Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model

Ning Xu, Song Ding, Yande Gong and Ju Bai

Energy, 2019, vol. 175, issue C, 218-227

Abstract: Facing the challenging problems of data quality, the ambitious targets of greenhouse gas emissions need practical forecasting methods to aid in formulating policy. A forecasting method, which has shown strong potential, is the rolling grey prediction model. To enable accurate forecasting, this study develops an adaptive grey model, which captures the essential features of a developing trend. The novel model is combined with a buffered rolling method to enhance accuracy. Compared with conventional models, the adaptive grey model with a buffered rolling method improves the adaptability in pursuit of data characteristics and it uses a nonlinear programming method to generate a satisfactory time response function for prediction. The proposed approach is constructed to forecast Chinese greenhouse gas emissions from 2017 to 2025 and compared with other benchmark models. Empirical applications show that the proposed model has advantages over others. The forecast data are decomposed by logarithmic mean Divisia index, and suggestions on emission mitigation are put forward based on factors analysis.

Keywords: Grey prediction; Buffered rolling mechanism; Greenhouse gas emission; Time response function (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219304608
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:175:y:2019:i:c:p:218-227

DOI: 10.1016/j.energy.2019.03.056

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().