working conditions and employee retention policies can contribute to long-term stability, as companies with lower staff turnover have recorded an average productivity increase of 8%.
Adopting a transparent reporting framework aligned with the CSRD and ESRS standards is essential to increasing a company’s attractiveness to investors. Companies that implemented detailed sustainability reports recorded an increase in access to finance of 12%, demonstrating that transparency is a competitive advantage.
Long-term risk assessment must become a central component of business strategies. This study confirms that firms that do not consider environmental and social risks are more vulnerable to economic fluctuations, and companies that do not invest in sustainability risk losing their long-term competitiveness. Therefore, integrating sustainability into strategic decisions is necessary for compliance and is an effective strategy to increase organizational resilience.
This study’s limitations include its dependence on available financial data, which may not fully reflect the impact of sustainability on long-term performance. Moreover, social variables require more precise definition to increase the model’s explanatory power. Future research directions should include extending the analysis to other economic sectors, integrating advanced data analysis methods (such as machine learning), and testing the model in different economic contexts.
The relatively low explanatory power of the social sustainability model (R2 = 0.057) is acknowledged. The constrained availability of granular social data at the firm level may explain this limitation. Due to the reliance on publicly reported accounting data under Directive 2013/34/EU, social performance was approximated using proxy variables such as the number of employees and labor productivity. While theoretically important, variables such as employee turnover, training expenditures, and community engagement were unavailable in a consistent and standardized format across the sample. This restricted the model’s ability to fully capture the social dimension, suggesting that future research should consider hybrid data sources (e.g., surveys or ESG self-disclosure) to improve the model precision.
One of the acknowledged limitations of this study lies in its national focus, as the analysis was conducted exclusively on Romanian construction companies. While this context ensures homogeneity and allows for the development of a model calibrated to a specific institutional and regulatory environment, it inevitably restricts the generalizability of the findings. The Romanian construction sector operates under EU-level sustainability directives (CSRD, ESRS), yet it is also shaped by the local market dynamics, financial disclosure practices, and access to capital. Therefore, although the econometric model aligns with broader European frameworks, its empirical validation remains context-dependent. Future research should explore the model’s applicability in other national or sectoral contexts, particularly in countries with differing regulatory systems, ESG maturity levels, or construction market structures. Such comparative studies would enhance the external validity of the model and contribute to building a more generalizable framework for corporate sustainability assessment.
Additionally, while helpful in identifying major thematic clusters, the bibliometric analysis presented in the literature review is limited by its dependence on term frequency and co-occurrence metrics. This may lead to the overrepresentation of popular topics at the expense of emerging but underrepresented themes. The method also does not fully capture the theoretical depth or causal relationships. As such, future research could complement bibliometric mapping with a qualitative content analysis of key works to ensure a more balanced and conceptually grounded foundation for model development.
Additionally, future research could investigate the impact of emerging digital technologies, such as artificial intelligence and blockchain, on corporate sustainability assessment processes in the context of the ongoing digital transformation of economic sectors.
In conclusion, this paper contributes to understanding the relationship between financial performance and sustainability, providing a methodology that is applicable to academia and business. The proposed model can serve as a basis for improvements in corporate sustainability strategies and in making informed decisions regarding investments and sustainable development in the construction sector.
Conceptualization, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; methodology, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; validation, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; formal analysis, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; investigation, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; resources, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; data curation, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; writing—original draft preparation, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; writing—review and editing, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; visualization, L.M.-D., C.-E.G.-I., F.V.J., D.F. and A.T.; supervision, L.M.-D.; project administration, L.M.-D. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data available on request.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| CSRD | Corporate Sustainability Reporting Directive |
| DEA | Data Envelopment Analysis |
| DW | Durbin–Watson |
| ESG | Environmental, Social, and Governance |
| ESRS | European Sustainability Reporting Standards |
| FMCDM | Fuzzy Multiple Criteria Decision-Making |
| GRI | Global Reporting Initiative |
| NACE/CAEN | Nomenclature of Economic Activities in the EU/Romanian Classification |
| OLS | Ordinary Least Squares |
| RCA | Ratio of Current Assets to Total Assets |
| RDA | Ratio of Total Debt to Assets |
| RDE | Debt-to-Equity Ratio |
| REL | Ratio of Equity to Total Liabilities |
| RFA | Ratio of Fixed Assets to Total Assets |
| RI | Inventory Ratio |
| ROA | Return on Assets |
| ROE | Return on Equity |
| ROS | Return on Sales |
| RR | Receivables Ratio |
| TBL | Triple Bottom Line |
| VIF | Variance Inflation Factor |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 Frequency and importance of key terms in the literature.
Figure 2 Conceptual structure of the domain: clusters and connections between terms.
Figure 3 Emerging directions in corporate sustainability research.
Figure 4 Evolution of the construction sector: (a) evolution of profit and turnover; (b) evolution of the number of companies and their employees.
Figure 5 Performance evolution of the selected construction companies: (a) evolution of turnover and ROA; (b) evolution of ROE and solvency.
Figure 6 Total sustainability score trend.
Figure 7 Correlation matrix between sustainability indicators.
Comparison between sustainability categories.
| Indicator | High Sustainability | Moderate Sustainability | Low Sustainability |
|---|---|---|---|
| ROA/ROE | High | Medium | Low/Negative |
| Degree of debt | Low | Medium | High |
| Liquidity | Good | Fluctuating | Low |
| Average number of employees | Stable or increasing | Stable | Low or declining |
| Environmental investments | High | Limited | Non-existent |
| Environmental provisions | Consistent | Limited | Absent |
| Risk exposure | Low | Moderate | High |
Summary of econometric model validation for sustainability scores in the construction sector.
| Model | Dependent Variables | R2 | Durbin–Watson | Significant Variables (p < 0.05) | Non-Significant Variables (p > 0.05) | Heteroscedasticity/Breusch–Pagan Test | Endogeneity/Breusch–Godfrey Test | Multicollinearity/VIF |
|---|---|---|---|---|---|---|---|---|
| Environmental Score | RR, RDE, ROS, REL, ROA, RDA, RI, RCA, RFA | 0.836 | 1.989 | REL, RI | RCA, RDA, RDE, RFA, ROA, ROS, RR | Lagrange multiplier statistic: 9200.61 | p-value: 0.008 (p < 0.05) | REL: 1.000 |
| Financial Score | 0.975 | 1.981 | ROA | RCA, RDA, RDE, RFA, RI, ROS, RR | Lagrange multiplier statistic: 25.69 | p-value: 7.01 × 10−5 (p < 0.05) | ROA: 1.000 | |
| Social Score | 0.006 | 1.963 | REL | RR, RDE, ROS, ROA, RDA, RI, RCA, RFA | Lagrange multiplier statistic: 0.693 | p-value: 5.36 × 10−18 (p < 0.05) | REL: 1.000 |
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Details
; Claudia-Elena, Grigoras-Ichim 1
; Jeflea, Florin Victor 2
; Dumitru, Filipeanu 3 ; Tugui Alexandru 4
1 Faculty of Economics, Administration and Business, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; [email protected]
2 Faculty of Economic Sciences, Ovidius University of Constanta, 900527 Constanta, Romania; [email protected]
3 Faculty of Constructions and Installations, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania; [email protected]
4 Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700506 Iasi, Romania; [email protected]