Search Results (467)

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

This study aims to investigate the greenhouse gas (GHG) emissions and environmental footprint of BOCOM Petroleum, a mid-sized downstream oil company operating in Douala, Cameroon. In response to the critical need for empirical data on industrial emissions in Sub-Saharan Africa, a mixed-methods approach combining Life Cycle Assessment (LCA), carbon accounting, and stakeholder interviews was adopted. Emissions were categorised following the GHG Protocol into Scope 1 (direct), Scope 2 (energy-related), and Scope 3 (value chain). Results reveal total annual emissions of 51,734 CO₂, kg/year, with Scope 3 accounting for 38%, Scope 2 for 33%, and Scope 1 for 29%. Major emission sources include stationary combustion, laboratory processes, and the use of electricity-intensive heat-generating machines. An Environmental Management Plan (EMP) was developed, proposing actionable measures such as process optimisation, adoption of energy-efficient equipment, electrification of vehicle fleets, and improved waste management. Findings underscore the need for systemic decarbonisation strategies among mid-sized oil firms and highlight the alignment of corporate initiatives with Cameroon’s climate commitments. This study contributes a replicable methodological framework for emission auditing in industrial enterprises across the region and calls for further integration of environmental and financial planning in corporate sustainability strategies. Full article

The climate emergency in Latin America and the Caribbean (LAC) has transformed sustainability from an aspirational goal into a strategic imperative, particularly in the context of decoupling economic growth from natural capital depletion. This research analyzes eco-innovation within the frameworks of the National Innovation System (NIS), open innovation, and absorptive capacity, with the objective of examining the moderating role of collaboration in overcoming financial, knowledge, and market-related obstacles to innovative behavior. Employing a quantitative methodology using firm-level microdata from the Latin American Harmonized Innovation Surveys (LAIS) between 2007 and 2017, this study focuses on eco-innovative outcomes specifically linked to reductions in energy and material consumption. By estimating models that assess the role of technical cooperation and public policy support, this study seeks to determine whether collaborative strategies operate as an effective buffer against uncertainty and the limitations of local innovation systems. Expanding the scope of previous analyses centered on a single country, this work provides a regional perspective that underscores institutional and sectoral disparities in emerging contexts. Ultimately, this research examines how integrating an environmental purpose into corporate strategy and strengthening absorptive capacity enable LAC firms to transform ecological pressures into sustainable competitive advantages, mitigating the barriers that traditionally hinder technological progress in the region. Full article

Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article

This study examines the causal impact of environmental, social, and governance (ESG) ratings on corporate green technology innovation using a panel of Saudi listed firms over the period 2015–2024. Adopting a multi-period difference-in-differences (DID) framework, the analysis evaluates three dimensions of innovation outcomes—quantity, quality, and efficiency (CGTI1–CGTI3). The results show that ESG ratings significantly enhance green technology innovation. Dynamic evidence indicates that these effects strengthen over time, reflecting gradual adjustment in firms’ innovation strategies. Mechanism analysis reveals that ESG ratings promote innovation primarily by alleviating financial constraints and mitigating agency problems. These effects are driven by improvements in the information environment, as ESG ratings reduce information asymmetry and enhance monitoring. From a theoretical perspective, ESG ratings are conceptualized as a digital information infrastructure that reduces informational entropy and provides algorithmic evaluation signals, thereby guiding managerial decision-making in R&D investment and project selection. Robustness tests, including propensity score matching difference-in-differences (PSM-DID), confirm that the results are not driven by selection bias. Focusing on Saudi Arabia under Vision 2030, the findings highlight the role of ESG information systems in shaping green innovation in an emerging market context. Full article

Enterprise bankruptcy prediction is a critical research issue in financial risk early warning, credit evaluation, and investment decision-making. To address the limitations of traditional methods in handling high-dimensional, nonlinear, and complex financial data, including parameter sensitivity, susceptibility to local optima, and insufficient prediction stability, this study proposes a multi-strategy enhanced Beaver Behavior Optimizer and applies it to optimize kernel extreme learning machines, constructing the MEBBO KELM prediction model. Three improvement mechanisms are introduced, including an elite pool enhanced exploration strategy, a stochastic centroid reverse learning strategy, and a leader guided boundary control strategy, which improve population diversity, global search capability, boundary handling capacity, and convergence accuracy. The proposed algorithm is evaluated on CEC2017 and CEC2022 benchmark datasets and compared with EWOA, HPHHO, MELGWO, TACPSO, CFOA, ALA, AOO, RIME, and BBO. Statistical analyses are conducted using the Wilcoxon rank sum test and the Friedman test. The results demonstrate that MEBBO achieves superior solution accuracy and stability, indicating strong global optimization capability and robustness. Further experiments on the Wieslaw Corporate Bankruptcy Dataset show that MEBBO-KELM achieves strong and robust performance across multiple evaluation metrics, including ACC, MCC, Sensitivity, Specificity, Precision, Recall, and F1 score. Specifically, ACC reaches 79.7578, MCC reaches 0.6050, and F1 score reaches 78.8504, confirming its effectiveness. Full article

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development. Full article

Despite the rapid institutionalization of ESG reporting mandates worldwide, the empirical question of whether ESG disclosure constitutes a structural, long-run determinant of corporate financial performance—rather than a cyclical or spurious co-trending artifact—remains unresolved. The prior literature predominantly employs short-panel estimators that assume stationarity and conflate long-run equilibrium effects with transitory associations. This study addresses that gap by applying a five-step non-stationary panel econometric framework to a sample of 479 S&P 500 firms across eleven GICS sectors over 2010–2022 (5084 firm-year observations), a period chosen to capture the full institutionalization of Bloomberg ESG reporting standards and to encompass two major macroeconomic stress episodes (the 2015–2016 commodity downturn and the COVID-19 shock). Im–Pesaran–Shin panel unit root tests confirm that ESG disclosure scores and financial performance measures are both integrated of order one. Pedroni residual-based panel cointegration tests decisively reject the null of no long-run relationship (Z = −62.38 for the ROA equation), establishing a stable cointegrating equilibrium. Fully Modified OLS and Dynamic OLS group-mean estimators yield bias-corrected long-run coefficients, and a panel error correction model quantifies short-run adjustment dynamics. The key finding is that a ten-point improvement in ESG disclosure is associated with a permanent nine-to-ten percentage-point gain in return on equity (FMOLS β = +1.023, p < 0.01; DOLS β = +0.914, p < 0.01), while the effect on return on assets is positive but more modest and sensitive to estimator choice. Complementary fixed-effects regressions reveal an asymmetric moderating role of macroeconomic uncertainty: equity market volatility (VIX) amplifies the ESG performance premium, whereas acute credit market stress (TED spread) attenuates it. Board governance variables are statistically insignificant across all five specifications, indicating that H3 (board governance) is not supported; this outcome is attributed to limited within-firm governance variation in the large-cap S&P 500 universe rather than a genuine absence of governance effects. The results are robust to lagged ESG measurement, winsorization, and alternative interaction specifications. The findings provide strong econometric evidence for the structural, permanent nature of the ESG–financial performance link in large-cap U.S. equities, with direct implications for mandatory disclosure policy and ESG-integrated investment strategies. Full article

This study explores the impact of internationalization on the financing decisions and finance costs of Chinese enterprises listed in Hong Kong, extending the pecking order theory to an international context. Utilizing data from 785 companies from 2010 to 2020, the research investigates how the degree of internationalization influences corporate finance strategies, with a focus on the mediating role of the pecking order and the moderating effects of international business factors. The findings reveal that while broader internationalization increases finance costs, deeper internationalization reduces them. Legal distance is found to negatively moderate this relationship, whereas the structure of the financial system positively influences it. The results suggest that multinational enterprises with extensive overseas resource allocation demonstrate greater flexibility in financing decisions, particularly in foreign markets characterized by strong investor protection and efficient direct finance mechanisms. Managers should be cautious about pursuing wide geographic expansion without adequate operating depth because a broad but shallow international presence may increase financing frictions. By contrast, deeper resource commitment abroad can strengthen financing flexibility and improve access to lower-cost funds, especially when institutional conditions in the financing market are favorable. Full article

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses. Full article

Supply chain risk management has become a core element of corporate strategy, yet systematic evidence on how innovation information disclosure affects supply chain risk remains scarce. We study how innovation information disclosure in firms’ MD&A sections affects supply chain risk. Using data on Chinese A-share listed firms from 2012 to 2023, we find that firms disclosing more innovation-related content face significantly a lower supply chain risk. This result remains true following instrumental variable estimation, propensity score matching, entropy balancing, and controlling for province- and industry-specific time trends. We provide supportive evidence for three circumstances: firms that disclose more have a broader and more diverse set of supply chain partners; they engage in more joint patenting with partners, consistent with higher switching costs and more stable relationships; and they exhibit stronger reputations and commercial credit capacity, consistent with partnerships reinforced through both trust and financial ties. The effect is concentrated among non-SOEs, high-tech firms, firms in competitive industries, and firms outside the digital economy, all settings in which information asymmetry is more severe and alternative channels for conveying innovation capabilities are limited. We also document asymmetric vertical spillovers: downstream customers’ innovation disclosure prompts upstream suppliers to become more transparent, but the reverse does not hold. Supply chain risk, by contrast, affects connected firms in both directions. These findings extend the literature on the economic consequences of innovation disclosure from capital markets to supply chain management. Full article

This study examines corporate carbon emissions of Korean firms from an ESG perspective and develops an AI-based screening framework to improve the identification of firms likely to exceed regulatory emission thresholds. As global climate policies and carbon pricing mechanisms expand, understanding the emission profiles of listed companies has become increasingly important for regulators, investors, and policymakers. Despite growing ESG disclosure, reliable firm-level screening tools for carbon emissions remain limited. Using a pooled annual panel of KOSPI-listed non-financial firms from 2019 to 2024, the study constructs a dataset of 552 firm-year observations. Firms are classified as high-emission when annual emissions exceed the Korean Emissions Trading Scheme (K-ETS) regulatory threshold of 125,000 tCO₂e. To evaluate predictive performance, the analysis compares multiple machine learning models (RF, SVM, XGBoost, LightGBM, and CatBoost) and deep learning models (CNN, RNN, GAN, LSTM, and Transformer). In addition, a hybrid ensemble combining CatBoost, GAN, and Transformer is proposed to enhance predictive reliability. The empirical results show that ESG-augmented models consistently outperform financial-only baselines across AUC and F1 metrics. Among individual models, the ESG-enhanced Transformer achieves the strongest discriminatory power, while the proposed hybrid ensemble delivers the best overall predictive performance. The findings contribute to the literature by demonstrating the incremental value of ESG information in predicting corporate carbon emissions and by presenting a practical AI-based framework for compliance-oriented screening under carbon regulation. From a policy and investment perspective, the model provides a useful decision support tool for anticipating potential inclusion in emissions trading schemes, assessing transition exposure, and supporting data-driven decarbonization strategies. Full article

This study constitutes the second part of a comprehensive investigation of the persistence of weighted average cost of capital (WACC) rates despite declining risk-free interest rates. While theory suggests that WACC should reflect lower risk-free interest rates and decline with falling government bond yields, empirical evidence reveals minimal adjustment in the reported WACC figures. Disclosed WACC of DAX40 companies remain between 7% and 8% as the yield of a ten-year German government bond fell from 4.1% to −0.2%. After the quantitative risk analysis (part I) systematically lacks market-based and fundamental explanations—demonstrating that neither systematic risk, overall market risk, earnings risk nor leverage increased sufficiently to justify this stability—this article addresses the resulting explanatory gap through qualitative inquiry. Employing a grounded theory methodology, we investigate the causes and consequences of persistent WACC through systematic analysis of 18 problem-centered semi-structured expert interviews (22 respondents comprising corporate finance executives, investment bankers, strategy consultants, auditors). The investigation reveals that behavioral economics (risk aversion, opportunism, subjectivity), organizational constraints (strategic path dependency, implementation complexity, financial criterion rigidity), and model-theoretic discretion (parameter averaging, analyst influence, supplementary risk adjustments) substantially shape practical WACC determination—factors that quantitative risk analysis cannot capture. Practitioners employ disclosed WACC strategically to reconcile investor return requirements with long-term operational stability, avoid audit friction, and hedge geopolitical–monetary risks—consequences that generate capital opportunity costs offsetting traditional value-maximization objectives. Combined quantitative and qualitative evidence yields actionable insights for value-based capital cost methodologies that are aligned with organizational and market realities. Full article

Corporate social responsibility (CSR) is increasingly adopted as a strategic tool to enhance firms’ sustainability and financial performance (CFP). However, despite extensive research, evidence on the underlying factors influencing CSR and CFP remains scarce. This study addresses this gap by exploring the role of employee engagement as one possible mechanism through which CSR initiatives may translate into CFP. Adopting a systematic literature review on papers published in 2019–2024 and a comparative case study methodology, the paper analyzes two Italian firms characterized by different configurations of CSR practices, including varying degrees of formalization and integration into organizational culture. The study leverages semi-structured interviews with management, employee surveys capturing perceptions of CSR and engagement, and firm-level financial indicators. The findings suggest that CSR contributes to CFP through some dimensions of higher engagement and only when CSR is perceived by employees as authentic and embedded in everyday organizational practices. The paper contributes to the literature on the factors influencing the relationship between firms’ CSR activities and CFP and the role played by employee engagement. Moreover, it offers implications for managers to design CSR strategies that create both sustainable and financial value. Full article