Growth and Change最新文献

The paper contributes to the growing literature on the relationship between relatedness, complexity and regional diversification. It explores regional diversification in an emerging economy, focusing on diversification opportunities of regions with distinct levels of local capabilities. We investigate the importance of relatedness and economic complexity for sectoral and technological diversification in all regions of Brazil during the period 2006–2019. Regions tend to diversify in sectors/technologies requiring similar capabilities to those already available locally. In general, the higher the sector/technology complexity, the lower the probability of diversification. However, in high-complex regions, complexity reverses into a positive force for diversification. Our analysis shows diversification prospects vary widely across different types of regions in Brazil.

Climate change disproportionately affects low-income and minority populations. This study identified vulnerable populations to extreme heat, focusing on home air conditioning. State and municipal laws and regulations usually consider home air conditioning an amenity rather than a requirement for habitability such as heat, water, and electricity. Using the historical census data and the American Housing Survey data, this study identified the vulnerable populations to extreme heat and their spatial dynamic in Los Angeles County, CA. This study found that low-income minority populations are more likely to live without home air conditioning, and they are more likely to be exposed to extreme heat in the coming years if their residential location patterns continue. Changing spatial patterns of low-income and minority populations need to be incorporated into urban and regional planning for climate change. State regulations and municipal codes should require air conditioning as a habitability requirement for cooling equity. Also, cooling stations that provide immediate relief for those without home air conditioning need strategic placements based on the locational concentration of the vulnerable populations.

Existing research on inter-regional capital flows commonly applied the proxy simulation method based on the location of financial firms in static snapshots, and took cities' centrality as the research object when discussing network influencing factors. It remains unclear how inter-regional capital flow networks are present actually and how city-dyad linkages are shaped. Based on real investment connections between listed firms and their investees in Yangtze River Delta (YRD) from 2005 to 2019, this study provides fresh insights into the spatial pattern and influencing factors of intercity capital flows from the perspective of dynamic urban bilateral relations. Results indicate that the network in the YRD is in the transition stage of provincial integration and cross-border integration. The central and western regions of the YRD are the current network “depression districts”, with cities in Anhui forming an independent network. The physical spatial scale of the origin city was the most robust facilitator of capital outflow, but it is not evident that obvious node attributes can match the destination city. Cultural proximity and functional proximity effectively promoted the shape of investment connections, but they also reflected the lack of long-distance intercity links and cross-border links. The establishment of metropolitan areas with overlapping members and the joint construction of industrial circles, living circles and transportation circles within metropolitan areas are effective ways to facilitate intercity capital flows in YRD.

Analyzing the spatial agglomeration and characteristics of urban functional elements and effectively identifying the polycentric pattern of a city can provide a scientific basis for urban planning. This study, on the basis of point-of-interest (POI) data, designs a weighted overlay-analysis method that is based on functional categories, spatial agglomeration, and POI weights to identify the spatial-agglomeration pattern and polycentric urban structure of Shenyang, China. Our results show that Shenyang has a significant polycentric urban structure. The POI elements in the municipal center in the second ring road show concentration and a contiguous distribution pattern. The POI element distribution near the city is scattered, and the spatial distribution of hotspots is uneven. The urban centers in the city planned by the government and those that we have identified differ. Because of the remote location and insufficient support facilities, the municipal subcenters and district centers do not play the role of decentralization as intended. Further strengthening the overall industrial planning and transportation apparatus is necessary in order to enhance the outward radiation and inward attraction of municipal subcenters. Our weighted overlay method realizes effective identification of the polycentric urban structure and can provide relevant reference for urban research and planning.

This paper explores the implementation of grand spatial planning narratives such as the compact city and polycentricity in planning practice. The effects of overlapping scales on the application of spatial imaginaries in metropolitan space are examined. Using post-socialist space, the research enriches the geographical context of metropolitan studies. On the basis of a spatial analysis of metropolitan form and centrality and a textual analysis of the relevant spatial plans of three Czech metropolitan areas, the key features in efforts of planning polycentric and compact metropolitan areas are identified as “Administrative blindness”, “(De)centralization ambiguity”, and “Reactive passivity”. By identifying the limits of translating spatial visions into the practical language of statutory regional and land-use plans, the paper contributes to the debate on the effectiveness of metropolitan planning based on the specific context of Central Europe.

This paper explores the roles of different types of technological proximity on the interregional spillovers of innovation growth. Using a panel dataset containing Chinese province patents and R&D inputs from 2002 to 2019, this paper estimates a knowledge production function with the spatial weight matrices constructed based on the linkage-based and structure-based technological proximity. We find that the two types of technological proximity are significantly different and their contributions to interregional innovation spillovers differ. The structure-based proximity shows a lower influence on spillovers and its effect in the process of interregional innovation spillovers is affected by the regional development level. This paper originates to clarify two conceptualizations of technological proximity, compare their influences on innovation spillovers, and explore their regional differences.

The economic hierarchy index, economic linkage model, and economic membership model are used to study the economic hierarchy, linkage, and direction of Northeast China and Russian Far East from 2005 to 2020, using the three northeastern provinces of China and 11 Far East federal subjects of Russia as the research objects. The findings are as follows: (1) There is a major polarization effect between developed affluent economic regions and stagnant isolated backward regions in the economic rankings in the 11 Russian Far East federal subjects from 2005 to 2020. (2) The three northeastern provinces of China have more economic linkages with their near Russian neighbors— the east-oriented Primorsky Territory, the north-oriented Amur Region, and the north-oriented Khabarovsk Territory, than with their farther neighbors— the Chukotka Autonomous Area, Magadan Region, and Kamchatka Territory. (3) From 2005 to 2020, the Primorsky Territory has been serving as the focal point of economic linkages between Russian Far East and Northeast China, showing the expanding outward pattern resembling the concentric circles.

The information services (IS) industry, which facilitates industrial transformation and upgrading, has emerged as a key driving force behind China's economic growth in recent years. More evidence can be accumulated from the Pearl River Delta (PRD), one of the largest transforming economies and IS gathering region in China. Firstly, this paper investigates the spatial patterns and dynamics of IS agglomeration in the PRD from 2003 to 2018, using the spatial autocorrelation analysis and Kernel density analysis. Secondly, a dynamic panel model with a system generalized method of moments (SYS-GMM) estimation is employed to identify the factors significantly influencing the industry agglomeration economy. Results show that: (1) Large firms continue to gather in the core area of major cities, while smaller firms show a more balanced distribution from the core to periphery regions. The spatial proximity of firms proves that micro firms benefit less from industrial agglomeration compared to larger ones. (2) The effects of agglomeration externalities on economic growth remains positive, with labor force and commuting costs playing a vital role in the expansion of the IS industry. By integrating geo-spatial information and empirical evidence, this study contributes to China's understanding and experience as a late-developing country in the digital economy era. Finally, policies supporting small and medium enterprises (SMEs), improving the effectiveness of government subsidies, and strengthening industry-university research cooperation are proposed.

Over the past few decades, China has undergone an unprecedented structural transformation, stupendously transforming its energy and environmental systems and decarbonizing pathways. Nevertheless, around the share of clean and renewable energy generation with the goal of low-carbon, it is still ambiguous how the dynamic relationship between the upgrading of grid infrastructure, interregional power trading and the investment and economic foundation that support their collaborative promotion forms. Thus, under the framework of panel vector autoregressive models, we study the bridge that links the key dominant factors in the pattern of electricity supply and demand such as grid infrastructure, power generation share of clean and renewable energy, net power input and carbon emissions. The results show that the governance framework dominated by power grid infrastructure upgrading, interregional power input dependence and carbon constraint mechanism to promote clean-oriented energy mix is still flawed. In addition to improve the utilization rate of power grid infrastructure, relevant policy makers should devote more efforts to software conditions including interregional and interprovincial power trading and grid-connecting mechanisms of clean and renewable energy.

Shift-share analysis has been extensively used to investigate the different drivers of changes in socioeconomic variables in both spatial and non-spatial contexts. This paper presents a comprehensive shift-share formulation that, by considering all the possible interactions between the geographical and sectoral variables that interplay in a non-spatial context, accounts for the intrinsic conditions of the regions that could affect the regional changes when spatial influences are not detected. The proposed technique is illustrated by analyzing the growth of industrial gross value added in the Spanish provinces between 2015 and 2019. Our results show the relevance of the intrinsic effects: the advantages (disadvantages) of the industrial sector within each province and the regional dynamism are important factors to explain the industrial changes in the Spanish regions. These findings provide evidence for the use of the comprehensive shift-share as a tool that contribute to the regional analysis by identifying the characteristics of vulnerable regions that require further attention to avoid or lessen the effects of low economic growth.