Search Results (76)

China’s healthcare expenditure tripled during 2010–2019, prompting the nationwide implementation of centralized volume-based procurement (CVBP). While effective in reducing drug prices, CVBP introduces sustainability challenges including supply chain vulnerabilities and welfare trade-offs. This study develops a pharmaceutical sector-embedded computable general equilibrium (CGE) model to quantify CVBP’s multidimensional sustainability impacts. Using China’s 2020 Social Accounting Matrix (SAM) with simulated 10–50% price reductions, key findings reveal that (1) >40% price reductions trigger sectoral output reversal; (2) GDP exhibits an inverted U-shape; (3) household income declines despite corporate/government gains; and (4) industrial contraction impairs innovation capacity and employment stability. Our analysis identifies potential sustainability risks, emphasizing the need for rigorous empirical validation prior to implementing aggressive price reduction policies, and underscores the importance of integrating supply chain considerations into procurement policy design. This approach maximizes resource allocation efficiency while advancing socioeconomic resilience in healthcare systems. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

This study uses a computable general equilibrium (CGE) model to analyze the impact of skilled and semi-skilled labor supply shocks on the Ethiopian economy and sectoral outputs. The study examines three policy scenarios: a 10% increase, a 15% increase, and a 20% increase in skilled and semi-skilled labor supply compared to a business-as-usual (BAU) scenario. The findings show that all three scenarios contribute to higher economic growth, investment, and exports. The impact on sectoral outputs is also significant, with the industry and services sectors performing better than the agriculture sector. In the 20% increase scenario, the real annual gross domestic product (GDP) growth rate is projected to be 0.79 percentage points higher than the business-as-usual scenario. Additionally, the annual growth rates of investments and exports are expected to be 2.69 and 2.31 percentage points higher, respectively, compared to their business-as-usual scenario counterparts. The agriculture sector experiences a slight increase of 0.16 percentage points in annual production compared to the business-as-usual scenario. Output in the industry sector also sees a rise of 1.61 percentage points higher than the business-as-usual scenario, while outputs in the services sector improve significantly. Overall, the study highlights the positive impact of increasing the supply of skilled and semi-skilled labor on the economy. This is mainly due to the higher productivity of skilled and semi-skilled workers, which contributes to increased economic growth. The findings suggest that governments should implement policies to enhance the supply of skilled and semi-skilled labor, such as investing in education and training programs. These measures would promote economic growth and improve living standards. Full article

The purpose of the Social Accounting Matrix (SAM) is to improve the quality of the database for modelling, including, but not limited to, policy analysis, multiplier analysis, price analysis, and Computable General Equilibrium. This article contributes to constructing the 2017 national SAM for South Africa, incorporating regional accounts. Only in Limpopo Province of South Africa are agricultural industries, labour, and households captured at the district level, while agricultural industry, labour, and household accounts in other provinces remain unchanged. The main data sources for constructing a SAM are found from different sources, such as Supply and Use Tables, National Accounts, Census of Commercial Agriculture, Quarterly Labour Force Survey, South Africa Revenue Service, Global Insight (regional explorer), and South Africa Reserve Bank. The dataset recorded that land returns for irrigation agriculture were highest (18.2%) in the Northern Cape Province of South Africa compared to other provinces, whereas the Free State Province of South Africa rainfed agriculture had the largest shares (22%) for payment to land. Regarding intermediate inputs, rainfed agriculture in the Western Cape, Free State, and Kwazulu-Natal Provinces paid approximately 0.4% for using intermediate inputs. In terms of the districts, land returns for irrigation were highest in the Vhembe district of Limpopo Province of South Africa with 0.3%. Despite Mopani district of Limpopo Province of South Africa having the lowest land returns for irrigation agriculture, it has the highest share (1.6%) of payment to land from rainfed agriculture. The manufacturing and community service sectors had a trade deficit, whereas other sectors experienced a trade surplus. The main challenges found in developing a SAM are scarcity of data to attain the information needed for disaggregation for the sub-matrices and insufficient information from different data sources for estimating missing information to ensure the row and column totals of the SAM are consistent and complete. Full article

Polycentric development facilitates urban–rural spatial reshaping and land use/land cover (LULC) protection. Previous studies have predominantly focused on urban areas, with spatial delineation methods biased towards the macro-level, lacking a holistic perspective that situates them within the urban–rural spatial framework. This study proposes a spatial delineation framework that is applicable to the polycentric structure, taking into account the social, economic, and natural characteristics of urbanization. It employs semivariance analysis and spatial continuous wavelet transform (SCWT) to analyze the effects of polycentric development on the urban–rural space of Wuhan from 2012 to 2021 and applies a land use transition matrix, landscape indices, and bivariate spatial autocorrelation to quantify the responses and differences of LULC within urban–rural space. The results indicate that $600\mathrm{m}\times 600\mathrm{m}$ is the best scale for exhibiting the multidimensional characterization of urbanization. The polycentric structure alleviates the compact development of the central city, and it drives rapid expansion at the urban–rural fringe, exacerbating the spatial heterogeneity in LULC change pattern, spatial configuration, and urbanization response within urban–rural spaces. The overall effects of urbanization on LULC are relatively weak along the urban–rural gradient, experiencing a transition from positive to negative and back to positive. This study employs a novel spatial delineation framework to depict the polycentric transformation of metropolitan areas and provides valuable insights for regional planning and ecological conservation in the urban–rural fringe. Full article

Decision-makers in cities have difficulties in implementing an effective climate policy for their own building resources due to the heterogeneous and dispersed distribution of buildings with low energy classes and different management specifics. Special zones include old towns, pre-war buildings (before 1945), and those built by the end of the 20th century. There is a noticeable shortage of methods for the comprehensive assessment of the emissions of urban complexes, taking into account social, economic, and environmental aspects. Exemplary individual examples of good thermal modernization practices towards low-emission and zero-energy solutions do not solve the problem of the poor-quality urban environment. This article proposes a simple integrated assessment of CO₂ emissions of separate urban zones using the example of a medium-sized city in Poland. The adopted ASEET assessment methodology takes into account socio-economic criteria, but above all, the technical and energy criteria of urban development. Sensitive information was collected from users and owners of buildings and gathered in a data matrix. From the inventory data on energy consumption and technical conditions related to socio-economic status, environmental indicators were introduced, which were called critical for their improvement. By analyzing local efficiency indicators Wei of individual development zones, we can influence TW^Ci, the total indicators for the city. In the case of the studied city of Gubin, the total final energy consumption indicator EK^C is 252.68 kWh/m²/year and is 58% lower than the most energy-intensive zone I, for which EK^I = 399.6 kWh/m²/year, similar to emission indicators EEj between zones. Therefore, energy efficiency or emission indicators as resultant characteristics of urbanized areas can be treated as sensitive parameters in administrative activities, for example when planning thermal modernization or health risk assessment. The recommended solutions for continuous monitoring of ecological identifiers of urban zones, especially those with the lowest technical status, are to facilitate the creation of own environmental urban policies in the future and directly affect the city’s climate in local and global terms. The environmental data obtained using the ASEET method can be digitized using various IT techniques and then the results can be visualized on a city map in the form of environmental urban mapping with an indication of the GIS system. As a result, simple methodological tools for city managers were indicated. In the authors’ opinion, the ASEET method can serve urban policy, especially energy and climate policy, because the instrument for calculation is a database of indicators from subsequent periods of monitoring one’s own urban development. Full article

Energy saving is an important measure to promote social green transformation. The traditional energy-saving ideas usually only focus on a specific loss, and seldom consider the possible relationship and influence among various losses. In relatively complex energy-using systems, there are often many kinds of losses, and each loss may have many influencing factors. There may be some relationship between these losses and the influencing factors. To solve this problem, this paper presents an analysis method of the variable association in multi-variable systems. First, the basic relationships between variables and the representation methods are discussed. The basic concept of a path between variables is given, and the analysis method of variable association based on path statistics is provided. This paper focuses on the analysis of the influencing factors and paths of the observed variables, as well as which observed variables will be affected by a control variable. Then, based on the correlation matrix, the quantitative analysis method of the influence between variables is given. Variable correlation analysis is innovatively applied in the field of energy saving to determine the correlation of losses through variable associations, guiding the preliminary screening of energy-saving measures and analyzing the collateral effects of these measures. Based on the correlations between energy losses, a scientific process for formulating energy-saving measures is proposed. The variable correlation analysis method proposed in this paper is a generalized method, which can judge the correlation between variables from the perspective of theoretical analysis and avoid the dependence on data. In addition to good applications in the field of energy conservation, it can also be widely used in construction, transportation, climate change, and other fields. The proposed energy-saving ideas take into account the intensity of influencing factors on loss and the correlation between loss, which improves the effectiveness of energy saving measures. Full article

The development of irrigation systems is strategically used to improve food security and achieve the Sustainable Development Goals (SDGs 2) of ending hunger and poverty. The objective of this research was to evaluate the effect of irrigation agriculture on the economy of the Limpopo Province, South Africa. This study used the 2017 national social accounting matrix (SAM) as a database with detailed information on irrigation and rainfed agricultural activities and land accounts to compute the effect of exogenous shock on output, income, land, and value added using SAM multiplier analysis. The findings showed that output multiplier effects were more significant for rainfed agriculture compared to irrigation agriculture. However, irrigation agriculture had the highest institutional income, land return, and value-added multiplier compared to rainfed agriculture. The type of crop did not influence the findings, with irrigation consuming more input per unit of output. We conclude that investing in irrigation agriculture and increasing the efficiency and sustainability of existing irrigation agriculture in Limpopo is significant and profitable because dry land production is hazardous when there is insufficient rainfall or recurrent drought. Full article

The globalization of markets has diversified the food supply, but it has also made the distribution chain more difficult, increasing the risk of microbial contamination. One strategy to obtain safer food and extend its shelf life is to develop active packaging with antimicrobial properties that prevent the growth of pathogenic microorganisms or spoilage in food products. In this context, and in line with the growing social awareness about the environmental impact generated by plastic waste, this work evaluated the effectiveness of polylactic acid (PLA) films loaded with different concentrations of copper (II) hydroxynitrate nanoparticles (CuHS) against the microbiota of fresh foods (chicken, fish and cheese). The results showed that the developed films containing 1, 3 and 5% w/w of CuHS in the polymeric matrix caused a decrease in the microbial abundance equal to or higher than 3 logarithmic units in all foods tested. Moreover, the mechanical and thermal properties of the formulated composites showed that the added CuHS concentrations did not substantially modify these properties compared to the PLA films. Taking into account the results obtained for antimicrobial activity, Cu (II) migration levels and the cytotoxicity of the films formulated, the PLA composite loaded with 1% CuHS (w/w) was the most suitable for its potential use as food packaging material. In addition, the biodegradation of this composite film was studied under conditions simulating intensive aerobic composting, demonstrating that almost 100% disintegration after 14 days of testing was achieved. Therefore, the innovative PLA-based films developed represent a promising strategy for the fabrication of packaging and active surfaces to increase food shelf life while maintaining food safety. Moreover, their biodegradable character will contribute to efficient waste management, turning plastic residues into a valuable resource. Full article

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance. Full article

This study formulates a mathematical dynamic Computable General Equilibrium (CGE) model within a rational expectations framework, adhering to neo-classical principles. It emphasizes the significant role of agents’ expectations in determining the broader economic trajectory over time. The model combines microeconomic and macroeconomic perspectives by merging the concept of intertemporal choice with savings behavior. Its mathematical foundations are derived and calibrated using data from a social accounting matrix to enhance its simulation capabilities. The paper presents a practical simulation investigating the economic implications of a strategic investment impact within an specific European region, Madrid as the case of study. Such demand shock affects sectors such as electronics, food, pharmaceuticals, and education. The study models the long-term effects of heightened investment and persistent demand-side shocks. The research demonstrates the CGE model’s ability to forecast economic shifts toward a new equilibrium after an investment shock, proving its utility for assessing the impacts of extensive environmental policies within a European context. The work’s originality lies in its detailed mathematical formulation, contributing to theoretical discourse and practical application in business analytics. Full article

Regulating the sharing economy is one of the most important aspects in the development of a business model that has developed rapidly due to the widespread adoption of digital technologies and is closely linked to the fast pace of institutional changes. The present study aims to develop strategies for regulating the sharing economy through the application of game theory. The authors identify common cooperative and non-cooperative strategies in the interaction of two participants: the state and the company. The matrix of strategies is based on the results of the analysis, which considers the interaction benefits, costs, and the positive and negative effects of this process. These strategies are exemplified in scenarios of interaction between the state and the sharing economy company in relation to three possible problems: environmental pollution, parking deficiency, and budget deficit. Furthermore, the study presents a comprehensive payoff matrix and provides a description of various sustainable and long-term scenarios. It also highlights the key parameters that should be taken into account when selecting a behavioral strategy for economic agents. In addition, the study establishes that supporting industries and projects of the sharing economy, as well as creating conditions or attracting investments, increasing public trust in government and business, and involving various social groups in resolving social problems are essential elements in the harmonious development of the sharing economy. These elements contribute to its potential to raise living standards. The practical significance of this study lies in the possibility of applying its results in the implementation of social and ecological objectives through the advancement of sharing economy initiatives. Full article

Emotion understanding (EU) ability is associated with healthy social functioning and psychological well-being. Across three studies, we develop and present validity evidence for the Core Relational Themes of Emotions (CORE) Test. The test measures people’s ability to identify relational themes underlying 19 positive and negative emotions. Relational themes are consistencies in the meaning people assign to emotional experiences. In Study 1, we developed and refined the test items employing a literature review, expert panel, and confusion matrix with a demographically diverse sample. Correctness criteria were determined using theory and prior research, and a progressive (degrees of correctness) paradigm was utilized to score the test. In Study 2, the CORE demonstrated high internal consistency and a confirmatory factor analysis supported the unidimensional factor structure. The CORE showed evidence of convergence with established EU ability measures and divergent relationships with verbal intelligence and demographic characteristics, supporting its construct validity. Also, the CORE was associated with less relational conflict. In Study 3, the CORE was associated with more adaptive and less maladaptive coping and higher well-being on multiple indicators. A set of effects remained, accounting for variance from a widely used EU test, supporting the CORE’s incremental validity. Theoretical and methodological contributions are discussed. Full article

While conventional innovation has boosted economic growth in certain regions, it has not contributed to closing the social and economic gap in most developing countries. Humanity is going through a historic moment of great challenges. One of them is social exclusion, a matrix of factors that prevent human beings from achieving well-being: poverty, hunger, inequality, lack of access to basic resources and services, and lack of social ties that help improve these circumstances, among others. This study holds two hypotheses: (1) in this context, inclusive innovation emerges as a response to the inability of conventional innovation to contribute to solve the persistent challenge of social exclusion and (2) universities—key actors in innovation dynamics—should play a fundamental role in the generation of inclusive innovation, especially considering their natural commitment to society. Although the role of the university in innovation has been widely acknowledged and studied, no formal theoretical model has represented inclusive innovation in developing countries adopting a systemic, complex, adaptive, and functional approach and incorporating a diversity of agents, interactions, capabilities, learning processes, knowledge, and directionalities—this would enable us to understand the role of the university in inclusive innovation. This paper argues that innovation dynamics should be understood from a systemic perspective and using computational modeling and simulation methods, so that the inherent complexity of these systems can be taken into account. The analysis of innovation scenarios based on a formal theoretical model and its operationalization through computer simulation should contribute to the understanding of the role of the university in these system dynamics, which can be used to propose effective strategies to strengthen its participation. Therefore, this paper proposes a formal systemic agent-based conceptual model that can be used to study the role of the university in inclusive innovation and establish guidelines to improve its performance. This study implemented standard computer modeling and simulation, specifically adapted for agent-based modeling. The results obtained from the simulation scenarios were comparatively analyzed using statistical tests (ANOVA and Tukey) to determine the presence of statistically significant differences. As the main finding of the research, the proposed conceptual model was validated and proved to be useful for studying the role of the university in reducing social exclusion in the Global South, through the design and execution of computer simulation scenarios. Full article

The escalating global energy demand, driven by heavy reliance on fossil fuels, worsens environmental degradation and triggers socio-economic shifts in extraction and refinery hubs. In Colombia, the energy matrix is predominantly fossil-based (76%), with hydroelectric power accounting for 70% of electricity generation. However, renewable energy sources only contribute 2% to the national energy mix. To reduce emissions by 20% by 2030, Colombia has presented an energy transition roadmap. The need for bioenergy production in Colombia arises from the residual biomass availability, the potential to provide sustainable energy access, and the potential to mitigate climate change impacts, while addressing energy poverty and enhancing energy security. This study presents an overview of biogas production in Colombia, emphasizing the need for financial resources to overcome barriers. Policy incentives, awareness campaigns, and research and development play a vital role in fostering social acceptance, technology adoption, and optimizing biogas production processes. Collaborative efforts among the government, private sector, and local communities are recommended to ensure wide-scale adoption of biogas, promoting economic, social, and environmental sustainability. By enabling informed decision-making, this research supports the transition to renewable energy sources and the achievement of sustainable development goals (SDGs), with a particular focus on bioenergy. The aim of this study is to explore the challenges and opportunities associated with biogas production in Colombia, including technical, economic, social, and environmental aspects, and provide recommendations for promoting its sustainable implementation and widespread adoption in the country. Full article