Search

On the basis of the theories of overseas foreign direct investment (OFDI) and New Economic Geography, the factors influencing the grain industry investment in the countries along the Belt and Road (herein after referred to as the “B&R countries”) were discussed, and the impacts of such investment in terms of the bilateral economic distance, institutional environments and the farmland resource levels of host countries were analyzed in depth in this study, thus expanding the theoretical analysis framework of OFDI. Empirically, the dependence on China's overseas investment was applied to measure the bilateral economic distance, and these two variables were incorporated into the empirical model along with the location characteristics of the institutional environments of host countries. The Zero-inflated Poisson Model was applied to analyze China and the B&R countries. A conclusion derived is as follows: the farmland resources of the B&R countries have a positive impact on China's overseas farmland investment, and the location characteristics of the B&R countries vary greatly. China should confer great importance to regional comparative advantages, conduct differentiated cooperation in farmland investment, strengthen the conservation of water and land resources and safeguard of farmers' livelihoods in the less developed regions, and guarantee the grain security in developing countries, while valuing the distribution and sales of agricultural products in developed regions and greatly enhance the ties between enterprises and local markets to ensure the sustainable development of grain industry investment projects in the B&R countries.

To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness.

Achieving mutualistic symbiosis is the core concept of China’s promotion of the Belt and Road Initiative (BRI). Therefore, how to guide China’s OFDI location decision-making in the Belt and Road (B&R) countries from the perspective of mutualistic symbiosis is of great significance. This paper constructs the B&R symbiotic system based on symbiotic system theory, to establish the investment suitability index from three dimensions: stability, coordination, and sustainability, then uses the entropy weight, TOPSIS, and GIS method to assess and analyze the investment suitability index of 60 major countries in B&R’s six economic corridors. The findings indicate that Russia, India, Indonesia, Thailand, Singapore, Vietnam, and Malaysia have the highest investment suitability and are China’s most favorable OFDI destinations for achieving mutualistic symbiosis. Conversely, Syria, Lebanon, Jordon, and Yemen have the lowest investment suitability. Based on these results, policymakers can formulate more scientific and reasonable policies to guide China’s OFDI to make location decisions in B&R countries with high investment suitability, so as to promote the high-quality and sustainable development of B&R construction. However, due to data acquisition and technical reasons, this study does not analyze investment suitability from time trends. The analysis of investment suitability from more B&R countries or different types of industries is an important direction for further research in the future.

Contrary to the conventional viewpoint that “high housing price promotes industrial upgrading”, this study finds that increasingly high housing prices are a pivotal factor that obstructs industrial value chain upgrading. Based on city-level data, micro-level data for Chinese industrial enterprises, and data for listed Chinese enterprises, this study examines the impacts of urban housing prices on value chain upgrading. We find that soaring housing prices in China since 2004 stunted industrial value chain upgrading, as indicated by the value-added rate. When housing prices increase by 100%, the enterprise value-added rate decreases by 12.4%. Intermediary mechanism analysis shows that housing price increases lead to innovation input suppression effects and resource misallocation effects, which in turn obstruct industrial value chain upgrading. Further analysis demonstrates that the impacts of housing price increases on industrial value chain upgrading, presenting an inverted-U shape, are varied in terms of time and region. Enterprises’ value chain upgrading also showcases different degrees of sensitivity to housing price increases, due to their respective features. The conclusions of this study carry significant policy implications for the sound development of the real estate market and industrial upgrading in China, as well as in developing countries at large.

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The standard approach to studying financial industrial agglomeration is to construct measures of the degree of agglomeration within financial industry. But such measures often fail to exploit the convergence or divergence of financial agglomeration. In this paper, we apply Markov chain approach to diagnose the convergence of financial agglomeration in China based on the location quotient coefficients across the provincial regions over 1993-2011. The estimation of Markov transition probability matrix offers more detailed insights into the mechanics of financial agglomeration evolution process in China during the research period. The results show that the spatial evolution of financial agglomeration changes faster in the period of 2003-2011 than that in the period of 1993-2002. Furthermore, there exists a very uneven financial development patterns, but there is regional convergence for financial agglomeration in China. [ABSTRACT FROM AUTHOR]