Financial Metrics and Environment, Social, Governance (ESG) Performance: A Cross Border Comparison of China and the UK Construction Industries

Abstract

Environmental, social, and governance (ESG) performance has become a pivotal factor for multinational corporations, especially within resource-intensive sectors like construction. This study explores how financial indicators—specifically liquidity, profitability, and leverage ratios—influence ESG outcomes under differing institutional conditions in China and the United Kingdom. Employing a quantitative approach via ridge regression analysis on data from 96 construction firms, the research identifies key financial predictors of ESG performance and develops a predictive model to assess cross-regional applicability. The results demonstrate that liquidity and profitability are significant drivers of ESG outcomes, with their impact varying according to institutional frameworks—where regulatory compliance and government incentives dominate in China, and market-driven pressures prevail in the UK. Although the predictive model exhibits strong accuracy, it also underscores the contextual sensitivity of financial metrics in shaping ESG practices. The extent to which ESG serves as a stabilising force or an amplifier of financial risk depends on disclosure levels and the deeper integration of ESG principles into corporate strategy, risk management, and capital allocation. These findings contribute to sustainable finance and resource dependence theories, offering opportunities for policymakers to refine ESG disclosure frameworks, investors to pinpoint financially resilient ESG leaders, and construction firms to align their financial strategies with sustainable development goals.

1. Introduction

The construction industry, a key engine of economic development, simultaneously stands as one of the foremost contributors to environmental degradation, resource depletion, and social inequities, underscoring the urgent need for robust environmental, social, and governance (ESG) integration [1,2]. Accounting for 38% of global CO₂ emissions [3] and 35% of global landfill waste [4], the sector must embrace sustainable practices that transcend conventional approaches. ESG frameworks advocate for the adoption of low-carbon materials, circular economy principles, and energy-efficient designs; however, inconsistent cross-border regulations continue to hinder their widespread implementation [3,5]. Socially, the construction sector is marred by hazardous working conditions and exploitative labour practices, highlighting the necessity for ESG-driven policies that improve wages, strengthen safety standards, and ensure supply chain transparency [6,7]. In terms of governance, weaknesses—especially in emerging markets—where firms are exposed to corruption, regulatory risks, and reputational harm [8] make stringent ESG compliance vital for ethical business practices and sustained investor confidence.

Integrating ESG performance into corporate strategies has gained momentum as organisations strive to reconcile profitability with sustainable development goals (SDGs). While SDGs articulate a broad vision for a sustainable future, ESG offers concrete mechanisms through which businesses and investors translate that vision into actionable outcomes. This convergence not only promotes long-term value creation [9], mitigating systemic risks [10], but also addresses pressing challenges related to climate change, inequality, and governance [11]. As regulatory landscapes evolve [12], stakeholder expectations intensify [13], and investors increasingly demand sustainability-linked returns [14]; aligning ESG with SDGs becomes a strategic imperative that enhances accountability and impact measurement [15].

Although significant research has focused on how ESG outcomes affect financial metrics such as the cost of capital and profitability, the results are inconclusive regarding whether ESG factors positively or negatively impact financial performance [16,17,18]. Also, the reverse relationship—where financial metrics predict ESG performance—remains underexplored. This gap is particularly critical in sectors like construction, where high environmental impact, protracted project timelines, and complex governance structures amplify the stakes of sustainability [19]. Moreover, many publicly listed construction companies operate across diverse geographic jurisdictions, further complicating the finance–ESG nexus. While ESG-financial metric prediction is a growing research field, significant gaps remain in data standardisation, alternative data integration, explainability, risk modelling, and AI-driven advancements [20,21,22]. This is compounded by a lack of understanding of how institutional pressures on sustainability drive improvements in triple bottom line performance through the adoption of ESG policies within firms [23]. This gap is particularly evident in emerging economies, where ESG initiatives are still developing.

This study addresses these shortcomings by questioning the following: To what extent do financial indicators predict ESG performance, and how does this relationship vary across different institutional frameworks, particularly between China and the UK? Thus, the aim is to compare the financial metrics and ESG performance within the construction industry across China and the UK using quantitative analysis. By examining the construction industry in China and the United Kingdom, the research delineates how policy-driven and market-driven environments shape the finance–ESG relations. In China, regulatory incentives and stringent compliance requirements steer ESG practices, aligning corporate strategies with national sustainability ambitions such as carbon neutrality by 2060 [24]. Conversely, the UK’s market-oriented system emphasises governance transparency and stakeholder engagement, with investor and consumer expectations driving ESG priorities [25]. Despite these institutional differences, cross-regional comparisons remain scarce, as predictive financial indicator models often exhibit regional biases that limit their generalisability [26,27]. Regionalised model calibration is, therefore, critical for a nuanced understanding of these dynamics.

Furthermore, ESG research has historically underrepresented the construction industry compared to sectors like manufacturing and finance. While extant studies have primarily concentrated on environmental aspects—such as the use of green materials and carbon reduction strategies [28]—the social and governance dimensions continue to receive insufficient attention [27,29]. Methodological limitations also persist, with many analyses relying on qualitative approaches and lacking robust quantitative validation [17,30].

Understanding how financial performance interacts with ESG disclosure in these settings contributes to the broader debate on whether ESG is primarily a risk-mitigation tool, a strategic differentiator, or a compliance-driven obligation. By using comparable datasets and advanced statistical techniques, this study identifies the financial indicators most strongly associated with ESG outcomes and validates a predictive model that accounts for regional differences. In doing so, it connects sustainable finance theory with resource dependence theory and offers valuable understandings for policymakers, investors, and construction firms. These insights can inform the design of more effective ESG regulations, facilitate the identification of financially resilient ESG leaders, and guide firms in aligning financial strategies with sustainability objectives. Ultimately, by providing a cross-regional perspective on the finance–ESG relationship, this study contributes a novel context to advance sustainability in one of the world’s most resource-intensive industries, reinforcing the role of ESG not merely as a regulatory mandate but as a strategic driver of innovation and long-term resilience.

2. Literature Review

The relationship between financial indicators and ESG performance remains a subject of both empirical inquiry and theoretical contestation, revealing unresolved tensions across disciplines. While Sustainable Finance Theory posits that strong ESG performance reduces firms’ risks and enhances long-term financial stability [16,31], this premise is increasingly challenged by divergent empirical findings. If ESG integration is universally beneficial to financial performance, why do firms exhibit such heterogeneity in ESG adoption? Why do some industries and regions prioritise ESG only when coerced by regulation, while others internalise it as a strategic imperative? The complexity of these questions stresses the limitations of a purely financial lens in understanding ESG dynamics.

The insufficiency of finance-centric explanations necessitates an engagement with broader theoretical paradigms. Stakeholder Theory [32] highlights the role of multi-actor influence, suggesting that firms do not operate in a vacuum but are continuously shaped by the expectations of investors, regulators, employees, and consumers. While this perspective explains why firms in market-driven economies such as the UK strategically align ESG commitments with investor sentiment [33], it fails to account for instances where firms engage in ESG for reasons that do not yield immediate financial benefits. For instance, why do some firms in China exceed mandated ESG requirements despite the absence of direct market incentives? This suggests an interaction between institutional pressures and firm agency that cannot be reduced to a simplistic profit-maximisation framework.

However, Resource Dependence Theory [34] extends the discourse by illustrating how financial and operational constraints shape a firm’s ability to respond to ESG imperatives. Firms with higher liquidity and stronger balance sheets are empirically shown to be more capable of implementing ESG initiatives [24]. However, this does not fully explain why resource-rich firms sometimes neglect ESG commitments or why resource-constrained firms, under certain conditions, prioritise ESG investments despite financial limitations. The construction sector exemplifies this paradox: while asset turnover is a strong predictor of ESG adoption in the UK [35], firms in China, regardless of liquidity, often comply with ESG mandates as part of state-driven industrial policies. This suggests that financial capacity alone does not determine ESG behaviour—rather, institutional forces mediate financial considerations.

Institutional Theory [36] provides a critical macro-level explanation, contending that firms conform to ESG standards in response to regulatory, normative, and cultural pressures. The dichotomy between China’s coercive regulatory ESG framework [37] and the UK’s investor-driven ESG orientation [38] exemplifies how institutional environments shape corporate behaviour. Yet, institutional theory raises further questions: why do some firms voluntarily exceed institutional expectations while others engage in ESG compliance only superficially? The phenomenon of “greenwashing,” wherein firms selectively disclose ESG-friendly practices to satisfy external pressures without substantive operational change, remains an unresolved challenge in institutional theory. The inability of existing frameworks to differentiate between genuine ESG integration and performative compliance signals a gap in understanding the motivations driving ESG adoption.

Beyond theoretical considerations, the empirical measurement of ESG remains fraught with methodological inconsistencies. ESG metrics, while standardised through frameworks such as the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) [39,40], vary significantly in their financial implications across industries and regions. Environmental performance indicators, such as carbon emissions and energy efficiency, are more predictive of financial outcomes in policy-driven contexts [41], whereas governance indicators, such as board independence, hold greater weight in market-driven settings [42,43]. The persistent challenge of ESG comparability raises a fundamental question: can a universal ESG metric ever be developed, or is ESG inherently context-dependent, requiring industry- and region-specific evaluation?

The limitations of conventional ESG measurement methodologies have prompted a shift towards predictive modelling. Traditional regression models have been instrumental in identifying correlations between financial indicators and ESG outcomes, yet their explanatory power diminishes in heterogeneous regulatory environments [44,45]. However, regression analysis is a widely used statistical technique that identifies causal relationships and assesses predictive power [46]. Regression modelling is particularly efficacious in studies involving multiple predictors, as it permits researchers to assess the distinctive contribution of each variable while controlling for others [47]. The limitations of regression prompted the adoption of machine learning (ML) and artificial intelligence (AI). Advanced techniques such as random forests and support vector machines have demonstrated great predictive accuracy by integrating non-linear ESG dynamics [45]. However, these models introduce their own set of epistemic challenges: how can AI-driven ESG predictions be interpreted when algorithmic decision-making processes remain opaque? The lack of transparency in deep learning models [48] raises concerns about their applicability in regulatory and investment decision-making [49].

The question of ESG predictive modelling is further complicated by the intersection of policy and financial markets. Bayesian optimisation has emerged as a tool for ESG portfolio strategies [50,51], yet its reliance on historical data risks reinforcing existing biases in ESG assessments. If past financial indicators shape future ESG predictions, how can such models account for paradigm shifts in sustainability expectations and regulatory frameworks? The evolution of ESG from a voluntary corporate initiative to a legally mandated requirement in many jurisdictions suggests that historical data alone are insufficient for predicting ESG trajectories. Future predictive modelling must incorporate not only financial indicators but also dynamic regulatory changes, geopolitical risks, and evolving consumer preferences.

This review highlights the need for an integrated framework that reconciles financial, institutional, and technological dimensions of ESG performance. Existing theories provide valuable but incomplete insights, each capturing only a fragment of the broader ESG landscape. The reliance on financial indicators as the primary mechanism for ESG evaluation is increasingly questioned, given the mediating effects of regulatory environments, stakeholder influences, and firm-level agency. Empirical metrics, while standardised, remain context-dependent, challenging the notion of a singular ESG assessment framework. Predictive modelling, while promising, must address concerns about transparency and adaptability to avoid reinforcing historical biases.

Future research must move beyond siloed theoretical approaches and methodological rigidity. A more holistic understanding of ESG requires an interdisciplinary synthesis that integrates finance, regulatory science, and behavioural economics. As ESG becomes an increasingly central determinant of corporate valuation and global economic policy, researchers must ask the following: Are we measuring ESG in ways that reflect its true societal and fiscal impact, or are we merely reinforcing existing institutional biases? The answer to this question will define the next stage of ESG scholarship and its role in shaping sustainable corporate governance.

The central research question being proposed in this study is as follows:

“How do financial metrics influence ESG performance, and how does this relationship vary across institutional frameworks, particularly between China and the UK?”

This study extends institutional theory by comparing ESG disclosure motivations in these contrasting institutional settings, highlighting the role of institutional pressure in shaping ESG-financial performance dynamics.

3. Method

3.1. Methodology

Figure 1 outlines a structured analytical process: Data preprocessing (handling missing values and outliers) ensures data integrity. Descriptive statistics (comparing ESG distributions between China and the UK) and correlation analysis (identifying linkages between financial metrics and ESG performance) inform the building of the regression model.

3.2. Research Design

This study employs a quantitative, cross-sectional research design to investigate the predictive power of financial ratios on environmental, social, and governance (ESG) ratings in the construction industry across China and the UK. The selection of China and the UK for this study is based on their significance in the global construction sector and the contrast in their ESG regulatory approaches, providing broader insights into global ESG adoption. By using a comparative approach, this study identifies regional variations influenced by institutional differences—China’s policy-driven environment versus the UK’s market-oriented framework. This juxtaposition allows for an in-depth analysis of how regulatory environments, market maturity, and business risks shape ESG performance in the construction industry.

3.3. Data Collection and Preprocessing

3.3.1. Data Sources

Secondary data were obtained from three financial databases for the financial year 2023:

• ifinD—Provides financial (e.g., ROA, current ratio, debt-to-equity ratio) and ESG data for 97 Chinese construction companies.
• Bloomberg—Supplies standardised ESG scores and financial metrics for 15 UK construction firms.
• Fame—Offers financial information on UK companies, ensuring a cross-validation of financial ratios.

These sources ensure high-quality, comparable datasets suitable for robust statistical analysis. Availability of data guided the inclusion of companies.

3.3.2. Data Cleaning and Reliability Measures

The following preprocessing steps were undertaken to maintain data integrity:

• Handling Missing Data—Companies with excessive missing values (≥10% of key financial and ESG metrics) were excluded, reducing the Chinese sample from 108 to 97 firms.
• Outlier Detection and Adjustment—Extreme values in financial ratios were identified using boxplots and histograms. Outliers were either capped at the 1st/99th percentiles or set to zero if their presence was deemed to distort the model.
• Ensuring Data Consistency—ESG scoring methodologies across databases were cross-validated to align weighting and definition structures.

3.3.3. Variable Selection for the Study

A firm’s financial health is fundamental to sustaining ESG investments without compromising overall performance. Profitability ensures that businesses can integrate ESG initiatives while maintaining financial returns, reinforcing the long-term viability of sustainability efforts [52]. Liquidity safeguards short-term stability, providing firms with the flexibility to uphold ESG commitments without jeopardising operational needs [53]. Efficiency further strengthens this equilibrium by enabling optimal asset and capital utilization, ensuring that sustainability efforts enhance rather than hinder performance [16]. Equally critical, financial stability underpins long-term ESG commitments, as firms with lower financial risk and controlled debt levels are better positioned to invest in sustainability while maintaining resilience [54].

The selection of financial ratios in this study aligns with the core financial indicators—profitability, liquidity, and leverage—ensuring a comprehensive assessment of the financial implications of ESG investments. Profitability is captured through Profit Margin, which measures the efficiency of converting revenue into profit while accounting for ESG-related costs, and Return on Shareholders’ Funds, which evaluates how effectively shareholder capital is utilised, a key concern for ESG investors prioritizing long-term value [55,56]. Additionally, Return on Capital Employed assesses overall capital efficiency, particularly significant in construction, where ESG initiatives demand substantial investment [57], while EBIT Margin isolates operational profitability, providing insights into ESG-driven efficiency gains, independent of tax incentives or subsidies [58].

Liquidity is reflected in the Current Ratio, which ensures firms can meet short-term obligations while financing ESG projects without financial strain [59]. Unlike the Quick Ratio, it includes inventory, a crucial component in construction. Leverage, a focal point of this study, is represented by gearing, which indicates financial leverage and the extent to which firms rely on debt to finance ESG investments. Lower gearing is often associated with stronger ESG performance and financial stability, justifying its selection as the primary leverage metric [60]. While alternative leverage ratios, such as debt-to-equity or interest coverage, are commonly used in financial analysis, gearing offers a direct measure of debt reliance, particularly relevant in assessing financial risk associated with ESG commitments.

Complementing these core financial indicators, efficiency ratios such as Return on Total Assets and Net Assets Turnover provide additional insight into financial sustainability. Return on Total Assets measures the effectiveness of asset utilization, a crucial factor in asset-intensive industries like construction, while Net Assets Turnover evaluates how efficiently firms deploy capital to generate revenue, revealing the impact of ESG-driven investments on operational performance [57,61]. Unlike other financial metrics, these selected ratios establish a clear link between ESG investments and financial performance, ensuring firms remain competitive while advancing sustainability goals [13]. By structuring the financial assessment around these key indicators, this study provides a nuanced understanding of how firms can integrate ESG commitments without undermining financial resilience.

3.4. Statistical Modelling

Regression analysis, particularly linear regression, is a well-established statistical methodology widely used due to its theoretical rigour, interpretability, and predictive capabilities. It provides a clear and intuitive framework for quantifying how variations in financial metrics influence ESG performance, offering critical insights into financial–sustainability linkages [62]. Additionally, regression models serve as powerful predictive tools, enabling the forecasting of ESG trends based on historical financial data, which is essential for investors, policymakers, and corporate executives [63]. Beyond prediction, regression analysis facilitates rigorous hypothesis testing and variable selection [64], allowing researchers to identify the most significant financial indicators affecting ESG ratings. Furthermore, the flexibility of regression models allows for extensions incorporating interaction terms, nonlinear transformations, or piecewise functions, enhancing their applicability across different institutional contexts, such as China’s policy-driven framework and the UK’s market-driven ESG approach. Given its capacity to establish causal relationships, generate reliable predictions, and rigorously test hypotheses, regression analysis is an appropriate and methodologically sound approach for investigating the financial-ESG performance nexus, particularly in capital-intensive sectors such as construction.

3.4.1. Regression Model Specification

Multiple linear regression models were constructed to analyse the relationship between financial ratios (independent variables X) and ESG score (dependent variable Y). The general model specification is as follows:

Y_i = β₀ + β₁X_1i + β₂X_2i + β₃X_3i + β₄X_4i + ………β_nX_ni + ϵ_i

(1)

where

• Y_i = ESG score of firm i.
• n = number of dependent variables.
• β₀, Intercept term.
• β₁…, β_n = Regression coefficients of independent variables.
• ϵ_i = Error term accounting for unexplained variance.

The significance test in regression analysis evaluates whether a linear relationship exists between the dependent variable Y and a selected subset of independent variables X₁, X₂,…, X_ni. This is formally assessed using the following hypotheses:

Null hypothesis (H₀)—β₁ = β₂ = …. = β_n = 0

Alternative hypothesis (H₁)—At least one β_j ≠ 0 where j = 1: n, suggesting that at least one independent variable significantly impacts Y.

Rejection of H₀ implies that at least one predictor variable meaningfully contributes to the model. The threshold for statistical significance was set at p ≤ 0.05, meaning that if this criterion is met, H₀ is rejected in favour of H₁. Standardised beta (β) coefficients provide insight into the relative impact of each independent variable on the dependent variable. Specifically, they indicate the expected change (in standard deviation units) in Y for a one-standard deviation increase in each predictor X_n. This standardisation is particularly useful when the predictor variables are measured in different units, as it allows for a direct comparison of their respective influences on Y. To mitigate multicollinearity among financial variables, ridge regression was employed to incorporate a regularisation term to prevent overfitting, with the optimal regularisation parameter (λ) determined via 10-fold cross-validation [65]. This approach ensured that the regularisation term adequately accounted for multicollinearity while maintaining the model’s predictive power. The penalty term shrinks the regression coefficients, thereby improving model stability and generalizability. It is proportional to the square of the magnitude of the coefficients and is added to the loss function as follows:

$$\mathrm{Loss} \mathrm{Function}=\sum {({\mathrm{Y}}_{\mathrm{i}}-{\widehat{Y}}_i)}^2+\lambda \sum {{\beta }_{\mathsf{j}}}^2$$

(2)

where

• Y_i represents the actual dependent variable values.
• ${\widehat{Y}}_i$ represents the predicted values.
• β_j are the regression coefficients.
• λ (the tuning parameter) controls the amount of regularisation.

3.4.2. Model Evaluation Metrics

To ensure model robustness, several statistical measures were used:

• R-Squared (R²) and Adjusted R²—Measures the proportion of variance explained by the model. Values closer to 1 indicate a better fit, while lower values suggest limited explanatory power. However, excessively high R-squared values may indicate multicollinearity among independent variables, potentially leading to instability and inaccurate coefficient estimates.
• ANOVA F-Test—Assesses the statistical significance of independent variables in predicting ESG scores. For categorical data, the test also identifies statistically significant group differences in the dependent variable [66].
• Durbin–Watson Statistic—Tests for autocorrelation in residuals.
• Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)—Evaluate model accuracy.
• Tolerance and Variance Inflation Factor (VIF) are used to detect multicollinearity among independent variables. From the data, it can be observed that the tolerance of all variables is greater than 0.1, and the VIF is less than 10, indicating that there is no serious multicollinearity problem among the independent variables.

For the Chinese dataset, the model exhibited a high explanatory power (R² = 0.974, p < 0.001), suggesting that financial ratios strongly predict ESG ratings.

3.5. Cross-Country Validation

Cross-regional validation with UK data reveals institutional discrepancies. The model trained on the Chinese dataset was applied to the UK dataset to test its generalisability. Discrepancies in predictive performance between the two markets highlight the influence of institutional frameworks on ESG determinants. Notably, UK firms demonstrated greater ESG score stability, while Chinese firms exhibited higher score variability, likely due to policy-driven reporting incentives.

4. Results and Analysis

4.1. ESG Disclosure and Financial Performance in the Construction Industry

This study examines the relationship between financial indicators and environmental, social, and governance (ESG) performance across 108 Chinese and 15 British construction enterprises in 2023. A linear regression model was constructed to assess the predictive power of financial metrics on ESG scores, with model validation through R-squared and ANOVA F-tests.

4.1.1. Descriptive Statistics and ESG Disclosure Trends of Chinese and British Construction Enterprises

Table 1. Summary statistics of Chinese construction enterprises.

Indicator	Mean	SD	Min	Max	Skewness
ESG Disclosure Score	51.49	8.49	30.99	72.52	0.067
Social Disclosure Score	46.67	8.63	26.25	71.60	−0.065
Governance Disclosure Score	50.65	13.97	22.73	82.74	0.043
Environmental Disclosure Score	60.84	5.91	46.35	73.56	0.123

summarises the ESG disclosure and financial indicators of Chinese firms. The mean ESG disclosure score is 51.49, with environmental disclosure scoring highest (mean = 60.84) and social disclosure lowest (mean = 46.67).

Table 2. Summary statistics of UK construction enterprises.

Indicator	Mean	SD	Min	Max	Skewness
ESG Disclosure Score	46.03	11.66	25.00	65.23	0.086
Social Disclosure Score	29.42	11.39	13.57	56.32	0.642
Governance Disclosure Score	73.40	19.37	35.00	109.65	−0.413
Environmental Disclosure Score	35.12	16.42	7.88	71.82	1.041

presents the UK firms’ financial and ESG performance. Governance disclosure is notably stronger (mean = 73.40) than both social (mean = 29.42) and environmental (mean = 35.12) disclosures. Chinese firms exhibit a higher mean environmental disclosure score (60.84) compared to governance (50.65) and social (46.67) disclosures, suggesting a regulatory emphasis on environmental reporting. UK firms demonstrate significantly higher governance disclosure (73.40), likely reflecting stricter corporate governance regulations, while environmental disclosure remains relatively low (35.12).

Chinese firms demonstrate lower variability (lower SD) in disclosure scores, suggesting more uniform reporting practices compared to the UK, where firms show substantial dispersion in governance and environmental transparency.

4.1.2. Financial Performance and Variability

Financial indicators display pronounced variability across firms, particularly among Chinese enterprises. The profit margin in Chinese firms is highly volatile, with a mean of −48.15% and extreme values ranging from −1519.37% to 28.23%, reflecting financial instability in the sector. See

Table 3. Financial indicators of Chinese and UK construction firms 2023.

Indicator	China (Mean ± SD)	UK (Mean ± SD)	Skewness (China/UK)
Profit Margin (%)	−48.15 ± 188.60	6.59 ± 7.77	−6.153/0.122
Return on Shareholders’ Funds (%)	4.27 ± 14.91	11.09 ± 14.17	9.019/−2.048
Return on Capital Employed (%)	−1.64 ± 43.16	9.31 ± 10.01	−2.129/−0.886
Current Ratio	1.3 ± 0.71	0.87 ± 0.28	2.316/0.696
Gearing (%)	71.97 ± 19.94	41.21 ± 35.30	0.362/1.457
Return on Total Asset	0.75 ± 4.40	5.21 ± 5.56	0.416/−1.95
EBIT margin	1.29 ± 12.06	6.60 ± 7.19	9.743/−0.161
Net Assets Turnover	2.55 ± 3.67	2.75 ± 2.72	3.204/1.456

. The financial discrepancies suggest that while some firms face severe financial distress, others maintain strong shareholder returns. Additionally, the return on shareholders’ funds and return on capital employed are relatively low at 4.27% and −1.64%, respectively, with a substantial dispersion in returns on shareholders’ funds (SD = 14.91%). However, some financial stability is observed in the current ratio (1.30) and return on total assets (0.75%). The skewness values indicate significant asymmetry in financial distributions, particularly for profit margin (−6.153) and return on shareholders’ funds (9.019). UK firms maintain moderate profitability (6.59%), with lower dispersion, suggesting a more stable financial landscape, but notable variations remain. Some firms exhibit extreme values, with profit margins ranging from −10.27% to 23.68%. Results suggest moderate efficiency in capital utilisation. However, liquidity concerns arise with an average current ratio of 0.87, which is below the standard benchmark, indicating potential short-term financial risks. The lower variability in gearing (mean = 41.21%), with values ranging from 12.15% to 120.12%, highlights significant differences in debt burden across firms.

Financial indicators show asymmetry, with some firms facing financial distress and others displaying resilience. Skewness analysis confirms extreme variations in return on shareholders’ funds and total assets. Thus, firms should optimise capital structures and liquidity management to enhance stability. The negative skewness in profit margin and return on capital employed suggests that a subset of enterprises is experiencing extreme financial losses, contributing to the sector’s overall instability.

4.1.3. Regression Analysis and Model Validation

A regression model was developed to explore the relationship between ESG disclosure and financial indicators using data from Chinese enterprises. The R-squared value for the model is 0.974, indicating that the selected indicators can explain 97.4% of the variability in ESG disclosure scores. The adjusted R-squared value of 0.971 confirms the model’s robustness and suggests that it is not overfitted. The Durbin–Watson statistic is 2.150, indicating minimal autocorrelation among residuals, further validating the model’s reliability. The ANOVA F-test results confirm the statistical significance of the model, with an F-value of 289.406 and a p-value of 0.000. These results indicate that at least one independent variable significantly influences ESG disclosure scores. See

Table 4. Regression model coefficients.

Model	Coefficients
	Unstandardised Coefficients		Standardised Coefficients	t	Sig.	95.0% Confidence Interval for β		Correlations
	β	Std. Error	β			Lower Bound	Upper Bound	Zero-Order
(Constant)	−0.198	2.160		−0.092	0.927	−4.493	4.097
Social Information Disclosure Score	0.392	0.021	0.399	18.288	0.000	0.350	0.435	0.777
Governance Disclosure Score	0.386	0.014	0.635	26.686	0.000	0.357	0.414	0.907
Environmental Disclosure Score	0.209	0.029	0.146	7.118	0.000	0.151	0.268	0.536
Profit Margin	0.000	0.001	0.010	0.524	0.602	−0.001	0.002	0.274
Return on Shareholders’ Funds	0.004	0.014	0.007	0.299	0.766	−0.023	0.032	0.144
Return on Capital Employed	−0.002	0.005	−0.012	−0.491	0.624	−0.012	0.007	0.269
Current ratio	0.255	0.339	0.021	0.753	0.454	−0.419	0.929	0.138
Gearing	0.007	0.012	0.016	0.545	0.587	−0.018	0.031	−0.095
Return on Total Assets	0.088	0.037	0.045	2.358	0.021	0.014	0.162	0.101
EBIT margin	−0.013	0.014	−0.018	−0.936	0.352	−0.040	0.014	−0.012
Net Assets Turnover	0.102	0.046	0.044	2.228	0.029	0.011	0.193	0.094

.

Governance disclosure has the highest impact among the key predictors, followed by social information disclosure and environmental disclosure. Financial indicators such as profit margin, return on shareholders’ funds, and return on total assets also show statistically significant effects, confirming the role of financial performance in determining ESG disclosure levels.

Data from UK construction firms were applied to validate the predictive model, and the predicted ESG disclosure scores were compared to actual values, see

Table 5. UK predicted ESG output 2023.

Name	Actual	Forecast/Prediction	Discrepancy	Proportion/Percentage
Watkin Jones PLC	25.00	24.42	0.58	2.33%
Vistry Group PLC	35.07	36.07	−1.01	−2.87%
Severfield PLC	42.13	47.39	−5.26	−12.49%
Renew Holdings PLC	41.11	40.89	0.22	0.53%
Persimmon PLC	43.30	43.10	0.20	0.46%
Morgan Sindall Group PLC	52.91	54.90	−1.99	−3.76%
Kier Group PLC	36.70	36.23	0.47	1.27%
Henry Boot PLC	47.68	50.93	−3.25	−6.81%
Galliford Try Holdings PLC	32.00	31.29	0.71	2.22%
Crest Nicholson Holdings Plc	43.89	46.53	−2.64	−6.01%
Costain Group PLC	56.25	59.68	−3.42	−6.09%
Berkeley Group Holdings PLC	62.75	64.59	−1.84	−2.94%
Bellway PLC	65.23	62.83	2.40	3.69%
Barratt Redrow PLC	60.31	58.08	2.24	3.71%
Balfour Beatty PLC	46.13	48.34	−2.21	−4.79%

. According to the given error range threshold of 5%, several companies predicted errors exceeding this range. The analysis revealed an error range between −12.49% and 3.69%, indicating that while the model is generally accurate, some deviations exist. Several firms, such as Severfield PLC and Henry Boot PLC, exhibited higher error margins, suggesting that unique ESG strategies or unaccounted external factors may influence their disclosure practices. More specifically, the environmental information disclosure score is markedly divergent from the reference mean, resulting in a diminished model score and augmented error. Nevertheless, the overall prediction accuracy confirms that financial indicators strongly predict ESG disclosure levels. Overall, the regression analysis confirms a significant relationship between financial performance and ESG disclosure, emphasising the role of financial health in shaping corporate transparency.

5. Discussion

The regression analysis shows that financial indicators and ESG disclosures explain 97.4% of the variation in ESG performance (R² = 0.974). Governance Disclosure Score (β = 0.635, p < 0.01) and Social Information Disclosure Score (β = 0.399, p < 0.01) are the primary contributors, whereas Environmental Disclosure Score (β = 0.146, p < 0.01) plays a secondary role. This outcome aligns with the Resource-Based View (RBV), which suggests that financially stable firms allocate resources preferentially to governance and social initiatives, reinforcing compliance, stakeholder trust, and corporate reputation. The dominance of governance reflects China’s regulatory environment, where transparency and accountability are central [39,40]. Stakeholder theory further supports these findings, emphasising governance and social investments as critical for meeting stakeholder expectations [32].

The weaker contribution of environmental factors suggests that financial resources alone may not drive proactive environmental engagement. Prior research highlights that capital-intensive industries, such as construction, often adopt a reactive approach to environmental investments, complying with regulatory mandates rather than integrating sustainability into core business strategies [24,67]. This pattern aligns with China’s policy-driven ESG framework, where environmental initiatives are largely shaped by disclosure requirements and carbon neutrality targets [68]. Moreover, operational efficiency, measured by net asset turnover (β = 0.044, p = 0.029), positively influences ESG performance, supporting the argument that efficient resource utilisation enhances sustainability investments [69]. Efficient firms optimise asset deployment, reducing waste and reinvesting in ESG initiatives, particularly in governance and social responsibility.

These findings validate RBV and stakeholder theory but underline the distinct characteristics of a policy-driven ESG landscape. Unlike market-driven economies, such as the UK, where investor expectations elevate environmental priorities [38], China’s regulatory framework emphasises governance and social compliance. Strengthening environmental contributions may require targeted policy interventions, such as tax incentives for green construction or subsidies for renewable energy adoption. Firms integrating environmental initiatives with operational efficiency strategies may achieve cost savings while improving ESG performance.

5.1. Financial Determinants of ESG Performance

Regression analysis of financial ratios highlights critical determinants of corporate sustainability in China’s construction industry. While financial health, operational efficiency, and profitability influence ESG performance, their effects are shaped by industry-specific and institutional dynamics. The current ratio (β = 0.21, p = 0.454) lacks statistical significance, suggesting that short-term liquidity does not directly drive ESG investments. This contrasts with findings from [18], who argue that liquidity facilitates sustainable practices, such as energy-efficient technologies. The discrepancy likely arises from the structure of China’s construction sector, where long-term strategic objectives and regulatory compliance outweigh immediate financial flexibility. Resource dependence theory [70] posits that firms with greater liquidity can allocate resources to sustainability, but this study suggests that companies in policy-driven environments rely more on external funding and government incentives.

In contrast, net asset turnover (β = 0.044, p = 0.029) exhibits a significant positive correlation with ESG performance, highlighting the role of operational efficiency in resource generation and reinvestment. Firms that optimise asset utilisation not only enhance profitability but also create financial capacity for ESG initiatives. This aligns with stakeholder theory, emphasising efficient resource management as a driver of corporate social responsibility [32]. Prior studies also confirm that operational efficiency encourages corporate engagement in sustainability, particularly in employee welfare and community development [71].

Profitability further strengthens ESG performance, as evidenced by the positive relationship between returns on total assets (β = 0.045, p = 0.021). High profitability enables firms to invest in long-term sustainability initiatives, including green technology and social responsibility programs, supporting the RBV [72]. Financially stable firms are more likely to adopt transparent ESG practices, reinforcing investor confidence and market competitiveness [69]. These findings highlight financial resilience as a key enabler of sustainability in capital-intensive industries, where regulatory compliance and long investment cycles necessitate stable financial foundations.

However, profit margin and return on capital do not exhibit statistically significant relationships with ESG scores. This may be attributed to the structural characteristics of China’s construction sector, where firms prioritise liquidity and efficiency over short-term profitability. Unlike in market-driven economies, where financial returns often dictate ESG engagement, many large Chinese construction firms operate under state ownership and align with national development goals, such as infrastructure expansion and sustainable urbanisation [73]. This institutional orientation prioritises regulatory imperatives over immediate financial returns, challenging conventional assumptions about the financial drivers of ESG engagement. These findings resonate with [74], who highlight the long-term strategic priorities of China’s state-owned enterprises, reinforcing the need for context-sensitive ESG analyses.

Interestingly, the findings indicate that while financial performance significantly influences ESG disclosure, leverage—typically measured by gearing—does not emerge as a primary determinant. Chinese firms exhibit substantially higher leverage (mean gearing of 71.97%) compared to their UK counterparts (41.21%). Despite the assumption that highly leveraged firms may allocate fewer resources to ESG initiatives due to financial constraints, regression analysis reveals that gearing has no statistically significant impact on ESG disclosure (p = 0.587). Moreover, financial instability, particularly among Chinese firms, where profit margins exhibit extreme volatility, underscores the broader challenge of maintaining ESG commitments amidst economic uncertainty. UK firms, with their more stable financial landscape, demonstrate a relatively consistent approach to ESG disclosure, highlighting the potential benefits of financial resilience in sustaining long-term corporate responsibility. These findings emphasise the complexities between financial health, regulatory environments, and ESG transparency, suggesting that while leverage influences corporate decision-making, it does not serve as a primary constraint on sustainability reporting. Instead, firms operating within strong governance frameworks and stable financial environments are better positioned to integrate ESG principles into their strategic objectives. Thus, the study stresses the imperative for firms to optimise capital structures while aligning with governance and sustainability frameworks to enhance ESG performance, ensuring that financial and non-financial imperatives are mutually reinforcing in the pursuit of corporate accountability and long-term value creation.

5.2. Are There Significant Regional Differences in the Financial-ESG Relationship?

The financial-ESG relationship exhibits significant regional disparities shaped by institutional structures, regulatory imperatives, and market incentives. The mechanisms through which ESG influences risk exposure differ markedly, offering an acute understanding of how sustainability imperatives interact with economic stability. In China, financial risk emerges as a dominant force overshadowing ESG commitments, with firms exhibiting extreme profit margin volatility and negative returns on capital as they align sustainability strategies with state-mandated objectives, such as carbon neutrality [75], to guide sustainability efforts. This regulatory dependency creates a fragile risk structure where firms that lack intrinsic ESG commitment may struggle to adapt to evolving policy directives, rendering their financial performance vulnerable to abrupt regulatory shifts. While governance disclosure appears relatively stable, China’s weakness in social disclosure suggests underlying labour risks, potential disputes, and reputational vulnerabilities that could further destabilise financial outcomes. The Chinese construction sector, therefore, presents a paradox in which ESG adoption is robust in a formal sense but tenuous in its capacity to mitigate deep-rooted financial risks. However, while operational efficiency metrics (e.g., net asset turnover, β = 0.044, p = 0.029) significantly influence ESG scores, short-term liquidity indicators (e.g., current ratio, β = 0.21, p = 0.454) are less relevant, reflecting a system where state-backed financial support reduces liquidity constraints and prioritises long-term strategic compliance. This view aligns with [76], who argue that China’s green finance initiatives, including mandatory environmental disclosures and carbon trading systems, have reinforced corporate alignment with national sustainability priorities.

UK firms’ high governance scores reinforce corporate accountability and regulatory adherence, yet this emphasis on governance disclosure contrasts with relatively weaker environmental and social commitments, exposing firms to growing investor scrutiny and potential shareholder activism. Conversely, in the UK’s market-driven ESG landscape, firms integrate sustainability within profitability models to enhance shareholder value and meet investor expectations, supporting [32] stakeholder theory, which posits that firms balance financial performance and social responsibility to satisfy diverse stakeholders. Unlike in China, where state influence dominates corporate ESG decisions, UK firms respond to normative and competitive pressures, aligning with [13] sustainable finance theory, which asserts that ESG integration enhances financial resilience by strengthening investor confidence. The UK’s emphasis on asset efficiency rather than liquidity reflects its market-oriented structure, where transparency and governance play a central role [25,39]. However, as indicated by suboptimal current ratios in UK firms, liquidity constraints introduce short-term financial risks that could limit firms’ ability to navigate economic downturns, particularly given the sector’s reliance on debt financing. Moreover, despite exhibiting more stable profit margins than their Chinese counterparts, UK firms reveal disparities in capital efficiency, suggesting that profitability is not uniformly secured across the industry. Institutional theory [36] explains these variations, where China’s coercive regulatory mechanisms compel firms to internalise ESG compliance, while the UK’s competitive financial markets foster a voluntary, profit-aligned ESG approach. The construction sector exemplifies these regional divergences—Chinese firms emphasise liquidity to meet stringent regulatory obligations [24], while UK firms leverage asset efficiency and governance transparency to attract sustainable investment [35]. These findings challenge the notion of a universal financial-ESG relationship, reinforcing [77] the argument that ESG-financial linkages are highly context-dependent, varying across industries and institutional settings. For policymakers, this highlights the need for regionally adaptive ESG frameworks—China may enhance transparency through standardised disclosures akin to the Task Force on Climate-related Financial Disclosures (TCFD), while the UK could refine market-based incentives to drive deeper sustainability commitments [78]. As ESG evolves into a core determinant of corporate strategy and financial resilience, future research should examine how shifting global sustainability mandates, regulatory reforms, and investor behaviours further shape regional ESG-financial dynamics, ensuring alignment with the sustainable development goals [26,79].

5.3. How ESG Performance Rating Influences ESG Leadership Across Geographic Regions

This study evaluated the feasibility of applying a predictive model, developed using financial indicators from the Chinese construction industry, to anticipate ESG ratings in the UK construction sector, revealing favourable reliability (MAE: 4.5); however, significant tensions remain between shareholder-centric financial metrics and the multi-dimensional demands of stakeholder capitalism. While financial health (e.g., total asset turnover) serves as a baseline proxy for ESG performance in standardised governance contexts—evidenced by prediction errors within ±5% for most UK firms—its inadequacy in outlier cases (e.g., Severfield PLC, error: 12.49%) stresses the limitations of prioritising shareholder primacy in ESG strategy, particularly in carbon-intensive sectors where resource dependency and decarbonisation gaps amplify systemic risks. The model’s failure to reconcile profitability metrics with sector-specific challenges, such as climate liability or ethical supply chain management, reflects a broader governance crisis: the incompatibility of traditional fiduciary duties with the ethical accountability and long-term resilience demanded by frameworks like the SDGs. In China’s policy-driven institutional environment, where state directives dominate ESG priorities, liquidity metrics (β = 0.21, p = 0.454) lack statistical significance, illustrating how financial flexibility is subordinated to compliance in state-led economies. Conversely, the UK’s market-driven framework prioritises investor-aligned ESG reporting, often conflating operational efficiency (e.g., asset turnover) with substantive progress on SDG 9 (sustainable infrastructure) or SDG 13 (climate action), thereby risking greenwashing through financialised metrics that obscure material outcomes like emission reductions or circular resource use. These divergences highlight the need for governance structures that adapt to institutional contexts while advancing a holistic sustainability ethos, particularly in bridging short-term returns with long-term investments in low-carbon innovation (SDG 9) and ethical labour practices. The predictive gaps further expose regulatory arbitrage—China’s focus on compliance over innovation contrasts with the UK’s investor-centric transparency—yet, both lack mechanisms like the EU’s double materiality assessments, which could mitigate greenwashing by mandating accountability for both financial and societal impacts. For ESG leadership, this necessitates redefining materiality to prioritise decarbonisation (SDG 13) and responsible production (SDG 12) over narrow financial proxies, requiring structural reforms such as embedding ESG committees to oversee climate resilience plans, promotion partnerships for construction technologies, and aligning capital allocation with just transition principles. The study’s core insight—that financial indicators inadequately predict ESG performance in heterogenous settings—mirrors global debates on reorienting corporate purpose from shareholder primacy to stakeholder welfare, particularly in industries facing existential sustainability pressures. For policymakers, harmonising fragmented standards (e.g., ISSB vs. ESRS) is critical to reducing regulatory arbitrage and incentivising genuine integration of SDG targets, while corporate leaders must expand fiduciary frameworks to internalise systemic risks, such as carbon lock-in or social inequality, that threaten long-term viability. Ultimately, the feasibility of ESG prediction models hinges less on algorithmic precision than on governance evolution: whether boards can reconcile profit motives with planetary boundaries, prioritise transparency over performative reporting, and institutionalise ethical accountability through reforms that reflect the interdependence of financial, social, and environmental health.

A limitation of the model is its implicit assumption that ESG ratings are a standardised benchmark when, in reality, ESG assessment methodologies vary significantly across agencies. Atz, Van Holt, Liu and Bruno [26] highlight how different weighting criteria—such as governance transparency (Sustainalytics) versus carbon risk exposure —can lead to divergent ESG scores for the same firm. Given this inconsistency, the observed prediction errors may partly reflect discrepancies in ESG scoring methodologies rather than actual sustainability performance. This raises important strategic considerations: Should ESG leaders prioritise alignment with dominant rating agencies, or should construction firms develop internal sustainability metrics that transcend third-party scores? Addressing this challenge is particularly relevant for multinational firms operating across different ESG rating ecosystems. This limitation also affects corporate contributions to SDG 13 (Climate Action), as it highlights the need for robust and transparent ESG evaluation methods that accurately reflect a firm’s climate impact rather than relying on fragmented rating systems and that also integrate climate change measures into corporate strategies and decision-making frameworks.

6. Conclusions and Recommendations

This study advances the theoretical and empirical understanding of the finance–ESG nexus by establishing a bidirectional relationship wherein financial health—measured through liquidity, profitability, and leverage—acts as both an enabler and outcome of ESG investment in the construction sector. The research challenges prior unidirectional frameworks by extending sustainable finance theory and integrating resource dependence theory across distinct institutional contexts, demonstrating that financial stability facilitates ESG adoption in policy-driven and market-oriented economies. The cross-regional analysis clarifies the role of institutional structures in shaping financial-ESG dynamics, revealing that China’s regulatory landscape prioritises compliance and governance disclosure, while the UK’s market-driven environment aligns ESG performance with investor expectations and operational efficiency. These findings reaffirm institutional theory, illustrating how financial conditions interact with policy imperatives and market mechanisms to drive sustainability outcomes.

The study’s predictive modelling highlights the capacity of financial indicators to forecast ESG performance with substantial accuracy. However, model performance exhibits variability across firms and regions, accentuating the need for adaptive approaches incorporating industry-specific and qualitative factors. In China, ESG adherence mitigates regulatory risk but does little to shield firms from extreme financial instability, while in the UK, strong governance structures enhance investor confidence yet fail to insulate companies from liquidity concerns and operational vulnerabilities. Ultimately, the extent to which ESG serves as a stabilising force or an amplifier of financial risk depends not only on disclosure levels but also on the deeper integration of ESG principles into corporate strategy, risk management, and capital allocation. In both contexts, firms that approach ESG as a fundamental driver of resilience—rather than a mere compliance requirement—will be better positioned to navigate the intersection of sustainability imperatives and financial exigencies in an increasingly complex global economy.

The results also emphasise that ESG leadership is not solely contingent on intrinsic corporate capabilities but is profoundly influenced by institutional norms, evolving disclosure standards, and regional economic priorities. This insight is particularly relevant for policymakers designing incentive structures and for investors seeking risk-aligned, ESG-integrated portfolios. By considering financial metrics with ESG predictors, this research offers useful insights for diverse stakeholders. Policymakers can use region-specific tools—targeted incentives in China and governance transparency frameworks in the UK—to enhance ESG integration. Construction firms, particularly those operating across multiple regulatory regimes, can refine financial strategies to support sustainability initiatives while maintaining economic resilience. Moreover, investors can incorporate financial-ESG linkages into predictive models for more robust portfolio risk assessments.

Despite its contributions, the study’s cross-sectional nature, which forms the basis of many models, acknowledges limitations, including the constraints of secondary data, the potential underrepresentation of UK firms, the exclusion of qualitative governance factors, and the inability to capture the temporal dynamics of ESG practices. Future research should expand dataset granularity, integrate non-financial metrics, and adopt longitudinal methodologies to capture the evolving nature of financial-ESG interdependencies for more robust and forward-looking predictions. Advancements in machine learning and AI-driven predictive models could further enhance the precision of ESG forecasting, offering deeper insights into firm-level and sector-specific sustainability trajectories.

In addition, using historical financial data in predictive models may prove difficult, particularly in rapidly evolving industries such as construction, where past trends may not accurately reflect future ESG trajectories. This discrepancy could affect the robustness and generalisability of the findings in the UK context, potentially underrepresenting the diversity of financial-ESG dynamics within the region. A larger sample size would provide greater statistical power and enable more comprehensive insights into the UK construction sector, but the number of firms in the market and available data constrain this. However, the role of liquidity in ESG engagement remains contingent on industry-specific and institutional conditions. In policy-driven economies, regulatory compliance and external funding mechanisms appear to outweigh the direct influence of internal liquidity. Future research should investigate how hybrid economies—where both regulatory pressures and market incentives shape ESG behaviour—could provide deeper insights into the financial mechanisms underlying sustainability commitments. By refining the understanding of financial determinants of ESG performance within a policy-driven context, this study contributes a more inciteful perspective on the relationship between financial structure, regulatory environments, and corporate sustainability strategies. Refinement of ESG rating methodologies incorporating an investigation into how differences in ESG rating agency methodologies affect prediction outcomes could reduce discrepancies across rating agencies. Lastly, assessing how firms can incorporate carbon pricing, emissions targets, and regulatory penalties into financial ESG forecasting is integral for incorporating climate risk.

Ultimately, this research establishes a foundational framework for understanding the relationship between financial stability and ESG performance, reinforcing the necessity of context-sensitive, adaptive strategies for sustainable finance. Clarifying the mechanisms through which financial health drives ESG investment contributes to the broader discourse on corporate sustainability, equipping firms, investors, and policymakers with the knowledge to nurture financial resilience and sustainable development in an increasingly complex global landscape.