Search Results (644)

Global policy narratives on livestock development increasingly emphasize environmental concerns, often overlooking the social dimensions of the sector. In the case of dairy, the world’s most valuable agricultural commodity, its role in social and economic development remains poorly quantified. Our study contributes to a more balanced vision of the UN SDGs thanks to the inclusion of a socio-economic dimension. Here we present a novel empirical approach to assess the socio-economic impacts of dairy development using a new global dataset and non-parametric modelling techniques (local polynomial regressions), with yield as a proxy for sectoral performance. We find that as dairy systems intensify, the number of farm households engaged in production declines, yet household incomes rise. On-farm labour productivity also increases, accompanied by a reduction in employment but higher wages. In dairy processing, employment initially grows, peaks, and then contracts, again with rising wages. The most substantial impact is observed among consumers: an increased milk supply leads to lower prices and improved affordability, expanding the access to dairy products. Additionally, dairy development is associated with greater agricultural value added, an expanding tax base, and the increased formalization of the economy. These findings suggest that dairy development, beyond its environmental footprint, plays a significant and largely positive role in social transformation, yet is having to adapt sustainably while tackling labour force relocation, and that dairy development’s social impacts mimic the general agricultural sector. These results might be of interest for the assessment of policies regarding dairy development. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders. Full article

Volcanic islands host exceptional geological features that illustrate complex endogenic processes and interactions with climatic and marine forces, while also being particularly vulnerable to the impacts of climate change. Despite their scientific, educational, touristic, and aesthetic values, such islands remain underrepresented within the UNESCO Global Geoparks (UGGp). This study reviews current volcanic island geoparks and evaluates territories with potential for future designation, based on documented geoheritage, geosite inventories, and geoconservation frameworks. Geoparks are categorized according to their dominant narratives—ranging from recent Quaternary volcanism to broader tectonic, sedimentary, and metamorphic histories. Through an analysis of their distribution, management strategies, and integration into territorial planning, this work highlights the challenges that insular territories face, including vulnerability to global environmental change, limited legal protection, and structural inequalities in access to international resources recognition. It concludes that volcanic island geoparks represent strategic platforms for implementing sustainable development models, especially in ecologically and socially fragile contexts. Enhancing their global representation will require targeted efforts in ecologically and socially fragile contexts. Enhancing their global representation will require targeted efforts in capacity building, funding access, and regional cooperation—particularly across the Global South. Full article

The rapid rise of the silver economy in the new era has become a new driving force for socio-economic development. From the perspective of Marxist political economy theory, this paper analyzes the intrinsic logic of the silver economy’s development through three dimensions: surplus value, labor market, and capital. The study finds that the silver economy in the new era faces challenges such as insufficient supply of high-quality elderly care services, simultaneous shortages in both total talent quantity and structural imbalances, and contradictions between capital’s profit-seeking nature and social welfare. By introducing the multiple streams model, the paper elucidates the coupling process of these three streams and the timing of policy window openings. It proposes targeted strategies, including strengthening technological innovation, deepening labor market reforms, and optimizing capital allocation, to promote the robust development of China’s silver economy and inject strong momentum into sustainable and high-quality economic growth. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

Developing agricultural new productive forces (ANPF) according to local conditions is a key strategy for agricultural modernization. Using panel data from 16 prefecture-level cities in Anhui Province from 2010 to 2022, this study constructed indicator systems for ANPF and the construction of a strong agricultural province (CSAP). The entropy-weight TOPSIS method was used to calculate the levels of ANPF and the SAP index. This study employed a modified gravity model and social network analysis (SNA) to investigate the spatial correlation and evolutionary characteristics of these networks. Geographical detectors were also used to identify the driving factors behind agricultural transformation. The findings indicate that both ANPF and CSAP showed an upward trend during the study period, with significant regional heterogeneity, with Central Anhui being the most prominent. This study revealed spatial spillover effects and strong network correlations between ANPF and CSAP, with the spatial network structure exhibiting characteristics of multi-core, multi-association, and multidimensional connections. The entities within the network are tightly connected, with no “isolated island” phenomenon, and Hefei, as the central hub, showed the highest number of connections. Laborer quality, tangible means of production, and new-quality industries emerged as the core driving forces, working in synergy to propel CSAP. This study contributes new insights into the spatial network dynamics of agricultural development and offers actionable recommendations for policymakers to enhance agricultural modernization globally. Full article

With accelerated population aging, the importance of older adults’ self-rated health is constantly increasing. Self-rated health is influenced by complex relationships between the built environment and psychosocial factors. Therefore, this study constructs a pathway framework of “material (housing quality and environmental pollution)–psychological (depression and social capital)–self-rated health” elements to explore the influencing mechanism of older adults’ self-rated health. This study utilized the 2018 China Labor Force Dynamics Survey Database to explore the relationship between built environment factors (housing quality and environmental pollution), depression, social capital, and older adults’ self-rated health, using structural equation modeling. The heterogeneity between urban and rural areas is also analyzed. Better housing quality and less environmental pollution were found to be related to higher levels of self-rated health. Depression and social capital were important mediators in the relationship between housing quality, environmental pollution, and self-rated health. Regarding urban–rural heterogeneity, the direct impact of environmental pollution on self-rated health was only significant among urban older adults. Secondly, the multiple mediating roles of social capital were only reflected among rural older adults. The government and relevant entities should promote improvements in housing quality and reduce environmental pollution to achieve a healthy and livable environment. Full article

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability. Full article

The agricultural products trade network is essentially a complex adaptive system formed by nonlinear interactions between countries. Based on the complex system theory, this study reveals the dynamic self-organization law of the RCEP regional agricultural products trade network by using the panel data of RCEP agricultural products export trade from 2000 to 2023, combining social network analysis (SNA) and the temporal exponential random graph model (TERGM). The results show the following: (1) The RCEP agricultural products trade network presents a “core-edge” hierarchical structure, with China as the core hub to drive regional resource integration and ASEAN countries developing into secondary core nodes to deepen collaborative dependence. (2) The “China-ASEAN-Japan-Korea “riangle trade structure is formed under the RCEP framework, and the network has the characteristics of a “small world”. The leading mode of South–South trade promotes the regional economic order to shift from the traditional vertical division of labor to multiple coordination. (3) The evolution of trade network system is driven by multiple factors: endogenous reciprocity and network expansion are the core structural driving forces; synergistic optimization of supply and demand matching between economic and financial development to promote system upgrading; geographical proximity and cultural convergence effectively reduce transaction costs and enhance system connectivity, but geographical distance is still the key system constraint that restricts the integration of marginal countries. This study provides a systematic and scientific analytical framework for understanding the resilience mechanism and structural evolution of regional agricultural trade networks under global shocks. Full article

Green innovation has become a core driving force for promoting sustainable development, making the accurate evaluation of enterprises’ green innovation efficiency an important research topic. Based on the Environmental, Social, and Governance (ESG) framework, this paper improves the SBM model to overcome shortcomings such as homogeneity in traditional SBM models during efficiency evaluation. By introducing an asymmetric slack measure, it breaks through the limitation of efficiency value ceilings, enabling gradient ranking of decision-making units and precisely distinguishing between efficient and inefficient enterprises, thereby better assessing the green innovation efficiency of hydrogen energy companies. The study shows that the improved SBM model significantly enhances the accuracy of enterprise efficiency evaluation. The contribution of this paper lies in constructing an improved SBM model integrated within the ESG framework, compensating for the lack of environmental dimensions in traditional evaluation methods, addressing issues of efficiency homogeneity and the static nature of the frontier, and achieving optimized ranking of frontier-efficient enterprises. Full article

Background/Objectives: Urolithin A (UA), a gut-derived metabolite of ellagitannins or ellagic acid, has recently gained attention for its potential benefits to brain health. The present research aimed to assess the antidepressant-like properties of UA in both in vitro and in vivo models and explored the molecular mechanisms underlying these effects. Methods: We investigated the antidepressant effects and mechanisms of UA in a model of corticosterone-induced damage to PC12 cells and in a model of chronic socially frustrating stress. Results: Our results demonstrate that UA treatment (5 and 10 μM) significantly alleviated cellular damage and inflammation in corticosterone (CORT)-treated PC12 cells. Furthermore, UA administration (50 and 100 mg/kg) significantly reduced immobility time in the mouse tail suspension test (TST) and forced swim test (FST), indicating its antidepressant-like activity. Additionally, treatment with UA led to the activation of the cAMP response element-binding protein (CREB)/brain-derived neurotrophic factor (BDNF) signaling cascade and triggered the activation of adenosine monophosphate-activated protein kinase (AMPK) during these processes. Importantly, pretreatment with AMPK-specific inhibitor Compound C abolished UA’s cytoprotective effects in PC12 cells, as well as its behavioral efficacy in the FST and TST, and its neurotrophic effects, highlighting the critical role of AMPK activation in mediating these effects. Furthermore, in the chronic social defeat stress (CSDS) mouse model, UA treatment (50 and 100 mg/kg) significantly alleviated depression-like behaviors, including reduced sucrose preference in the sucrose preference test, increased social avoidance behavior in the social interaction test, and anxiety-like behaviors, including diminished exploration, in the elevated plus maze test, suggesting the antidepressant-like and anxiolytic-like activities of UA. Moreover, UA treatment reversed elevated serum stress hormone levels, hippocampal inflammation, and the decreased AMPK/CREB/BDNF signaling pathway in the hippocampus of CSDS mice. Conclusions: Together, these results provide compelling evidence for UA as a viable dietary supplement or therapeutic option for managing depression. Full article

Pedestrian flow simulations play a pivotal role in urban planning, transportation engineering, and disaster response by enabling the detailed analysis of crowd dynamics and walking behavior. While physical models such as the Social Force model and Boids have been widely used, they often struggle to replicate complex inter-agent interactions. On the other hand, reinforcement learning (RL) methods, although adaptive, suffer from limited interpretability due to their opaque policy structures. To address these limitations, this study proposes a pedestrian simulation framework based on the Anticipatory Classifier System 2 (ACS2), a rule-based evolutionary learning model capable of extracting explicit behavior rules through trial-and-error learning. The proposed model captures the interactions between agents and environmental features while preserving the interpretability of the acquired strategies. Simulation experiments demonstrate that the ACS2-based agents reproduce realistic pedestrian dynamics and achieve comparable adaptability to conventional reinforcement learning approaches such as tabular Q-learning. Moreover, the extracted behavior rules enable systematic analysis of movement patterns, including the effects of obstacles and crowd composition on flow efficiency and group alignment. The results suggest that the ACS2 provides a promising approach to constructing interpretable multi-agent simulations for real-world pedestrian environments. Full article

This study investigates how climate-resilient city construction (CRCC) influences the Environmental, Social, and Governance (ESG) performance of Chinese listed firms, employing a difference-in-differences (DID) model with firm-year data from 2012 to 2023. The empirical results demonstrate that CRCC exerts a significant positive effect on firms’ ESG performance, with particularly pronounced improvements in the environmental and social dimensions. The mechanism analysis reveals that strengthening government environmental guidance and stimulating firms’ environmental response strategies are the key channels via which CRCC improves firms’ ESG performance. The heterogeneity tests show more pronounced effects for the central–eastern regions, state-owned firms, non-regulated industries, and non-heavily polluting sectors. A further analysis indicates that better ESG performance drives firms to increase their environmental investment, upgrade their value chains, and enhance new quality productive forces. This study extends the framework of ESG determinants by integrating climate adaptation policies, offering insights for urban climate governance and firms’ low-carbon transitions. Full article

Emergency displacement has become an increasingly salient global phenomenon, precipitated by the intensification of climate crises and persistent geopolitical conflicts. These events forcibly displace millions each year and generate complex social, political, and institutional challenges. While the literature on displacement is expanding, much of it centers on individual and household experiences, often overlooking the collective dimensions of displacement. This article addresses this gap by critically examining the concept of the displaced community, a term used to describe collectivities formed in host societies comprising individuals who have been forcibly uprooted. The article undertakes a conceptual investigation of displaced communities, seeking to define their constitutive features while accounting for their internal heterogeneity and contextual variability. To sharpen analytical clarity, the study contrasts displaced communities with healthy communities, thereby situating two polar ends of a continuum. Based on these two types of community, the question arises, “can displaced communities be healthy communities?” The article advances a conceptual model of a healthy displaced community, positing that such a construct extends conventional understandings of resilience by foregrounding the processual dynamics of recovery and adaptation. Specifically, it is argued that community health in contexts of forced displacement must be understood as the outcome of iterative processes intentionally involving community-based intervention, empowerment, and long-term sustainability. Drawing on published case studies and empirical accounts of work with displaced populations, the article demonstrates how these three pillars—community intervention, empowerment, and sustainability—are implemented in practice. It concludes with policy and practice recommendations designed to prevent further deterioration and promote the development of health and well-being within displaced communities. Full article