Study of carbon metabolic processes and their spatial distribution in the Beijing-Tianjin-Hebei urban agglomeration

It is necessary to reduce carbon emission, and land-use adjustment is one of the potential strategies. In this paper, we calculated the carbon emission and absorption rates in China's Beijing-Tianjin-Hebei agglomeration. We used ArcGIS to simulate their spatial patterns based on land-transfer matrices (land-use transitions) at 5-year intervals. From 2000 to 2015, carbon emission first increased and then decreased, but carbon absorption increased steadily. Transportation and industrial land accounted for 73.0% of carbon emission, whereas forest accounted for 55.0% of carbon absorption. The spatial distributions of emission and absorption evolved, developing obvious spatial gradients. Land transfers leading to increased emission or decreased absorption (negative transfers) increased by 115.1% from 2000 to 2015; the corresponding area of land transferred first decreased and then increased. The area of land transferred increased more slowly than negative carbon transfers, mainly due to transfers to transportation and industrial land, which accounted for 90.0% of the total; the land transfers leading to reduced emissions or increased absorption (positive carbon transfers) and the amount of carbon transferred decreased or increased simultaneously. The area of land transferred increased more than positive carbon transfers. The positive carbon transfers increased slightly at the start of the study, but showed no significant overall change, mainly due to transfers from transportation and industrial land to urban land. However, positive carbon transfers from 2010 to 2015 increased to 21 times the starting value, mainly due to transfers from transportation and industrial land (92% of the total). Our analysis revealed un-favorable transfer directions and sizes and their spatial distribution, thereby providing a scientific basis for regional planning to reduce carbon emission through adjustment of the land use pattern. (c) 2018 Elsevier B.V. All rights reserved.