Search Results (524)

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

This study examines the associations between linearly scored phenotypic traits and auction sales prices of young event horses in Ireland, aiming to identify key traits influencing market value. Data from 307 horses sold at public auctions (2022–2023) were analysed using regression analysis, binary optimisation, and Principal Component Analysis (PCA). Regression identified Head–neck Connection, Quality of Legs, Walk length of Stride, and Scope as highly significant predictors of sales price (p < 0.001), with Length of Croup, Trot Elasticity, Trot Balance, and Take-off Direction also significant (p < 0.05). Optimised regression reduced the number of relevant traits from 37 to 8, streamlining evaluation. PCA highlighted eight principal traits, including Scope, Elasticity, and Canter Impulsion, explaining 61.19% of variance in the first four components. These results demonstrate that specific conformation, movement, and athleticism traits significantly affect auction outcomes. The findings provide actionable insights for breeders and stakeholders, suggesting that targeted selection for high-impact traits could accelerate genetic progress and improve market returns. Furthermore, these traits could underpin the development of economic or buyer indices to enhance valuation accuracy and transparency, with potential application across equestrian disciplines to align breeding objectives with market demands. Full article

In this study, we utilize a unique quarterly dataset of non-GAAP earnings to re-examine the “informational” role of non-GAAP earnings from the perspective of value relevance and earnings predictability in the post-Reg G period. We find that non-GAAP earnings are more value relevant and can better predict future operating earnings of a firm compared to equivalent GAAP earnings. Additionally, we also find empirical evidence suggesting that the difference in the value relevance and earnings predictability between non-GAAP and equivalent GAAP earnings can vary across but cannot be completely mitigated by firm-level characteristics, such as the market value of equity, accruals quality, analyst coverage, and managerial ability of a firm. Moreover, our supplementary analysis reveals that the superior value relevance and predictive power of non-GAAP earnings persist even after the SEC’s release of the Compliance and Disclosure Interpretations (C&DI) in 2010. Overall, our empirical evidence suggests a superior “informational” role of non-GAAP earnings to equivalent GAAP earnings in terms of valuation and predictability on future operating performance in the post-Reg G period. Full article

Drylands cover approximately 41% of Earth’s land surface, supporting about 500 million people and 45% of global agriculture. Groundwater is essential in drylands and is crucial for maintaining ecosystem services and offering numerous benefits. This article, for the first time, analyses and valuates the hydrological ecosystem services (HESs) provided by groundwater in a region of the Colorado River Delta in Mexico, an area with uncertain economic impact due to water scarcity. The main water sources are the Colorado River and groundwater from the Mexicali and San Luis Rio Colorado valley aquifers, both of which are overexploited. Valuation techniques include surrogate and simulated market methods for agricultural, industrial, urban, and domestic uses, the shadow project approach for water conservation and purification cost avoidance, and the contingent valuation method for recreation. Data from 2013 to 2015 and 2020 were used as they are the most reliable sources available. The annual value of HESs provided by groundwater was USD 883,520 million, with water conservation being a key factor. The analyzed groundwater uses reflect differences in efficiency and economic value, providing key information for decisions on governance, allocation, conservation, and revaluation of water resources. These results suggest reorienting crops, establishing differentiated rates, and promoting payment for environmental services programs. Full article

This study examines the influence of ethical leadership on corporate sustainability and financial performance, highlighting the moderating effect of firms’ commitment to the United Nations Sustainable Development Goals (SDGs). Utilizing panel data from 420 automotive companies spanning 2015 to 2024, the analysis applies the System Generalized Method of Moments (GMM) to control for endogeneity and unobserved heterogeneity. All data were gathered from the Refinitiv Eikon Platform (LSEG) and annual reports. Panel GMM regression is used to estimate the relationship to deal with the endogeneity problem. The results reveal that ethical leadership significantly improves corporate sustainability performance—measured by ESG scores from Refinitiv Eikon and Bloomberg—as well as financial indicators like Return on Assets (ROA) and Tobin’s Q. Additionally, firms that demonstrate breadth (the range of SDG-related themes addressed), concentration (the distribution of non-financial disclosures across SDGs), and depth (the overall volume of SDG-related information) in their SDG disclosures gain greater advantages from ethical leadership, resulting in enhanced ESG performance and higher market valuation. This study offers valuable insights for corporate leaders, policymakers, and investors on how integrating ethical leadership with SDG alignment can drive sustainable and financial growth. Full article

Prior research often portrays Corporate Social Responsibility (CSR) as a coercive institutional force compelling firms to passively conform for legitimacy. More recent studies, however, suggest firms actively pursue CSR to gain sustainable competitive advantages. Yet, how and when CSR buffers firms against adverse shocks of crises remains insufficiently understood. This study addresses this gap by using multiple regression analysis to examine the buffering effects of CSR investments during the COVID-19 crisis, which severely disrupted capital markets and firm valuation. Drawing on signaling theory and CSR literature, we analyze the stock market performance of China’s A-share listed firms using a sample of 2577 observations as of the end of 2019. Results indicate that firms with higher CSR investments experienced significantly greater cumulative abnormal returns during the pandemic. Moreover, the buffering effect is amplified among firms with higher debt burdens, greater financing constraints, and those operating in regions with stronger social trust and more severe COVID-19 impact. These findings are robust across multiple robustness checks. This study highlights the strategic value of CSR as a resilience mechanism during crises and supports a more proactive view of CSR engagement for sustainable development, complementing the traditional legitimacy-focused perspective in existing literature. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Background/Objectives: Ancient grains are increasingly recognized for their nutritional value, environmental sustainability, and connection to traditional agriculture. This study examines Italian consumers’ awareness, purchasing habits, and willingness to pay (WTP) for ancient grain pasta, focusing on the influence of product origin, price, and flour type on preferences. Methods: An online survey was conducted with 3020 Italian household grocery shoppers. Descriptive statistics assessed awareness and purchasing behavior, while conjoint analysis (CA) evaluated the relative importance of key product attributes (origin, price, and flour type) in pasta choices. The sample was segmented based on consumer knowledge of ancient grains. Results: A significant portion of respondents reported familiarity with ancient grains, perceiving them as “less refined” and “more digestible”; pasta emerged as the most purchased product. CA results indicated product origin as the most influential factor, followed by price, with flour type having comparatively lower influence. Notably, consumers more familiar with ancient grains showed a slight preference for ancient flour types and were less sensitive to price. Conclusions: While origin and price are primary drivers for pasta choices, knowledgeable consumers show greater valuation for flour type and accept higher prices. These findings provide strategic insights for stakeholders seeking to promote traditional, sustainable agri-food products through targeted marketing and transparent value communication. Full article

This study investigates the relationship between CEO characteristics and firm performance across emerging and developed economies using both panel regression and machine learning techniques. Drawing on Upper Echelons Theory, we examine whether CEO age, tenure, gender, founder status, and appointment origin influence Return on Assets (ROA), Return on Equity (ROE), and market-to-book ratio. We apply the fixed and random effects models for inference and deploy random forest and XGBoost models to determine the feature importance of each CEO trait. Our findings show that CEO tenure consistently predicts improved ROE and ROA, while CEO age and founder status negatively affect firm performance. Female CEOs, though not consistently significant in the baseline models, positively influence market valuation in emerging markets according to interaction models. Firm-level characteristics such as size and leverage dominate CEO traits in explaining performance outcomes, especially in machine learning rankings. By integrating machine learning feature importance, this study contributes an original approach to CEO evaluation, enabling firms and policymakers to prioritise leadership traits that matter most. The findings have practical implications for succession planning, diversity policy, and performance-based executive appointments. Full article

Global biodiversity is in crisis, with wildlife populations declining 69% since 1970 (WWF). Preserving and restoring ecosystems is essential for sustaining life on Earth. However, many countries rely on market-based instruments like biodiversity offsets, despite little evidence of their effectiveness. This study critically examines biodiversity offsets, identifying institutional, data, ecological, economic, and social failures that undermine their success. Using Indonesia, a global biodiversity hotspot, as a case study, we develop an econometric model to analyze key drivers of deforestation. The findings reveal that biodiversity offset schemes are fundamentally flawed: they lack scientific credibility, rely on arbitrary ratios, lack auditing and transparency, create value conflicts, and fail to achieve “No Net Loss” even over a 100-year timeframe. Offsets do not compensate for lost biodiversity, especially for affected communities, and are rarely supported by ecosystem mapping or robust valuation metrics. Without major reforms, they cannot halt or reverse biodiversity loss. A stronger, evidence-based approach is urgently needed. Rather than relying on ineffective offset schemes, the global community must prioritize genuine ecosystem restoration and sustainable conservation strategies to protect biodiversity for future generations. Full article

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

This study investigated how corporate maturity—measured through firm age and lifecycle stage—moderates the value relevance of Environmental, Social, and Governance (ESG) disclosures in a frontier market context, using Bangladesh as a case study. Drawing on panel data from 2011–2012 to 2023–2024 for 86 publicly listed non-financial firms, the study employed a modified Ohlson valuation framework, panel regression analysis, and multiple robustness techniques (2SLS, PSM). ESG disclosure was measured using a researcher-developed index aligned with international reporting standards (GRI, SASB, TCFD, UN SDGs). ESG disclosures are positively associated with firm value, but this relationship is significantly moderated by corporate maturity. Younger firms exhibit a stronger valuation effect from ESG transparency, driven by higher signaling and legitimacy needs. In contrast, mature firms experience a diminished marginal benefit, reflecting routine compliance rather than strategic differentiation. These findings challenge the uniform application of ESG assessment models and suggest the need for lifecycle-adjusted disclosure ratings, particularly in nascent regulatory environments like Bangladesh. Investors and regulators should tailor ESG evaluation criteria by firm age and industry sustainability exposure. Younger firms, often overlooked, may carry outsized ESG signaling value in emerging markets. Enhancing ESG transparency among younger firms can foster greater stakeholder trust, support inclusive growth, and strengthen social accountability in emerging economies. This study contributes to the ESG literature by introducing corporate maturity as a key moderating variable in value relevance analysis. It provides new empirical insights from a developing economy and proposes lifecycle-based adaptations to global ESG rating methodologies. Full article