Optimal strategies for carbon reduction at dual levels in China based on a hybrid nonlinear grey-prediction and quota-allocation model

In this research, a hybrid nonlinear grey-prediction and quota allocation model (HNGP-QAM) was developed for supporting optimal planning of China's carbon intensity reduction at both departmental and provincial levels in 2020. At such dual levels, HNGP-QAM can not only help forecast carbon intensity and its fluctuations over the concerned period, but also facilitate the identification of China's carbon intensity reduction target in 2020 and the corresponding quotas for minimizing the total abatement cost. Two scenarios were developed based on multiple governmental policies and allocation schemes among provinces and departments. The results showed that the total abatement cost would be 92.07 and 98.93 x 10(9), as well as 180.57 and 194.19 x 10(9) RMB (It is another shortname for China Yuan) for provincial and departmental allocation schemes under the reduction ratios of 40 and 45%, respectively. Furthermore, the west, the east, and the central China would be allocated the emission assignments that would be accounting for 48.53, 28.26, and 23.21% of the total national emission reduction, respectively. The obtained results were particularly useful for multi-level governments in providing information to identify the carbon intensity reduction target, conducting emission reduction assignments among provinces and departments, as well as supporting relevant policy-making. The results also suggested that the developed HNGP-QAM be applicable to similar engineering and planning problems. (C) 2014 Elsevier Ltd. All rights reserved.