Search Results (24)

The environmental challenges posed by global warming have significantly increased the global pursuit of renewable and clean energy sources. Among these, solar energy stands out due to its abundance, renewability, low environmental impact, and favorable long-term economic viability. However, its intermittent nature and dependence on weather conditions hinder consistent and efficient utilization. To address these limitations, nanoparticle-enhanced phase change materials (NPCMs) have emerged as a promising solution for enhancing thermal energy storage in solar thermal systems. NPCMs incorporate superior-performance nanoparticles within traditional phase change material matrices, resulting in improved thermal conductivity, energy storage density, and phase change efficiency. This review systematically examines the recent advances in NPCMs for solar energy applications, covering their classification, structural characteristics, advantages, and limitations. It also explores in-depth analytical approaches, including mechanism-oriented analysis, simulation-based modelling, and algorithm-driven optimization, that explain the behavior of NPCMs at micro and macro scales. Furthermore, the techno-economic implications of NPCM integration are evaluated, with particular attention to cost-benefit analysis, policy incentives, and market growth potential, which collectively support broader adoption. Overall, the findings highlight NPCMs as a frontier in materials innovation and enabling technology for achieving low-carbon, environmentally responsible energy solutions, contributing significantly to global sustainable development goals. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

We investigate the affine term structure models (ATSMs) with unspanned stochastic volatility (USV). Our aim is to test their ability to generate accurate cross-sectional behavior and time-series dynamics of bond yields. Comparing the restricted models and those with USV, we test whether they produce both reasonable estimates for the short rate variance and cross-sectional fit. Essentially, a joint approach from both time series and options data for estimating risk-neutral dynamics in ATSMs should be followed. Due to the scarcity of derivative data in emerging markets, we estimate the model using only time-series of bond yields. A Bayesian estimation approach combining Markov Chain Monte Carlo (MCMC) and the Kalman filter is employed to recover the model parameters and filter out latent state variables. We further incorporate macro-economic indicators and GARCH-based volatility as external validation of the filtered latent volatility process. The $A_1\left(4\right)$USV performs better both in and out of sample, even though the issue of a tension between time series and cross-section remains unresolved. Our findings suggest that even without derivative instruments, it is possible to identify and interpret risk-neutral dynamics and volatility risk using observable time-series data. Full article

In the context of global climate change, carbon emission trading (CET) has become a critical tool for driving urban green economic transformation. Since 2011, China has launched CET pilot programs, supporting the achievement of the “dual carbon” goals. Studying the relationship between CET and urban green economic efficiency is essential for advancing urban green economic transitions. However, the existing research is limited by its single-perspective approach, insufficient exploration of mechanisms, and weak heterogeneity analysis, which restricts a comprehensivethe comprehensiveness of our understanding of policy effects. To address these gaps, this study is the first to integrate macro-regional data with micro-enterprise behavior, evaluating the impact of CET on urban green economic efficiency from a dual macro–micro perspective, thereby filling the research void in macro–micro data integration. At the macro level, this study employs panel data from 281 Chinese cities spanning 2007 to 2020, using fixed-effects and difference-in-differences (DID) models to assess the impact of CET on urban green economic efficiency. At the micro level, a game-theoretic pricing decision model is constructed to reveal behavioral differences among enterprises in complete and incomplete information markets and their indirect effects on green economic efficiency. The findings indicate that CET significantly enhances urban green economic efficiency, with technological innovation, green finance, and industrial structural upgrading serving as mediating mechanisms. Heterogeneity analysis shows that the effects are more pronounced in eastern, non-resource-based, small-to-medium-sized, and non-old industrial cities. The game-theoretic model further demonstrates that enterprises in complete information markets more effectively indirectly enhance green economic efficiency through CET mechanisms. By combining macro and micro perspectives, this study provides a new theoretical framework and practical insights for understanding the policy effects of CET. However, limitations such as data confined to Chinese pilots and model simplifications remain. Future research should expand data dimensions, allowing researchers to more comprehensively evaluate policy outcomes. Full article

This research explores the use of treated hybrid natural fibers—wheat straw and bamboo—as reinforcements in concrete for pavement applications. Motivated by environmental and economic benefits, the study investigates how these fibers can enhance the mechanical and durability properties of concrete. Wheat straw fibers, abundant in Ethiopia due to extensive wheat farming, help control micro-cracks and increase the tensile strength of concrete, while bamboo fibers, also locally available, reduce macro-crack propagation and improve concrete toughness. To prepare these fibers, wheat straw was cut to 25 mm in length and bamboo fibers were treated with a 5% sodium hydroxide solution before being cut into lengths of 30, 45, and 60 mm. A concrete mix targeting a cube compressive strength of 30 MPa incorporated 0.1% wheat straw fibers, with varying bamboo fiber contents (0.5%, 1%, and 1.5%) by weight of cement. The results indicate that the uniquely treated hybrid natural fiber-reinforced concrete mix exhibits noticeable enhancements in mechanical properties, with approximate increases of 4.16%, 8.80%, and 8.93% at 7, 28, and 56 days, respectively. Furthermore, the split tensile strength, flexural strength, and durability properties of the concrete were significantly improved by the proposed fiber concentration and length compared to the control concrete mix design. This treatment also shifted the failure mode of the concrete from brittle to ductile and enhanced its energy absorption capacity up to 7.88% higher than that of the control concrete. Based on the AASHTO 1993 pavement design guidelines, this fiber-reinforced concrete reduces pavement thickness by 11% compared to the control concrete while improving post-cracking behavior. This hybrid natural fiber-reinforced concrete presents a promising, sustainable, and eco-friendly alternative for rigid pavement construction. Full article

Recognizing the factors that influence business formation in developing contexts is critical for promoting economic growth. This study examines the drivers of entrepreneurship among university alumni in Ecuador, addressing gaps in the literature regarding the roles of individual, organizational, and institutional factors in business creation. Drawing on established theories such as the Theory of Planned Behavior and Resource-Based Theory, a multilevel causal model was developed and tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 550 alumni through a structured questionnaire, complemented by semi-structured interviews. The model integrates micro-level (entrepreneurial attitudes and funding experience), meso-level (entrepreneurial knowledge acquired from working experience), and macro-level determinants (opportunity cost). Results indicate that while individual traits play a role, organizational knowledge and institutional context have more pronounced impacts on entrepreneurial actions. In particular, opportunity costs have a negative impact on the business formation process, reflecting the challenges of entrepreneurship in unfavorable environments. The findings highlight the importance of fostering entrepreneurial ecosystems within universities, emphasizing education and support mechanisms tailored to overcoming institutional barriers. This study contributes to the understanding of entrepreneurship in Latin America, offering insights for policymakers and academic institutions aiming to enhance entrepreneurial activity and economic development. Full article

This paper contributes to the literature by demonstrating that behavioral macroeconomic models better explain macroeconomic volatility in emerging economies compared to traditional rational expectations frameworks. We explore behavioral macroeconomics as a foundation for sustainable economic policies by comparing New Keynesian models under rational expectations and behavioral heuristics across multiple economies. The model parameters are estimated using the Generalized Method of Moments (GMM) for rational expectations and the Simulated Method of Moments (SMM) for the behavioral framework, evaluating their ability to replicate empirical second moments of output, inflation, and interest rates. The GMM, suited for linear models, provides analytical solutions, ensuring computational efficiency, while the SMM, designed for non-linear models, enables greater flexibility by generating simulated data and departing from restrictive DSGE assumptions. Our findings reveal that the behavioral model—incorporating heterogeneity, heuristic switching, and bounded rationality—better captures the persistent and volatile macroeconomic conditions observed in Central and Eastern European (CEE) economies. In contrast, rational expectations models perform better in advanced economies, where agents rely more on forward-looking information. These results emphasize the need to integrate behavioral features into macroeconomic modeling to enhance empirical accuracy and inform sustainable monetary policy tailored to diverse economic environments. Full article

The sustainability and adequacy of pension systems are central to public policy debates in aging societies. This paper introduces a novel microsimulation model with probabilistic behavior to assess these dual challenges in the Spanish pension system. The model employs a mixed-projection method, integrating a macro approach—using economic and demographic aggregates from official sources such as the Spanish Statistics Office (INE) and Eurostat—with a micro approach based on the Continuous Sample of Working Lives (MCVL) dataset from Spanish Social Security. This framework enables individual-level projections of key labor market variables, including work time, salary, and initial pensions, under diverse reform scenarios. The results demonstrate the model’s ability to predict initial pensions with high accuracy, providing detailed insights into adequacy by age, gender, and income levels, as well as distributional measures such as density functions and quantiles. Sustainability findings indicate that pension expenditures are projected to stabilize at 13.9% of Gross Domestic Product (GDP) by 2050. The proposed model provides a robust and versatile tool for policymakers, offering a comprehensive evaluation of the long-term impacts of pension reforms on both system sustainability and individual adequacy. Full article

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies. Full article

There is a need to investigate the flexural behavior and mechanical properties of super high-performance concrete (SHPC) for a better understanding of its response to compression, tension, and bending. Super-high-performance concrete (SHPC) lies between high-performance concrete (HPC) and ultra-high-performance concrete (UHPC) in strength, durability, and workability and is suitable for sustainable buildings. This paper presents an extensive experimental and analytical study to investigate the effect of the hybridization of micro-polypropylene and macro-steel fibers on the flexural behavior and mechanical properties of super-high-performance concrete (SHPC). The hybridization of both micro-PP fibers and macro-hooked-end ST fibers gathers the benefits of their advantages and offsets their disadvantages. Three types of fibers (micro polypropylene fibers (PP), macro hooked-end steel fiber (ST), and hybrid fiber (PP + ST)) with different fiber content up to 2% were tested to study their effect on the following: (a) the workability of fresh concrete, (b) concrete compressive strength, (c) splitting tensile strength, (d) flexural behavior, including flexural tensile strength and toughness, and (e) the optimum percentage of each of the two fibers, PP and ST, in the hybrid to get the maximum structural and economic benefits of hybridization. Based upon the experimental results and using a statistical program, formulae to calculate both the tensile splitting strength ($f_{sp}$) and the flexural tensile strength in the form of the modulus of rupture ($f_{ctr}$) were obtained. These formulae were able to predict accurately both the splitting tensile strength and modulus of rupture for SHPC with each of the three types of fibers used in this research. Also, they were in very good agreement with the values corresponding to different experimental results of other research, which means the ability to use these equations more generally. In addition, the prediction of the additional ultimate moment provided for all fibers was investigated. This research confirms the structural and the economical efficiency of hybridization in the behavior of SHPC. It was found that the optimum percentage of the fiber volume content for the hybrid of ST and PP is 1%; 0.5% for each of the two kinds. Full article

The Circular Economy (CE) concept has acquired a prominent role in both the academic and political fields, accelerated by the realization of a need to change the current pathway of economic development towards a more sustainable one. This transition depends upon a transformation in production and industrial processes, but also in consumption practices. Consumer behaviors and perceptions of circular solutions have been overlooked in the literature and in policy measures, often limited to eco-labelling and information campaigns. This paper argues for a greater definition and centrality of the role of consumption within the CE. Based on a systematic literature review covering the years 2012–2023, the article offers an overview of the main tendencies and challenges of market valorization in the CE, showing a greater concentration of papers at the macro level and micro levels (47% and 35%, respectively) and a lower concentration at the meso level (18%). Results show a steady number of publications regarding consumption in the CE over the years. The mapping of keywords shows greater clustering between terms such as policies, sustainable development and the CE and a lesser focus on the practices that support it. The article concludes that consumption dynamics in the CE must take into account the balance between individual agency, institutional structures, and normative values and develop a paradigm that comprehends sectorial boundaries. Full article

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes. Full article

Innovation is the main driving force of the sustainable development of enterprises. Economic policy uncertainty has increased dramatically in recent years due to events such as COVID-19, which will alter the business environment of enterprises and ultimately affect their innovation behavior. How economic policy uncertainty will affect corporate innovation has become a crucial topic, but empirical studies have not reached consistent conclusions, and few have noted the heterogeneity of different firms’ perceptions of uncertainty. This study used a textual analysis approach to create firm-level economic policy uncertainty indicators from the texts of annual reports of Chinese A-share listed firms. Based on the effectiveness of our measure of economic policy uncertainty, we further examined its impact on firm innovation. We find that our uncertainty measure has negative effects on enterprise innovation activity, and this negative impact is more significant among non-state-owned enterprises, and firms with higher financial constraints and lower government subsidies. We extend the measurement of economic policy uncertainty from the micro level and provide some suggestions for policymakers at the macro level. In the period of increasing uncertainty in the external environment, the government should try to maintain the stability and transparency of economic policies, and provide more targeted policy support to enterprises, such as by broadening their financing channels and providing innovation subsidies. Full article

It is well known that most particle technology studies attempting to predict secondary properties based on primary properties such as size and shape begin with particle characterization, which means the process of determining the primary properties of particles in a wide spectrum from macro to nanoscale. It is a fact that the actual shape of engineering particles used in many industrial applications or processes is neglected, as they are assumed to be “homogeneous spheres” with easily understood behavior in any application or process. In addition, it is vital to control the granular materials used in various industries or to prepare them in desired shapes, to develop better processes or final products, and to make the processes practical and economical. Therefore, this review not only covers basic shape definitions, shape characterization methods, and the effect of particle shape on industrial material properties, but also provides insight into the development of the most suitably shaped materials for specific applications or processes (from nanomaterials used in pharmaceuticals to proppant particles used in hydrocarbon production) by understanding the behavior of particles. Full article

(1) Background: Poor adherence to management behaviors in Chinese Type 2 diabetes mellitus (T2DM) patients leads to an uncontrolled prognosis of diabetes, which results in significant economic costs for China. It is imperative to quickly locate vulnerability factors in the management behavior of patients with T2DM. (2) Methods: In this study, a thematic analysis of the collected interview materials was conducted to construct the themes of T2DM management vulnerability. We explored the applicability of the pre-trained models based on the evaluation metrics in text classification. (3) Results: We constructed 12 themes of vulnerability related to the health and well-being of people with T2DM in Tianjin. We considered that Bidirectional Encoder Representation from Transformers (BERT) performed better in this Natural Language Processing (NLP) task with a shorter completion time. With the splitting ratio of 6:3:1 and batch size of 64 for BERT, the test accuracy was 97.71%, the completion time was 10 min 24 s, and the macro-F1 score was 0.9752. (4) Conclusions: Our results proved the applicability of NLP techniques in this specific Chinese-language medical environment. We filled the knowledge gap in the application of NLP technologies in diabetes management. Our study provided strong support for using NLP techniques to rapidly locate vulnerability factors in T2DM management. Full article