Suchergebnisse

Purpose– This study aims to analyze the relationships between Chinese students' behavioral intentions, satisfaction, service quality, perceived value, and university image. A multi-level modeling approach is used to examine the relationships between these higher order constructs. In addition, service quality consists of three primary dimensions and 13 sub-dimensions. Finally, this study compares students' perceptions of the dimensions of service quality, overall perceived service quality, university image, perceived value, satisfaction and favorable behavioral intentions based on the demographic characteristics of the sample (gender, age, year of study, and major).Design/methodology/approach– The data used in this study were based on a sample of 350 students studying at a public university in China. Data was analyzed using factor analysis, regression analysis, and analysis of variance (ANOVA).Findings– The findings support using a multi-level model consisting of three primary dimensions and 13 sub-dimensions to conceptualize and measure perceived service quality. Service quality is the main determinant of satisfaction and has a significant influence on university image and perceived value. Perceived value has a significant moderating effect on the relationship between service quality and satisfaction. Satisfaction significantly influences recommending the university and future attendance. The results of this study also indicate that students' demographic characteristics (gender, age, year of study, and major) influence their perceptions of several of the constructs.Originality/value– This is the first study synthesizing behavioral intentions, satisfaction, service quality, perceived value and university image in a Chinese university setting.

AbstractStarting in 2006, China launched a project to carry out Basic Farmland Construction in 116 representative counties. The objective of this project was to enhance agricultural productivity. China invested 30 billion yuan in this project from 2006 to 2013. Using a difference‐in‐differences approach, this study estimates that Basic Farmland Construction increased per capita agricultural output by 6.3%, crop sown area by 5%, and agricultural mechanization by 7.3%. The total benefits of the project far exceeded the total investments. By incorporating with rainfall shocks, this study finds that the project could mitigate most of the damage from droughts and floods.

The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.

AbstractDecarbonization of the energy system is the key to China's goal of achieving carbon neutrality by 2060. However, the potential of wind and photovoltaic (PV) to power China remains unclear, hindering the holistic layout of the renewable energy development plan. Here, we used the wind and PV power generation potential assessment system based on the Geographic Information Systems (GIS) method to investigate the wind and PV power generation potential in China. Firstly, the high spatial-temporal resolution climate data and the mainstream wind turbines and PV modules, were used to assess the theoretical wind and PV power generation. Then, the technical, policy and economic (i.e., theoretical power generation) constraints for wind and PV energy development were comprehensively considered to evaluate the wind and solar PV power generation potential of China in 2020. The results showed that, under the current technological level, the wind and PV installed capacity potential of China is about 56.55 billion kW, which is approximately 9 times of those required under the carbon neutral scenario. The wind and PV power generation potential of China is about 95.84 PWh, which is approximately 13 times the electricity demand of China in 2020. The rich areas of wind power generation are mainly distributed in the western, northern, and coastal provinces of China. While the rich areas of PV power generation are mainly distributed in western and northern China. Besides, the degree of tapping wind and PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the wind and PV potential-rich areas.