Search Results (89)

Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract relevant information through deep learning (DL) methods. However, the effectiveness of these methods is often limited by the scarcity of high-quality labeled data. To address this, we propose a new data augmentation technique, Concatenation Augmentation (CA). This is designed to overcome the challenges of processing unstructured text, particularly in analyzing professional profiles from corporate governance reports. Based on Mixup and Label Smoothing Regularization principles, CA generates new text samples by concatenating inputs and applying a convex additive operator, preserving its spatial and semantic coherence. Our proposal achieved hit rates between 92.4% and 99.7%, significantly outperforming other data augmentation techniques. CA improved the precision and robustness of the DL models used for extracting critical information from corporate reports. This technique offers easy integration into existing models and incurs low computational costs. Its efficiency facilitates rapid model adaptation to new data and enhances overall precision. Hence, CA would be a potential and valuable data augmentation tool for boosting DL model performance and efficiency in analyzing financial and governance textual data. Full article

In modern digital transactions involving government agencies, financial institutions, and commercial enterprises, reliable identity verification is essential to ensure security and trust. Traditional methods, such as submitting photocopies of ID cards, are increasingly susceptible to identity theft and fraud. To address these challenges, this study proposes a novel and robust identity verification framework that integrates super-resolution preprocessing, a convolutional neural network (CNN), and Monte Carlo dropout-based Bayesian uncertainty estimation for enhanced facial recognition in electronic know your customer (e-KYC) processes. The key contribution of this research lies in its ability to handle low-resolution and degraded facial images simulating real-world conditions where image quality is inconsistent while providing confidence-aware predictions to support transparent and risk-aware decision making. The proposed model is trained on facial images resized to 24 × 24 pixels, with a super-resolution module enhancing feature clarity prior to classification. By incorporating Monte Carlo dropout, the system estimates predictive uncertainty, addressing critical limitations of conventional black-box deep learning models. Experimental evaluations confirmed the effectiveness of the framework, achieving a classification accuracy of 99.7%, precision of 99.2%, recall of 99.3%, and an AUC score of 99.5% under standard testing conditions. The model also demonstrated strong robustness against noise and image blur, maintaining reliable performance even under challenging input conditions. In addition, the proposed system is designed to comply with international digital identity standards, including the Identity Assurance Level (IAL) and Authenticator Assurance Level (AAL), ensuring practical applicability in regulated environments. Overall, this research contributes a scalable, secure, and interpretable solution that advances the application of deep learning and uncertainty modeling in real-world e-KYC systems. Full article

In the context of global efforts to address climate change and pursue sustainable development, green finance (GF) and energy efficiency (EE) have become key issues of focus for academics and policymakers. This study explores the spatiotemporal coupling characteristics and driving factors of China’s green finance and energy efficiency from 2011 to 2022, aiming to help China achieve its dual carbon goals. This study used a three-dimensional framework to assess 30 provinces, considering factor inputs, expected outputs, and undesirable outputs. The study employed the global benchmark super-efficiency EBM model, entropy method, coupling coordination model (CCD), Dagum Gini coefficient decomposition, and spatiotemporal geographic weighted regression model (GTWR). Key findings include a “high in the east, low in the west” gradient distribution of both green finance and energy efficiency, expanding regional disparities, and a strong synergistic effect between technological innovation and energy regulation. Based on the findings, this paper proposes a three-tier governance framework: regional adaptation, digital integration, and institutional compensation. This study contributes to a deeper understanding of the coupling theory of environmental financial systems and provides empirical support for optimizing global carbon neutrality pathways. Full article

Climate-smart agriculture (CSA) has emerged as a critical strategy to address the intertwined challenges of climate change, food insecurity, and environmental degradation, particularly among smallholder farmers in Southern Africa. This study reviews the existing literature on the adoption and scaling of CSA innovations among smallholder farmers in South Africa, focusing specifically on the roles played by institutional mechanisms, policy frameworks, and market dynamics. The findings reveal that while CSA interventions—such as conservation agriculture, drought-tolerant crop varieties, and precision irrigation—have demonstrated positive outcomes in enhancing productivity, food and nutritional security, and climate resilience, adoption remains uneven and limited. Key barriers include insecure land tenure, insufficient extension and climate information services, limited access to credit and inputs, and fragmented institutional support. The analysis highlights the importance of secure land rights, functional farmer cooperatives, effective NGO involvement, and inclusive governance structures in facilitating CSA adoption. Further, the review critiques the implementation gaps in South Africa’s climate and agricultural policy landscape, despite the existence of comprehensive strategies like the National Climate Change Response Policy and the Agricultural Policy Action Plan. This study concludes that scaling CSA among smallholder farmers requires a holistic, multi-level approach that strengthens institutional coordination, ensures policy coherence, improves market access, and empowers local actors. Targeted financial incentives, capacity-building programs, and value chain integration are essential to transform CSA from a conceptual framework into a practical, scalable solution for sustainable agricultural development in South Africa. Full article

Climate change-induced floods and droughts pose significant threats to rice farm development in Indonesia, particularly in regions reliant on pump-based irrigation systems. The urgency of this study lies in the increasing vulnerability of rice production to extreme weather events, necessitating institutional adaptations to enhance irrigation sustainability and financial risk sharing. This study examines the role of irrigation institutions in supporting sustainable rice farming in Wajo District, Indonesia. Using a case study approach, qualitative data were collected from four irrigation service provider (ISP) units across three subdistricts through in-depth interviews and focus group discussions. The analysis focuses on institutional mechanisms, including irrigation payment structures, input credit systems, and cost-sharing arrangements. The findings reveal that institutional frameworks are crucial in mitigating financial risks by promoting adaptive payment schemes and equitable cost-sharing mechanisms. Farmers’ access to critical agricultural inputs, such as fertilizers and pesticides, is enhanced through collaborative financing models, ensuring resilience against climate-induced production risks. However, variations in institutional support led to disparities in irrigation fees, credit access, and financial sustainability across study sites. This study underscores the need for risk-based irrigation pricing models and public–private partnerships to invest in climate-resilient infrastructure, such as water storage facilities and sustainable irrigation systems. In conclusion, it is important to remember that each of us, including agricultural policymakers, researchers, and stakeholders, plays a crucial role in implementing these solutions. By strengthening institutional governance, promoting flexible financial mechanisms, and integrating climate-adaptive pricing models, we can all contribute to enhancing the long-term sustainability of rice farming in Indonesia. Full article

The livestock sector is crucial for global food security and economic development, particularly in developing nations, as it supports the livelihoods of approximately 1.3 billion people. However, with the global population expected to reach 9.2 billion by 2050, the sector must address increasing demand for livestock products while ensuring environmental sustainability. This study used the available literature to evaluate the economic viability of sustainable pasture and rangeland management practices to enhance livestock production. The key findings demonstrate that strategies such as rotational grazing and nitrogen fertilization can decrease winter feed costs by up to 40% while simultaneously improving pasture productivity and animal weight gains. Initial investments in these improved forage practices offer high internal rates of return, indicating their profitability. To guide sustainable pasture production and rangeland management, we propose a conceptual framework that balances cultivated pastures and natural rangelands. This framework assesses critical factors, including input costs, expected outputs (enhanced biodiversity and livestock production), and interventions to mitigate land degradation. For successful adoption of these practices, targeted policies are essential. Governments should develop financial support mechanisms for smallholder farmers, improve transportation infrastructure for efficient feed logistics, and provide technical assistance to educate producers on sustainable practices. Engaging stakeholders to align policies with local needs is also vital. By implementing these strategic interventions, the resilience of livestock systems can be strengthened, contributing to long-term sustainability and supporting food security and rural community well-being. Full article

Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effective—and not just more efficient—solutions, based on value co-creation between several providers and a customer. Full article

Historically, innovation has been a milestone in achieving sustainable agriculture for small-scale producers. For several centuries, innovation has improved agricultural activity. However, there is still the challenge of introducing technologies pertinent to the knowledge and practices of small producers to achieve sustainability. Therefore, the present study explores the traditional knowledge embedded in the activities of Planting–Harvest and First Disposal circuit (PHFDc) of beans (Phaseolus vulgaris L.) for its innovation involving the social, economic, and environmental context. Applying the methodology of roadmapping technology to 73 small-scale producers in Guanajuato, Mexico, combining the SDGs catalogue, in addition to statistical analysis, the results show access to government financial support; improving sales price, production, area, and profitability; having accessible tools; creating their inputs; in addition to having more excellent knowledge for plant care and advice as strategies to develop within economic sustainability. In this sense, based on the assertion that social and productive conditions are directly related to innovation, the proposal for reorientation is towards the creation of word credit, improving bean varieties, sustainable practices, mechanical seeders, bean corridors, and the connection with associations and institutes as the most pertinent ones that are developing in similar contexts. This research can be significant for small producers and the general population regarding food security, zero hunger, and the fight against climate change, as well as for researchers and politicians who support continuing new studies. Full article

Currently, urban land use in China faces many challenges, such as irrational land use structure and inefficiency, which is especially obvious in resource-based cities. In order to improve this situation, this paper uses the super-efficient Slack-Based Measure (SBM) model to measure the green land use efficiency (GLUE) of 113 resource-based cities in China, analyzes its spatial-temporal evolution law, and identifies the formation law of heterogeneous GLUE in resource-based cities using the Tobit model. The research results show that: (1) GLUE in resource-based cities shows year-on-year growth and has certain stage characteristics, in which the eastern region is the best, followed by the western and central regions, and the northeastern region is the worst; regenerative cities are significantly better than mature, growth, and declining cities; oil and gas cities are better than non-metal, forest, metal, and coal cities in turn; (2) High-value resource-based cities are concentrated in the eastern and western regions, while low-value ones are concentrated in the central and northeastern regions. Moreover, the number of high-value resource-based cities is continuously increasing, while the number of low-value ones is significantly decreasing; (3) The level of economic development, industrial structure, level of technological input, number of green patents granted, government financial support, sewage treatment rate, and policy constraints all exhibit significant positive effects on the GLUE of resource-based cities. Furthermore, there is notable heterogeneity among resource-based cities in different regions, development stages, and resource types. In the future, policies should be implemented on a city-by-city basis, and a sound long-term mechanism for policy implementation should be established to enhance the long-term awareness of managers and land users so as to improve the GLUE in resource-based cities. Full article

The question of whether microeconomic shocks induce aggregate fluctuations constitutes a central issue in economic research. This paper introduces a general equilibrium model with production networks to explore the propagation mechanisms of microeconomic shocks. A novel triangular production network structure is introduced, and simulations are performed using China’s input-output table to analyze the propagation of these shocks within the Chinese economy. The model demonstrates that the first-order effects of microeconomic shocks propagate downstream along the industrial chain, while the second-order effects of microeconomic productivity shocks propagate both upstream and downstream along the chain. The first-order propagation mechanism of microeconomic shocks involves changes in prices within the affected sector and its downstream sectors. Additionally, the second-order effects of microeconomic shocks rely on the reallocation of factors. The simulation results indicate that China’s production network matrix is triangular, and that the financial sector plays a crucial role in amplifying the effects of microeconomic shocks. Government should prioritize supporting upstream fundamental sectors to mitigate the adverse impacts of external shocks on economic fluctuations and to address systemic financial risks. Full article

Improving the rural living environment is of great significance in enhancing the life quality of rural residents and promoting rural sustainable development. The Chinese government initiated a nationwide three-year action in 2018, followed by a five-year campaign starting in 2021, to improve the rural living environment. Despite these efforts, comprehensive assessment covering multiple facets of the rural living environment at the national level remained scarce. A novel evaluation method was proposed in this study that included seven aspects and nineteen indicators, applied AHP for weighting, and PLS-SEM to analyze the relationships between variables. Then, the completion of key tasks of rural living environment improvement in 37 counties, which were incentivized by the State Council on account of its excellent governance results, was comprehensively investigated and evaluated. The assessment of key tasks in the 37 incentivized counties revealed high completion in rural household solid waste disposal, domestic sewage treatment, and toilet improvement. However, the level of the rural living environment was uneven among different regions, and the progress varied in different key tasks. The funds input had a greater impact on the comprehensive level of rural living environment than social and economic factors. Accordingly, the government should provide more targeted financial and policy support to underdeveloped areas. And priority should be given to the sewage treatment and sanitation, especially in rural and remote areas. Furthermore, local governments should diversify funding sources to ensure the sustainability of rural living environment development. These findings provide a theoretical basis for developing policies and specific plans to address the challenges of financial investment and rural living environment management in China and other developing countries and regions worldwide. Full article

Global environmental issues are becoming increasingly prominent and environmental, social and governance (ESG) ratings may play a key role in green development by stimulating informal environmental regulation from stakeholders. As a pivotal criterion for measuring green development, green total factor productivity (GTFP) refers to maximizing output while minimizing the environmental pollution for the required input production factors. Existing research neglects the impact of ESG ratings on GTFP that indicates the balance between economic growth and ecological protection. This study examines the impact of ESG ratings and mechanisms on GTFP using a sample of Chinese A-share listed manufacturing firms between 2010 and 2021. The findings indicate that ESG ratings promote corporate GTFP, a result which remains robust after a series of robustness tests. The mechanism analysis reveals that ESG ratings improve corporate GTFP by alleviating financial constraints, mitigating managerial myopia, and enhancing supply chain efficiency. A moderating analysis verified that managerial power weakens the positive impact of ESG ratings on corporate GTFP. The positive effect of ESG ratings on GTFP is more pronounced among non-state-owned firms and firms in non-heavily polluting and highly competitive industries. This study confirms that ESG ratings can achieve the benefits of productivity growth, energy conservation, and pollution reduction at the micro-enterprise level, offering a policy foundation for promoting ESG disclosure and achieving green development. Full article

Green finance (GF) is a new financial service that supports green and low-carbon (GLC) transformation. Whether green finance (GF) can effectively improve regional green innovation performance (GIP) by optimizing resource allocation and increasing factor input is a key question for achieving sustainable development goals (SDGs): environmental, economic, and society. Based on panel data from 30 provinces in China from 2007 to 2021, this paper explores the impact of GF on GIP and analyzes the mechanisms of the effect. The findings of this paper indicate that GF plays a significantly positive role in promoting regional GIP, increasing both quantity and quality. Heterogeneity analysis reveals that GF has a considerable incentive effect on carbon reduction technology, and the innovation incentive effect of GF is much greater in regions where the attention paid to the environment is high or there is a low natural endowment. Mechanism analysis reveals GF improves GIP by optimizing financial resource allocation and increasing R&D factor input. Therefore, this paper proposes the following suggestions: (1) The central government should improve the top-level design of the GF policy system and provide financial support for GLC transformation and the achievement of SDGs. (2) Local governments should explore diversified development paths for GF according to their own characteristics, stimulate market entities’ enthusiasm for GLC transformation, and improve regional green innovation performance so as to achieve coordinated and sustainable development of the environment, economy, and society. Full article

This study offers detailed recommendations on strengthening government support without harming digital finance benefits, especially in negatively affected areas, which is critical for enhancing the inclusiveness of the digital financial landscape and reducing social disparities. This paper uses year 2011–2022 panel data from China’s 31 provinces to empirically analyze digital finance’s effects, mechanisms, and heterogeneity on agricultural economy resilience with a two-way, fixed-effect model. It further explores each feature’s impacts using machine learning methodologies like the random forest, GBRT, SHAP value method, and ALE plot. The findings show that digital finance boosted agri-economy resilience, varying by food-producing status and marketization. Among all the features analyzed, government input, urbanization level, and planting structure emerged as the most critical factors influencing agri-economy resilience. Notably, government input negatively moderated this relationship. The ALE plot revealed non-linear effects of digital finance and planting structure on agri-economy resilience. Full article

The face of corporate governance is changing as new technologies in the scope of artificial intelligence and data analytics are used to make better future-oriented decisions on performance management. This study attempts to provide empirical results to analyze when the impact of diversity on the board of directors is most evident through the multi-breaks model and artificial neural networks. The input data for the simulation includes 853 electronic companies listed on the Taiwan Stock Exchange from 2000 to 2021. The empirical results show that the higher the percentage of female board members, the more influential the company’s performance is, which is only evident when the company is in good business condition. By integrating ANNs with multi-breakpoint regression, this study introduces a novel approach to management research, providing a detailed perspective on how board diversity impacts firm performance across different conditions. The ANN results show that using the number of business board members for predicting Return on Assets yields the highest accuracy, with female board members following closely in predictive effectiveness. The presence of women on the board contributes positively to ROA, particularly when the company is experiencing favorable business conditions and high profitability. Our analysis also reveals that a higher percentage of male board members improves company performance, but this benefit is observed only in highly favorable and unfavorable business conditions. Conversely, a higher percentage of business members tends to affect performance during periods of high profitability negatively. The power of the board of directors and significant shareholders is positively correlated with performance, whereas CEO power positively impacts performance only when it is not extremely low. Independent board members generally do not have a significant effect on profits. Additionally, the company’s asset value positively influences performance primarily when the return on assets is high, and increased financial leverage is associated with reduced profitability. Full article