This paper aims to examine the determinants of green purchasing intentions among different resident groups in a developing-country context. We first expand the theory of planned behaviour (TPB) and build a theoretical model based on green purchasing intention, including attitude, perceived behavioural control, subjective norms, environmental concern, habit, and socio-demographic characteristics (i.e., age, gender, residential area, and educational level). Following this, we collect 552 questionnaires from residents in Tianjin Municipality, China. We use partial least squares structural equation modelling (PLS-SEM) to analyse the green purchasing intention of the population sample and then employ a multi-group analysis (MGA) to explore the group differences in residents' green purchasing intention. The results show that green purchasing intention is significantly and positively influenced by attitude, perceived behavioural control, subjective norms, and environmental concern but not by habit. The relationship chain of environmental concern→subjective norms→purchasing intention is the strongest. The results of the MGA show that for residential-area groups, the relationships between attitudes, perceived behavioural control, and habits and purchasing intention differ significantly between the downtown group and the outside-the-city group. For the educational-level groups, the relationship between environmental concern and subjective norms differs significantly between the high-education group and the low-education group. Finally, these findings contribute to the literature on the TPB model on green purchasing intention and provide some suggestions for the local government and green marketers.

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3 and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

No abstract

Quality accreditation is an effective way to ensure and improve the quality of higher education. AACSB is the first accrediting organization for commercial college, and it is the most authoritative in the field. In January 2007, AACSB revised its former attestation standards and published the new one. The paper describes the meaning of AACSB, analyzing its accreditation standards and the eligibility procedures. Based on the analysis, the author found the following features of the AACSB: the rigorous and demanding process; the application-oriented procedures, the equal importance between the teaching process and teaching results in the accrediting procession.