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This study reveals the evolution of the Belt and Road trade network, and discusses the determinant factors of trade relationships by employing network analysis methods. Using 65 countries’ trade fl...

Regional multi-pole growth is an important spatial form of regional economic organization pattern. We construct a model of regional multi-pole growth evolution to study its mechanism based on the assumption of heterogeneous space and inseparability of the economic subject. The study concludes that the evolution of regional multi-pole growth is affected by factors such as transportation costs, economies of scale, the crowding-out effect, and the factor endowments of superior location and inferior location. Based on the condition that the factor endowment of a location remains the same, with an increased economy of scale effect and a decline in the marginal production cost advantage, location will strengthen the agglomeration layout, which does not help form regional multi-pole growth patterns. With an increase of the crowding-out effect and a decline in the marginal production cost disadvantage, location will strengthen the dispersed layout, which fosters the formation of the regional multi-pole growth pattern. When transportation costs are high, a decline in such costs strengthens the links among locations, fostering the evolution of the regional spatial organization form to shift from dispersion to agglomeration. However, when transportation costs are somewhat reduced, their further reduction leads the regional spatial organization pattern into dispersion. Therefore, under certain conditions and with a decline in transportation costs, regional multi-pole growth weakens first and then strengthens.

This study constructs a market area evolution model from the perspective of economic subject and location matching under the hypothesis of non-homogeneous space. It is proved that transportation development can reduce transport costs, which is closely related to location factor endowments. As such, we believe that when analyse the economic impact of new means of transportation, both overall analysis and different line analysis should be taken into account. Therefore, the economic impact of high-speed rail were studied by DID and PSM-DID methods. The results show that China's high-speed rail construction has a positive effect on economic growth and will become an important force in reshaping the organization of China’s spatial economy. However, the effect is different for different high-speed rail lines. Thus, Chinese government needs to fully consider the economic impact of high-speed rail. In addition, the government needs to formulate a high-speed rail economic belt plan in accordance with the characteristics of different rail lines. Furthermore, China needs to adjust its regional development strategy and regional policies accordingly, because the role of high-speed rail is mainly depends on whether a location has the necessary conditions to achieve the desired effect. Therefore, a city should adjust and recombine the local factor endowment to align with high-speed rail if it wants to capitalize on the new high-speed rail. Otherwise, the introduction of high-speed rail likely to adversely impact economic growth, which make the city a peripheral city.

This paper evaluates the economic benefits of high-speed rail (HSR) in China, with a focus on the connectivity change resulting from HSR development. The effect of HSR, measured in degree centrality, is assessed using a spatial econometric modeling technique based on a panel dataset that covers 268 Chinese cities from 2008-2015. To provide a robust assessment, statistical issues including heterogeneous effects, endogeneity, and spatial dependence are addressed simultaneously in the spatial panel modeling process. Our empirical results confirm that connectivity improvement brought by HSR plays a vital role in facilitating economic growth. Specifically, the contribution of HSR to urban economic growth is found to be 0.11, most of which comes from a local effect rather than a spillover effect. Overall, the research findings suggest that urban economic growth can benefit from the development of HSR.

Due to the rapid economic growth and urbanization, China’s real estate industry has been undergoing a fast-paced development in recent decades. However, the spatial imbalance between the economic growth in urban and that in rural areas and the excessive growth and fluctuations of house prices in both areas had quickly caught public’s attention. Not surprisingly, these issues had become a focus of urban and regional economic research. Efficient and accurate prediction of housing prices remains a much needed but disputable topic. Currently, based on the trends and changes in the financial market, population migration and urbanization processes, numerous case studies have been developed to evaluate the mechanism of real estate’s price fluctuations. However, few studies were conducted to examine the space-time dynamics of how housing prices fluctuated from a big data perspective. Using data from China’s leading online real estate platform {sofang.com}, we investigated the spatiotemporal trends of the fluctuations of housing prices in the context of big data. This paper uses spatial data analytics and modeling techniques to: first, identify the spatial distribution of housing prices at micro level; second, explore the space-time dynamics of residential properties in the market; and third, detect if there exist geographic disparity in terms of housing prices. Results from our analysis revealed the space-time patterns of the housing prices in a large metropolitan area, demonstrating the utility of big data and means of analyzing big data.

Spatial club convergence is a group of regions which are adjacent to each other in space and have similar initial conditions and structural features while converging towards the same steady state in the economic development. Based on the Solow model, this paper builds a theoretical model to prove the mechanism of spatial club convergence. The spatial club convergence process is explored in inland China, using the case of the Zhongyuan urban agglomeration during the years of 1993–2009. This region has been experiencing a dramatic economic development and serves as an ideal test bed of the theory of spatial club convergence. The results show that in the two periods of 1993–1999, and 1993–2009, there was spatial club convergence in the 56 regions of Zhongyuan urban agglomeration of China. The respective convergence rates were 2.0% and 1.0%. Hence, both theoretical deduction and empirical studies verify the hypothesis of spatial club convergence.

The paper focuses on the constant elasticity of variance (CEV) model for studying a defined-contribution pension plan where benefits are paid by annuity. It also presents the process by which the Legendre transform and dual theory can be applied to find an optimal investment policy for a participant’s whole life in the pension plan. Finally, it reveals two explicit solutions for the logarithm utility function in two different periods (before and after retirement). Hence, the optimal investment strategies in the two periods are obtained.

In this paper, we examine the influence of environmental regulation on sustainable economic growth from both theoretical and empirical perspectives. Our research is twofold. First, we apply a modified NEG (New Economic Geography) model to analyze how environmental regulation influences firms’ location choices and cities’ sustainable economic growth. Second, we test a spatial econometric model employing panel data of the three largest urban agglomerations in China from 2003 to 2013 to study the relationship between environmental regulation and sustainable economic growth as well as the spillover channels of economic activities. The results reveal a remarkable negative effect of environmental regulation on economic growth. In addition, we find no sufficient evidence to prove the existence of long-term effects of environmental regulation on economic growth in the three urban agglomerations. Furthermore, using different weight matrices to illustrate the different economic networks of the urban agglomeration, we validate the difference in spillover mechanisms across these three urban agglomerations. Specifically, the disparity in environmental regulation acts as a spillover channel for the Yangtze River Delta and the Pearl River Delta, while it is not significant for Jing-Jin-Ji.