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.

It is crucial to study ecological footprint production intensity (EFPI) in the ecological compensation strategy of designated industries and to delineate high-polluting industries. Environment-extended input-output (EE-IO) tables are suitable for analyzing embodied pollution or land occupation among its economic sectors. The ecological footprint (EF) and input-output tables (IOTs) were used to analyze China’s EFPI and its (net) flow among sectors in 2005, 2010, and 2015. With the environment-extended matrix and Leontief inverse matrix of EE-IO analysis, the direct pollution coefficient (DPC) and total pollution coefficient (TPC) of China’s EF were studied. The (net) embodied EFPI flows between any two sectors were decomposed and demonstrated in detail. The key embodied EFPI component transfer paths among sectors were tracked and analyzed. The results for China’s EFPI in 2005, 2010, and 2015 show that the averages of the TPC component and net embodied EFPI transfer components showed a downward trend from 2005 to 2015. The sector of electricity, heat, gas, and water (S11) and the sector of Agriculture (S1) provided larger component values of both TPCs and net embodied EFPI transfers. From the analysis of the three transfer levels of EFPI, high-value transfer paths were further marked for key governance. Imposing an ecological tax and controlling high-EFPI industries were recommended as optimizations from the production and consumption ends. Additionally, this paper provides a reference for the division of ecological responsibility among Chinese sectors.

The ecological footprint (EF), as a set of land-based ecological indicators, plays an important role in land ecology and evaluations of ecological pressure. Multi-scale levels of Jiangsu’s three-dimensional EF were analyzed, and 3D maps were presented to demonstrate the geographical distribution of the ecological footprint depth (EFD) of Jiangsu’s counties in 1995–2015 at the geographic scales of prefecture-level cities and counties. The results show that the overall EFD of Jiangsu gradually increased during the study period. The county-scale results show that the distribution of EFDs was high in the south and low in the north, and EFDs were mainly concentrated in urban areas of prefecture-level cities. The logarithmic mean Divisia index (LMDI) was used to decompose the factors in explaining the change in EFD. The LMDI analysis shows that the changes in factors every year differ among geographical units on different scales. Affluence is the main factor that promotes EFD, and the change in the ratio between EFD and scientific and technological level is the main factor that suppresses EFD. Countermeasures and suggestions for balancing ecological pressure in specific regions and reducing the depth of the EF from various factors with multi-scale heterogeneity are suggested.