The rapid development of social media platforms has fundamentally reshaped large-scale group decision-making (LSGDM), introducing complex information flows that extend beyond traditional trust-based relationships. Existing consensus reaching processes (CRPs) primarily rely on trust networks, which emphasize interpersonal ties but often fail to capture the broader, real-world diffusion of information. To address this challenge, we propose a novel consensus model for LSGDM based on information diffusion theory in social networks. First, experts are clustered into subgroups using the Infomap algorithm, which reflects the underlying structure of information flow. Second, the independent cascade (IC) model is applied to simulate the information diffusion process, thereby quantifying expert influence and determining subgroup and individual weights. Third, a hybrid feedback mechanism is developed to guide consensus formation, where experts are categorized into leaders and followers with distinct interactive and automatic adjustment strategies. Finally, a hierarchical framework is introduced to manage noncooperative behavior among leaders. This framework balances the preservation of minority opinions with group consensus by protecting the weights of highly influential leaders while penalizing less influential ones. The feasibility and effectiveness of the proposed model are demonstrated through an illustrative example and extensive simulation experiments. Furthermore, comprehensive comparative analyses with classical opinion dynamics models and traditional trust-based CRPs demonstrate that our information diffusion mechanism significantly accelerates consensus convergence and robustly protects valuable minority insights.

Purpose A disconnect between the intention to participate and the actual engagement among private enterprises is impeding the effective promotion of new infrastructure construction (NIC). To address this critical barrier, this study aims to identify the determinants of this intention-behavior gap and to uncover the causal relationships and hierarchical structures, thereby revealing its underlying formation mechanisms. Design/methodology/approach A hybrid research design is employed. First, LDA topic modeling is used to extract 16 determinants from a heterogeneous dataset of academic literature, policy documents and industry reports. These determinants are then categorized within the technology-organization-environment (TOE) framework. Subsequently, a combined Fuzzy-DEMATEL-ISM-MICMAC approach is applied to identify key factors and elucidate the hierarchical structures among them. Findings Technological innovation benefits, data security assurance capabilities and corporate digital transformation strategy are identified as pivotal emerging determinants in the NIC field. Notably, the macroeconomic environment serves as a fundamental driver that profoundly influences the intention-behavior gap through the intermediate factors of market environment, organizational readiness and data security assurance capabilities. Originality/value By integrating data-driven factor discovery with expert-led structural analysis, this study overcomes the perceptual lag inherent in traditional factor identification and clarifies the gap's formation mechanisms. It extends the TOE framework to the NIC field and provides actionable guidance for policymakers and practitioners to facilitate substantive private enterprise engagement, ultimately fostering the high-quality development of NIC.

As climate-related extreme events intensify in frequency and severity, sustaining access to employment, healthcare, and other essential activities increasingly depends on the resilience of human mobility. This study advances mobility-resilience assessment by explicitly incorporating behavioral constraints that shape how travelers adapt under disruption. While conventional approaches largely equate resilience with transportation network connectivity, we conceptualize resilience as a behaviorally grounded outcome that depends on whether technically feasible trips remain acceptable within travelers’ limited tolerance for disruption-induced detours. Using large-scale mobile phone origin–destination data from Chongqing, China, we develop a behaviorally informed framework that integrates empirically estimated detour tolerance into resilience measurement, enabling the quantification of functional mobility loss under flood-induced network disruption. Results show that ignoring travel behavior systematically overestimates realized mobility resilience and masks critical spatial and social inequities, with low-income populations exhibiting consistently lower mobility resilience, particularly under more severe climate scenarios.

As urbanization accelerates, urban blue and green spaces are increasingly recognized as critical nature-based solutions for enhancing human well-being, delivering climate, environmental, and psychological benefits. Yet, the mechanisms by which access to these natural spaces and the equality of access by urbanites in diverse residential locations shape well-being remain poorly understood in the context of sustainable urban governance. Here, we systematically evaluated the spatiotemporal dynamics of urban blue and green space accessibility and equality across 279 Chinese cities from 2000-2100 and assessed their combined effects on well-being. We found that accessibility has a significant positive influence on well-being but follows an inverted-U-shaped pattern with distinct optimal thresholds. By contrast, equality shows a consistent, positive linear relationship with well-being. Although future accessibility is projected to improve, persistently high disparities among different segments of urban populations highlight the urgent need for equity-centered governance of blue and green spaces in cities. This perspective extends current approaches to environmental justice while revealing a structural mismatch between resource abundance and equitable distribution. To address this mismatch, we propose a prioritization framework that emphasizes context-specific, spatially targeted interventions, guided by four key drivers: the natural space to built-up area ratio, urban population size, the proportion of natural spaces with high accessibility, and urban landscape connectivity. By operationalizing these insights in an online toolkit for local governments, this work advances the fields of sustainable urban governance and equitable environmental planning.

Enhancing the sustainability of old neighbourhood renewal projects is pivotal for achieving the strategic goal of sustainable development across urban areas. However, limited attention is placed on the mechanism driving the sustainability of neighbourhood renewal. Acknowledging this void, a conceptual framework is developed using systems thinking for assessing and understanding the sustainability of old neighbourhood renewal. Drawing upon the ‘production–living–ecological spaces (PLES)’ and stakeholder perspectives, we identify a total of six driving factors covering the capabilities and inputs of the public sector, private entity and general public. Anchored on them, a system dynamics model is constructed to conduct a scenario-based analysis and empirically examine the developed framework through a case study of Baotou, China. The simulation results reveal that the stakeholder decisions and actions aimed at optimising inputs and sustainable capacity-building can enhance the long-term sustainability of old neighbourhood renewal throughout its lifecycle. This study engenders a novel paradigm to (1) clarify the mechanism driving the sustainability of neighbourhood renewal projects from a multidimensional, systematic and dynamic perspective and (2) have a systematic approach in place for enabling project sustainability. It also facilitates the practices in relation to pertinent decision-making processes and sustainable management.

Highway projects that harness digital technologies during operation (known as digital highway projects; DHPs) can stimulate economic growth, but limited efforts have been made to fully unravel this mechanism. To address this void, this study examined the impact of DHPs on the economic growth. Specifically, under the auspices of the regional competitiveness theory, the development level of DHPs, transportation demand, new factor endowments, and related and supporting industries were identified and measured first, and their impact on economic growth was then unearthed using data from 11 operational DHPs and a partial least squares structural equation modeling (PLS-SEM) framework. It was observed, from the perspective of stakeholders of our DHPs, that DHPs increase transportation demand, which in turn has a positive effect on a new factor endowment (i.e., data flow) and the development of related and supporting industries, with the former (beta = 0.859, p < 0.001) being impacted more than the latter (beta = 0.363, p < 0.001). In addition, new factor endowment has a statistically significant impact (beta = 0.666, p < 0.001) on the economy, while the development of related and supporting industries is insignificant (beta = 0.095, p > 0.05). Finally, although DHPs promote economic growth, this path can be mediated by increased transportation demand and subsequently by the new factor endowment including data flow. As such, this study further develops the regional competitiveness theory in the context of DHPs and provides new empirical evidence on the 'transportation induced demand' effect and the growth theory. Practically, this study arms policymakers with a better understanding of how DHPs influence the regional economy, and offers effective and targeted recommendations for managing these projects.

This study employs a mediating effect model and the Generalized Method of Moments (GMM) approach to examine the direct effect of ICT on the economy and the mediating role of data flow in the ICT – economic growth nexus. The results indicate that ICT significantly enhances data flow intensity, which in turn promotes economic growth. Moreover, both the direct effect of ICT and the mediating role of data flow are more pronounced in developed regions compared to underdeveloped areas. Further analysis shows that with the implementation of the policy, the mediating effect of data flow shifted from being insignificant (2006–2010) to significant (2011–2019). This study contributes to the understanding of the digital divide, highlighting potential drivers such as disparities in ICT infrastructure and data flow inequality. The government should develop tailored ICT development policies based on the region’s economic level to fully harness the benefits of digitalization. First published online 18 May 2026

In real world, a phenomenon is often observed and recorded by multiple institutions. Based on the recorded data, institutions can construct their own predictive models to understand the phenomenon. However, it is often difficult for a single institution to obtain a comprehensive understanding of the complex phenomenon based on its own data. In this study, we select the fuzzy rule-based model as a representative prediction method and introduce how institutions can effectively use multi-source data to construct their own predictive models with full consideration of data privacy. The originality of the study is summarized as that the overlap degree of data sets belonging to different institutions is considered when building collaborative predictive models, and corresponding modeling strategies are designed for scenarios where data sets of different institutions are either with a high (Scenario A) or low (Scenario B) degree of overlap. In Scenario A, the structure of data sets from different sources varies greatly and institutions can take Union-like strategies to achieve collaboration. For different levels of privacy-retention requirements, we propose strategies based on either sharing data structure or sharing local predictive models. In Scenario B, the structure of data sets from different sources is similar, and individual institutions can take Intersection-like strategies to achieve collaboration. For different levels of efficiency requirements, strategies based on either a single-stage collaboration or a two-stage collaboration are proposed to share local predictive models. Through experiments on a series of synthetic and publicly available data sets, we demonstrate the effectiveness of the proposed approach.

Sustainable development embodies the principle of equity while placing an emphasis on addressing climate change. This paper treats the National Sustainable Development Pilot Zones policy as a quasi-natural experiment to explore the extent to which sustainable development can facilitate equity in carbon mitigation. Based on a sample of 179 prefectural-level cities from 2004 to 2017, we employ a staggered difference-in-differences model to examine the impact of the National Sustainable Development Pilot Zones on carbon emissions. The results show that the National Sustainable Development Pilot Zones significantly slowed the growth of carbon emissions and promoted intragenerational equity, as confirmed by extensive robustness tests. Furthermore, the study examines the heterogeneity of the impact and the underlying mediating mechanisms, and also analyzes the policy linkage effects. Finally, the impact of equitable mitigation tends to favor the pilot zones with higher initial carbon emission growth rates, thus achieving intergenerational equity. Understanding whether National Sustainable Development Pilot Zones can effectively facilitate equitable carbon mitigation is crucial for designing policies that balance economic development with environmental sustainability. This paper contributes to both the theoretical foundation and empirical evidence of the impact of establishing the National Sustainable Development Pilot Zones on equitable carbon emissions mitigation.

Delivering infrastructure assets capitalized by real estate investment trusts (REITs) is emerging and challenging due to the complexities and uncertainties that characterize the project lifecycle. Having effective management of risks in place is key to project success. However, there remains limited knowledge about the dynamic mechanism of the infrastructure-REIT-related risks. The result has hindered the delivery process and investors' benefits. Acknowledging this void, we identify a set of risk factors with a reference to the five categories identified from a case study of China's infrastructure REITs, and then examine them using the directed-weighted-network risk assessment model followed by the dynamic propagation paradigm. The results reveal the front-end attribute of those risks including the principal agent, macroenvironment, and policies, and indicate that identifying the anomalies early can minimize the scale of risk propagation. The contribution of this study is twofold: (1) we originally develop the risk network for explaining the interactive mechanism of the infrastructure-REIT risks; and (2) we identify the relevant risk dynamic propagation pattern.

Within the framework of Digital China, creating effective policies to support the development of smart cities remains a longstanding challenge. Yet, there has been insufficient focus on evaluating and optimizing these policies. To address this knowledge gap, our study advances policy process theory by integrating the multiple streams framework (MSF) with the policy model consistency Index (PMC-Index) and fuzzy set qualitative comparative analysis (fsQCA). This novel hybrid framework systematically evaluates the effectiveness of China's smart city policies (SCPs) and identifies data-driven optimization strategies. The findings reveal that central guidance notwithstanding, the innovative nature of local policies results in marked differences from the central government's approach. The findings reveal that local governments exhibit superior policy entrepreneurship, translating central directives into contextualized innovations. Additionally, while SCPs demonstrate strong overall consistency, significant challenges persist-including institutional fragmentation, field restrictions, and tool imbalances. Accordingly, three advanced optimization strategies are identified: top-level guidance, domain expansion, and comprehensive synergy. These insights not only enrich the theoretical understanding of policy design but also provide policymakers with actionable guidance to tailor policies according to urban conditions and resource endowments, thereby enhancing adaptive governance to secure urban digital transformation. 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En el marco de la iniciativa China Digital, la creaci & oacute;n de pol & iacute;ticas eficaces para impulsar el desarrollo de ciudades inteligentes sigue siendo un reto persistente. Sin embargo, se ha prestado insuficiente atenci & oacute;n a la evaluaci & oacute;n y optimizaci & oacute;n de estas pol & iacute;ticas. Para abordar esta brecha de conocimiento, nuestro estudio profundiza en la teor & iacute;a del proceso pol & iacute;tico mediante la integraci & oacute;n del marco de flujos m & uacute;ltiples (MSF) con el & Iacute;ndice de Consistencia del Modelo de Pol & iacute;ticas (PMC-Index) y el an & aacute;lisis comparativo cualitativo de conjuntos difusos (fsQCA). Este novedoso marco h & iacute;brido eval & uacute;a sistem & aacute;ticamente la eficacia de las pol & iacute;ticas de ciudades inteligentes (SCP) de China e identifica estrategias de optimizaci & oacute;n basadas en datos. Los resultados revelan que, a pesar de la orientaci & oacute;n central, la naturaleza innovadora de las pol & iacute;ticas locales genera diferencias notables con respecto al enfoque del gobierno central. Los hallazgos demuestran que los gobiernos locales exhiben una mayor capacidad de innovaci & oacute;n pol & iacute;tica, traduciendo las directivas centrales en innovaciones contextualizadas. Adem & aacute;s, si bien las SCP demuestran una s & oacute;lida coherencia general, persisten desaf & iacute;os importantes, como la fragmentaci & oacute;n institucional, las restricciones de campo y los desequilibrios en las herramientas. En consecuencia, se identifican tres estrategias de optimizaci & oacute;n avanzadas: orientaci & oacute;n de alto nivel, expansi & oacute;n del dominio y sinergia integral. Estos conocimientos no solo enriquecen la comprensi & oacute;n te & oacute;rica del dise & ntilde;o de pol & iacute;ticas, sino que tambi & eacute;n brindan a los responsables pol & iacute;ticos orientaci & oacute;n pr & aacute;ctica para adaptar las pol & iacute;ticas a las condiciones urbanas y la disponibilidad de recursos, mejorando as & iacute; la gobernanza adaptativa para garantizar la transformaci & oacute;n digital urbana.

In public-related large-scale group decision-making (PL-LGDM) events, the public disclosure of phased decision-making results affects the trajectory of public opinion, while the evolution of public opinion in turn shapes the direction of decisions that need to keep public interest in mind. Inconsistencies between public opinion and decisions can hinder or even overturn the implementation of decision outcomes, affecting social stability. Considering this interaction, this paper proposes an opinions and behaviors-based public opinion management (OB-POM) model. The model takes the fluidity of information within social networks into account and proposes the public opinion dissemination process, shifting the focus of public opinion evolution research from static to dynamic populations. A set of negative public opinion trigger conditions, encompassing both quantitative and qualitative criteria, is proposed considering public opinion and phased decision-making results. Opinion leaders are classified into four categories based on their opinions and behaviors, and targeted public opinion management strategies are proposed accordingly. Simulation results demonstrate the feasibility of the model and its effectiveness in eliminating negative public opinion and facilitating consensus among decision-makers.

Procedural fairness has long been recognized as critical to citizen acceptance of urban renewal initiatives. However, research examining the underlying mechanisms of this relationship remains limited, hindering effective policy implementation. This study addresses this gap by investigating how relationship quality dimensions-specifically citizen satisfaction, citizen recognition, and citizen trust-mediate the link between procedural fairness and citizen acceptance. Drawing on survey data from 409 respondents across three communities in China that experienced urban renewal projects and employing structural equation modeling (SEM) and artificial neural network (ANN) analysis, our findings demonstrate that procedural fairness positively influences citizen acceptance, with citizen satisfaction and citizen recognition serving as partial mediators in this relationship, while citizen trust shows no significant mediating effect. These findings advance the literature by elucidating the distinct procedural fairness pathways through which citizens develop acceptance toward urban renewal policies, while also revealing the nuanced roles that different relational dimensions play in this process.

Improving infrastructure investment efficiency is critical to address global investment gap and resource constraints, thereby promoting sustainable economic development. This study aims to explore the peer effects in local governments’ new infrastructure investment (NII) decision-making and uncovers the underlying mechanisms driving these effects. Utilizing panel data from 258 Chinese prefecture-level cities (2012–2022), this study employs a spatial econometric model to examine how intergovernmental spatial interactions shape the NII decision-making. Furthermore, the impacts of learning mechanism, competition mechanism, and exogenous shocks on the peer effects are explored. The findings show that: (1) Significant peer effects exist in NII decision-making of local governments across all seven spatial correlation contexts, with transportation networks and industrial linkages exerting the strongest influence. (2) Cities exhibit asymmetric imitation patterns based on their level of economic development: underdeveloped cities exhibit stronger imitation of developed counterparts, which primarily serve as benchmarks. This behaviour reflects rational adaptation strategies under uncertainty, as local governments seek to minimise risks and leverage experiential learning. (3) Learning mechanisms reinforce irrational imitation through accumulated experience, leading to self-reinforcing path dependence. Competition mechanisms—especially industrial competition in central China—are major drivers, complemented by fiscal and talent competition in central and western regions. Moreover, national policy pilots (e.g. National Big Data Comprehensive Pilot Zones) serve as pivotal reference points, accelerating policy convergence among local governments. This study offers a comprehensive understanding of the existence, asymmetry, and multidimensional drivers of peer effects in NII decision-making. It provides both theoretical and policy insights for optimizing regional infrastructure coordination and promoting sustainable inclusive development.

In this study, we propose a series of methods to build fuzzy rule-based models (FRBMs) in the presence of the big data environment such that the formed predictive models are more accurate, efficient, and robust. We follow two major steps to realize this target. In the first step, we build numeric FRBMs with the big data set such that the formed predictive models are more accurate and efficient. Specifically, based on the divide-and-conquer strategy, the big data set is divided into subsets through either the hyperplane division-based method or the K-Means clustering-based method; then either a global-based strategy or a local-based strategy is used to build numeric FRBMs. As a result, four Options are generated to develop numeric FRBMs. In the second step, we build the granular FRBMs based on the four Options developing the numeric FRBMs. Specifically, given a certain Option, based on the Principle of Justifiable Granularity (PJG), we granulate both condition parts and conclusion parts of the rules, forming the granular FRBMs; then the predictive models are further evaluated based on the PJG and optimized based on the Particle Swarm Optimization (PSO) algorithm to enhance the robustness. Finally, experimental studies on both synthetic datasets and publicly available datasets are conducted to prove the effectiveness of the proposed methods.

Blame avoidance behavior by civil servants has become an obstacle to government innovation and social governance, and it is crucial to explore the causes of this behavior. This study explored how institutional pressures affect the blame avoidance behavior of Chinese civil servants. Data obtained through a survey experiment showed that high institutional pressure induces more blame avoidance behavior in Chinese civil servants by increasing their risk perception and reducing their public service motivation. The influence path of risk perception is stronger than that of public service motivation. Additionally, positive incentives reduce the impact of institutional pressures on risk perception, while negative incentives increase this impact. This study confirmed that institutional pressures influence blame avoidance behavior through a combination of risk perception, public service motivation, and incentive strategies. Theoretical guidance and practical experience are provided for the governance of blame avoidance behavior.

Climate change is causing a significant increase in the number of compound extreme events that pose significantly greater threats to public safety. Chongqing is a megacity in southwestern China that took the brunt of temporally compounding events (TCEs) in the summer of 2022. We developed an approach based on the Intergovernmental Panel on Climate Change (IPCC) risk framework to assess the public health risks posed by TCEs. This approach was then applied to reveal temporal and spatial discrepancies in risks, which depend on natural endowments, socioeconomic conditions, and population demographics. High public health risks posed by heatwaves are caused by high exposure and vulnerability, which are primarily influenced by poor living conditions and living alone, together with underlying medical conditions such as mental disorders. The risks can be further magnified when TCEs emerge along with heatwaves, resulting from accumulated effects associated with noncommunicable diseases and vulnerable populations, including children and elderly individuals. Although a reduction in exposure to heatwaves can directly moderate these risks, prioritizing a reduction in exposure while simultaneously mitigating climate hazards and actively protecting people from TCEs, including alleviating vulnerability, is unequivocally necessary to minimize the risks to TCEs. Our findings indicate that highly vulnerable population groups are mostly exposed to TCEs and susceptible to impacts. These impacts exacerbate inequalities, engender environmental injustice, and hinder sustainable development. Efforts to reduce these risks by strengthening the health system and improving dwelling conditions are essential.

Poverty remains one of the most pressing global challenges of this era, affecting millions of people across both developing and developed countries. The poverty alleviation resettlement (PAR) is a policy with Chinese characteristics for eradicating poverty. By integrating the Maslow’s Hierarchy of Needs and Amartya Sen’s Capability Approach, this study developed a theoretical framework to analyze the factors influencing the well-being of poverty alleviation migrants (PAMs). A telephone survey conducted between July and August 2022 in Hubei Province, Guizhou Province, Shaanxi Province, and Chongqing Municipality of China yielded 259 valid questionnaires. Using the partial least squares-structural equation modeling (PLS-SEM), this study revealed that financial accessibility, health level, living conditions, and social networks significantly enhanced the well-being of PAMs, with living conditions having the strongest impact on the well-being of PAMs. Furthermore, the factors affecting well-being varied across age groups. Social networks played a more significant role in the elderly group, whereas health level had a greater impact on the young and middle-aged group. These findings deepen the understanding of the PAR and its effects on the well-being of PAMs, offering valuable insights for policy-makers and practitioners to refine poverty alleviation strategies and enhance social welfare.

Understanding community disaster resilience is critical to mitigating the disproportionate impacts of climate change and natural disasters on socially vulnerable populations. However, despite extensive discussion on disaster resilience, a systematic analysis of the extent of social inequity across climate scenarios, geographic locations, spatial scales, and sociodemographic groups remains underexplored. Our study introduces a human-centric framework to investigate social inequities in community disaster resilience related to human well-being. We combined flood hazard maps under both historical and future SSP scenarios with a compound multilayer urban spatial network model consisting of roads, communities, and essential services to evaluate the residents' service resilience during flood events. Then, we utilized the Gini coefficient and Lorenz curve to quantify the degree of inequities in resilience among different sub-populations. With Central Chongqing as a case study, our analysis reveals a significant increase in both the number of affected communities and their vulnerability under future climate conditions. We further observed a striking spatial polarization in community resilience due to the islanding effect, whereby communities are increasingly divided into those with severely limited service availability and those with sufficient resources. In addition, we found that the extent of social inequity in resilience is highly spatial and scale-specific, with moderate levels of inequity at the city level, but the degree of inequity varies greatly across sociodemographic groups at a localized level. This widening socio-spatial differentiation may trigger widespread dissatisfaction in disadvantaged communities, hindering the collective disaster response actions and engagements to enhance community resilience. Our research highlights the importance of embedding future climate variabilities, human well-being, and social equity in inclusive disaster response policies, processes, and practices.