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.

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The traditional data envelopment analysis (DEA), bootstrap-DEA and Malmquist models are employed to measure different tourism efficiencies and their spatial characteristics of 61 cities in six coastal urban agglomerations in eastern China. The following conclusions are drawn. (1) The comprehensive efficiency (CE) of urban tourism using the bootstrap-DEA model is lower than the CE level using the DEA-CRS model, which confirms the significant tendency of the DEA-CRS model to overestimate results. (2) The geometric CE averages of urban tourism in the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) have changed from ineffective to effective since 2000, the averages in the Beijing-Tianjin-Hebei (BTH) and the Shandong Peninsula (SDP) have changed from ineffective to moderately effective since 2000, and those in the Central and Southern Liaoning (CSL) and the West Bank of Taiwan Strait (WBTS) have been ineffective since 2000. (3) The CE values of urban tourism in the PRD, the YRD, the BTH and the SDP have been slightly affected by the pure technical efficiency (PTE), whereas the CE values in the CSL and the WBTS have been slightly affected by the scale efficiency (SE) since 2000. (4) Spatially, the range of geometric averages of the total factor productivity (TFP) for the PRD, the YRD, the BTH, the SDP, the WBTS and the CSL has decreased sequentially, while the one for most cities in six urban agglomerations has exhibited a downward trend since 2000. (5) Collectively, the natural conditions, the economic policies and the tourism capital drive the SE change of urban tourism of the CSL and the WBTS. The tourism enterprises for increasing returns to scale and imitating innovative technology have an effect on the CE change of urban tourism in the BTH and the SDP. The tourism market competition drives the PTE change of urban tourism in the PRD and the YRD. Although the PTE and the SE of urban tourism in six coastal urban agglomerations suffer from uncertain events, the CE maintained overall sound momentum since 2000.

This study applies the dynamic spatial Durbin model (SDM) to explore the direct and spillover effects of tourism development on economic growth from the perspective of domestic and inbound tourism. The results are compared with those from the static SDM. The results support the tourism-led-economic-growth hypothesis in China. Specifically, domestic tourism and inbound tourism play a significant role in stimulating local economic growth. However, the spatial spillover effect is limited to domestic tourism, and the spatial spillover effect of inbound tourism is not significant. Furthermore, the long-term effects are much greater than the short-term impact for both domestic and inbound tourism. Plausible explanations of these results are provided and policy implications are drawn.

Conflicts between ecological conservation and socio-economic development persisted over many decades in the Beijing–Tianjin–Hebei urban agglomeration (BTH). Ecosystem services were affected drastically by rapid urbanization and ecological restoration programs in the BTH since 2000. This study aims to identify the spatial patterns of the four types of ecosystem services (net primary productivity (NPP), crop production, water retention, and soil conservation) in 2000 and 2010, and to make clear the impacts of urbanization and associated factors on the spatial patterns of ecosystem services. Based on the quantification of ecosystem services, we assessed the spatial patterns and changes, and identified the relationships between the type diversity of ecosystem services and land-use change. We also analyzed the effect of the spatial differentiation of influencing factors on ecosystem services, using the geographical detector model. The results showed that the average value of crop production increased substantially between 2000 and 2010, whereas the net primary productivity decreased significantly, and the water retention and soil conservation decreased slightly. The ecosystem services exhibited a spatial similar to that of influencing factors, and the combination of any two factors strengthened the spatial effect more than a single factor. The geomorphic factors (elevation and slope) were found to control the distribution of NPP, water retention, and soil conservation. The population density was responsible for crop production. We also found that the urbanization rate plays a major indirect role in crop production and water retention when interacting with population density and slope, respectively. The normalized difference vegetation index (NDVI) indirectly influences the spatial distribution of NPP when interacting with geomorphic factors. These findings highlight the need to promote new strategies of land-use management in the BTH. On the one hand, it is necessary to carefully select where new urban land should be located in order to relieve the pressure on ecosystem services in dense urban areas. On the other hand, the maintenance of ecological restoration programs is needed for improving vegetation coverage in the ecological functional zones in the medium and long term.

The 3Ds, namely, density, distance, and division, are important for regional economic development and are integrated into a “3D” analytical framework in the 2009 World Development Report. Few empirical studies have examined the relevance of the 3D framework for explaining rural poverty in a developing‐country context. The effects of density on poverty are seldom studied, and distances to different layers of city centers on poverty may vary across different contexts. This paper aims to fill these gaps. Examining the case of Guizhou Province in China and adopting methods of the ordinary least square, instrument variable, and spatial econometrics, we find the evidence of the 3D framework for explaining rural poverty at the county level. Population density has a negative effect on rural poverty, while division, as measured by share of the ethnic minority population, has a positive effect. The effects of distance are mixed. Distance to Guiyang, which is the provincial political‐economic center of Guizhou province, has a negative effect on rural poverty, whereas distance to the local city center has no effect. These results can provide important policy implications for local poverty‐alleviation.

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.

Most economists measure labor productivity based on activities conducted at places of work and do not consider leisure time in their calculations. In contrast, psychologists and sociologists argue that leisure has a positive role in the production process: leisure can improve individuals' labor productivity by affecting their self-development. Using empirical data from 21 OECD countries, this study finds that leisure time has a dual effect on labor productivity in terms of per capita per hour GDP. Moreover, leisure time is nonlinearly associated with labor productivity (inverted U-shaped). When leisure time reaches the optimal level (5,813 hours), leisure has a compensatory effect on work and can positively influence labor productivity, but when leisure time exceeds the optimal value, leisure has a substitution effect on work and can negatively influence labor productivity.