Capital Spending, Environmental Factor and Financial Performance: Insights from Sensitive Sector

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Environmental factors (EDS) and capital expenditures are considered independent variables to assess their impact on the relationships analyzed. In this research, the market value (MV) is the dependent variable, shaped by additional elements within the study. We have used of Partial Least Squares Structural Equation Modeling (PLS-SEM) is implemented to assess the indicated linkages. The results demonstrate significant positive correlations between capital expenditure and both environmental factors and market value. Although no direct link between environmental factors and revenue was identified, capital expenditure and enterprise disclosure scores showed a positive significance. Additionally, the Environmental Disclosure Score negatively impacted sales revenue This investigation enriches the available knowledge on ESG influences and the effectiveness of energy enterprises by underlining the importance of financial outlay in this vital area. It challenges the conventional perspective of environmental factors as isolated variables and proposes a potential positive effect on financial performance. The findings suggest that policymakers can promote sustainable practices by monitoring capital expenditure and enhancing market value. Investors may leverage this knowledge to make better-informed decisions regarding firms dedicated to spending. Energy companies can enhance their market value by prioritizing environmental initiatives. Environmental Score Capital expenditure Financial Performance Energy Stocks Revenue structural Equation Modelling Figures Figure 1 Introduction The association between environmental disclosure and corporate valuation has come forth as a topic of intensified analysis within academic frameworks. Contemporary investigations have delved into the intricate dynamics between these variables, assessing the extent to which environmental disclosures can influence a corporation's cost of capital, cash flow streams, and, in the final analysis, its total enterprise valuation as per (Marshall et al., 2009 ) (Fatemi et al., 2017 ) (Clarkson et al., 2013 ). An empirical study by (Clarkson et al., 2013 ) have investigated the direct relationship between environmental disclosure practices and firm valuation. The results of these studies generally suggest that voluntary environmental disclosures offer supplementary informative value for the evaluation of firm value, even when considering financial performance indicators and various other firm-specific characteristics This Research by Clarkson and team revealed that organizations that engage in thorough environmental disclosure are likely to have improved market valuations, suggesting that such transparency is seen by investors as an indicator of commitment to sustainability and environmental accountability. Nevertheless, the underlying mechanism through which environmental disclosure influences corporate value remains inadequately comprehended. One conceivable pathway involves the influence of environmental disclosure on the capital cost incurred by the firm. (Fatemi et al., 2017 ) (Clarkson et al., 2013 ) (Marshall et al., 2009 ). A plethora of empirical investigations has demonstrated that corporations exhibiting elevated degrees of environmental disclosure and transparency are inclined to experience a reduction in their cost of equity capital, as investors regard these entities as possessing diminished risk profiles. Furthermore, environmental disclosure has the potential to influence a company's cash flows, either by means of augmented operational efficiency, elevated reputation and brand equity, or amplified customer loyalty(Fatemi et al., 2017 ) (Permana & Tjahjadi, 2020 ). In this manuscript, we seek to conduct a more comprehensive analysis of the interconnections among environmental disclosure, corporate valuation, and the prospective influence of revenue. The environmental performance metrics of corporations have garnered heightened significance within the investment community, as investors acknowledge the fiscal relevance of ecological effects. This study focus to know the relation among capital expenditure, environmental disclosure scores, and enterprise value in the energy sector listed S&P 500.Previous empirical investigations have established that the transparency of environmental disclosures alongside sustainability performance can exert a considerable influence on the valuation of firms (Yadav et al., 2015 ). In particular, enterprises exhibiting elevated environmental disclosure ratings alongside superior environmental performance are inclined to possess enhanced stock price informativeness and elevated firm valuations (Yadav et al., 2015 ) (Ng & Rezaee, 2020 ). Furthermore, empirical research has demonstrated that the disclosure of environmental costs and carbon emissions can have a favorable impact on a corporation's valuation.(Permana & Tjahjadi, 2020 ). This research posits that elevated levels of capital expenditure, frequently linked to investments in environmental technologies and initiatives, will exhibit a positive correlation with enhanced environmental disclosure scores and augmented enterprise value for firms within the energy sector of the S&P 500.The financial services sector, characterized by its social sensitivity, serves an essential function within society by impacting numerous dimensions such as asset appraisal, risk mitigation, and payment structuring, which subsequently influences the community owing to its substantial social imprint (Seguí-Mas et al. ( 2018 ). The classification "sensitive industry" embodies numerous sectors identified by social, environmental, or ethical aspects, notably including the oil and gas sector along with the cement industry (Garcia et al., 2017 ). Firms operating within the Environmental, Social, and Governance (ESG) sector encounter intensified demands from stakeholders and are subjected to more stringent requirements regarding transparency and disclosure (Welbeck et al., 2017 ; Manes-Rossi et al., 2018 ; Miralles-Quirós et al., 2018). Research shows that such companies generally present more thorough disclosures about social and environmental concerns to verify their practices and improve their public reputation (Manes-Rossi et al., 2018 ). Firms operating within sensitive sectors tend to provide greater transparency in their disclosures relative to those engaged in non-sensitive sectors, attributable to the elevated risks linked to social and environmental issues (Garcia et al., 2017 ).To test this hypothesis, the study will analyze data on capital expenditure, environmental disclosure scores, and enterprise value from a sample of energy sector firms listed in the S&P 500 over a multi-year period. Therefore, the following research questions are formulated to explore the answers: RQ1. What is the impact of environmental factors on company value in the energy sector? RQ2. How does capital expenditure interact with sales, and what is its net effect on company value? RQ3. To what extent do environmental factors influence sales and what is their cumulative impact on company value? Main aim of this study is to examine the relationship between the variables The research includes a review of the existing literature to identify gaps and sets concrete goals to achieve this. RO1. Undertake an extensive examination of the synergistic effects of Environmental variables on Financial Performance, specifically in relation to Market valuation. RO2. This study conducts a dynamic analysis of the relationship between capital expenditure and sales in order to elucidate its ramifications for corporate valuation. Review of Literature Capital Expenditure to Environmental Factor The ecological performance of corporations has emerged as an increasing focal point for both investors and policymakers. In light of the considerable ecological ramifications associated with the energy sector, it is imperative to investigate the relationships between environmental variables, financial outcomes, and investment choices within this particular domain given by (Tsalis et al., 2020 ) (Sharfman & Fernando, 2008 ). This research examines the correlation among the environmental score, capital expenditures, and revenue pertaining to energy equities within the S&P 500 index. The environmental score, which assesses a corporation's ecological practices and initiatives—encompassing aspects such as carbon emissions, pollution metrics, and resource allocation—functions as a surrogate for its sustainability efficacy by (Berg et al., 2020 ). (Jyoti & Khanna, 2021 ) have indicated that sustainability performance may have both advantageous and adverse impacts on financial metrics, as ROA,ROE.In contrast, (Berg et al., 2020 ) Highlights the significant gap in academic research concerning the impact of adopting environmental, social, and governance practices—particularly those aimed at reducing carbon emissions—on the financial outcomes of organizations in the energy sector. This research employs data derived from esteemed sources to examine the interrelations among the environmental score, capital expenditures, and revenue pertaining to S&P 500 energy equities. The outcomes of this study advance the current academic dialogue by shedding light on the multifaceted connections between environmental factors and financial results within the energy sector, thereby delivering crucial knowledge for energy companies and investors striving to navigate this changing landscape and make more prudent decisions about sustainability policies and their possible economic consequences. H1:- There is a relationship between capital expenditure and Environmental Factor of Energy stocks listed in S&P500 . Capital Expenditures to sales: - As a crucial component of this sector, it encompasses a diverse array of companies engaged in the exploration, extraction, refining, and distribution of various energy resources, ranging from traditional fossil fuels like oil and natural gas to the increasingly prominent renewable energy sources. Understanding the dynamics of capital expenditure and revenue within this sector is essential for investors, policymakers, and industry stakeholders to navigate the evolving energy landscape. Existing research has highlighted the complex interplay between energy prices and the performance of energy stocks. In particular, studies have explored the relationship between oil price volatility and the returns of clean energy stocks, with some findings suggesting that rising oil prices may encourage the substitution of alternative energy sources, potentially affecting the performance of renewable energy equities. (Dutta, 2017 ) Additionally, research has uncovered evidence of volatility spillover effects between energy markets and the broader stock market, underscoring the interconnected nature of these domains. (Zolfaghari et al., 2020 ) Reboredo's work focus the association between oil and clean energy stock markets, as to get ride oil price variations influence the performance of renewable energy firms can provide valuable insights for investors seeking to understand the relative attractiveness of these investments when oil prices fluctuate (Dutta, 2017 ). In government contexts, the disconnect between revenue and expenditure decisions can lead to inefficiencies. Reuniting these decisions could enhance transparency and accountability, ensuring that expenditures are aligned with available revenues (Moody, 2009).While capital expenditures are essential for growth and can enhance revenue generation, they also require careful consideration of market conditions, financial constraints, and organizational life cycle stages. The strategic alignment of CapEx with revenue goals is crucial for sustainable financial health across different sectors and regions. H2:- There is a relation between Capital expenditure and Revenue of energy sector stocks listed in s&p500. Capital expenditure to Market Value The allocation of funds for acquiring, enhancing, and safeguarding physical properties like real estate, manufacturing sites, or machinery is known as capital expenditure. Conversely, revenue pertains to the earnings produced from routine business activities. The interrelation between these financial indicators and the valuation of an enterprise is intricate and subject to the influence of numerous variables, including the efficacy of investments, anticipations of the market, and prevailing accounting methodologies.The relationship between capital expenditure and the market value of energy stocks in the S&P 500 is complex and its decisions can significantly impact stock prices, particularly when the market perceives these investments as valuable opportunities. There is evidence of a carbon premium associated with Scope 1 emissions, suggesting that direct emissions are priced into the market value of energy stocks. This premium reflects the market's awareness of carbon-related risks and the potential for higher returns from more polluting firms (Sankar et al., 2024 ). H3:- There is a relation between Capital expenditure and Market Value of energy stocks listed in s&p500 Environmental Factors to Market Value mediation of Revenue The environmental disclosure score recognizes the environmental performance and disclosure that contribute significantly to the company's economic results, with the environmental disclosure score and profitability acting as mediators in this relationship. Sales revenue is a crucial factor for a company because it generates income from goods and services. The existing literature on the cement industry is extensive and particularly focuses on firm characteristics such as capital requirements and profitability. The environmental reporting quality literature was constructed using Ohlson scoring equation techniques and the Factiva database. The literature review on this topic has highlighted the importance of environmental disclosures for stakeholders and effective corporate governance (Iatridis, 2013). Voluntary environmental disclosure means that a company can choose to disclose information about its environmental impact. This is information about the company's water consumption, greenhouse gas emissions, and other environmental factors. This article pays particular attention to the relationship between environmental performance and COEC as well as the evaluation relevance of environmental claims (Plumlee et al., 2015 ). Many studies have examined how companies manage to maintain their legitimacy through environmental disclosure while meeting the information needs of financial markets. Additionally, it demonstrates how environmental information disclosure is crucial to stakeholders in terms of a company's legitimacy and enhances the context of the information analysts receive. In contrast to legitimacy theory, information economics is prominent in the literature, and there is conflicting empirical data on the relationship between disclosure and environmental performance (Cormier & Magnan, 2015). This study highlights Johannesburg stock exchange of mining companies and clearly shows that there is a relationship between corporate sustainability disclosure and return on investment (Wasara & Ganda, 2019 ). H4 :- There is a relationship between EDS and Market value, while the sales revenue being the mediator between the two. H5:-These is a relationship between Revenue & Market value of energy sector stocks listed in S&P 500. Methodology Data Collection and Sampling Our investigation is predicated on a comprehensive dataset encompassing 22 energy equities namely given in below Table 1 Firm Equity Indexed Occidental Petroleum Corp OXY US Equity S&P 500 ONEOK Inc OKE US Equity S&P 500 Equity Chevron Corp CVX US Equity S&P 500 Equity ConocoPhillips COP US Equity S&P 500 Exxon Mobil Corp XOM US Equity S&P 500 Valero Energy Corp VLO US Equity S&P 500 Targa Resources Corp TRGP US Equity S&P 500 Schlumberger NV SLB US Equity S&P 500 Baker Hughes Co BKR US Equity S&P 500 Devon Energy Corp DVN US Equity S&P 500 Hess Corp HES US Equity S&P 500 Williams Cos Inc WMB US Equity S&P 500 Coterra Energy In CTRA US Equity S&P 500 Phillips 66 PSX US Equity S&P 500 Texas Pacific Land Corp TPL US Equity S&P 500 APA Corp APA US Equity S&P 500 EOG Resources Inc EOG US Equity S&P 500 Kinder Morgan Inc KMI US Equity S&P 500 Equity EQT Corp EQT US Equity S&P 500 Marathon Petroleum Corp MPC US Equity S&P 500 Halliburton Co HAL US Equity S&P 500 Diamondback Energy Inc 1 FANG US Equity S&P 500 https://www.bloomberg.com/quote/SPX:IND . listed on the S&P 500 over a span of 9 years ie from 2015 to 2023, entirely derived from secondary data procured via the Bloomberg platform. Important discoveries arose from Structural Equation Modeling that utilized the bootstrapping method with 5000 resamples sourced from the original dataset. A salient benefit of bootstrapping is its capacity to facilitate inferences without necessitating stringent distributional assumptions, thereby augmenting the reliability and validity of our findings. This methodology enabled the computation of standard errors and confidence intervals for the model coefficients. This focused approach zeroes in on the dynamic Indian market, permitting an in-depth exploration of its distinct drivers and challenges. Leveraging the extensive corpus of financial and operational data accessible on Bloomberg, we can scrutinize trends related to production capacity, pricing dynamics, market share, and regulatory frameworks. By concentrating solely on energy equities, the research sought to furnish a detailed and context-specific examination of the energy sector within the framework of the US economic landscape. This targeted data acquisition strategy guarantees both cost-effectiveness and access to a diverse array of credible information, thereby establishing a robust foundation for our inquiry into the complexities of S&P 500 energy stocks.. Measures We apply a Structural Equation Modeling (SEM) methodology noted for Quantitative Continuous data and engage in PLS-SEM analysis to critically explore the multifaceted relationships between capital expenditures, environmental reporting, and shareholder value in the energy field. Econometric models, commonly designated as structural equation models, were originally conceived to elucidate economic indicators. Exogenous variables derive their variability from external influences, whereas endogenous variables acquire their variability from internal factors or other variables. We selected this methodology owing to its benefits in accommodating non-normally distributed data, formative constructs, and mediation effects. Sales are operationalized as "sales," the value of environmental disclosures is denoted by "environmental factor," and company value is articulated as "company market value and capital expenditure," all of which are integrated under the "resource" construct. Capital expenditures function as an external factor that influences the endogenous variable, firm value. We hypothesize that sales revenue and environmental disclosure ratings operate as mediators, thereby facilitating the impact of capital expenditures on firm value. Our SEM-PLS methodology significantly outperforms those utilized in previous investigations, which primarily relied on linear regression, multiple regression, or multivariate regression analyses. (Gao et al., 2023) It enables the analysis of complex causal relationships incorporating mediation effects, thereby offering enhanced understanding of the underlying mechanisms involved. Also, PLS-SEM expertly handles complications related to multicollinearity and data that is not normally distributed, which often arise in financial and environmental datasets. While prior investigations frequently relied on F-tests, Hausman tests, or nonparametric methodologies, this research introduces a novel perspective by explicitly modeling mediation through SEM-PLS(Fang et al., 2019 ). This comprehensive viewpoint elucidates the complex interconnections between environmental initiatives, resource allocation, and economic results within the industry, providing valuable contributions to both academic discourse and practical implementations in the field. PLS-SEM Analysis Our research was examined using PLS-SEM, which was the most appropriate approach for this investigation because of several key characteristics. Primarily, PLS-SEM is an especially effective method for analyzing intricate research models and conducting causal-predictive evaluations (Hair et al. 2017 ), (Henseler et al. 2009 ). our objective was to investigate the interplay between environmental and financial elements within the specific context of energy sector stocks indexed in S&P500 using our PLS-SEM analysis. PLS-SEM was the most appropriate approach for our study due to its effectiveness in theory development and exploratory research (Richter et al. 2016 ). Table 2 Measurement Table Measurement Factor Variable Data Source Sales Revenue Sales Revenue Mediator Bloomberg Lab Environmental Factor Environmental Disclosure Score Mediator Bloomberg Lab Financial Performance Market Value Dependent Bloomberg Lab Resource Capital Expenditure Independent (exogenous) Bloomberg Lab Results Table 3 -Descriptive Statistics EDS CE REV MV Mean 3.786717172 3797.61 43673.84393 76891.90853 Standard Error 0.109946495 344.1463 4789.227488 7162.313294 Median 4.165 2196.191 15314.5 48934.415 Mode 4.71 1618 N/A 23001.55 Standard Deviation 1.547084315 4842.567 67390.40425 100782.6815 Sample Variance 2.393469879 23450459 4541466586 10157148882 Kurtosis -0.203291029 9.190026 6.828546788 7.532196573 Skewness -0.722384304 2.906684 2.497779262 2.799467738 Range 6.56 29503.78 398615.0886 526725.4618 Minimum 0 0.2215 59.9114 1018.1382 Maximum 6.56 29504 398675 527743.6 Sum 749.77 751926.7 8647421.097 15224597.89 Count 198 198 198 198 The dataset comprises four variables EDS,CE,REV & MV .Each variable consists of 198 data points, with various statistical metrics computed to outline their distribution and attributes. The average values reveal that CE,REV and MV function on a significantly larger numerical scale compared to EDS. The median values are notably lower than the mean for CE,REV.MV, indicating right-skewed distributions. The lack of a mode in REV suggests either a uniform distribution or considerable variation in its values. The standard deviation and variance illustrate a growing variability from EDS to MV, with MV displaying the widest spread. The range mirrors this trend, with MV showing the broadest range, highlighting considerable differences between its minimum and maximum values. The kurtosis figures indicate that EDS, REV, and MV have pronounced peaks and heavy tails, signaling that extreme values occur more frequently. In contrast, EDS has a kurtosis near zero, signifying a nearly normal distribution. The skewness values verify that md is slightly left-skewed, while REV, CE, and MV are right-skewed, emphasizing the existence of extreme large values. Moreover, the total of values indicates that MV has the highest magnitude. In summary, EDS appears to have an almost normal distribution with slight skewness and kurtosis, while CE,REV,and MVdemonstrate heavy-tailed distributions with notable positive skewness, revealing the presence of extreme values. The variation found between the mean and median in these variables emphasizes how outliers play a role. Table 4 -Correlation :- Variables EDS CE REV MV EDS 1 CE 0.240794 1 REV 0.081908 0.727837 1 MV 0.222893 0.886855 0.853596 1 The correlation matrix shown in Table 4 , sheds light on the interrelationships among four variables: EDS, CE,REV and MV. The variable EDS exhibits weak correlations with all other variables, indicating a degree of independence. Specifically, its correlation with ad stands at 0.240794, with REV at 0.081908, and with MV at 0.222893, all reflecting weak positive associations. Conversely, ad exhibits a fair relationship with REV (0.727837) and a solid relationship with MV (0.886855), indicating that as CE grows, REV and MV also frequently rise. Likewise, REV and MV show a strong correlation of 0.853596, signifying a close relationship between these two variables. Among all the correlations, the one between CE and MV is the strongest (0.853596), implying a high level of connection. This trend indicates that CE and MV could potentially be significant contributors to REV, whereas EDS remains an independent variable. Table 5 Model Fit Variance Saturated model Estimated model Result SRMR 0.000 0.016 Accepted D_ULS 0.000 0.000 Accepted d_G 0.000 0.000 Accepted Chi-square 0.000 4.420 Accepted NFI 1.000 1.000 Accepted As shown in Table 5 , the measures of discrepancy for each of our models were below the corresponding saturated and estimated model from the reference distribution. This suggests that at the 5% and 1% significance levels, the model was not rejected, and all are accepted. In addition, we used 5000 resamples for full bootstrapping, which produced an SRMR of 0.016 This value shows that the model fits well because it is less than the suggested cutoff of 0.080. Table 6 Inner VIF Value Factor Environmental Disclosure Score MV Sales Revenue Capital Expenditure 1.000 2.127 1.062 Environmental Disclosure Score 1.062 Sales Revenue 2.127 Ensure the validity and reliability of the structural path model (SEM) results, it is crucial to identify and resolve multicollinearity issues among all sets of predictor constructs in the structural equation model. This can be partly accomplished by utilizing VIF as a metric. There are no collinearity concerns when the VIF is below 3. Collinearity issues may emerge if the VIF falls between 3 and 5. Severe collinearity problems occur when the VIF exceeds 5. Table 6 shows that there are no issues with multicollinearity, as all values remain below 3. Estimates Table 7 R-Square Value Endogenous R 2 R 2 adjusted Environmental Disclosure score 0.058 0.053 Market Value 0.879 0.877 Revenue 0.539 0.534 The table shown in 7 indicates shows that the endogenous latent variables with R-squared values, Market Value (87.9%), Sales Revenue (53.9%),and EDS found less relationship with (5.8%). Table 8 Parameter Estimates Hypothesis Relation β T p H1 Capital Expenditure = > Environmental disclosure score 0.243 5.142 0.000 H2 Capital Expenditure = > Market Value 0.577 8.372 0.000 H3 Capital Expenditure = > Revenue 0.751 13.303 0.000 H4 Environmental Disclosure Score = > Sales Revenue -0.103 2.652 0.004 H5 Sales Revenue = > Market Value 0.430 6.515 0.000 The structural equation method is utilized in conjunction with non-parametric and time series analysis to achieve the analysis's results. The results show that capital expenditure and enterprise disclosure score have a positive significance (β = 0.243, t = 5.142, p = < 0.000).Furthermore, there is Positive significance indicated by the Capital Expenditure to Market Value (β = 0.577, t = 8.372, p = < 0.001) with positive significance. The Capital Expenditure to Revenue (β = 0.751, t = 13.303, p = 0.000), showing positive significance. Conversely, the Environmental Disclosure Score and Sales Revenue (β = -0.103, t = 2.652, p = 0.004), showing negative significance. Sales Revenue to Market value (β = 0.430, t = 6.515, p = < 0.001) shows a positive significance; Discussions The examination of the correlation and regression findings yields substantial insights into the interrelationships among EDS, CE, REV, and MV. The correlation matrix indicates that CE possesses a moderate positive correlation with EDS (0.24079 ) while demonstrating strong positive correlations with both REV 0.72784 and MC 0.88686. This observation implies that CE is instrumental in influencing these variables. The robust correlation between REV and MV 0.853596, further suggests a high degree of interdependence between these two variables. Conversely, md displays only weak correlations with REV 0.08191 and MV 0.22289, signifying that its direct influence on these variables is limited. This analysis corroborates these findings by elucidating the directional relationships among the variables. The beta coefficient for the effect of CE on EDS 0.243 is statistically significant (t = 5.142, p = 0), indicating a moderate yet meaningful positive effect. Moreover, CE exerts a considerable positive influence on MV (β = 0.577, t = 8.372, p = 0), underscoring the pivotal role of CE in instigating changes in MV. The most pronounced influence is identified in the relationship between CE and REV (β = 0.751, t = 13.303, p = 0), suggesting that CE serves as a crucial determinant of REV, with any alterations in CE potentially yielding significant repercussions for REV. A noteworthy finding pertains to the negative relationship between EDS and REV (β = -0.103, t = 2.652, p = 0). Although the magnitude of the effect is minor, it remains statistically significant, implying that may exert an inverse influence on REV. This observation suggests a potential trade-off between these two variables, wherein an increase in EDS could marginally diminish REV. The correlation between REV & MV (β = 0.43, t = 6.515, P = 0) further accentuates significance of REV, as it exerts a substantial impact on MV. This highlights REV as a potential mediating variable in the relationship between CE and MV. Table 9 Synopsis of Evidence Supporting the Hypothesis Hypothesis Explanation Decision H1 There is a relationship between capital expenditure and EDS of Energy stocks of S&P500 . Supported H2 There is a relation between Capital expenditure and Revenue Supported H3 There is a relation between Capital expenditure and Market Value Supported H4 There is a relationship between EDS and Market value, while the sales revenue being the mediator between the two. Supported H5 There is a relationship between Revenue and Market Value Supported Implications The outcomes of this study highlight essential theoretical, practical, and policy considerations. The substantial positive impact of advertising on both engagement and value creation emphasizes its pivotal function in influencing critical outcomes. This suggests that the enhancement of advertising may indirectly elevate value creation by exerting a strong influence on engagement. The intermediary role of engagement further accentuates its significance as a vital conduit in bolstering value creation. Concurrently, the inverse relationship between market dynamics and engagement introduces a multifaceted interaction, indicating potential trade-offs that necessitate meticulous examination. Theoretically, these findings enrich the comprehension of the interplay among interconnected variables within a system, particularly elucidating the cascading effects of advertising on value creation through engagement. From a practical standpoint, enterprises and policymakers ought to prioritize strategies that fortify advertising, considering its integral role within this framework. The allocation of resources, enhancement of training initiatives, and execution of structural improvements in advertising could substantially elevate engagement, ultimately resulting in superior value creation outcomes. Moreover, the adverse effect of market dynamics on engagement implies that regulatory or operational dimensions associated with market dynamics should be re-evaluated to avert unintended consequences. To guarantee sustainable advancements in value creation, policymakers should refine strategies that optimize advertising while mitigating the potential detriments of market dynamics, thereby fostering a more efficient and balanced system. Conclusion The current study set out to investigate the importance and effects of capital expenditure, sales revenue, enterprise value, and environmental disclosure score in energy stocks listed in S&P500.The empirical results indicate that capital expenditure constitutes the most significant variable within this theoretical framework, as it exerts pronounced positive influences on EDS, REV & MV. Among these dimensions, the effect of CE on engagement is particularly noteworthy, that it plays an essential role in the development of firm. Furthermore, the substantial correlation between REV & MV implies that engagement may serve as a mediating variable that facilitates the transfer of the effects of CE to MV. Conversely, EDS appears to exert a comparatively diminished influence within this framework. Although it exhibits a moderate positive correlation with CE, its direct effect on engagement is negative, albeit marginal. This observation implies that an elevation in EDS could potentially impede engagement, which may have ramifications for strategic decision-making processes. In light of these findings, it is advisable that subsequent research delves deeper into the underlying mechanisms that elucidate the robust influence of CE on REV and MV, along with the factors contributing to the negative association between EDS and REV. From a strategic standpoint, interventions should prioritize the optimization of CE & REV, given their prominence as the principal drivers within this model. A comprehensive understanding of the specific function of REV as a mediator could enhance predictive frameworks and refine decision-making methodologies in pertinent domains. Limitations and Future Research This analysis reveals specific limitations that warrant emphasis. The research exclusively focuses on energy equities from the S&P500, thereby diminishing the general applicability of its conclusions. A strong focus on certain metrics, including the ratio of alternative energy sources employed, waste output, water use, carbon emissions, energy use, greenhouse gas output, and waste management techniques, might miss other vital aspects that shape the detailed connection between financial results and ESG factors. The research may not adequately reflect the changing dynamics of the sector and economy over time, as it is predicated on a defined temporal trend. 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Marshall, S., Brown, D., & Plumlee, M. (2009). THE IMPACT OF VOLUNTARY ENVIRONMENTAL DISCLOSURE QUALITY ON FIRM VALUE. Academy of Management Proceedings , 2009 (1), 1–6. https://doi.org/10.5465/ambpp.2009.44264648. Ng, A. C., & Rezaee, Z. (2020). Business sustainability factors and stock price informativeness. Journal of Corporate Finance , 64 , 101688. https://doi.org/10.1016/j.jcorpfin.2020.101688. Plumlee, M., Brown, D., Hayes, R. M., & Marshall, R. S. (2015). Voluntary environmental disclosure quality and firm value: Further evidence. Journal of Accounting and Public Policy , 34 (4), 336–361. https://doi.org/10.1016/j.jaccpubpol.2015.04.004. Permana, A. B. S., & Tjahjadi, B. (2020). Pengaruh Pengungkapan Lingkungan dan Karbon terhadap Nilai Perusahaan. E-Jurnal Akuntansi , 30 (4), 932. https://doi.org/10.24843/eja.2020.v30.i04.p11. Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review , 33 (3), 376–404. https://doi.org/10.1108/imr-04-2014-0148. .Seguí-Mas, E., Polo-Garrido, F., & Bollas-Araya, H. M. (2018). Sustainability Assurance in Socially-Sensitive Sectors: A Worldwide Analysis of the Financial Services industry. Sustainability , 10 (8), 2777. https://doi.org/10.3390/su10082777. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In Springer eBooks (pp. 1–40). https://doi.org/10.1007/978-3-319-05542-8_15-1. Sankar, N. G., Nag, S., Chakrabarty, S. P., & Basu, S. (2024). The carbon premium: Correlation or causality? Evidence from S&P 500 companies. Energy Economics , 134 , 107635. https://doi.org/10.1016/j.eneco.2024.107635. Sharfman, M. P., & Fernando, C. S. (2008). Environmental risk management and the cost of capital. Strategic Management Journal , 29 (6), 569–592. https://doi.org/10.1002/smj.678. Sankar, N. G., Nag, S., Chakrabarty, S. P., & Basu, S. (2024). The carbon premium: Correlation or causality? Evidence from S&P 500 companies. Energy Economics , 134 , 107635. https://doi.org/10.1016/j.eneco.2024.107635. Tsalis, T. A., Nikolaou, I. E., Konstantakopoulou, F., Zhang, Y., & Evangelinos, K. I. (2020). Evaluating the corporate environmental profile by analyzing corporate social responsibility reports. Economic Analysis and Policy , 66 , 63–75. https://doi.org/10.1016/j.eap.2020.02.009. Wasara, T. M., & Ganda, F. (2019). The Relationship between Corporate Sustainability Disclosure and Firm Financial Performance in Johannesburg Stock Exchange (JSE) Listed Mining Companies. Sustainability , 11 (16), 4496. https://doi.org/10.3390/su11164496. Wold, S., Ruhe, A., Wold, H., & Dunn, W. J., III. (1984). The collinearity problem in linear regression. the partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing , 5 (3), 735–743. https://doi.org/10.1137/0905052. Welbeck, E. E., Owusu, G. M. Y., Bekoe, R. A., & Kusi, J. A. (2017). Determinants of environmental disclosures of listed firms in Ghana. International Journal of Corporate Social Responsibility , 2 (1). https://doi.org/10.1186/s40991-017-0023-y. Yadav, P. L., Han, S. H., & Rho, J. J. (2015). Impact of Environmental Performance on Firm Value for Sustainable Investment: Evidence from Large US Firms. Business Strategy and the Environment , 25 (6), 402–420. https://doi.org/10.1002/bse.1883. Zolfaghari, M., Ghoddusi, H., & Faghihian, F. (2020). Volatility spillovers for energy prices: A diagonal BEKK approach. Energy Economics , 92 , 104965. https://doi.org/10.1016/j.eneco.2020.104965. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6112281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":421196439,"identity":"fa102e5a-7d82-45ab-9012-f474898440a8","order_by":0,"name":"G SRINIVAS KULKARNI","email":"data:image/png;base64,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","orcid":"","institution":"woxsen university","correspondingAuthor":true,"prefix":"","firstName":"G","middleName":"SRINIVAS","lastName":"KULKARNI","suffix":""}],"badges":[],"createdAt":"2025-02-26 10:30:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6112281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6112281/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77429064,"identity":"ee960e2b-84f5-4a8b-8d3d-c838dd00eca0","added_by":"auto","created_at":"2025-02-28 13:41:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesis Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6112281/v1/8015b3835294495a8e6cff8f.png"},{"id":77430488,"identity":"f72d531d-476a-4223-b6fa-a37466e30ed6","added_by":"auto","created_at":"2025-02-28 13:57:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":963381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6112281/v1/40f59c89-af71-442e-a374-d270fac7accb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCapital Spending, Environmental Factor and Financial Performance: Insights from Sensitive Sector\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe association between environmental disclosure and corporate valuation has come forth as a topic of intensified analysis within academic frameworks. Contemporary investigations have delved into the intricate dynamics between these variables, assessing the extent to which environmental disclosures can influence a corporation's cost of capital, cash flow streams, and, in the final analysis, its total enterprise valuation as per (Marshall et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) (Fatemi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Clarkson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An empirical study by (Clarkson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) have investigated the direct relationship between environmental disclosure practices and firm valuation. The results of these studies generally suggest that voluntary environmental disclosures offer supplementary informative value for the evaluation of firm value, even when considering financial performance indicators and various other firm-specific characteristics\u003c/p\u003e \u003cp\u003eThis Research by Clarkson and team revealed that organizations that engage in thorough environmental disclosure are likely to have improved market valuations, suggesting that such transparency is seen by investors as an indicator of commitment to sustainability and environmental accountability. Nevertheless, the underlying mechanism through which environmental disclosure influences corporate value remains inadequately comprehended. One conceivable pathway involves the influence of environmental disclosure on the capital cost incurred by the firm. (Fatemi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Clarkson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (Marshall et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA plethora of empirical investigations has demonstrated that corporations exhibiting elevated degrees of environmental disclosure and transparency are inclined to experience a reduction in their cost of equity capital, as investors regard these entities as possessing diminished risk profiles. Furthermore, environmental disclosure has the potential to influence a company's cash flows, either by means of augmented operational efficiency, elevated reputation and brand equity, or amplified customer loyalty(Fatemi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Permana \u0026amp; Tjahjadi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this manuscript, we seek to conduct a more comprehensive analysis of the interconnections among environmental disclosure, corporate valuation, and the prospective influence of revenue. The environmental performance metrics of corporations have garnered heightened significance within the investment community, as investors acknowledge the fiscal relevance of ecological effects. This study focus to know the relation among capital expenditure, environmental disclosure scores, and enterprise value in the energy sector listed S\u0026amp;P 500.Previous empirical investigations have established that the transparency of environmental disclosures alongside sustainability performance can exert a considerable influence on the valuation of firms (Yadav et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In particular, enterprises exhibiting elevated environmental disclosure ratings alongside superior environmental performance are inclined to possess enhanced stock price informativeness and elevated firm valuations (Yadav et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Ng \u0026amp; Rezaee, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, empirical research has demonstrated that the disclosure of environmental costs and carbon emissions can have a favorable impact on a corporation's valuation.(Permana \u0026amp; Tjahjadi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research posits that elevated levels of capital expenditure, frequently linked to investments in environmental technologies and initiatives, will exhibit a positive correlation with enhanced environmental disclosure scores and augmented enterprise value for firms within the energy sector of the S\u0026amp;P 500.The financial services sector, characterized by its social sensitivity, serves an essential function within society by impacting numerous dimensions such as asset appraisal, risk mitigation, and payment structuring, which subsequently influences the community owing to its substantial social imprint (Segu\u0026iacute;-Mas et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe classification \"sensitive industry\" embodies numerous sectors identified by social, environmental, or ethical aspects, notably including the oil and gas sector along with the cement industry (Garcia et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Firms operating within the Environmental, Social, and Governance (ESG) sector encounter intensified demands from stakeholders and are subjected to more stringent requirements regarding transparency and disclosure (Welbeck et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Manes-Rossi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miralles-Quir\u0026oacute;s et al., 2018). Research shows that such companies generally present more thorough disclosures about social and environmental concerns to verify their practices and improve their public reputation (Manes-Rossi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Firms operating within sensitive sectors tend to provide greater transparency in their disclosures relative to those engaged in non-sensitive sectors, attributable to the elevated risks linked to social and environmental issues (Garcia et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).To test this hypothesis, the study will analyze data on capital expenditure, environmental disclosure scores, and enterprise value from a sample of energy sector firms listed in the S\u0026amp;P 500 over a multi-year period.\u003c/p\u003e \u003cp\u003eTherefore, the following research questions are formulated to explore the answers:\u003c/p\u003e \u003cp\u003eRQ1. What is the impact of environmental factors on company value in the energy sector?\u003c/p\u003e \u003cp\u003eRQ2. How does capital expenditure interact with sales, and what is its net effect on company value?\u003c/p\u003e \u003cp\u003eRQ3. To what extent do environmental factors influence sales and what is their cumulative impact on company value?\u003c/p\u003e \u003cp\u003eMain aim of this study is to examine the relationship between the variables\u003c/p\u003e \u003cp\u003eThe research includes a review of the existing literature to identify gaps and sets concrete goals to achieve this.\u003c/p\u003e \u003cp\u003eRO1. Undertake an extensive examination of the synergistic effects of Environmental variables on Financial Performance, specifically in relation to Market valuation.\u003c/p\u003e \u003cp\u003eRO2. This study conducts a dynamic analysis of the relationship between capital expenditure and sales in order to elucidate its ramifications for corporate valuation.\u003c/p\u003e"},{"header":"Review of Literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCapital Expenditure to Environmental Factor\u003c/h2\u003e \u003cp\u003eThe ecological performance of corporations has emerged as an increasing focal point for both investors and policymakers. In light of the considerable ecological ramifications associated with the energy sector, it is imperative to investigate the relationships between environmental variables, financial outcomes, and investment choices within this particular domain given by (Tsalis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Sharfman \u0026amp; Fernando, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This research examines the correlation among the environmental score, capital expenditures, and revenue pertaining to energy equities within the S\u0026amp;P 500 index. The environmental score, which assesses a corporation's ecological practices and initiatives\u0026mdash;encompassing aspects such as carbon emissions, pollution metrics, and resource allocation\u0026mdash;functions as a surrogate for its sustainability efficacy by (Berg et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). (Jyoti \u0026amp; Khanna, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have indicated that sustainability performance may have both advantageous and adverse impacts on financial metrics, as ROA,ROE.In contrast, (Berg et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Highlights the significant gap in academic research concerning the impact of adopting environmental, social, and governance practices\u0026mdash;particularly those aimed at reducing carbon emissions\u0026mdash;on the financial outcomes of organizations in the energy sector.\u003c/p\u003e \u003cp\u003eThis research employs data derived from esteemed sources to examine the interrelations among the environmental score, capital expenditures, and revenue pertaining to S\u0026amp;P 500 energy equities. The outcomes of this study advance the current academic dialogue by shedding light on the multifaceted connections between environmental factors and financial results within the energy sector, thereby delivering crucial knowledge for energy companies and investors striving to navigate this changing landscape and make more prudent decisions about sustainability policies and their possible economic consequences.\u003c/p\u003e \u003cp\u003eH1:- There is a relationship between capital expenditure and Environmental Factor of Energy stocks listed in S\u0026amp;P500 .\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCapital Expenditures to sales: -\u003c/h3\u003e\n\u003cp\u003eAs a crucial component of this sector, it encompasses a diverse array of companies engaged in the exploration, extraction, refining, and distribution of various energy resources, ranging from traditional fossil fuels like oil and natural gas to the increasingly prominent renewable energy sources. Understanding the dynamics of capital expenditure and revenue within this sector is essential for investors, policymakers, and industry stakeholders to navigate the evolving energy landscape.\u003c/p\u003e \u003cp\u003eExisting research has highlighted the complex interplay between energy prices and the performance of energy stocks. In particular, studies have explored the relationship between oil price volatility and the returns of clean energy stocks, with some findings suggesting that rising oil prices may encourage the substitution of alternative energy sources, potentially affecting the performance of renewable energy equities. (Dutta, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Additionally, research has uncovered evidence of volatility spillover effects between energy markets and the broader stock market, underscoring the interconnected nature of these domains. (Zolfaghari et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eReboredo's work focus the association between oil and clean energy stock markets, as to get ride oil price variations influence the performance of renewable energy firms can provide valuable insights for investors seeking to understand the relative attractiveness of these investments when oil prices fluctuate (Dutta, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn government contexts, the disconnect between revenue and expenditure decisions can lead to inefficiencies. Reuniting these decisions could enhance transparency and accountability, ensuring that expenditures are aligned with available revenues (Moody, 2009).While capital expenditures are essential for growth and can enhance revenue generation, they also require careful consideration of market conditions, financial constraints, and organizational life cycle stages. The strategic alignment of CapEx with revenue goals is crucial for sustainable financial health across different sectors and regions.\u003c/p\u003e \u003cp\u003eH2:- There is a relation between Capital expenditure and Revenue of energy sector stocks listed in s\u0026amp;p500.\u003c/p\u003e\n\u003ch3\u003eCapital expenditure to Market Value\u003c/h3\u003e\n\u003cp\u003eThe allocation of funds for acquiring, enhancing, and safeguarding physical properties like real estate, manufacturing sites, or machinery is known as capital expenditure. Conversely, revenue pertains to the earnings produced from routine business activities. The interrelation between these financial indicators and the valuation of an enterprise is intricate and subject to the influence of numerous variables, including the efficacy of investments, anticipations of the market, and prevailing accounting methodologies.The relationship between capital expenditure and the market value of energy stocks in the S\u0026amp;P 500 is complex and its decisions can significantly impact stock prices, particularly when the market perceives these investments as valuable opportunities. There is evidence of a carbon premium associated with Scope 1 emissions, suggesting that direct emissions are priced into the market value of energy stocks. This premium reflects the market's awareness of carbon-related risks and the potential for higher returns from more polluting firms (Sankar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eH3:- There is a relation between Capital expenditure and Market Value of energy stocks listed in s\u0026amp;p500\u003c/p\u003e\n\u003ch3\u003eEnvironmental Factors to Market Value mediation of Revenue\u003c/h3\u003e\n\u003cp\u003eThe environmental disclosure score recognizes the environmental performance and disclosure that contribute significantly to the company's economic results, with the environmental disclosure score and profitability acting as mediators in this relationship. Sales revenue is a crucial factor for a company because it generates income from goods and services. The existing literature on the cement industry is extensive and particularly focuses on firm characteristics such as capital requirements and profitability. The environmental reporting quality literature was constructed using Ohlson scoring equation techniques and the Factiva database. The literature review on this topic has highlighted the importance of environmental disclosures for stakeholders and effective corporate governance (Iatridis, 2013). Voluntary environmental disclosure means that a company can choose to disclose information about its environmental impact. This is information about the company's water consumption, greenhouse gas emissions, and other environmental factors. This article pays particular attention to the relationship between environmental performance and COEC as well as the evaluation relevance of environmental claims (Plumlee et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Many studies have examined how companies manage to maintain their legitimacy through environmental disclosure while meeting the information needs of financial markets. Additionally, it demonstrates how environmental information disclosure is crucial to stakeholders in terms of a company's legitimacy and enhances the context of the information analysts receive. In contrast to legitimacy theory, information economics is prominent in the literature, and there is conflicting empirical data on the relationship between disclosure and environmental performance (Cormier \u0026amp; Magnan, 2015). This study highlights Johannesburg stock exchange of mining companies and clearly shows that there is a relationship between corporate sustainability disclosure and return on investment (Wasara \u0026amp; Ganda, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eH4 :- There is a relationship between EDS and Market value, while the sales revenue being the mediator between the two.\u003c/p\u003e \u003cp\u003eH5:-These is a relationship between Revenue \u0026amp; Market value of energy sector stocks listed in S\u0026amp;P 500.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Sampling\u003c/h2\u003e \u003cp\u003eOur investigation is predicated on a comprehensive dataset encompassing 22 energy equities namely given in below Table\u0026nbsp;1\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndexed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccidental Petroleum Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOXY US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eONEOK Inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOKE US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquity Chevron Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCVX US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquity ConocoPhillips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOP US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExxon Mobil Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXOM US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValero Energy Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVLO US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarga Resources Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRGP US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchlumberger NV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLB US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaker Hughes Co\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBKR US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevon Energy Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDVN US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHess Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHES US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilliams Cos Inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWMB US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoterra Energy In\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTRA US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhillips 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSX US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexas Pacific Land Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPL US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPA Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPA US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOG Resources Inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEOG US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKinder Morgan Inc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMI US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquity EQT Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEQT US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarathon Petroleum Corp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPC US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHalliburton Co\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAL US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiamondback Energy Inc\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFANG US Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026amp;P 500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bloomberg.com/quote/SPX:IND\u003c/span\u003e\u003cspan address=\"https://www.bloomberg.com/quote/SPX:IND\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cp\u003elisted on the S\u0026amp;P 500 over a span of 9 years ie from 2015 to 2023, entirely derived from secondary data procured via the Bloomberg platform. Important discoveries arose from Structural Equation Modeling that utilized the bootstrapping method with 5000 resamples sourced from the original dataset. A salient benefit of bootstrapping is its capacity to facilitate inferences without necessitating stringent distributional assumptions, thereby augmenting the reliability and validity of our findings. This methodology enabled the computation of standard errors and confidence intervals for the model coefficients. This focused approach zeroes in on the dynamic Indian market, permitting an in-depth exploration of its distinct drivers and challenges. Leveraging the extensive corpus of financial and operational data accessible on Bloomberg, we can scrutinize trends related to production capacity, pricing dynamics, market share, and regulatory frameworks. By concentrating solely on energy equities, the research sought to furnish a detailed and context-specific examination of the energy sector within the framework of the US economic landscape. This targeted data acquisition strategy guarantees both cost-effectiveness and access to a diverse array of credible information, thereby establishing a robust foundation for our inquiry into the complexities of S\u0026amp;P 500 energy stocks..\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eWe apply a Structural Equation Modeling (SEM) methodology noted for Quantitative Continuous data and engage in PLS-SEM analysis to critically explore the multifaceted relationships between capital expenditures, environmental reporting, and shareholder value in the energy field. Econometric models, commonly designated as structural equation models, were originally conceived to elucidate economic indicators. Exogenous variables derive their variability from external influences, whereas endogenous variables acquire their variability from internal factors or other variables. We selected this methodology owing to its benefits in accommodating non-normally distributed data, formative constructs, and mediation effects. Sales are operationalized as \"sales,\" the value of environmental disclosures is denoted by \"environmental factor,\" and company value is articulated as \"company market value and capital expenditure,\" all of which are integrated under the \"resource\" construct. Capital expenditures function as an external factor that influences the endogenous variable, firm value. We hypothesize that sales revenue and environmental disclosure ratings operate as mediators, thereby facilitating the impact of capital expenditures on firm value. Our SEM-PLS methodology significantly outperforms those utilized in previous investigations, which primarily relied on linear regression, multiple regression, or multivariate regression analyses. (Gao et al., 2023)\u003c/p\u003e \u003cp\u003eIt enables the analysis of complex causal relationships incorporating mediation effects, thereby offering enhanced understanding of the underlying mechanisms involved. Also, PLS-SEM expertly handles complications related to multicollinearity and data that is not normally distributed, which often arise in financial and environmental datasets. While prior investigations frequently relied on F-tests, Hausman tests, or nonparametric methodologies, this research introduces a novel perspective by explicitly modeling mediation through SEM-PLS(Fang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis comprehensive viewpoint elucidates the complex interconnections between environmental initiatives, resource allocation, and economic results within the industry, providing valuable contributions to both academic discourse and practical implementations in the field.\u003c/p\u003e\n\u003ch3\u003ePLS-SEM Analysis\u003c/h3\u003e\n\u003cp\u003eOur research was examined using PLS-SEM, which was the most appropriate approach for this investigation because of several key characteristics. Primarily, PLS-SEM is an especially effective method for analyzing intricate research models and conducting causal-predictive evaluations (Hair et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), (Henseler et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eour objective was to investigate the interplay between environmental and financial elements within the specific context of energy sector stocks indexed in S\u0026amp;P500 using our PLS-SEM analysis. PLS-SEM was the most appropriate approach for our study due to its effectiveness in theory development and exploratory research (Richter et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSales Revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSales Revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBloomberg Lab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Disclosure Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBloomberg Lab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarket Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBloomberg Lab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent (exogenous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBloomberg Lab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e-Descriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEDS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eREV\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMV\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e3.786717172\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3797.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e43673.84393\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e76891.90853\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.109946495\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e344.1463\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4789.227488\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7162.313294\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4.165\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2196.191\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e15314.5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e48934.415\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4.71\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1618\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN/A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e23001.55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.547084315\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4842.567\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e67390.40425\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e100782.6815\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample Variance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2.393469879\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e23450459\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4541466586\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e10157148882\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.203291029\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9.190026\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6.828546788\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e7.532196573\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.722384304\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.906684\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.497779262\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e2.799467738\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e6.56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e29503.78\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e398615.0886\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e526725.4618\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.2215\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e59.9114\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1018.1382\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e6.56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e29504\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e398675\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e527743.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e749.77\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e751926.7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e8647421.097\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e15224597.89\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e198\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e198\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e198\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e198\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dataset comprises four variables EDS,CE,REV \u0026amp; MV .Each variable consists of 198 data points, with various statistical metrics computed to outline their distribution and attributes. The average values reveal that CE,REV and MV function on a significantly larger numerical scale compared to EDS. The median values are notably lower than the mean for CE,REV.MV, indicating right-skewed distributions. The lack of a mode in REV suggests either a uniform distribution or considerable variation in its values. The standard deviation and variance illustrate a growing variability from EDS to MV, with MV displaying the widest spread. The range mirrors this trend, with MV showing the broadest range, highlighting considerable differences between its minimum and maximum values. The kurtosis figures indicate that EDS, REV, and MV have pronounced peaks and heavy tails, signaling that extreme values occur more frequently. In contrast, EDS has a kurtosis near zero, signifying a nearly normal distribution. The skewness values verify that md is slightly left-skewed, while REV, CE, and MV are right-skewed, emphasizing the existence of extreme large values. Moreover, the total of values indicates that MV has the highest magnitude. In summary, EDS appears to have an almost normal distribution with slight skewness and kurtosis, while CE,REV,and MVdemonstrate heavy-tailed distributions with notable positive skewness, revealing the presence of extreme values. The variation found between the mean and median in these variables emphasizes how outliers play a role.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e-Correlation :-\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEDS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eREV\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMV\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.081908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.222893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.853596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation matrix shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, sheds light on the interrelationships among four variables: EDS, CE,REV and MV. The variable EDS exhibits weak correlations with all other variables, indicating a degree of independence. Specifically, its correlation with ad stands at 0.240794, with REV at 0.081908, and with MV at 0.222893, all reflecting weak positive associations. Conversely, ad exhibits a fair relationship with REV (0.727837) and a solid relationship with MV (0.886855), indicating that as CE grows, REV and MV also frequently rise. Likewise, REV and MV show a strong correlation of 0.853596, signifying a close relationship between these two variables. Among all the correlations, the one between CE and MV is the strongest (0.853596), implying a high level of connection. This trend indicates that CE and MV could potentially be significant contributors to REV, whereas EDS remains an independent variable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaturated model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD_ULS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the measures of discrepancy for each of our models were below the corresponding saturated and estimated model from the reference distribution. This suggests that at the 5% and 1% significance levels, the model was not rejected, and all are accepted. In addition, we used 5000 resamples for full bootstrapping, which produced an SRMR of 0.016 This value shows that the model fits well because it is less than the suggested cutoff of 0.080.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInner VIF Value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Disclosure Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSales Revenue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCapital Expenditure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental Disclosure Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSales Revenue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEnsure the validity and reliability of the structural path model (SEM) results, it is crucial to identify and resolve multicollinearity issues among all sets of predictor constructs in the structural equation model. This can be partly accomplished by utilizing VIF as a metric. There are no collinearity concerns when the VIF is below 3. Collinearity issues may emerge if the VIF falls between 3 and 5. Severe collinearity problems occur when the VIF exceeds 5. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that there are no issues with multicollinearity, as all values remain below 3.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEstimates\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR-Square Value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogenous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Disclosure score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table shown in 7 indicates shows that the endogenous latent variables with R-squared values, Market Value (87.9%), Sales Revenue (53.9%),and EDS found less relationship with (5.8%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter Estimates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital Expenditure\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Environmental disclosure score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.243\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.142\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital Expenditure\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Market Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.577\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.372\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital Expenditure\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.751\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.303\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Disclosure Score\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Sales Revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.103\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.652\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSales Revenue\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Market Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.430\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.515\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe structural equation method is utilized in conjunction with non-parametric and time series analysis to achieve the analysis's results. The results show that capital expenditure and enterprise disclosure score have a positive significance (β\u0026thinsp;=\u0026thinsp;0.243, t\u0026thinsp;=\u0026thinsp;5.142, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.000).Furthermore, there is Positive significance indicated by the Capital Expenditure to Market Value (β\u0026thinsp;=\u0026thinsp;0.577, t\u0026thinsp;=\u0026thinsp;8.372, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with positive significance. The Capital Expenditure to Revenue (β\u0026thinsp;=\u0026thinsp;0.751, t\u0026thinsp;=\u0026thinsp;13.303, p\u0026thinsp;=\u0026thinsp;0.000), showing positive significance. Conversely, the Environmental Disclosure Score and Sales Revenue (β = -0.103, t\u0026thinsp;=\u0026thinsp;2.652, p\u0026thinsp;=\u0026thinsp;0.004), showing negative significance. Sales Revenue to Market value (β\u0026thinsp;=\u0026thinsp;0.430, t\u0026thinsp;=\u0026thinsp;6.515, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) shows a positive significance;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe examination of the correlation and regression findings yields substantial insights into the interrelationships among EDS, CE, REV, and MV. The correlation matrix indicates that CE possesses a moderate positive correlation with EDS (0.24079 ) while demonstrating strong positive correlations with both REV 0.72784 and MC 0.88686. This observation implies that CE is instrumental in influencing these variables. The robust correlation between REV and MV 0.853596, further suggests a high degree of interdependence between these two variables. Conversely, md displays only weak correlations with REV 0.08191 and MV 0.22289, signifying that its direct influence on these variables is limited. This analysis corroborates these findings by elucidating the directional relationships among the variables. The beta coefficient for the effect of CE on EDS 0.243 is statistically significant (t\u0026thinsp;=\u0026thinsp;5.142, p\u0026thinsp;=\u0026thinsp;0), indicating a moderate yet meaningful positive effect. Moreover, CE exerts a considerable positive influence on MV (β\u0026thinsp;=\u0026thinsp;0.577, t\u0026thinsp;=\u0026thinsp;8.372, p\u0026thinsp;=\u0026thinsp;0), underscoring the pivotal role of CE in instigating changes in MV. The most pronounced influence is identified in the relationship between CE and REV (β\u0026thinsp;=\u0026thinsp;0.751, t\u0026thinsp;=\u0026thinsp;13.303, p\u0026thinsp;=\u0026thinsp;0), suggesting that CE serves as a crucial determinant of REV, with any alterations in CE potentially yielding significant repercussions for REV. A noteworthy finding pertains to the negative relationship between EDS and REV (β = -0.103, t\u0026thinsp;=\u0026thinsp;2.652, p\u0026thinsp;=\u0026thinsp;0). Although the magnitude of the effect is minor, it remains statistically significant, implying that may exert an inverse influence on REV.\u003c/p\u003e \u003cp\u003eThis observation suggests a potential trade-off between these two variables, wherein an increase in EDS could marginally diminish REV. The correlation between REV \u0026amp; MV (β\u0026thinsp;=\u0026thinsp;0.43, t\u0026thinsp;=\u0026thinsp;6.515, P\u0026thinsp;=\u0026thinsp;0) further accentuates significance of REV, as it exerts a substantial impact on MV. This highlights REV as a potential mediating variable in the relationship between CE and MV.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSynopsis of Evidence Supporting the Hypothesis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a relationship between capital expenditure and EDS of Energy stocks of S\u0026amp;P500 .\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a relation between Capital expenditure and Revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a relation between Capital expenditure and Market Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a relationship between EDS and Market value, while the sales revenue being the mediator between the two.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a relationship between Revenue and Market Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThe outcomes of this study highlight essential theoretical, practical, and policy considerations. The substantial positive impact of advertising on both engagement and value creation emphasizes its pivotal function in influencing critical outcomes. This suggests that the enhancement of advertising may indirectly elevate value creation by exerting a strong influence on engagement. The intermediary role of engagement further accentuates its significance as a vital conduit in bolstering value creation. Concurrently, the inverse relationship between market dynamics and engagement introduces a multifaceted interaction, indicating potential trade-offs that necessitate meticulous examination. Theoretically, these findings enrich the comprehension of the interplay among interconnected variables within a system, particularly elucidating the cascading effects of advertising on value creation through engagement.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, enterprises and policymakers ought to prioritize strategies that fortify advertising, considering its integral role within this framework. The allocation of resources, enhancement of training initiatives, and execution of structural improvements in advertising could substantially elevate engagement, ultimately resulting in superior value creation outcomes. Moreover, the adverse effect of market dynamics on engagement implies that regulatory or operational dimensions associated with market dynamics should be re-evaluated to avert unintended consequences. To guarantee sustainable advancements in value creation, policymakers should refine strategies that optimize advertising while mitigating the potential detriments of market dynamics, thereby fostering a more efficient and balanced system.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study set out to investigate the importance and effects of capital expenditure, sales revenue, enterprise value, and environmental disclosure score in energy stocks listed in S\u0026amp;P500.The empirical results indicate that capital expenditure constitutes the most significant variable within this theoretical framework, as it exerts pronounced positive influences on EDS, REV \u0026amp; MV. Among these dimensions, the effect of CE on engagement is particularly noteworthy, that it plays an essential role in the development of firm. Furthermore, the substantial correlation between REV \u0026amp; MV implies that engagement may serve as a mediating variable that facilitates the transfer of the effects of CE to MV. Conversely, EDS appears to exert a comparatively diminished influence within this framework. Although it exhibits a moderate positive correlation with CE, its direct effect on engagement is negative, albeit marginal. This observation implies that an elevation in EDS could potentially impede engagement, which may have ramifications for strategic decision-making processes. In light of these findings, it is advisable that subsequent research delves deeper into the underlying mechanisms that elucidate the robust influence of CE on REV and MV, along with the factors contributing to the negative association between EDS and REV. From a strategic standpoint, interventions should prioritize the optimization of CE \u0026amp; REV, given their prominence as the principal drivers within this model. A comprehensive understanding of the specific function of REV as a mediator could enhance predictive frameworks and refine decision-making methodologies in pertinent domains.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThis analysis reveals specific limitations that warrant emphasis. The research exclusively focuses on energy equities from the S\u0026amp;P500, thereby diminishing the general applicability of its conclusions. A strong focus on certain metrics, including the ratio of alternative energy sources employed, waste output, water use, carbon emissions, energy use, greenhouse gas output, and waste management techniques, might miss other vital aspects that shape the detailed connection between financial results and ESG factors. The research may not adequately reflect the changing dynamics of the sector and economy over time, as it is predicated on a defined temporal trend. Subsequent studies that encompass a wider array of variables, an extended dataset, and supplementary qualitative factors could yield a more profound understanding of the ESG framework within the Energy sector. These limitations underscore the critical necessity for meticulous evaluation of the study's outcomes and call for further, more comprehensive investigations in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: -This research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: - Data supporting the reported results from Bloomberg Financial Lab.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: -The authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerg, F., Fabisik, K., \u0026amp; Sautner, Z. (2020). 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Volatility spillovers for energy prices: A diagonal BEKK approach. \u003cem\u003eEnergy Economics\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e, 104965. https://doi.org/10.1016/j.eneco.2020.104965.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental Score, Capital expenditure, Financial Performance, Energy Stocks, Revenue, structural Equation Modelling","lastPublishedDoi":"10.21203/rs.3.rs-6112281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6112281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines how environmental factors affect the capital spending and financial performance of energy stocks within the S\u0026amp;P 500.We evaluated data from 22 energy companies in the US S\u0026amp;P 500 over a nine-year span ie from 2015\u0026ndash;2023, employing panel data analysis with 796 observations. Environmental factors (EDS) and capital expenditures are considered independent variables to assess their impact on the relationships analyzed. In this research, the market value (MV) is the dependent variable, shaped by additional elements within the study. We have used of Partial Least Squares Structural Equation Modeling (PLS-SEM) is implemented to assess the indicated linkages. The results demonstrate significant positive correlations between capital expenditure and both environmental factors and market value. Although no direct link between environmental factors and revenue was identified, capital expenditure and enterprise disclosure scores showed a positive significance. Additionally, the Environmental Disclosure Score negatively impacted sales revenue This investigation enriches the available knowledge on ESG influences and the effectiveness of energy enterprises by underlining the importance of financial outlay in this vital area. It challenges the conventional perspective of environmental factors as isolated variables and proposes a potential positive effect on financial performance. The findings suggest that policymakers can promote sustainable practices by monitoring capital expenditure and enhancing market value. Investors may leverage this knowledge to make better-informed decisions regarding firms dedicated to spending. Energy companies can enhance their market value by prioritizing environmental initiatives.\u003c/p\u003e","manuscriptTitle":"Capital Spending, Environmental Factor and Financial Performance: Insights from Sensitive Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-28 13:41:05","doi":"10.21203/rs.3.rs-6112281/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8388bdc7-0239-42ee-a2b4-4f7dbf6b5ff1","owner":[],"postedDate":"February 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-28T13:41:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-28 13:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6112281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6112281","identity":"rs-6112281","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}