Youjin Liu

This study examines the factors driving pro-environmental behavior by focusing on energy-saving, waste sorting, and reducing single-use plastics. Utilizing 380 participants' survey data from China and Pakistan, our study considers the Theory of Planned Behavior to examine attitudes, subjective norms, perceived behavioral control, and climate change concern in shaping environmental intentions. Employing a structural equation modeling, we find that intentions related to climate change were positive and significantly associated with all three targeted behaviors, i.e., energy saving, waste sorting, and single-use plastic behavior. Across both countries, attitudes, perceived behavioral control, subjective norms, and climate change concern are significant predictors of pro-environmental intention. Country-specific analysis reveals that attitudes and climate change concern in China significantly predict environmental intention, whereas in Pakistan, environmental intentions significantly predict single-use plastic use behavior. Moderation analysis shows that climate change concern did not significantly moderate the relation between intention to take climate action and waste sorting behavior for China, whereas it substantially moderates for Pakistan. However, single-use plastic reduction and climate change concern significantly moderate the relationship between intention and climate change concern and single-use plastic reduction for both countries. The findings highlight the evolving adoption of eco-friendly practices and emphasize the need for environmental policies to enhance pro-environmental behavior.

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.

Tian, J.J.; Liu, Y.J., and Yin, Z.D., 2020. Relationship between coastal foreign direct investment & industrial upgrading of China under the condition of the Belt and Road initiative. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 695–699. Coconut Creek (Florida), ISSN 0749-0208.At present, the issuance of the relationship between foreign direct investment & the transformation and upgrading of domestic industrial structures has been controversial under the condition of the construction of the “Belt and Road” of China. Based on the data from 2008 to 2018, the VAR model is applied for studying the interrelationship between foreign direct investment in sixty four countries along the “Belt and Road” and the transformation and upgrading of domestic industrial structures according to the experience since China has joined in Word Trade Organization (“WTO”); and it is found that in this kind of countries, the direct investment is without any statistical significance on the industrial upgrading of China so far; nevertheless, the industrial restructuring of China still in turn accelerates the going out intention of companies and promotes direct investment in this kind of countries.