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AbstractWhile suburban areas have been typically related with urban sprawl, low density, cheap land price and low‐end manufacturing industries, relatively little attention has been paid to the spatial organization of innovation in suburban areas. In some Chinese cities, development zones (kai fa qu) in their suburban areas which are generally regarded as major spaces for traditional manufacturing industries have been transforming into spaces for innovation. This paper provides a case study of Nanjing, the capital of Jiangsu province in Eastern China by investigating and explaining the learning and innovation processes in suburban development zones. Drawing upon a unique database on 733 state‐certified high‐tech firms in Nanjing from 2008 to 2012 which contains detailed information on their addresses and number of patents, we observe strong spatial disparity in the distribution of innovation. Suburban districts of Nanjing where its three national‐level development zones and many provincial‐level development zones are located have become major spaces for innovation of the city. The planning of industrial clusters in development zones, the place‐based investment in the suburbs, and the suburbanization of universities are three major mechanisms behind the learning and innovation processes in Nanjing's suburban development zones.

World's human dynamics can be parameterized with metrics that explain the current model of economic growth and its sustainability. Changes in the world's human dynamics are crucial for understanding the current state of the world, which is faced with increasing challenges related to globalization. In this paper, we propose to analyze the shifting locations of centers of gravity of four basic global indicators (these are Gross Domestic Product, carbon dioxide emissions, population, and urban population) for the period 1960–2016. The spatial locations of the respective centers of gravity (one per year) draw some traces that explain, at least partially, relevant changes on different world's human dynamics at a global level. These traces and dynamics are further discussed. In addition, these traces are fundamental for predicting upcoming trends for the next few years. Results shown here may help political leaders and policymakers for solving upcoming and future global challenges related to the current economic system and its impact on the environment.

World's human dynamics can be parameterized with metrics that explain the current model of economic growth and its sustainability. Changes in the world's human dynamics are crucial for understanding the current state of the world, which is faced with increasing challenges related to globalization. In this paper, we propose to analyze the shifting locations of centers of gravity of four basic global indicators (these are Gross Domestic Product, carbon dioxide emissions, population, and urban population) for the period 1960–2016. The spatial locations of the respective centers of gravity (one per year) draw some traces that explain, at least partially, relevant changes on different world's human dynamics at a global level. These traces and dynamics are further discussed. In addition, these traces are fundamental for predicting upcoming trends for the next few years. Results shown here may help political leaders and policymakers for solving upcoming and future global challenges related to the current economic system and its impact on the environment.

This article analyzes the impact of the level of urban polycentricity (UP) on particulate matter emissions from vehicles (PMV) across 102 prefecture-level cities in China between 2011 and 2015. We adopt a spatial panel modeling approach to our measures of UP and PMV, controlling for (possible) intervening effects such as population density and economic output. We observe an inverted U-shaped relationship between both measures: When UP is low, an increase in polycentricity is associated with higher levels of PMV; however, when UP reaches a certain threshold, the increase in polycentricity is associated with a reduction in PMV. We find a similar relationship between economic output and PMV and demonstrate how the effects of population density on PMV consist of two opposite processes that likely offset each other. Nonetheless, jointly, population density and UP have a significant effect on PMV. We use our results to discuss policy implications and identify avenues for further research.