Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

Michael J. Scott, Don S. Daly, John E. Hathaway, Carina S. Lansing, Ying Liu, Haewon C. McJeon, Richard H. Moss, Pralit L. Patel, Marty J. Peterson, Jennie S. Rice and Yuyu Zhou

Energy, 2015, vol. 90, issue P2, 1682-1694

Abstract: In this paper, an IAM (integrated assessment model) uses a newly-developed Monte Carlo analysis capability to analyze the impacts of more aggressive U.S. residential and commercial building-energy codes and equipment standards on energy consumption and energy service costs at the state level, explicitly recognizing uncertainty in technology effectiveness and cost, socioeconomics, presence or absence of carbon prices, and climate impacts on energy demand. The paper finds that aggressive building-energy codes and equipment standards are an effective, cost-saving way to reduce energy consumption in buildings and greenhouse gas emissions in U.S. states. This conclusion is robust to significant uncertainties in population, economic activity, climate, carbon prices, and technology performance and costs.

Keywords: Building-energy demand; Integrated assessment models; Uncertainty analysis (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421500883X
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:90:y:2015:i:p2:p:1682-1694

DOI: 10.1016/j.energy.2015.06.127

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 ().