While female representation in social sciences is increasing, systemic gender disparities may persist in research funding and academic performance. Some argue that female scholars now receive equal opportunities, yet evidence suggests that gender imbalances remain, particularly in specific research areas. This study examines 12,945 National Science Foundation (NSF)-funded principal investigators in social sciences from 2000 to 2019 to assess gender disparities in grant allocation, research topics, and post-award academic performance. Findings reveal a dual imbalance. First, despite similar overall funding success rates, female scholars remain underrepresented in high-impact and traditionally male-dominated research topics. Males dominate most funded topics, especially STEM-related ones, while female-led topics align with traditional gender stereotypes. Second, post-award performance patterns suggest that females outperform males in male-dominated fields, whereas males excel in female-dominated ones, undermining any presumed advantage of female scholars in their own research areas. These disparities contribute to the risk of both genders prematurely exiting the science pipeline. Furthermore, early-career experiences shape these outcomes asymmetrically: postdoctoral experience benefits both genders in female-dominated fields, with stronger effects for males, but disadvantages females in male-dominated fields by reducing their output and citation impact. Longer postdoctoral tenure enhances male researchers' citation impact across all fields but has mixed effects for females depending on field gender composition. These findings underscore the need for policies that address not just overall funding equality, but also gendered disparities across research topics and career trajectories.

Urban complexity represents one of the most significant challenges for sustainable development in the AI era. Traditional planning methods often struggle to navigate their dynamic and complex nature. Drawing upon the "Urban Iceberg" theoretical framework, we launch the special issue “Integrating AI into Sustainable Urban Development: Planning Approaches and Strategies.” The studies published in this issue provide robust evidence that AI has matured into a powerful tool for decoding the visible physical city environment through high-precision sensing and pattern optimization. However, building truly sustainable cities necessitates penetrating the submerged social environment—the domain of conflicting values, institutional logic, and human needs. This demands a structural transition from observing physical phenomena to decoding underlying socio-economic mechanisms. We advocate for the advancement of cognitive AI to simulate stakeholder negotiation and facilitate human-AI co-creation. This special issue serves not only as a repository of technical advances but as a roadmap for bridging the divide between physical intelligence and social rationality. Only by integrating these dimensions can we transform AI from a tool of measurement into an instrument of humane and sustainable development.

Housing discrimination hinders labour mobility and economic equality. The level of housing discrimination has not been significantly reduced over recent decades, with notable issues in less diverse countries, especially former colonial powers. We studied London's rental housing market, where ethnic minorities face barriers and housing is unaffordable. An analytical framework based on the taste-based and statistical discrimination theories were developed to investigate the nature of housing discrimination. Blue-collar and white-collar employment information was included in the correspondence test to differentiate between taste-based and statistical discrimination. Our findings indicate UK's racial discrimination is primarily taste-based, and providing job information does not bridge the racial gap. Addressing this requires government and societal efforts, emphasizing inclusive urban policies that influence how individuals perceive and interact with different ethnicities.

Allocating resources effectively for the recovery of facilities and services after a disaster is essential to improve the sustainability and well-being of affected residents who face various challenges and pressures. A significant challenge in this process is identifying vulnerabilities and strategically prioritizing resource to address residents' postdisaster needs. These needs change over time, including both their urgency and the extent to which they are fulfilled, as facilities recover. However, currently, no quantitative computational model characterizes these needs and further guides resilience evaluation and infrastructure rush repair. This study introduces an evaluation framework that monitors residents' unmet needs by keeping track of the needs they report and addressing feedback gathered from social sensing. Residents' postdisaster needs are first identified by clustering 336,928 pieces of appeal and feedback data using latent Dirichlet allocation (LDA) topic modeling. These needs are then validated through a Delphi study and modeled into a three-level hierarchy. Then, we characterized residents' expected recovery curve over time as a benchmark and used the actual recovery based on needs fulfillment to quantify the discrepancy, which served as the proxy for resilience. The proposed framework was validated using empirical data from a regular and an extreme rainstorm that occurred in 2020 and 2023 in Beijing. The findings offer valuable insights into how residents' most urgent needs and the corresponding types of infrastructure repair needed change across different recovery phases and for different types of rainstorms. These insights help guide policymakers in improving the effectiveness of disaster response and in taking proactive measures for infrastructure maintenance to prevent future disasters.

Sustainable property development in developing economies requires a careful balance between attracting foreign capital and maintaining housing affordability for local residents. While foreign direct investment (FDI) serves as a crucial engine for economic growth by enhancing productive capacity and international competitiveness, its effects on local housing markets remain inadequately understood in policy frameworks. This study examines how economic development strategies can be designed to harness FDI benefits while preventing residential market distortions in rapidly industrializing regions. Using Malaysia’s Kulim Hi-Tech Park and Batu Kawan Industrial Park as empirical cases, we analyze the relationship between foreign capital inflows and residential property prices from 2000 to 2022 through time-series regression analysis supplemented by stakeholder consultations. Our findings reveal that FDI significantly influences housing price dynamics in industrial zones, with both positive economic spillovers and challenges for housing affordability. The results demonstrate that targeted policy interventions—including affordable housing mandates, developer incentives, and strategic land use planning—can effectively moderate price appreciation while maintaining investment attractiveness. This research contributes to evidence-based policymaking by identifying integrated mechanisms that promote sustainable and inclusive growth in emerging economies seeking to balance industrial advancement with equitable housing access. The Malaysian experience offers valuable practical insights for policymakers in developing nations navigating the complex relationship between international investment, housing markets, and social welfare.

This study used randomised controlled trials to test the effectiveness of three behavioural interventions, i.e., focalism, social norm, and visualisation, in changing people’s housing and commuting preferences. The experiment was conducted online via Credamo, one of the largest online panel data providers in China. It included only renters who needed to commute in the city of Xi’an, China, as participants in the study. The results show that behavioural interventions significantly increased respondents’ willingness to adopt more sustainable commute modes, such as walking or cycling, and reduced the tendency to use private cars. Among the three behavioural interventions, the social norm intervention had the largest and most significant impact. The findings shed light on the potential of applying behavioural interventions in sustainable urban transport management. More importantly, the results demonstrate the possibility of using behavioural interventions to incorporate sustainable urban development goals into housing decisions.

This study develops an analytical framework to investigate the complex relationship between local government debt issuing for infrastructure financing, state control, land finance, and development activities in the private sector in China. Using local government financing vehicles’ accounting data, we find that local governments are working creatively to meet infrastructure development targets handed down by the central government. Moreover, local government financing vehicles became more responsive to development activities from the private sector in their debt-issuing decisions after the regulations of local government debt issuing in 2013/14. By modelling the effect of three distinct forces, i.e., the central government, local governments, and the market, in one unified framework, our study provides reliable evidence of how infrastructure financing works in China. Our research extends the studies of land finance into the infrastructure development domain. The findings are also helpful for studies on China's land use policy under its leasehold land right system, particularly the impacts of different land planning uses on infrastructure development.

This paper investigates how three forms of social capital, namely, social norms, social network and trust, influences the effectiveness of land use policies. Both long- and short-term policy outcomes are considered in the proposed analytical framework. Benefiting from a comprehensive household survey dataset covering 17 provinces in China, we adopt multiple measurements for each social capital form and policy outcome in our empirical investigation. We use a specific rural land use policy (i.e. reform to confirm, register and certify rural land rights) as a natural experiment to estimate the effect of social capital. By revealing the complex relationships amongst various forms of social capital and a wide range of policy outcome measurements, our empirical findings confirm the validity, reliability and tractability of the proposed analytical framework. Policy implications are also derived regarding how to utilise social capital to improve the effectiveness of land use policies.

This consensus statement is the outcome of comprehensive collaboration through an international working group on the disparities in the legacies of major sporting events, specifically for communities and individuals from disadvantaged backgrounds (CIDBs). The workshop brought together scholars to discuss current challenges and develop four propositions and recommendations for event leveraging, policy stakeholders, and researchers. The propositions included (1) the nature of ‘disadvantage’ needs to be recognised and the specific targeted CIDBs in each event context must be carefully identified or clearly defined; (2) CIDBs should be considered as an integral part of the whole event hosting cycle to ensure legacy inclusivity; (3) dedicated event leverage, sufficient financial backing and resource commitments for CIDBs are needed; and (4) it is critical to establish a system of legacy governance for CIDBs. The recommendations aim to inform change in practice and ensure lasting positive legacies for the communities that need them most.

This study investigates whether behavioural interventions can reduce racial and gender discrimination in the rental housing market. In our correspondence tests, we incorporated two specific behavioural interventions: providing employment details to assist letting agents in overcoming statistical discrimination and incorporating anti-discrimination messages to encourage adherence to the 'Equality, Diversity and Inclusion' social norm. Although these strategies notably influenced the likelihood of prospective renters receiving responses to their housing inquiries, the outcomes were not consistent across genders or ethnic groups and were not always positive. Racial and gender discrimination in housing markets is a complex issue. There are no 'one-size-fits-all' solutions when using behavioural tools to address complex social problems such as racial and gender discrimination. Behavioural interventions demand rigorous field testing prior to widespread adoption.

Through a systematic and critical review of the literature, we assembled a list of behavioural biases identified in the housing market and a taxonomy of behavioural interventions tested extensively in the last two decades. Based on these findings, we developed an analytical framework for behavioural interventions for housing decisions. We suggest that behavioural interventions have the most significant potential in areas where market incentives and government regulations are ineffective. The application of behavioural interventions in the housing market should focus on encouraging and supporting decision-makers to narrow the intention-action gap.

We examine how carbon emission trading systems (ETS) address both government and market failures in the Cat and Mouse game of controlling emissions. We investigate how Chinese cities responded to two natural experiments: unannounced inspections by the central government and turnovers of senior local government officials. Using both theoretical and empirical analysis, we show that cities tend to rein in their emissions when inspection teams are in town because "the cat is around." And they are less stringent on emission controls when local political powers change hands because "the cat is away". However, cities with ETS exchanges will be less responsive to these political events than those without, as the ETS system regulates firms' behaviour and consequently reduces both the incentives and opportunities for gaming the system. Our theoretical model indicates an efficient Carbon Market condition when the price is high enough. And empirical works confirms the essentiality of the condition. The findings remain robust when alternative event windows and estimation methods are employed. We conclude that ETS is an effective way to address government and market failures in carbon emission control. When the cat is away, the mice will not play because there is a system in place to discourage such behaviour. Our findings provide additional support for the development of ETS in China and beyond.

This study explores the impact of political uncertainty on sustainable urban development by examining carbon emission trading systems (ETS) in four major markets in China (Beijing, Shanghai, Guangdong, and Hubei) from 2014 to 2022. As an alternative to carbon taxes, carbon ETS markets have become increasingly popular due to their success in reducing greenhouse gas emissions. However, their effectiveness is often hindered by political instability and uncertainty. Utilising logistic regression and AR(1)-GARCH estimations, we identify a negative relationship between political uncertainty and carbon trading volume. Our study also reveals significant variations in the responses of these markets to political uncertainty. The paper contributes to the understanding of how ETS markets operate in a complex and constantly changing political environment. We suggest that policymakers need to consider the impact of political uncertainty on carbon trading when designing and implementing urban policies that promote sustainable development. Additionally, our research contributes to the development of urban policies that can be effectively implemented in both developed and developing regions.

Rural homestead development rights (RHDR) reform is considered a pivotal tool for promoting rural revitalization in China. Thus, identifying the impact of RHDR reform on rural revitalization is crucial for comprehensively understanding the ongoing rural homestead system reform in China. We propose a unified theoretical framework to unpack the effectiveness of RHDR reform by contracting the effects of two approaches, i.e., the collective-oriented and the household-oriented strategies. Our theoretical analysis suggests that the two approaches affect rural revitalization differently through five channels, and the overall effects are stronger for the collective-oriented approach. Based on an unbalanced multi-period panel dataset from 2006 to 2018, we develop a comprehensive index system to measure rural revitalization. We then use propensity score matching combined with a difference-in-differences model and a two-way fixed effects model to identify the net effect of RHDR reform on rural revitalization. The baseline empirical results show that the rural revitalization performance of the treatment group with the RHDR reform is significantly higher, on average, than that of the control group. Further impact heterogeneity analysis shows that collective-oriented RHDR reform has a stronger impact than household-oriented RHDR reform on promoting rural revitalization. The findings not only underpin the significance of further conducting rural homestead system reform to comprehensively promote revitalization in China, but also provide a reference for the validity of the rural community as an effective organizational subject to reuse land resources intensively in a developing economy with an imperfect rural land market.

The unprecedented urbanisation observed in leading developing countries has placed immense pressure on effective and efficient land management. The significance of land-use efficiency in the Chinese context has been addressed in the literature, particularly on the measurements of land-use efficiency and key influencing factors. However, quantifying the interdependence between land-use efficiency, local government revenue, employment and infrastructure development whilst controlling for significant cross-city differences remains a gap in the literature. Based on data for 272 prefecture-level Chinese cities between 2012 and 2017, this study employs a novel modelling approach, combining latent class analysis (LCA) in a generalised structural equation model. The incorporation of LCA helps to control for the significant, non-linear heterogeneity across city samples. The empirical model identifies both the direct (one-off land conveyance fee and transaction-related tax revenue from land transactions) and indirect (corporate and personal taxes generated from employment and business growth) channels, through which land development contributes to local government revenue. It also provides one of the first quantified evidence, confirming that employment growth provides higher long-term return than a one-off, land conveyance fee to government revenue in China, controlling for significant cross-city heterogeneity in land-use efficiency and wage.

Housing discrimination hinders labour mobility and economic equality. The level of housing discrimination has not been significantly reduced over recent decades, with notable issues in less diverse countries, especially former colonial powers. We studied London's rental housing market, where ethnic minorities face barriers and housing is unaffordable. An analytical framework based on the taste-based and statistical discrimination theory was developed to investigate the nature of housing discrimination. Blue-collar and white-collar employment information was included in the correspondence test to differentiate between taste-based and statistical discrimination. Our findings indicate UK's racial discrimination is primarily taste-based, and providing job information doesn't bridge the racial gap. Addressing this requires government and societal efforts, emphasizing inclusive urban policies that influence how individuals perceive and interact with different ethnicities.

The study of reference dependence in housing markets is of practical importance due to the unusual characteristics of property transactions, such as high information asymmetry caused by many individuals' lack of experience in housing markets. The overall low transaction frequency and general illiquidity of housing markets can exacerbate and reinforce behavioural anomalies such as reference dependence. The knowledge gained through an empirical investigation in the UK housing market can assist in the understanding of these behavioural biases. By conducting an online experiment at a UK online panel data platform, we identify the presence of reference dependence in the UK housing market, and the extent to which they are caused by both historical and recent prices. The influence of expectations and social norms is also investigated in this novel context. The findings of this study pave the way for reliable economic modelling of such anomalies and a better understanding of behaviours in the housing market.

Coastal land reclamation has been practiced widely to accommodate rapid urbanization. But the exploitation of coastal wetlands also imposes ecological risks and jeopardizes ecological security in many countries. The overall effects of coastal land reclamation must therefore be assessed. We developed a conceptual framework to evaluate the ecological effects of coastal land reclamation on cropland protection along three dimensions: land quantity, ecological environment, and land quality. An integrated index system was constructed accordingly and tested by using data from Cixi, a coastal city in eastern China. Our index system generated rich information to assess the impacts of reclamation on added cropland area, landscape ecological risk, and cropland soil quality. The results showed that between 1985 and 2020, a total area of 393.71 km(2) of coastal wetlands were reclaimed in Cixi City, which played a crucial part in upholding the equilibrium between the land supply and demand during urbanization. However, its adverse ecological effects were also evidenced. Coastal reclamation not only increased the landscape disturbance and ecological risk but also significantly decreased the overall cropland soil quality. The city's original reclamation-driven development strategy is unable to meet the updated requirements of "quantity-quality-ecological balance" for cropland protection. Our index system can help land use policymakers monitor the ecological effects of coastal reclamation. The knowledge gained will have major policy implications for the land use management that promotes sustainability in coastal regions.