EconPapers    
Economics at your fingertips  
 

Improved Grey Forecasting Model for Taiwan’s Green GDP Accounting

Shin-li Lu, Ching-I Lin and Shih-hung Tai ()
Additional contact information
Shin-li Lu: Aletheia University
Ching-I Lin: Lunghwa University of Science and Technology
Shih-hung Tai: Lunghwa University of Science and Technology

Chapter Chapter 166 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 1575-1584 from Springer

Abstract: Abstract This paper applies the grey forecasting model to forecast the green GDP accounting of Taiwan from 2002 to 2010. Green GDP accounting is an effective economic indicator of human environmental and natural resources protection. Generally, Green GDP accounting is defined as the traditional GDP deduces the natural resources depletion and environmental degradation. This paper modifies the original GM(1,1) model to improve prediction accuracy in green GDP accounting and also provide a value reference for government in drafting relevant economic and environmental policies. Empirical study shows that the mean absolute percentage error of RGM(1,1) model is 2.05 % lower than GM(1,1) and AGM(1,1), respectively. Results are very encouraging as the RGM(1,1) forecasting model clearly enhances the prediction accuracy.

Keywords: Grey theory; Forecasting; Green GDP accounting (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-642-38391-5_166

Ordering information: This item can be ordered from
http://www.springer.com/9783642383915

DOI: 10.1007/978-3-642-38391-5_166

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-642-38391-5_166