Multi-Regional Input-Output (MRIO) Study of the Provincial Ecological Footprints and Domestic Embodied Footprints Traded among China’s 30 Provinces

Abstract

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.

Spatial and Temporal Variations in the Ecological Footprints in Northwest China from 2005 to 2014

“…On spatial distribution, it is consistent with the results of Wu et al [40]. On temporal variation, it is also in line with the results of Li et al [21] who founded an increasing trend from 1990 to 2010 in Shanxi, Gansu, Ningxia and Xinjiang, and of Yue et al [41] who showed a steadily growth in Gansu from 1995 to 2003.…”

Large inter-city inequality in consumption-based CO2 emissions for China's pearl river basin cities

“…where I is the identity matrix, and L is called Leontief inverse matrix (Wu and Liu 2016). Supposing there is a row vector D, each of its elements, [d r i ], represents the direct carbon emission intensity of the sector i in region r, that is, the production-based emissions of the sector i in region r divided by the total output of this sector.…”

Determinants of Oil Footprints Embodied in Sino-US Trade: A Perspective from the Globalizing World

“…Zhang et al, measure the virtual energy trade situation between various regions of China by using MRIO [41]. Decun and Jinping analyzed the energy footprint of various provinces in China in 2007 [42]. Chen et al tracked the use of freshwater resources in global supply chains [43].…”