Understanding the difference of the multi-relationships between tourism, environment, and economic growth is an effective way to reduce inequality, but little is known about multivariate differences induced by different income levels. Thus, we empirically explored these multi-relationships between tourism, economic growth, CO₂ emissions, and primary energy consumption at different development stages using a series of panel models, leading to enlightening results. A cointegration relationship exists between CO₂ emissions and all other variables in all countries selected. CO₂ emissions in low-income countries increase by 0.084% for a 1% increase in expenditure of inbound tourists per capita, and CO₂ emissions in the low-income countries and high-income countries increase by 0.072% and 0.059%, respectively, for a 1% increase in the net inflow of international tourists. In addition, the lower the income countries, the greater the impact of tourism on CO₂ emissions and the higher the income countries, the higher the number of feedback Granger causality. Our results imply that the differences in multi-relationships induced by different income levels are of great importance for government policy decisions.

In the past fifteen years, the high-speed railway (HSR) network in China has experienced unprecedented rapid growth. The development of the HSR has profound impacts in terms of redistributing accessibility in space and affecting the travel behaviors of people. The impacts of HSR development on the land use system have not been well investigated, however, because of the lack of large-scale, high-resolution land use data. Hence, this is the first time that impacts of the construction of the HSR network in China on the land use patterns at the national level are examined using high-resolution satellite land use data. In detail, a difference-in-differences model was used to evaluate the impacts of the HSR network on different landscape metrics in three land use categories: urban land, agricultural land, and natural land. We also compared the differences in land use transformations between HSR and non-HSR cities over three periods: 2005 to 2008, 2008 to 2010, and 2010 to 2013. The analysis yields the following findings: (1) for urban land, the HSR had no universal effect on the absolute size (quantity) of urban land but had a negative effect on the mean patch size (MPS) in matched samples, and regional and network endowments might lead to various effects in different regions; (2) HSR could have a negative effect on the absolute size of agricultural land, especially in the west; and (3) for natural land, patches in HSR cities tended to aggregate and regularize, whereas in non-HSR cities natural land continued to be fragmented and consumed.

Cultivated land in Middle-lower Yangtze Plain has been greatly reduced over the last few decades due to rapid urban expansion and massive urban construction. Accurate and timely monitoring of cultivated land changes has significant for regional food security and the impact of national land policy on cultivated land dynamics. However, generating high-resolution spatial-temporal records of cultivated land dynamics in complex areas remains difficult due to the limitations of computing resources and the diversity of land cover over a complex region. In our study, the annual dynamics of cultivated land in Middle-lower Yangtze Plain were first produced at 30 m resolution with a one-year interval in 1990–2010.Changes of vegetation and cultivated land are examined with the breakpoints inter-annual Normalized Difference Vegetation Index (NDVI) trajectories and synthetic NDVI derived by the enhanced spatial and temporal adaptive reflectance fusion model (ESTRAFM), respectively. Last, cultivated land dynamics is extracted with a one-year interval by detecting phenological characteristic. The results indicate that the rate of reduction in cultivated land area has accelerated over the past two decades, and has intensified since 1997.The dynamics of cultivated land mainly occurred in the mountains, hills, lakes and around towns, and the change frequency of these area was mainly one or two times. In particular, the changes in cultivated land in Nanjing have been most intense, perhaps attributed to urban greening and infrastructure construction.

The identification of fine particulate matter (PM_2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM_2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM_2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R² higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

There are debates on whether the implementation of the Belt and Road (BR) initiative could significantly increase CO₂ emissions in participating countries. Nevertheless, the policy effect of the BR initiative on both consumption- and production-based CO₂ emissions has not been fully explored. In this study, we quantified the consumption- and production-based CO₂ emissions in BR countries with a multiregional input–output model. Then, a difference-in-differences (DID) model was used to identify the CO₂ emissions caused by the BR initiative. Results showed that the production-based CO₂ emissions (20.77 Gt) were 11 percent higher than the consumption-based CO₂ emissions (18.71 Gt) in BR countries. The BR initiative had a significant, positive effect on per-capita consumption-based CO₂ emissions (p value < 0.1), with an average increase of 0.51 t/cap/year, but had no significant effect on per-capita production-based CO₂ emissions (p value > 0.1). These results imply that the BR initiative promoted regional consumption to achieve common prosperity and boosted green transformation of regional economy. Moreover, non-BR countries that have consumed CO₂-embedded products should take responsibility for the CO₂ emitted in BR countries. Participants should give priority to strengthening the cooperation based on their country’s infrastructure conditions rather than the proximity to China. These findings clarify the policy effect of the BR initiative and distinguish the CO₂ emission responsibility from both consumption- and production-based perspectives.