Search Results (498)

This study examines which bond-level, issuer-level, and institutional characteristics are associated with external recognition or alignment in the global corporate green bond market and whether these associations differ between Climate Bonds Initiative (CBI) alignment and formal CBI certification. The database contains 6009 green bond issuances by 822 corporate issuers between 2016 and 2025. The main complete-case analysis uses 4875 bonds issued by 682 firms and estimates a multilevel logistic model with issuer random intercepts, Fitch-rating, sector, and issuance-year controls. Larger issuance amounts are positively associated with external recognition or alignment across every specification. Issuance in a developed market and average operating margin, which is treated as an exploratory covariate, are also positively associated with the broad outcome, whereas issuer size is negatively associated; however, these relationships are more sensitive to outcome definition, temporal measurement, or estimator choice. Financial leverage and credit-rating categories show no consistent association. Separate aligned-versus-self-labelled, certified-versus-aligned, and multilevel multinomial analyses reveal substantial heterogeneity between CBI alignment and formal certification, confirming that the two categories should not be interpreted as equivalent verification mechanisms. These findings identify transaction scale as the most stable correlate of external recognition or alignment and show that issuance-market context and issuer characteristics operate differently across recognition categories. Full article

This paper examines directional quantile dependence from green bonds, clean energy markets, and carbon markets to the return states of five literature-classified green cryptocurrencies. Using daily returns from 25 September 2019 to 23 May 2025, we estimate static cross-quantilograms on a $19\times 19$ quantile grid at lags 1, 5, and 22, 500-observation rolling cross-quantilograms, bootstrap surface tests, and green-specificity comparisons with five cryptocurrencies that used proof-of-work (PoW) consensus throughout the comparison sample. Point estimates display heterogeneous short-run patterns in selected green-bond and carbon-market pairs, but none of the 45 forward surfaces rejects the omnibus null at the 5% level. Rolling estimates vary across windows and tail cutoffs. The largest raw green-group contrast occurs for carbon quota prices at lag five, although its time-series bootstrap interval includes zero and factor-adjusted tests do not detect systematic green-minus-PoW separation. Descriptive quantile-on-quantile connectedness estimates are higher at extreme quantiles than at the median–median state. Overall, the evidence is more consistent with broad cryptocurrency-market conditions than with a uniform dependence pattern associated with the environmental label. Full article

Conventional macroeconomic indicators are released with a time lag and may therefore provide limited information about rapidly changing market expectations, creating a need for complementary high-frequency indicators capable of capturing the informational content of financial narratives. This study examines whether sentiment extracted from unstructured financial text contains incremental predictive information for short-horizon market dynamics in the context of Eastern Europe’s green and digital transition. Using financial news and discourse collected between May 2025 and May 2026, a FinBERT-based Daily Sentiment Index (DSI) is constructed for selected technology, energy, and sustainability-related narratives and linked to market indicators and representative assets, including the DAX, UiPath, and OMV Petrom. The empirical strategy combines Granger predictability tests, vector autoregression (VAR), GARCH(1,1) models with sentiment effects, lead–lag analysis, Bai–Perron structural-break tests, and Markov-switching specifications to assess predictive temporal relationships, volatility dynamics, and time variation in sentiment–market interactions. Forecasting utility is further evaluated through an expanding-window out-of-sample exercise in which a sentiment-augmented VAR is compared with a benchmark autoregressive specification using one-day-ahead DAX returns, with relative predictive accuracy evaluated via the Diebold–Mariano test. The results indicate that lagged sentiment contains statistically significant predictive information for subsequent market returns and that incorporating the DSI significantly improves out-of-sample forecast accuracy relative to the benchmark specification. The evidence also reveals heterogeneous and time-varying responses across the selected cases: technology-related assets exhibit comparatively differentiated sensitivity to macro-sentiment conditions, whereas the energy case displays stronger exposure to transition-related narratives. Structural-break and regime-dependent estimates further suggest instability in the relationship between ESG sentiment and market performance over the sample period, but do not establish a permanent structural transformation in investor preferences. The study contributes to the narrative-economics and computational-finance literature by providing applied evidence that domain-specific textual sentiment contains incremental short-horizon information beyond conventional market dynamics and can complement lagged macroeconomic indicators in financial forecasting and risk-monitoring applications. Full article

This paper investigates the impact of corporate financial asset holdings on green innovation and explores the internal mechanisms through which financialization shapes firms’ sustainability-oriented technological strategies. Using a comprehensive panel dataset of Chinese A-share non-financial firms from 2010 to 2023, the analysis confirms that corporate financialization significantly inhibits green innovation, supporting the crowding-out hypothesis. Mechanism tests reveal three economic channels: reduced innovation capability (measured by R&D staffing), diminished innovation willingness (proxied by environmental investment), and a deteriorated innovation environment (captured through financial constraints). Heterogeneity analysis further shows that this negative effect is particularly pronounced among non-state-owned enterprises, firms operating in low-pollution industries, and those facing high market competition. These findings highlight how financialized capital allocation undermines long-term sustainable investment, especially where institutional protections are weak or absent. The study offers important policy implications for regulating excessive financialization, enhancing green financing mechanisms, and designing targeted incentives to foster sustainability-driven innovation across different industry and ownership structures. Full article

We study the effects of green finance reform on firms’ long-term investment using data from a Chinese green finance pilot policy introduced in 2017 and panel data on listed firms covering an eleven-year period. We find that the policy significantly increases long-term investment, with the effect accounting for approximately 17.86% of the sample mean. This positive effect mainly operates by easing firms’ financing constraints, improving firms’ financing maturity structure, and mitigating uncertainty at both the operational and market levels. Heterogeneity analyses reveal that the policy effect is more pronounced among mature firms, non-state-owned enterprises, firms with financially experienced directors, supervisors, and senior managers, and those in non-pollution-intensive industries and competitive markets. Full article

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

In the context of deepening data assetization, this paper explores the strategic role of data assets in sustaining corporate competitiveness. Utilizing a dataset of China’s A-share listed companies from 2010 to 2023, we examine the influence of data assets on continuous innovation from a synergistic perspective of robust anchoring and dynamic reconstruction. The findings indicate that data assets can significantly empower continuous innovation, with the effect being more pronounced in specific contexts defined by ownership, regional marketization, the intensity of intellectual property protection, and market structure. In further analysis, we also found that this effect extends to green continuous innovation, offering more direct evidence for the sustainability implications of data-driven innovation. Crucially, our mechanism analysis reveals that data assets facilitate innovation through two pathways: (1) a “de-financialization” pathway, which reduces reliance on financial speculation and consolidates the real economy; and (2) a capability-building pathway, which enhances firms’ dynamic capabilities. These findings offer practical implications for policymakers and managers seeking to promote sustainable corporate growth, as they demonstrate that embedding innovation continuity into core strategies serves as a foundational driver of long-term environmental, social, and economic resilience. Full article

Using 2143 daily observations from January 2018 to March 2026, this study examines risk transmission among European Union Allowance (EUA) prices, green-bond indices, environmental, social, and governance (ESG) equity indices, conventional financial assets, and digital assets. The empirical design combines vector autoregression with an exogenous variable and generalised forecast-error variance decomposition (VAR-X/GFEVD) for the full-sample and rolling analyses with quantile vector autoregression with an exogenous variable and GFEVD (QVAR-X/GFEVD) for the quantile-specific analysis. The preferred eight-asset VAR-X specification produces a full-sample Total Connectedness Index (TCI) of 39.31%, while the corresponding 100-day rolling VAR-X analysis produces a mean TCI of 43.03%. The rolling TCI reaches a maximum of 54.17% on 20 July 2022 and a COVID-19-period maximum of 48.80% on 8 June 2020, indicating that system-wide connectedness increases during periods of market stress. The Cboe Volatility Index (VIX), S&P 500, and ESG equity index are consistently the dominant net transmitters, whereas EUAs, Bitcoin, crude oil, and green bonds are net receivers. The EUA net-receiver position becomes less negative after 2024, but this temporal association should not be interpreted as evidence that strengthened Phase IV provisions caused greater carbon-market stability. The quantile-specific results reveal non-monotonic state dependence, with the TCI ranging from 18.28% at τ = 0.6 to 23.52% at τ = 0.4 rather than increasing uniformly toward the lower tail. Full article

The global energy transition is reshaping the relationship between traditional fossil-fuel and renewable energy sectors, making the transmission of financial shocks across brown and green energy equity markets increasingly important. This paper examines connectedness between brown and green energy equity markets using a unified quantile–frequency framework that captures both market-state dependence and investment-horizon heterogeneity, and it further investigates how innovations in transition and physical climate risk are associated with changes in connectedness. Using global energy equity ETFs representing carbon-intensive and renewable energy sectors, we obtain three main findings. First, connectedness is substantially stronger in both lower- and upper-tail market states than around the median, indicating pronounced state dependence in risk transmission. Second, the frequency decomposition reveals that short- and long-horizon connectedness contribute similarly on average, but their relative importance varies markedly across market states: long-horizon connectedness is relatively more important in the lower-tail state, whereas short-horizon connectedness is relatively more important around the median. Third, transition and physical climate risks exhibit distinct frequency-specific associations with connectedness. Physical risk innovations are negatively associated with changes in total connectedness, whereas transition risk innovations are positively associated with changes in short-horizon connectedness; the latter association is also stronger at the short than at the long horizon, although this cross-horizon difference is only marginally significant. These findings show that risk transmission across brown and green energy equity markets is jointly state- and horizon-dependent and that the association between climate risk and connectedness differs across climate risk dimensions and investment horizons. The results have implications for portfolio diversification, risk management, renewable energy investment, and the assessment of financial risks associated with the global energy transition. Full article

As an integral component of environmental, social, and governance (ESG) reporting, climate risk disclosure (CRD) has received growing attention from firms, investors, and regulators. Using 4501 Chinese A-share listed firms over 2009–2024, this study examines the association between CRD and corporate financial performance using return on assets (ROA) and Tobin’s Q (TQ) as separate accounting- and market-based outcomes. We construct a firm-year CRD index from annual-report text and interpret it as a normalized measure of climate-related disclosure intensity. CRD is positively associated with both ROA and TQ, and the results remain robust across alternative disclosure construction, sample windows, future outcomes, high-dimensional fixed effects, and complementary endogeneity analyses. Pathway tests show that greater CRD is associated with lower financing costs and greater green innovation, both of which are associated with stronger financial outcomes. Institutional ownership and accounting information quality positively moderate the CRD–performance relationship. Heterogeneity analyses indicate stronger associations among non-state-owned and heavily polluting firms, while both physical and transition risk disclosure are positively associated with financial performance. Overall, the findings support a conditional value-relevance interpretation of climate risk disclosure. Full article

Amid rising population pressure, heightened volatility in international markets, and increasingly binding domestic resource and environmental constraints, safeguarding food security while maintaining ecological security has become a salient policy and academic concern in China. Within this context, our study investigates whether and how green finance contributes to the dual security of food and ecology. Building on a theoretical analytical framework, we elaborate on the functional role of green finance and empirically assess its effects using provincial panel data from China. We construct a composite index to gauge the level of green finance development and then employ a two-way fixed-effects model with province and year fixed effects to identify the overall impact of green finance, followed by a mediation model to uncover the underlying transmission mechanisms and sub-sample regressions to examine heterogeneity. The empirical evidence indicates that, while green finance significantly improves the dual security of food and ecology, substantial heterogeneity is observed, with stronger effects in southern regions, major grain-producing functional areas, and plain regions. Mechanism analyses further show that green finance advances dual security through three primary channels of agricultural structure adjustment, planting specialization, and agricultural green technological innovation, among which shifts in agricultural production structure, greater centralization of planting, and enhanced agricultural green innovation capacity constitute the dominant transmission mechanisms. By integrating food security and ecological security within a unified framework, we clarify the multidimensional pathways through which green finance supports sustainable agricultural development and provide new empirical evidence on how financial instruments can reconcile production and environmental objectives. Full article

China’s “dual carbon” targets and stricter green finance regulations have increased compliance pressures on manufacturing firms. In response, some firms engage in greenwashing—exaggerating their environmental performance or concealing negative information. If greenwashing spreads through interlocking directorate networks, it poses a threat to green financial stability. However, existing research primarily focuses on individual firm motivations, leaving the mechanisms of network contagion and their boundary conditions insufficiently understood. Using panel data on A-share manufacturing firms from 2009 to 2023, we employ two-way fixed-effects models to test for peer greenwashing contagion and examine how financing constraints moderate this effect. Our findings reveal significant contagion within manufacturing interlocking directorate networks: firms facing fewer financing constraints are more sensitive to peer greenwashing. This effect is more pronounced in highly marketized regions, in high-tech industries, and among firms with advanced digital transformation. Full article

Nature-based solutions (NBSs) are central to urban climate-neutrality strategies, but their implementation still lags behind policy ambition. One reason is that most NBS benefits are public goods and rarely generate direct revenue, so we know far less about the institutional and financial conditions needed to deliver them than about their ecological performance. This paper develops a business model framework linking NBS interventions to financing and governance configurations, applying established NBS typologies and the Pestoff Triangle of state, market, and community provision to thirteen interventions across eleven sites in the Railway District of Thessaloniki, Greece. The interventions are organised into five business model categories: public space greening, private space upgrades, public–private hybrids, building retrofits, and renewable energy. Their financing logic is shaped less by intervention type than by ownership structure and stakeholder configuration. To address this gap, we followed category-specific blended finance strategies combining grants, private investment, regulatory incentives, and community resources, underpinned by stewardship-oriented governance. The findings suggest that scaling NBSs depends less on technical readiness than on institutional capacity to match financing and governance to local ownership and stakeholder contexts, offering a pathway from climate-neutrality strategy to implementable urban regeneration. Full article

This study investigated the dynamic volatility connectedness among financial technology (FinTech), green indices, and global uncertainty metrics between June 2018 and May 2025. The research was conducted to understand how technological innovation and sustainability indices interact with systemic risk during periods of extreme global stress, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the market disruptions of early 2025. The analysis employed a time-varying parameter vector autoregression (TVP-VAR) framework to capture time-varying interdependencies and risk spillovers across multiple market regimes. Key findings indicated that total dynamic connectedness intensified significantly during crisis events, with major spikes occurring during the 2020 pandemic onset and the 2025 shocks possibly related to the “DeepSeek” AI disruption and the US tariff announcements. FinTech indices and green assets consistently functioned as net transmitters of shocks, while uncertainty indices, particularly the VIX, served as net recipients. Notably, the Alternative Finance Index (AFI) exhibited regime-dependent behaviour, transitioning from a transmitter to a recipient during the COVID-19 pandemic. These results imply that innovative and sustainable sectors have evolved into systemic drivers of global market sentiment rather than mere recipients of external shocks. The findings provide critical insights for stakeholders in financial markets, helping them to rethink their current approaches and prevent financial losses amid market upheaval. Full article

This paper analyzes the viability of Indian equity markets in response to global energy supply shocks. This study attempts to correct the missing-variable bias in earlier literature by using the USD-to-INR exchange rate as an exogenous explanatory variable. This will help determine the intricate synthetic relationship between Brent Crude Oil Returns and the carbon-efficient S&P BSE GREENEX. Vector Autoregressive with exogenous variables (VARX) models are employed to analyze the effects of structural shocks to Brent Crude Oil prices on the S&P BSE GREENEX. The empirical results found that global oil price shocks might immediately affect green equity values in India. Even without foreign currency changes, the Indian Green Exchange Index (GREENEX) maintains its long-term values, showing structural resilience. Institutional investors and Indian financial authorities, such as SEBI and the Reserve Bank of India, gain better risk-management insights amid international energy crises from this information. It also shows that carbon-efficient standards can hedge inflation induced by foreign import supply chain interruptions. Full article