Search Results (4,628)

As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets. Full article

Emissions Trading Systems (ETSs) have become some of the most widely adopted market-based instruments for reducing greenhouse gas emissions. However, their environmental performance varies considerably across jurisdictions, suggesting that carbon pricing mechanisms operate under heterogeneous structural and institutional conditions. This study analyzes the factors influencing ${\mathrm{CO}}_2$ emissions performance in economies implementing ETSs. Grounded in Ecological Modernization Theory and Institutional Theory, the research combines a k-prototypes clustering model and an Artificial Neural Network (ANN). First, 58 ETSs across 53 countries were classified into four archetypes according to their institutional maturity, regulatory scope, and structural characteristics. Second, an ANN model was estimated using annual data from 2000–2021 to examine the influence of environmental, socio-demographic, economic, and development-related variables on ${\mathrm{CO}}_2$ emissions per capita. The results show that ETS performance depends not only on economic development levels but also on broader structural and institutional factors. Renewable energy consumption and renewable energy production emerge as the most influential drivers of lower ${\mathrm{CO}}_2$ emissions, particularly in developing economies. Conversely, urbanization, export-oriented activities, and governance weaknesses are associated with greater emissions pressures. Corruption also exhibits a stronger negative effect on environmental performance in emerging economies. Overall, the findings suggest that ETSs should not be viewed as standalone climate instruments; their effectiveness depends on complementary policies that promote renewable energy deployment, strengthen institutional quality, and address the pressures associated with trade and urbanization. Full article

In light of growing concerns about sustainable development and green innovation, the green market has progressively taken center stage in the financial markets. From the nonlinear information transmission angle, we look into the interconnectedness between the global banking sectors and the green markets using transfer entropy causal networks, containing the Dow Jones Green Bond Index (SPGB), Dow Jones Sustainability Index (DJSI), The S&P Global Clean Energy Index (SPCL), and MSCI World ESG Leaders Index (ESGL). We observe significant bidirectional causal relationships between two markets. The banking industries of developed nations and emerging economies like South Korea, Indonesia, and India are the most important, while four green markets are vital. Furthermore, using the CEEMDAN-SE-KM approach, this study also investigates the two markets’ heterogeneous performance at various time scales. The causal relationships between two markets exhibit heterogeneity at time scales, and that is most noticeable at the short-term scale. Additionally, after the COVID-19 pandemic and the conflict between Russia and Ukraine, there is an increase in the causal relationships between the two markets and a higher efficiency of information transmission. These results help regulatory bodies and green market players have a more thorough understanding of and dynamic regulation of the green market. Full article

A This study examines the interplay between environmental, social, and governance (ESG) practices, artificial intelligence (AI) adoption, and financial performance within Saudi Arabia’s financial sector. It investigates whether AI adoption moderates the ESG–performance relationship, reflecting the sector’s ongoing digital transformation under Vision 2030. Drawing on 224 firm-year observations across banks, diversified financials, real estate investment trusts (REITs), and insurance companies, the study employs content analysis of annual reports to identify AI implementation. Panel regression models are used to test the effects of ESG practices on both accounting-based (ROE) and market-based (Tobin’s Q) performance measures, while examining AI’s moderating role. The results reveal that ESG practices significantly enhance accounting-based performance, particularly return on equity, while board size exerts a positive and board independence a negative influence. However, ESG does not significantly affect market-based valuation (Tobin’s Q). Notably, AI adoption negatively moderates the ESG–financial performance link, suggesting short-term challenges in integrating digital transformation with sustainability strategies. This study contributes to literature in three key ways. First, it provides new evidence from financial institutions in a developing economy—Saudi Arabia—where ESG and AI integration remains underexplored. Second, unlike previous research that proxies AI adoption through R&D expenditure, this study captures actual deployment of AI tools in operational activities. Third, it extends the ESG–performance debate by introducing AI adoption as a novel moderating factor. The findings offer actionable insights for managers and policymakers in emerging markets, underscoring the importance of developing organizational capabilities that harmonize AI-driven innovation with ESG principles to foster sustainable long-term value creation. Full article

Early-stage entrepreneurship involves making decisions under conditions of high uncertainty, which makes risk appetite an important component of the entrepreneurial decision-making process. This paper analyzes the influence of non-financial factors on risk appetite in early-stage entrepreneurship in Romania and their relevance in shaping the entrepreneurial decision-making process. The research uses an exploratory qualitative approach, based on focus groups and semi-structured interviews conducted with entrepreneurs in the early stages of business development. The results highlight that entrepreneurs’ risk appetite is not merely an impulsive reaction or the exclusive result of economic reasoning, but reflects a process of evaluation and adaptation to conditions of uncertainty, influenced by the interaction between psychological and human capital factors, such as previous experience and entrepreneurial education. In the analyzed context, the results suggest a moderate risk appetite of the entrepreneurs participating in the study, which favors the adoption of entrepreneurial decisions oriented towards adaptability, continuity of activity and sustainable business development. Based on the results, the authors propose an integrated conceptual framework that highlights the relationship between entrepreneurial education, previous experience, psychological factors, risk appetite and the entrepreneurial decision-making process, with implications for sustainable business development in the context of an emerging economy. Full article

To solve ecological challenges, green innovation plays a crucial role. As a result, this study examines the impact of supply chain digitalization (SCD), carbon dioxide emissions (CO₂), and financial globalization (FIG) on green innovation (GINV) in Kuwait from 2000Q1 to 2022Q4 using the Multiple Quantile-on-Quantile (MQQ), Quantile-on-Quantile Regression (QQ), and Wavelet Quantile Regression (WQR) non-parametric techniques. The variables, methods, and country examined in this research contribute to the existing literature. More specifically, Kuwait has been underexplored in existing studies. In addition, the study is motivated by Kuwait’s growing commitment to economic diversification, digital transformation, and sustainable development. Thus, the results are as follows: Firstly, the MQQ outcome confirms that the combination of SCD, CO₂, and FIG can drive GINV. The strength of this relationship is pronounced in the upper quantiles. Secondly, the QQ result shows that SCD, CO₂, and FIG diminish GINV at the lower and middle quantiles. However, at the upper quantiles, a positive relationship is ascertained. Lastly, the WQR confirms that SCD has a negative association with GINV in the short term. However, in the medium and long term, across all quantiles, the relationship is predominantly positive. CO₂ drives GINV significantly in the medium term, while FIG shows evidence of an asymmetric connection in the medium and long term. The findings suggest that policymakers in Kuwait should promote targeted incentives and digital infrastructure development to support SCD as a driver of green innovation. Full article

Amid rising global economic uncertainty and frequent external shocks, strengthening marine economic resilience has become a core priority for coastal nations to stabilize industrial supply chains and achieve sustainable marine development. China’s traditional resource-driven marine economy faces persistent structural bottlenecks, including homogeneous industrial structure, low value addition and weak risk resistance. As a landmark national policy for sustainable marine economic growth, the Marine Economic Innovation and Development Policy (MEIDP) has been piloted in 15 coastal cities across two batches, yet its causal impact on marine economic resilience remains under systematic evaluation. Using panel data of 51 Chinese coastal cities from 2008 to 2023, this study employs a multi-period difference-in-differences approach with supporting analyses to systematically evaluate the MEIDP’s impact on marine economic resilience, as well as its moderating mechanisms and heterogeneous patterns. The key findings are threefold. First, the MEIDP significantly improves coastal cities’ marine economic resilience, and this positive effect remains stable after multiple robustness tests. Second, public health emergencies exert a significant positive moderating effect, where the industrial support capacity and risk-resilience foundations established through policy implementation function more effectively under shock conditions, thereby amplifying the enhancement of resilience. Third, the policy effect shows prominent heterogeneity, being more pronounced in high-tourism cities and the Northern Marine Economic Circle, while statistically insignificant in low-tourism cities and the Southern Marine Economic Circle. This study enriches the theoretical framework of marine economic policy evaluation and provides empirical evidence from a major developing country for global marine governance, confirming that marine policies that promote innovation are an effective path to strengthen economic risk resistance. In light of these findings, we propose targeted policy recommendations to steadily enhance overall marine economic resilience. Coastal regions should deepen marine policies that promote innovation to bolster industrial upgrading and technological empowerment, adopt differentiated schemes aligned with local industrial foundations and resource endowments, promote marine industrial diversification and chain extension to reduce structural vulnerability, and improve public risk response mechanisms to strengthen the counter-cyclical buffering capacity of the marine economy. Full article

This study examines the effectiveness of Genetic Algorithm (GA)-based image augmentation in enhancing Convolutional Neural Network (CNN) performance for Romblon marble texture classification. Five CNN architectures, AlexNet, InceptionV3, VGG16, MobileNet, and ResNet50, were trained and evaluated on both raw and GA-augmented datasets. Unprocessed dataset achieved limited accuracy, with InceptionV3 performing best at 35.33%, followed closely by VGG16 at 33.78%. In contrast, GA-augmented data significantly boosted performance, with VGG16 achieving 94.68% accuracy, followed by MobileNet (92.68%) and InceptionV3 (92.46%). Entropy loss values consistently decreased across all models, indicating improved convergence and reduced overfitting. Although ROC-AUC scores remained close to 0.5, reflecting modest improvements in class separability, overall results confirm that evolutionary augmentation enriches dataset diversity and strengthens CNN learning capacity. MobileNet showed a solid balance between accuracy and computational economy, underscoring the possibility of GA-based augmentation as a workable option for real-world marble categorization, while VGG16 emerged as the most accurate of the studied architectures. Full article

Cities in the Majority World face a widening climate investment gap that is often attributed to the absence of suitable financing instruments. Green bonds promise to mobilise private capital for low-carbon urban infrastructure, yet they have diffused unevenly, leaving the economies with the greatest needs at the market’s margins. This study asks whether macroeconomic constraints—the cost of finance, monetary instability, and public indebtedness—systematically shape green bond issuance across emerging and developing economies. We assemble an original panel of 24 such economies over 2015–2024 (240 country-year observations) and estimate pooled ordinary least squares (OLS), random-effects, two-way fixed-effects, Tobit, and probit models with robust standard errors. The public debt-to-GDP ratio is positively associated with issuance in most specifications, though the strength of this relationship varies across estimators and it is not statistically significant in the preferred two-way fixed-effects model; the renewable energy share is consistently positive, while consumer price inflation shows no significant suppressive effect. A probit model of the extensive margin shows that public debt, the renewable energy share, and income per capita raise the probability of issuing among the economies for which the data permit estimation. The four lower-income Sub-Saharan economies in the sample fall outside this estimation owing to missing data, yet record no issuance whatsoever over the decade—a descriptive pattern consistent with the structural barriers the model identifies. The findings challenge the assumption that monetary stabilisation is a precondition for climate finance, pointing instead to capital-market depth and subnational fiscal capacity as the more binding constraints. Full article

As a booming application domain in sharing economy, the crowd logistics services (CLSs) have emerged in recent years to take advantage of under-utilized resources for generating economic value. However, unduly designed CLS system platforms may suffer substantial problems such as sensitive information exposure, excessive commission fees, and trust issues. To mitigate such problems, we propose an approach comprising four initiatives: (1) exploring the applicability of blockchain technology and its affiliated technology, smart contract, in CLSs to manifest blockchain-enabled benefits including service traceability, process transparency, system automation and disintermediation; (2) adopting blockchain and smart contract technologies to design a blockchain application system architecture (BASA) suitable for reengineering current CLSs; (3) demonstrating the blockchain-based crowd logistics services (BCLSs) system design, implementation, and deployment details; and (4) evaluating the functionality and benefit of BCLSs approach to confirm its feasibility and applicability. After presenting the system design and implementation outcomes, this study elaborates the benefits and implications of BCLS systems through four theoretical frameworks: e-Commerce Value Creation Theory (VCT), Innovation Diffusion Theory (IDT), Principal Agent Theory (PAT), and Transaction Cost Analysis (TCA), deriving important findings and implications. Such benefits and implications suggest that BCLSs may help CLSs (1) mitigate PAT frictions and reduce transaction costs; (2) redefine value creation and accelerate innovation diffusion; (3) eliminate platform monopolies and achieve real-time settlement; (4) implement data sovereignty and enhance privacy/security; and (5) reconfigure trust mechanisms and generate digital credit assets. The research results of this study may help the CLS industry clarify the BCLS system’s upgrade path, promote business model innovation, and enhance fair governance and social sharing. Full article

Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. DEA allows the simultaneous consideration of economic performance, resource utilization, environmental impacts, and recycling results in the evaluation of the circular economy (CE). This review investigates the evolution of DEA models in the last years and the variables used to measure the CE performance. We analyze 209 peer-reviewed articles to systematically explore the evolution of DEA applications from conventional single-stage efficiency models to advanced network-based structures that better reflect the intricacy of circular systems. The review discusses the most advanced DEA approaches to date in the literature on CE assessment and uncovers specific factors that affect the choice of models in empirical studies. Finally, it points to promising directions of future research by showing interest in the development of comprehensive DEA models adapted to the specificity of the CE systems. Full article

Colombia condenses a paradox common across Latin America’s extractive economies: one of the region’s cleanest electricity matrices—close to 60% of installed capacity is hydropower—coexists with large-scale coal and oil exports, while renewable potential concentrates in ethnic and peasant territories that remain energy-poor. Although the country has enacted an ambitious just-transition framework, conflicts persist. This study asks which epistemological and ontological factors constrain or enable that transition. Using an interpretive-critical qualitative design, it triangulates four evidentiary pillars: critical discourse analysis of policy instruments, a structured critical review of the academic and policy literature, six megaproject case studies, and ten participatory workshops with 259 territorial actors across nine departments. Six recurrent, cross-case findings emerge: epistemological asymmetry, ontological irreducibility of territory, participation reduced to procedure, fragmented governance, the Caribbean territorial paradox (La Guajira combines the country’s highest renewable potential with its lowest electricity coverage), and unresolved fiscal dependence on hydrocarbons. The study’s novelty is Territorial Energy Sustainability (SET), a six-condition framework that articulates sustainability-transitions theory with Latin American critical thought and operationalizes it into five levels of policy intervention, offering a transferable framework for energy-exporting countries of the Global South pursuing transitions that transform rather than reproduce inequality. Full article

This study examines the effectiveness of government support mechanisms in fostering venture capital (VC) ecosystems in emerging economies, with a particular focus on Kazakhstan. While venture capital is widely recognized as a key driver of innovation, entrepreneurship, and economic diversification, many developing countries face persistent structural barriers, including underdeveloped financial markets, limited private investment, and regulatory inefficiencies. To address these challenges, this research adopts a multi-source qualitative design, combining a systematic literature review with semi-structured interviews conducted with venture capital experts and practitioners. The analysis evaluates key policy instruments—such as co-investment schemes, tax incentives, startup accelerators, and regulatory reforms—and assesses their role in shaping VC activity. The findings indicate that although Kazakhstan has made significant progress in establishing institutional support for venture capital, critical constraints remain, including bureaucratic complexity, investor risk aversion, and limited exit opportunities. The study contributes to the literature by integrating global best practices with context-specific evidence, offering policy-relevant insights into how governments can more effectively design and implement interventions to strengthen venture capital ecosystems in developing economies. Full article

Decoupling economic growth from carbon emissions is central to the sustainable development of rapidly urbanizing economies, and urban agglomerations are the pivotal spatial units for delivering this transition under China’s dual-carbon goals, yet systematic cross-agglomeration comparisons that could inform differentiated sustainability policy remain scarce. Using panel data for 107 prefecture-level cities in five agglomerations—the Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH), Pearl River Delta (PRD), Chengdu–Chongqing (CY), and the middle reaches of the Yangtze River (MRYR)—across five benchmark years spanning 2005–2023, we combined a two-way fixed-effects environmental Kuznets curve (EKC) model, the Tapio decoupling model, and cross-sectional quadrant analysis to examine the growth–emission relationship in shape, decoupling dynamics, and spatial structure. All five agglomerations traced an inverted-U trajectory, with turning-point per capita gross domestic product (GDP) rising in the order CY < PRD < BTH < MRYR < YRD. Once fixed effects and structural controls were added, most quadratic terms became insignificant and reversed sign after the secondary-industry share and carbon intensity entered; only the PRD and BTH retained a significant nonlinear form. The net income effect is therefore largely monotonic, with the inverted U carried by industrial upgrading and energy-efficiency gains. Tapio decoupling followed a non-monotonic “improve-then-regress” path, with expansive negative decoupling re-emerging across all agglomerations during 2020–2023. Spatially, high-value clustering persisted in the YRD, weakened in the BTH after 2020, and concentrated on single cores in Chengdu and Wuhan. We accordingly propose sustainability-oriented low-carbon pathways differentiated jointly by agglomeration and quadrant. By showing that decoupling is stage-dependent and reversible rather than an automatic by-product of income growth, our findings indicate that durable progress toward regional sustainability hinges on structural transformation and coordinated governance tailored to each agglomeration’s stage of development. Full article

This scientometric and bibliometric study analyzes global research trends on polyhydroxyalkanoate (PHA)-producing microorganisms and their applications in bio-based packaging between 2014 and 2025. Using the Scopus database and advanced tools such as Bibliometrix in RStudio and VOSviewer, scientific output and international collaboration networks were evaluated. The results demonstrate exponential growth in publications, driven by the urgent need to mitigate the petrochemical plastics crisis and develop biodegradable alternatives within the circular economy. Multidisciplinary analysis reveals a thematic shift from basic physiological and taxonomic studies towards complex applications in metabolic engineering, synthetic biology, the optimization of low-cost substrates such as industrial effluents, multi-omics tools, gene editing with CRISPR-Cas9, and, as an emerging exploratory approach, quantum modeling to optimize cell performance. Despite significant progress, critical technological gaps were identified related to challenges in downstream processing, the management of mixed microbial communities, and insufficient funding for the characterization of physicochemical and biocompatibility properties. It is concluded that, to ensure the commercial scalability and sustainability of PHAs, future research must prioritize overcoming these economic and technological bottlenecks, fostering strategic collaboration between academia and the biotechnology industry. Full article