Growth and Change最新文献

Urban retrofitting is a fundamental approach for achieving sustainable and resilient urban development in the face of contemporary challenges. The increasing prevalence of urban big data presents an opportunity to establish a robust analytical framework for urban retrofitting, enabling more effective comparative studies and informed decision-making. This paper introduces a comprehensive 5R framework—Re-inhabitation, Re-building, Re-transportation, Re-capitalization, and Re-greening—to provide a multidimensional perspective on urban retrofitting. The 5R framework facilitates a holistic understanding of urban transformation processes and establishes standardized metrics for analyzing urban retrofitting initiatives using diverse urban big data sources. To demonstrate the adaptability and effectiveness of the 5R framework in a real-world context, we conduct a case study of Charlotte, North Carolina. By applying innovative methods for the integration and analysis of extensive datasets, our study offers new insights into the evaluation of urban retrofitting efforts, such as transportation accessibility, micro-scale building improvements, investment patterns, green space enhancements, and overall livability. This approach addresses existing research gaps by providing a structured set of indicators that assess each dimension of urban transformation comprehensively. Beyond academic advancements, the 5R framework offers practical tools for policymakers and urban planners to evaluate retrofitting interventions, quantify their outcomes, and understand the dynamics of evolving urban spaces. The insights gained through our research highlight the importance of using big data to enhance the scope and impact of urban development strategies, ultimately bridging the gap between theoretical concepts and real-world urban retrofitting applications. Our findings demonstrate the potential of the 5R framework to serve as a guiding model for more sustainable, data-driven urban growth and revitalization.

The spatial configuration of urban systems has garnered significant interest from various disciplines, including urban planners, economists, and ecologists, due to its interconnectedness with various aspects of sustainable development. Research on urban form suggests a departure from the conventional model of a gradually declining density gradient from the city center, giving way to a “spiky” urban landscape characterized by a heterogeneous polycentric pattern. This study aims to examine the recently emerging spiky structure of an urban agglomeration and its determinants, providing insights into the potential prospects of cities. We adopt a new quantitative modeling approach inspired by spatial econometrics and coined here ‘urbanometrics’. By utilizing and testing spatial dependence urbanometric models, we seek to elucidate the factors driving these changes, with a specific focus on pluriform urban sprawl in the Mediterranean region, specifically the Izmir city-region. The findings indicate that since the early 2000s, the Izmir city-region has experienced simultaneous decentralization and the emergence of multiple centers, with sharp differences. Furthermore, the results demonstrate that the expansion of highway infrastructure, population growth, and existing convertible (agricultural or forest) land contribute to urban sprawl and the emergence of a “spiky” urban landscape.

This paper introduces eigenvector centrality to capture partner heterogeneity and examines its impact on urban innovation. By constructing and empirically testing a co-invention patent database of Chinese cities over the period from 2014 to 2018, we find that eigenvector centrality has a positive effect on urban innovation. Technology breadth and technology depth increases are two important underlying mechanisms. A counterfactual analysis shows that laggard cities with low levels of innovation can increase their innovative productivity by establishing collaborative links with other cities, especially with well-connected cities, which indicates the potential gains from policies that improve the inter-city innovation collaboration network structure. And this result is further confirmed by the comparative analysis of Erdos and Chifeng.

The increasing connectedness between neighboring metropolitan areas anchored by global economic centers highlights the relevance of the megaregional scale for government and governance. Yet, there is a lack of data to examine the expansion of megaregions and understand prevalent challenges to coordination. We use data from the Census Bureau and the California Franchise Tax Board (FTB) to analyze migration patterns within the Northern California Megaregion that combines the San Francisco Bay Area and California Central Valley and highlight different trends underlying regional expansion. We find that people are leaving Bay Area zip codes at the edge of the urbanized area where population growth is robust, migration rates lower, job accessibility is low, rents are nearly as high as the more central locations, and home values are lower, making it difficult to move elsewhere within the Bay Area. Moves into the Central Valley are divided between the suburbs of the main urban centers and isolated towns leading to fragmented growth that increases stress on transportation infrastructure and worsens spatial inequality in the region.

Numerous studies have shown that agglomeration externalities play a crucial role in the survival prospects of logistics firms. However, previous studies have largely ignored the role that agglomeration externalities play at a more granular level. Therefore, this paper used Chengdu International Railway Port as a case study, constructed a distance-based agglomeration measure to explore the impact of agglomeration externalities on the survival of logistics firms located within a publicly developed logistics park (PDLP). The results show that the three types of agglomeration externalities coexist and are highly localized in the PDLP, and their effects change with distance from the logistics firm. Specifically, the specialization externalities operate within 1.5 km of the logistics firm and produce competitive effects within immediate proximity (0.5 km). Second, while diversification externalities cause significant congestion effects, interindustry knowledge spillovers arising from related diversification externalities play a key role in the 0.5 km proximity of logistics firms, and unrelated diversification externalities play a positive role in reducing the market transaction costs of logistics firms outside the 0.5 km range.

As China's economy advances toward high-quality development, how to promote the rational spatial distribution of skilled labor to achieve sustainable regional economic growth has become an urgent issue. This study utilizes microdata from the 2000–2015 Chinese census and tariff data from China covering the same period to empirically regress input tariff cuts on changes in the proportion of skilled labor in cities. The results show that for every 1% decrease in input tariffs, the proportion of skilled labor in cities increases by 0.78%, accounting for 15.22% of the spatial distribution of skilled labor. Mechanism analysis indicates that input tariff cuts facilitate technological progress and subsequently increase skilled labor demand. From an equilibrium perspective, increased skilled labor demand attracts skilled worker inflows, thereby increasing the proportion of skilled labor in cities. Additionally, input tariff cuts have a more significant impact on cities with low trade costs, a high historical proportion of skilled labor, and high labor market flexibility. These findings provide favorable evidence of the micro-level impact of input tariff cuts on regional skilled labor markets, offering important policy insights into how developing countries similar to China can optimize the spatial distribution of skilled labor through input tariffs.