Search Results (377)

Objectives: Patients with rare diseases in China face extremely high medical expenses. The current coverage framework remains inadequate in terms of coverage depth and proactive risk control, underscoring an urgent need for institutional reform. Methods: This study employs a policy content analysis approach to review the current landscape of rare disease protection in China. Drawing on risk management theory and the health capital model, it constructs an analytical framework to examine potential institutional reforms through the lens of risk response pathways and the efficiency of health investment. Results: The findings reveal that basic medical insurance (BMI) provides limited financial protection for patients with rare diseases. Among China’s 31 provincial-level administrative centers, 24 have set general outpatient reimbursement ceilings under the urban and rural resident basic medical insurance (URRBMI) at 1000 RMB or less. In comparison, 24 cities have set outpatient reimbursement limits under the urban employee basic medical insurance (UEBMI) at 6000 RMB or less. The security system relies predominantly on the BMI, while supplementary mechanisms have failed to provide effective support or continuity in coverage. Current policies are generally reactive, with coverage typically triggered only after a confirmed diagnosis and often lacking early intervention or preventive strategies. Conclusions: China’s rare disease security system urgently requires structural improvements in coverage depth and proactive risk management. The proposed Dedicated Insurance Scheme for Rare Diseases (DISRD) presents a feasible and sustainable model for China’s multi-tiered system of securing rare diseases. It provides valuable institutional insights for other countries and regions seeking to build public health systems with proactive risk control capabilities. Full article

In Pakistan and most other developing nations, the residential building sector is one of the highest energy-consuming domains. The residential sector has the highest share of 50% of final electricity use of the country. Though Energy Conservation Building Codes (ECBC-2023) provide structured energy guidelines, no work has been performed to quantify the actual energy-saving potential of code-compliant retrofits in residential buildings. This study investigates the performance of ECBC-compliant retrofitting strategies for residential buildings under Pakistan’s diverse climatic conditions. The Passive House Planning Package (PHPP), a validated simulation tool, was used to assess energy performance improvements through building envelope interventions such as thermal insulation, solar shading, window glazing, and optimal orientation. Field data were collected from three representative cities, Multan (hot desert), Taxila (humid subtropical), and Quetta (cold semi-arid), to simulate both conventional and energy-efficient building scenarios. The results showed substantial seasonal energy savings in all three climates. During the heating period, energy savings were 48%, 50%, and 60% for Taxila, Multan, and Quetta, respectively. Similarly, energy savings during the cooling season were 44%, 33%, and 16%. Life cycle economic analysis revealed that these retrofits yielded Net Present Values (NPVs) of USD 752 (Taxila), USD 1226 (Multan), and USD 1670 (Quetta) over a 30-year period, with discounted payback periods ranging from 6 to 10 years. Furthermore, a life cycle assessment demonstrated that retrofitted buildings yielded up to 26% reduction in overall carbon emissions, combining both embodied and operational sources. The findings highlight that ECBC-2023 is not only a technically viable solution for energy savings but also financially attractive in residential retrofitting. By incorporating localized climate responsiveness into ECBC-compliant building design, the study provides a practical roadmap for achieving Pakistan’s energy efficiency goals. Additionally, the outcomes serve as a basis for informing policy initiatives, supporting building code adaptation, and raising public awareness of sustainable housing practices. Full article

Autonomous public transport (APT) is revolutionizing urban mobility by integrating advanced technologies, including electric autonomous buses and shared autonomous vehicles (SAVs). This paper examines the historical evolution of APT, from early automation efforts in the 1920s to the deployment of autonomous shuttles in contemporary cities. It highlights technological milestones, legislative developments, and shifts in public perception that have influenced the adoption of APT. The research identifies key benefits of APT, including enhanced road safety, reduced greenhouse gas emissions, and improved cost-efficiency in public transport operations. Additionally, the environmental potential of SAVs to reduce traffic congestion and emissions is explored, particularly when integrated with renewable energy sources and sustainable urban planning. However, the study also addresses significant challenges, such as handling emergencies without human intervention, rising cybersecurity threats, and employment displacement in the transportation sector. Social equity concerns are also discussed, especially regarding access and the risk of increasing urban inequality. This paper contributes to the broader discourse on sustainable mobility, transportation innovation, and the future of smart cities by providing a comprehensive analysis of both opportunities and obstacles. Effective policy frameworks and inclusive planning are essential for the successful implementation of APT systems worldwide. Full article

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

In the digital age, social media significantly shapes public attitudes and emotional responses towards health interventions, such as HPV vaccination, which is critical in developing countries. This study employed a deep learning model to identify fine-grained emotions of 38,615 HPV-related tweets from 2016 to 2024, revealing significant shifts in public emotions. Notably, skepticism about vaccine commercialization motives heightened anger, while university outreach initiatives fostered positive emotions. Structural entropy analysis highlighted polarized emotional communication networks: the network of joy exhibited lower entropy with centralized information flow, whereas other emotions displayed higher entropy, fragmented dissemination, and enhanced cross-community communication efficiency. New communicators, such as campus accounts and music bloggers, played pivotal roles in spreading positive emotions, while individual bloggers in specific fields amplified negative emotions like anger, particularly in closed networks. This research underscores the intricate dynamics of online health communication and the need for targeted interventions to address stigma and enhance public awareness of HPV vaccination, providing valuable insights for future public health policy. Full article

To advance global climate governance, this study investigates the carbon emission efficiency (CEE) of 110 Chinese resource-based cities (RBCs) using a super-efficiency SBM-GML model combined with kernel density estimation and spatial analysis (2006–2022). Spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) further elucidate the driving mechanisms. The results show that (1) RBCs achieved modest CEE growth (3.8% annual average), driven primarily by regenerative cities (4.8% growth). Regional disparities persisted due to decoupling between technological efficiency and technological progress, causing fluctuating growth rates; (2) CEE exhibited high-value clustering in the northeastern and eastern regions, contrasting with low-value continuity in the central and western areas. Regional convergence emerged through technology diffusion, narrowing spatial disparities; (3) energy intensity and government intervention directly hinder CEE improvement, while rigid industrial structures and expanded production cause negative spatial spillovers, increasing regional carbon lock-in risks. Conversely, trade openness and innovation level promote cross-regional emission reductions; (4) the influencing factors exhibit strong spatiotemporal heterogeneity, with varying magnitudes and directions across regions and development stages. The findings provide a spatial governance framework to facilitate improvements in CEE in RBCs, emphasizing industrial structure optimization, inter-regional technological alliances, and policy coordination to accelerate low-carbon transitions. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

This study explores the application of modular design in the regeneration of community public spaces within rapidly transforming urban environments, using Haikou as a case study. The objective is to improve spatial quality and community sustainability while preserving cultural identity and community engagement. Through a mixed-methods approach involving questionnaires, GIS-based spatial analysis, and case studies, the research identifies key challenges such as fragmented layouts, limited accessibility, and insufficient green space. In response, a “policy–design–community” integration mechanism is proposed to guide bottom-up and top-down coordination. A multidimensional evaluation framework is developed to assess the effectiveness of modular interventions across functional, spatial, and cultural dimensions. The findings suggest that modular design—owing to its standardization and flexibility—enhances spatial adaptability and construction efficiency, and strengthens cultural identity and community engagement. This research provides a replicable and data-informed strategy for the renewal of public spaces in Chinese urban environments. Full article

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience. Full article

Residents’ values in social housing retrofit extend beyond energy efficiency targets to encompass complex social and physical considerations, shaping their acceptance and experience of interventions. These values appear to influence retrofit success yet are often overlooked in policy and practice that prioritise technical performance. This paper investigates what residents value in social housing retrofit through a focus group method with the Sutton Estate in London, combining institutional stakeholder (housing association and architect) and resident perspectives. Content analysis of focus group questionnaires, discussions, and consensus-building activities revealed four key resident values: (1) good stakeholder relationships, emphasising trust-building through personal connections; (2) access to information, requiring clear, continuous communication through multiple channels; (3) comfort, health, and safety, integrating physical and mental wellbeing; and (4) building and community longevity, focusing on preserving building character and social sustainability. The findings suggest that social housing retrofit may benefit from a holistic approach integrating residents’ values throughout the project lifecycle, addressing the social, physical, and long-term sustainability of homes and communities. The findings highlight the importance for housing associations, architects, and policymakers to consider residents’ values when developing retrofit strategies, balancing technical requirements with residents’ needs and priorities. Full article

Digitalization is rapidly reshaping the global construction industry; however, its adoption in developing countries, such as Pakistan, remains limited and uneven. Hence, this study investigates and evaluates the current status of digital technology integration in Pakistan’s construction industry, with a primary focus on key tools, implementation challenges, and necessary policy interventions. Using a three-phase mixed-method approach involving a literature review, expert interviews, and a nationwide survey, this research identifies Building Information Modeling, Geographic Information Systems, and E-Procurement as essential technologies with strong potential to improve transparency, efficiency, and collaboration. However, adoption is hindered by a lack of awareness, limited technical expertise, and the absence of a cohesive national policy. This study also highlights that the private sector shows greater readiness compared to the public sector; however, systemic barriers persist across both sectors. Based on stakeholder insights, a three-part policy strategy was also proposed. This includes establishing a national regulatory framework, investing in capacity-building programs, and providing financial or institutional incentives to encourage the adoption of these measures. The findings emphasize that digitalization is not just a technical upgrade; it represents a pathway to improved governance and more efficient infrastructure delivery. With timely and coordinated policy action, the construction industry in Pakistan can align itself with global innovation trends and move toward a more sustainable and digitally empowered future. Full article

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

Under China’s ‘Dual Carbon’ strategy, the power sector plays a central role in achieving carbon neutrality. This study develops a bi-level game-optimization model involving the government, power producers, and technology suppliers to explore the dynamic coordination between shadow carbon pricing and emission trajectories. The upper-level model, guided by the government, focuses on minimizing total costs, including emission reduction costs, technological investments, and operational costs, by dynamically adjusting emission targets and shadow carbon prices. The lower-level model employs evolutionary game theory to simulate the adaptive behaviors and strategic interactions among power producers, regulatory authorities, and technology suppliers. Three representative uncertainty scenarios, disruptive technological breakthroughs, major policy interventions, and international geopolitical shifts, are incorporated to evaluate system robustness. Simulation results indicate that an optimistic scenario is characterized by rapid technological advancement and strong policy incentives. Conversely, under a pessimistic scenario with sluggish technology development and weak regulatory frameworks, there are substantially higher transition costs. This research uniquely contributes by explicitly modeling dynamic feedback between policy and stakeholder behavior under multiple uncertainties, highlighting the critical roles of innovation-driven strategies and proactive policy interventions in shaping effective, resilient, and cost-efficient carbon pricing and low-carbon transition pathways in the power sector. Full article

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, examining both theoretical foundations and implementation practices, this study aims to quantitatively assess and compare the effectiveness of two dominant PWSs models—the EC-like model (Phase I: October 2008–April 2017) and the PESs-like model (Phase II: 2017–December 2021). Using the Liaohe River in China as a case study, utilizing ecosystem service value (ESV) as an indicator and employing the corrected unit-value transfer method, we compare the effectiveness of different PWSs models from October 2008 to December 2021. The results reveal the following: (1) Policy Efficiency: The PESs-like model demonstrated significantly greater effectiveness than the EC-like model, with annual average increases in ESV of 3.23 billion CNY (491 million USD) and 1.79 billion CNY (272 million USD). (2) Functional Drivers: Water regulation (45.1% of total ESV growth) and climate regulation (24.3%) were dominant services, with PESs-like interventions enhancing multifunctionality. (3) Stakeholder Impact: In the PESs-like model, the cities implementing inter-county direct payment showed higher growth efficiency than those without it. The operational efficiency of PWSs increases with the number of participating stakeholders, which explains why the PESs-like model demonstrates higher effectiveness than the EC-like model. Our findings offer empirical evidence and actionable policy implications for designing effective PWSs models across global watershed ecosystems. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article