Rapid development in China has led to imbalances and inequities of ecological resources among the provinces and regions. In this study, an environmentally extended multi-regional input-output (MRIO) model was used to analyze the imbalances, inequities and pressures of the ecological footprints (EF) of China’s 30 provinces in 2007. In addition, by decomposing the total product consumption coefficients, we calculated the net embodied EF of the flows among the provinces by the total amount, land type and sector. The results showed that most provinces presented EF deficits. Significant differences were observed between the ecological pressure in consumption (EPC) and ecological pressure in production (EPP) for each province because of the net embodied EF trade; the EPCs of Shanghai (15.16), Beijing (7.81) and Tianjin (7.81) were the largest and presented descending EPPs, whereas the EPCs of Heilongjiang (0.98), Hebei (0.98), Xinjiang (0.98) and Guangxi (0.98) were under the threshold value (1) and presented ascending EPPs. The carbon footprint in the secondary sector was the main embodied EF of the flows among the provinces responsible for inequities. Finally, based on the various conditions of the provinces in different geographical regions, we have provided suggestions for regionally balanced development that can maintain the EPP and EPC values under the threshold for each province.

Due to the high ecological pressure that exists in the process of rapid economic development in Jiangsu Province, it is necessary to evaluate its ecological footprint intensity (EFI). This article focuses on ecological footprint intensity analysis at the county scale. We used county-level data to evaluate the spatial distributions and temporal trends of the ecological footprint intensity in Jiangsu’s counties from 1995 to 2015. The temporal trends of counties are divided into five types: linear declining type, N-shape type, inverted-N type, U-shape type and inverted-U shape type. It was discovered that the proportions of the carbon footprint intensity were maintained or increased in most counties. Exploratory spatial data analysis shows that there was a certain regularity of the EFI spatial distributions, i.e., a gradient decrease from north to south, and there was a decline in the spatial heterogeneity of EFI in Jiangsu’s counties over time. The global Moran’s index (Moran’s I) and local spatial association index (LISA) are used to analyze both the global and local spatial correlation of EFIs among counties of Jiangsu Province. The high-high and low-low agglomeration effects were the most common, and there were assimilation impacts of counties with strong agglomeration on adjacent units over time. The results implied the utility of differentiated EFI reduction control measures and promotion of low-low agglomeration and suppression of high-high agglomeration in EFI-related ecology policy.

This study uses a panel threshold model to explore the nonlinear relationship between restraining factors and ecological footprint (EF) evolution from 2003 to 2015 in China. In addition, the heterogeneity of the environmental Kuznets curve (EKC) hypothesis is identified. The results show that the four regime-dependent variables, i.e., technology level, openness, industrial structure and energy efficiency, have significant single-threshold effects on the EF in China, and the negative correlations between these variables and EF are significantly enhanced when the threshold variable urbanization exceeds 86.20%, 68.71%, 86.20% and 47.51%, respectively. As the urbanization level increases, more factors begin to play a high restraining role on the EF. The single-threshold effects on the EKC are significant under the threshold variables of urbanization and industrial structure. Meanwhile, the significant inverted-U relationship trends emerge when the two variables exceed the thresholds of 86.2% and 69.1%, respectively. Based on an empirical study, to restrain the EF of China’s 30 provinces more effectively, the urbanization process should be accelerated, while energy efficiency, foreign capital investment, technology level and service sector proportion should be promoted according to the urbanization level. Compared to other studies, this study is more focused on EF restraining factors and it contributes to the identification of the heterogeneity of EF’s restraining factors and EKC hypothesis, which would be useful for the EF reduction policy in the case of China.