Search Results (1,156)

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

This study investigates the dynamic interactions between Bitcoin, global financial indicators, and the Kazakhstan Stock Exchange (KASE) index within a VAR-based econometric framework, addressing a notable gap in the literature on emerging and shallow financial markets. While prior research predominantly focuses on developed economies, evidence suggests that cryptocurrency–stock market linkages are time-varying, crisis-sensitive, and often asymmetric. In this context, the present study examines both short-term causality structures and shock transmission mechanisms among KASE, Bitcoin (BTC), oil prices, the U.S. dollar index (DXY), and the VIX using monthly data for the period 2017M01–2026M04. Empirical findings indicate that, despite the absence of statistically significant Granger causality from individual global variables to KASE, the joint dynamics suggest a non-negligible, albeit indirect, interaction structure. Variance decomposition and impulse-response analyses further reveal that KASE dynamics are predominantly driven by its own shocks, reflecting the relatively segmented and internally driven nature of the market. Diagnostic tests confirm the robustness of the model, with no evidence of serial correlation or heteroskedasticity in residuals. These findings are consistent with the structural characteristics of the Kazakh financial system, including limited market depth, lower investor participation, and high sensitivity to domestic macroeconomic conditions. Unlike developed markets where stronger integration is observed, KASE appears only weakly connected to global financial and cryptocurrency markets. The study contributes to the literature by providing empirical evidence from a frontier market and highlights the importance of considering country-specific structural factors when evaluating financial integration. Policy implications emphasize the need to enhance market depth, transparency, and investor confidence to strengthen the responsiveness of KASE to global financial developments. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

This study examines the impact of corporate environmental, social, and governance (ESG) practices on corporate stock prices, with a particular focus on the mediating role of board gender diversity (BGD). Using a dataset of 9543 firm-year observations from non-financial companies across 15 countries in the S&P 1200 global index between 2012 and 2020, the analysis evaluates ESG performance through the Refinitiv ESG Combined Score, which incorporates disclosures across ESG pillars and an overlay for ESG controversies. BGD is measured as the proportion of female directors on corporate boards, while stock prices are assessed using annual closing prices. The findings reveal a positive relationship between ESG performance and corporate stock prices, both at the aggregate level and across individual ESG pillars. Additionally, BGD is shown to enhance stock price performance and serves as a mediator in the ESG-stock price relationship. These results highlight the critical role of board diversity in amplifying the financial benefits of ESG practices. Further analysis suggests that the value relevance of ESG performance varies across institutional settings, with stronger effects observed in emerging/offshore markets and in the North American and European regions. The study offers important implications for companies, investors, and policymakers, emphasizing the need to integrate ESG strategies and promote gender diversity at the board level to enhance corporate valuation and long-term sustainability. Full article

This study provides a structured operational-to-financial roadmap for soil organic carbon (SOC) sequestration in mineral soils as a specific carbon-farming pathway. It integrates SOC management; Monitoring, Reporting, and Verification (MRV) execution; financial recognition; and farmer adoption barriers. A comparison of carbon farming pathways is first presented to investigate their strengths and limitations, highlighting the specific importance of SOC management in mineral soils. For high-integrity carbon accounting, SOC gains should be assessed not only for quantity, but also for additionality, permanence, uncertainty, leakage, lifecycle emissions, and transparent verification. Credible MRV frameworks operationalize this logic: monitoring quantifies SOC changes, reporting ensures transparency, and verification provides independent assurance for carbon credit issuance and financial recognition. However, MRV execution faces several challenges, including high spatial variability of SOC, slow accumulation rates, methodological uncertainty, and high costs that limit scalability and reduce trust among stakeholders. Financial incentives are available from both public and private sources, supporting long-term soil carbon stabilization, verified carbon removals, and corporate insetting projects. Yet, adoption remains constrained by uncertain payments, poor transparency, contract and permanence concerns, as well as learning and operational costs for farmers. Addressing these bottlenecks is essential for transforming mineral-soil SOC sequestration into a scalable, high-integrity climate and economic opportunity. Full article

This study investigates Bitcoin price forecasting using integrated sentiment analysis and gradient boosting within digital financial ecosystems. Two XGBoost models were developed using sentiment scores derived from Bitcoin news (2021–2024) and technical indicators, including GARCH-estimated volatility, Bollinger Bands, MACD, and RSI. The analysis uses 1042 daily Bitcoin observations and 10,025 sentiment records. Two model configurations were evaluated: one using only technical indicators and another incorporating daily aggregated sentiment scores. Model performance was assessed using Diebold–Mariano tests with Newey–West HAC variance estimation and walk-forward validation across 40 rolling windows. Contrary to expectations, sentiment features provided no statistically significant improvement over the technical-only model (p = 0.4888). Both models achieved identical test performance (R² = −0.16%). Walk-forward validation revealed substantial temporal instability (Mean R² = −126.30%, Std = 233.05%), highlighting the challenges of forecasting daily Bitcoin returns. Nevertheless, both XGBoost models significantly outperformed the random walk benchmark (DM statistic = −8.58, p < 0.0001), indicating that technical indicators capture exploitable market structure despite limited predictive accuracy for practical trading. These findings support the efficient market hypothesis and have implications for digital financial ecosystems integrating multimodal information. Full article

In the last 10 years, the global green bond market has reached an estimated value of USD 6.8 trillion. However, credibility concerns persist due to greenwashing risks and issues regarding the reporting system. The current measurement, reporting, and verification systems (MRV) have high uncertainty levels of 10–30%, and so they contribute to information asymmetries and fuel investor skepticism when allocating capital to green bond instruments. The scope of this study is to develop an integrated management approach that links air quality and greenhouse gas monitoring with financial incentives throughout the lifecycle of green bonds. The central contribution is a four-phase lifecycle model covering issuance, allocation, monitoring, and impact reporting, which systematically identifies where greenwashing risks and verification gaps arise across the investment cycle. Methodologically, the study combines qualitative content analysis, a novel Disclosure Quality Score (DQS) instrument, based on the Regulation (EU) 2023/2631, four documentary case studies, and an advanced verification framework. The content analysis shows that regulatory and market-performance studies dominate the literature, while integrated lifecycle verification frameworks remain less explored. The DQS uses eight indicators, applied to a matched sample of green bonds, in accordance with the European Green Bond Standard (EuGB) and the ICMA Green Bond Principles (GBP). The results demonstrate that bonds issued under the EuGB present higher disclosure quality (mean DQS = 15.4/16) compared to GBP-aligned bonds (mean DQS = 11.4/16). Case studies show strong issuance-stage disclosure, but weak post-issuance verification. The framework enables lifecycle-wide accountability by reducing information asymmetry. The proposed lifecycle framework and DQS instrument offer a replicable model for improving disclosure quality and ESG performance standards, with direct implications for sustainable investment screening and ESG fund selection. Overall, the findings show that improving green bond credibility requires moving beyond issuance-focused disclosure toward lifecycle-wide verification. Full article

This study investigates the impact of green leadership on financial performance in the hospitality sector, focusing on the mediating roles of resource management and market positioning. Drawing upon the Resource-Based View and Stakeholder Theory, data were collected from 390 employees working in five-star hotels in Egypt and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that green leadership positively influences financial performance both directly and indirectly. Specifically, green leadership enhances resource management practices and strengthens market positioning, which in turn contribute to improved financial outcomes. The mediation analysis confirms that both resource management and market positioning serve as significant mechanisms through which green leadership translates sustainability-oriented strategies into economic benefits. These findings extend current knowledge on sustainable leadership by identifying key organizational pathways linking environmental responsibility with financial success. This study also provides practical implications for hospitality managers seeking to integrate sustainability initiatives into strategic and operational decision-making to achieve long-term competitive advantage. Full article

Carbon has increasingly been incorporated into economic and financial architectures as a tradable commodity within contemporary climate governance. Yet, carbon is not produced, stored, or mobilized in abstract space; it emerges from territorially specific land-use systems, ecological processes, and socio-spatial trajectories. This study examines carbon as a territorial commodity by analyzing long-term land-use and land-cover (LULC) dynamics in the municipality of Alegrete, located in the Brazilian Pampa biome, between 1985 and 2024. Based on MapBiomas Collection 10, and using cloud-based processing in Google Earth Engine combined with reproducible statistical workflows in R, the analysis identifies structural land-use trajectories shaping carbon-relevant territorial conditions. Results reveal a strong contraction of native grasslands, corresponding to approximately 17.8% of the municipal territory and a 24.2% reduction relative to the 1985 grassland area, alongside the expansion of mechanized agriculture, particularly soybean cultivation (+10.8% of the territory; +1343% relative to 1985 soybean area), and the consolidation of flooded rice systems (+7.6% of the territory; +146% relative to 1985 rice area). Rather than estimating carbon stocks or fluxes, the study establishes a territorial baseline linking land-use trajectories to key carbon-relevant processes, including soil carbon stability, disturbance intensity, permanence constraints, and multi-gas trade-offs. From a historical–structural perspective, these trajectories contrast with prevailing policy narratives and market-based instruments that assume an expanding carbon sequestration capacity, revealing a governance gap between valuation mechanisms and land-use realities. By conceptualizing carbon as a territorially embedded economic asset linked to land-use trajectories, the article contributes to interdisciplinary debates on climate governance, MRV integrity, environmental valuation, and the structural limits of market-based environmental instruments. Full article

The paper investigates the evolution of earnings management across corporate life-cycle stages and evaluates the moderating role of leverage within an emerging market context. Utilising a dataset of 273 non-financial firms listed on Vietnamese stock exchanges over the 2012–2022 period (3003 firm-year observations), we delineate life-cycle phases—introduction, growth, maturity, and decline—using net cash flow patterns. Earnings quality is proxied via diverse discretionary accrual models. Methodologically, the study employs fixed-effects regressions with firm-clustered standard errors, incorporating interaction terms to capture stage-specific leverage dynamics. The empirical evidence reveals a non-linear and stage-dependent trajectory of earnings management, with introduction- and decline-stage firms exhibiting higher discretionary accruals compared to benchmarks. Crucially, the institutional impact of debt financing is contingent upon corporate maturity; while leverage exhibits a baseline positive association with earnings management, this relationship diminishes or reverses during the introduction and decline phases. These insights withstand rigorous robustness checks, including different discretionary accrual models and alternative life-cycle classifications. This study advances current literature by integrating capital structure into the corporate life-cycle framework, demonstrating that leverage effects are dynamic and shaped by shifting financial constraints and monitoring environments. Ultimately, the findings offer valuable insights into financial reporting incentives in emerging markets characterised by concentrated ownership and transitional corporate governance, yielding critical implications for regulators, investors, and auditors. Full article

Agricultural production is increasingly threatened by climate variability, limited access to quality inputs, and market shocks in developing countries. Insurance–input bundles, which integrate crop insurance with inputs like fertiliser and seed, have emerged as a promising tool for improving productivity and resilience among smallholder farmers. This study adopts a structured (systematic narrative) literature review approach, synthesising evidence from 152 studies to examine farmers’ awareness, attitudes, willingness to pay, participation, satisfaction, and productivity outcomes associated with insurance–input bundles. The findings show that awareness remains uneven and often limited by weak extension systems and low financial literacy, while farmers’ attitudes are strongly shaped by past experiences, cultural perceptions, and institutional trust. Furthermore, affordability constraints and risk misinterpretation reduce willingness to pay, whereas perceived value and institutional credibility significantly enhance demand for bundled products. Across the reviewed literature, adoption is shown to be a non-linear and interdependent process influenced by behavioural, economic, and institutional factors, where breakdowns in trust, affordability, or information can limit participation. Evidence further indicates that insurance–input bundles promote the adoption of improved inputs, increase yields, and enhance income stability, although these impacts are highly context-dependent and mediated by implementation quality, including timely payouts and effective service delivery. The review contributed to the literature by advancing a systems-based understanding of bundled insurance adoption, highlighting the central role of institutional reliability, behavioural responses, and implementation quality. Lastly, the review underscores the need for strong institutions, integrated extension systems and farmer-centred design to ensure sustainable scaling. Full article

This study examines the evolution of dependence structures and tail risk transmission among technology equities, Bitcoin, Gold, and Crude Oil during 1 January 2016–1 January 2026. The analysis focuses on NVIDIA (NVDA), AMD, Tesla (TSLA), Bitcoin (BTC), Gold and Oil, covering major disruptions including the COVID-19 pandemic and the Russia–Ukraine conflict. An integrated methodological framework combines DCC-GARCH modeling, R-vine copulas, tail dependence analysis, complexity measures and machine learning-based forecasting techniques. The findings reveal volatility persistence and time-varying correlations, especially between technology equities and BTC during crisis periods. Regime analysis reveals that dependence structures are not stable in time. Lower-tail dependence intensifies during periods of market stress, indicating increased downside risk transmission. Gold remains weakly connected to the other assets, while Bitcoin has the strongest exposure to extreme downside co-movements. Complexity analysis based on the Scale-Dependent Lyapunov Exponent (SDLE) indicates heterogeneous dynamics across scales, characterized by local divergence and stabilization at broader scales. Forecast results based on Random Forest and XGBoost models provide limited predictive gains over benchmark specifications, suggesting that dependence and tail risk modeling offer better insight than short-horizon return predictions. These results are important for monitoring tail risk transmission for financial stability policies. Full article

The COVID-19 pandemic prompted the introduction of South Africa’s Social Relief of Distress (SRD) grant to mitigate income shocks among individuals excluded from existing social programs. This study utilizes a three-year national representative dataset from the General Household Survey and applies a fixed effects regression framework to estimate the association between COVID-19 SRD grants and unemployment. The findings indicate a positive and statistically significant association between grant receipt and unemployment. Specifically, the results suggest that grant recipients are approximately 27–32% more likely to be unemployed than non-recipients. The findings suggest that while the grant provided necessary initial financial relief, in the absence of complementary labour market interventions, income support alone may be insufficient to address unemployment. The study concludes by recommending the coordination of temporary income relief integrated with active labour market policies to promote sustainable employment outcomes. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

Electric vehicles (EVs) are increasingly recognized as a sustainable transportation solution; however, mass-market adoption in Thailand remains limited due to infrastructure constraints, technological complexity, and evolving consumer perceptions. This study examines the effects of charging infrastructure accessibility, perceived ease of use, and psychological driving experience on consumers’ willingness to pay (WTP) for EVs. A quantitative approach was employed using survey data collected from 400 EV users and analyzed through Structural Equation Modeling (SEM). Grounded in the Technology Acceptance Model (TAM) and the Theory of Consumption Values (TCV), the study reveals that charging infrastructure accessibility significantly enhances perceived ease of use, driving experience, and WTP. In addition, perceived ease of use and driving experience positively influence consumers’ financial commitment toward EV adoption and partially mediate the relationship between infrastructure accessibility and WTP. The findings indicate that EV consumer valuation is shaped by both functional infrastructure readiness and psychological user experience. The study contributes to EV consumer behavior literature by integrating cognitive and experiential perspectives and provides practical implications for policymakers and industry stakeholders seeking to accelerate EV adoption in Thailand. Full article