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Improving green innovation quality is a critical component for speeding green transformation and generating high-quality growth. This study examines the link between the pilot zone for green finance reform and innovations (PZGFRI) policy and the quality of green innovation in Chinese A-share listed firms from 2010 to 2020. This study demonstrates that the PZGFRI may greatly enhance the quality of enterprises’ green innovation. Additionally, by promoting environmental investment and reducing financial barriers, we use the mediating effect model to confirm that the PZGFRI improves the enterprises’ quality of green innovation. Meanwhile, the heterogeneity analysis demonstrates that the PZGFRI is more successful in raising the green innovation quality in state-owned, large-sized, and heavily polluting businesses. Our study’s findings offer a strong theoretical basis for improving the PZGFRI and encouraging businesses to undergo high-quality transformation. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

Introduction and Objectives: Risk-sharing agreements (RSAs) have emerged as a key strategy for financing high-cost medical technologies while ensuring financial sustainability. These payment mechanisms mitigate clinical and financial uncertainties, optimizing pricing and reimbursement decisions. Despite their widespread adoption globally, Latin America has reported limited implementation, particularly for high-cost medical devices. This study aims to share insights from designing RSAs in the form of Outcome Protection Programs (OPPs) for medical devices in Latin America from the perspective of a medical devices company. Methods: The report follows a structured approach, defining key OPP dimensions: payment base, access criteria, pricing schemes, risk assessment, and performance incentives. Risks were categorized as financial, clinical, and operational. The framework applied principles from prior models, emphasizing negotiation, program design, implementation, and evaluation. A multidisciplinary task force analyzed patient needs, provider motivations, and payer constraints to ensure alignment with health system priorities. Results: Over two semesters, a panel of seven experts from the manufacturer designed n = 105 innovative payment programs implemented in Argentina (n = 7), Brazil (n = 7), Colombia (n = 75), Mexico (n = 9), Panama (n = 4), and Puerto Rico (n = 3). The programs targeted eight high-burden conditions, including Coronary Artery Disease, atrial fibrillation, Heart Failure, and post-implantation arrhythmias, among others. Private providers accounted for 80% of experiences. Challenges include clinical inertia and operational complexities, necessitating structured training and monitoring mechanisms. Conclusions: Outcome Protection Programs offer a viable and practical risk-sharing approach to financing high-cost medical devices in Latin America. Their implementation requires careful stakeholder alignment, clear eligibility criteria and endpoints, and robust monitoring frameworks. These findings contribute to the ongoing dialogue on sustainable healthcare financing, emphasizing the need for tailored approaches in resource-constrained settings. Full article

The study examines economic and non-economic endeavors to explore the association between family involvement and financial decisions within family firms. The non-economic factors of a family drive the need to analyze the impact of socioemotional factors on the financial policies of the family firms. The study explores the impact of family ownership, family management, and family control drawn from agency theory and socioemotional wealth perspectives on the financial decisions of family firms. Our findings in support of the socioemotional wealth perspective show a positive relationship between family ownership and debt financing with a desire to finance growth and avoid control dilution, with an increase in the level of debt. However, the involvement of family members in management and the top management team leads to an adverse relationship between family ownership and debt level, exhibiting the risk-averse behavior of a firm, which drives firms to reduce debt levels. Overall, our findings suggest that the perceptions of the socioemotional wealth theoretical paradigm are important in determining capital structure decisions in family enterprises. The results are resilient to potential endogeneity and heterogeneity difficulties, which may assist scholars and practitioners in assessing capital structure decisions in emerging economies. Full article

A multilayer linear response model (MLRM) is proposed to generate time-series data based on linear response theory. The proposed MLRM is designed to generate data for anomalous dynamics by extending the conventional single-layer linear response model (SLRM) into multiple layers. While the SLRM is a linear equation with respect to external forces, the MLRM introduces nonlinear interactions, enabling the generation of a wider range of dynamics. The MLRM is applicable to various fields, such as finance, as it does not rely on machine learning techniques and maintains interpretability. We investigated whether the MLRM could generate anomalous dynamics, such as those observed during the coronavirus disease 2019 (COVID-19) pandemic, using pre-pandemic data. Furthermore, an analysis of the log returns and realized volatility derived from the MLRM-generated data demonstrated that both exhibited heavy-tailed characteristics, consistent with empirical observations. These results indicate that the MLRM can effectively reproduce the extreme fluctuations and tail behavior seen during high-volatility periods. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article

Understanding the entrepreneurial profile is essential for developing effective regional policies that promote business growth. The path of an entrepreneur is shaped not only by individual decisions but also by the inherent risks of managing a business. This study aims to identify the characteristics of entrepreneurs at the regional level, specifically highlighting the impact of accumulated experience in their fields. Our central hypothesis asserts that entrepreneurial experience significantly influences how business owners perceive and respond to economic challenges. Utilizing survey data from 120 entrepreneurs in Romania’s Center Region (a NUTS 2 area), we reveal that entrepreneurial experience profoundly affects perceptions of key business challenges, such as legislative instability, taxation predictability, governmental support strategies, and access to SME financing. Importantly, our findings demonstrate that entrepreneurs with less than 10 years of experience express greater concerns about these challenges compared to their more seasoned peers. This novel insight highlights the need for tailored policy interventions aimed at enhancing regional economic resilience and fostering entrepreneurial sustainability. By addressing the specific needs of less experienced entrepreneurs, our study contributes to a deeper understanding of how experience shapes business dynamics in the region. Full article

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article