ABSTRACT The Healthy Streets Approach has emerged as a human‐centered framework for evaluating urban streets in terms of health, safety, and quality of life. Despite its growing prominence in contemporary urban design and policy, its applicability to historic urban environments, characterized by conservation constraints and unique spatial qualities, remains underexplored. Addressing this gap, this study reinterprets the healthy streets within historic contexts by integrating with the streetscape characteristics of heritage environments, and proposes a “healthy historic streets” perspective. The study focuses on Belediye Street, located in the historic city center of Kastamonu, Türkiye. A multi‐criteria assessment framework was developed based on six key dimensions: walkability and active mobility, safety, perceptual‐physical comfort, air environmental quality, social interaction and inclusivity and place identity attachment. Data were collected through field observations, noise measurements, pedestrian traffic counts and user surveys, and were analyzed using statistical methods. The findings reveal both synergies and tensions between health‐oriented street criteria and heritage conservation principles, particularly regarding accessibility, environmental quality, and spatial usability. Based on these insights, context‐sensitive design and planning recommendations were developed to enhance public use while preserving the street's historic character. The study contributes to the literature by demonstrating that the Healthy Streets Approach can be effectively adapted to historic urban contexts and offers a transferable assessment framework for integrating public health objectives with heritage conservation. It highlights the potential of historic streets not only as preserved cultural assets but also as active, health‐supportive public spaces within contemporary urban systems.
ABSTRACT Ontario accounts for the overwhelming majority of Canada's motor vehicle production, yet its exposure to Mexican manufacturing shocks has generally been treated as indirect, transmitted only through Canada's relationship with the United States. This paper tests that assumption using a purpose‐built panel of 10 Canadian provinces and 12 US states, with Mexico's manufacturing cluster growth as a common shock variable (2006Q1–2024Q4). Granger causality tests, the primary evidence, show that Mexico's nearshoring shock predicts Ontario directly ( χ 2 = 15.21, p < 0.001) while the US automotive corridor does not ( χ 2 = 0.83, p = 0.661). A placebo test confirms automotive specificity: motor vehicles respond on impact, aerospace does not. Markov‐switching regression shows Mexico and the US achieve 97.4% regime concordance while Ontario enters recession independently in 16 of 76 quarters, including a rising episode from 2023Q4 without a continental counterpart. Local projections corroborate this response, though differential estimates do not survive wild cluster bootstraps and are treated as directional. These results are consistent with a triangular rather than bilateral network, Mexico as the upstream source, with Ontario and the US Midwest as parallel downstream receivers, with implications for regional‐science models of stratified production geographies and for the USMCA review initiated in July 2026.
ABSTRACT Graduate migration plays a key role in the spatial reallocation of human capital, yet little is known about how graduate migration mechanisms differ across directions within a hierarchical urban system. Using LinkedIn resume data, this study classifies intercity graduate migration in China into upward, lateral, and downward modes and examines the economic opportunities and urban amenities associated with each migration trajectory. We show that three migration modes exhibit distinct spatial patterns, with upward migration towards first‐tier and new first‐tier cities dominating national flows and downward migration occurring mainly between higher‐ and lower‐tier cities within regions. Over time, new first‐tier cities have become increasingly important destinations for graduates. Empirical results indicate that graduate migration is jointly associated with economic opportunities and urban amenities in both origin and destination cities. The temporal analysis shows that wages, climatic conditions, and healthcare provision in destination cities have become increasingly important over time. Migration mechanisms also differ across the urban hierarchy: upward migration is primarily associated with destination economic opportunities, whereas downward migration is associated with both push factors from higher‐tier origin cities and pull factors from improving opportunities and living conditions in lower‐tier destinations. These relationships further vary across human capital groups, while talent‐attraction policies are associated with shifts in graduates’ responses to destination characteristics. By extending the traditional push‐pull framework to incorporate urban hierarchy, human capital heterogeneity, and institutional context, this study provides a more nuanced understanding of the factors associated with intercity graduate mobility.
ABSTRACT AI‐related hiring demand is unevenly distributed across urban labor markets. Using online job‐posting data from Chinese listed firms between 2016 and 2024, this study constructs a city–occupation–year panel and examines whether expanding AI‐related hiring demand is associated with differences in the structure of occupational hiring demand within cities. The results show that: (1) higher city‐level AI‐related hiring demand is significantly associated with greater relative hiring demand for occupations that are more closely related to AI. This finding indicates that AI‐related opportunities are not evenly distributed across occupations; instead, hiring demand is selectively concentrated in occupations with stronger links to AI. The result remains robust across a series of robustness checks; (2) AI‐related hiring expansion is associated with stronger hiring demand for digitally complementary occupations and a larger share of AI‐related vacancies within occupations; and (3) these associations are more pronounced in cities with greater higher‐education capacity and more developed innovation ecosystems. This study extends the literature on AI and the structure of urban labor demand and provides recruitment‐based evidence relevant to cities seeking to improve talent‐development systems, strengthen their innovation environments, and address the growing divergence in occupational demand associated with AI.
ABSTRACT Spatial competition is the strongest determinant of Big Mac prices across the United States. Using 10,585 store‐level observations collected in 2023, we examine how local labor market, geographic characteristics, and market structure shape price variation. We find that prices are positively correlated among nearby McDonald's outlets, suggesting limited intra‐brand competition, while the presence of rival chains significantly lowers prices, indicating stronger cross‐brand competitive pressure. Higher wages and unionization rates are associated with higher prices, while higher unemployment reduces them. Urban restaurants charge 2.5%–4% more than rural establishments. Overall, local competitive structure plays a central role in shaping fast‐food prices.
