Energy Demand U.S. Commercial Buildings: An Econometric Approach
Lucy Yueming Qiu, StanfordUniversity, Phone +1-623-209-4725, E-mail:
Overview
Adopting energy efficient technologies is widely regarded as an investment with high profitability and low payback period. However, the diffusion of energy efficient technologies has been slow. The concern around rebound effect, the fact that adopting energy efficient technologies might lead to an increase of energy use, is an obstacle for the diffusion. Thus evaluating the net energy savings of adopting energy efficient technologies, in other words, comparing the energy consumption before and after adopting energy efficient technologies, taking into consideration the effect of all the social factors such as rebound effect, can provide solid evidence to assist policy makers and project investors in making their energy efficiency related decisions. Despite of the importance of this evaluation, there have been very few studies conducting this kind of empirical work to quantify the net energy savings - net of rebound effect and all other social factors- of energy efficient technologies, especially in commercial building sector. There are two possible reasons for this lack of literature in this area: first, conducting behavioural experiments in commercial buildings is costly; second, without doing experiment, using cross-sectional data seems to be the only way, but cross-sectional data has the technical challenge of sample selection bias. Selection bias arises in the context of adopting energy efficient technologies because normally it is the buildings with higher energy intensity that tend to realize the necessity to adopt energy efficient technologies. Thus without correcting for the sample selection bias, a positive correlation between high energy consumption and the adoption of energy efficient technologies could lead to a misleading conclusion that rebound effect is big enough to offset all energy savings and even more. This paper aims to fill in the above gap by conducting empirical analysis to quantify the net energy savings of adopting energy efficient technologies in U.S. commercial buildings, with a correction of sample bias. It also contributes to the literature of finding price elasticties of energy demand in commercial sector. Building level cross-sectional data is used in this paper.
The paper is organised as follows: After the introduction with a literature survey, the second chapter describes the structural and econometric models. Chapter Three describes the data, followed by Chapter Four which discusses the estimation results from the models. Chapter Five derives policy implications, followed by the final chapter of conclusions.
Methods
Heckmanselectionmodel to correct for the sample selection bias of adopting energy efficient technologies.
Two stage least squares to tackle the endogeneity of average electricity price.
Results
First, Energy Management and Control System can have a net electricity savings of 16.22~27.22% for commercial buildings. Adopting the Economizer technology could save electricity consumption by 0.26~11.03%. Adopting the Variable Air Volume system could have a net saving of 14.69~26.47%. All these savings are the net savings after rebound effect.
Second, the own price elasticity of electricity consumption of commercial buildings ranges from -0.9034 to -0.6854; the cross price elasticity of electricity consumption, the response to natural gas price change, is not significant.
Conclusions
The empirical results of the net energy savings estimated in this paper are consistent with the theoretical rebound effect literature which states that for commercial buildings, the magnitude of rebound effect is smaller than the absolute energy savings, which means that there are still net energy savings of adopting energy efficient technologies. These results provide strong arguments for policy makers to promote building energy efficiency. Commercial buildings are price sensitive in terms of their electricity consumption.The long run own price elasticity of electricity consumption estimated by this paper, using disaggregate data, is in the higher end of the range of the long run elasticity in residential sector. This indicates that commercial buildings have great potential for reducing energy use via pricing or tax policy instruments.
References
Dahl C. 1993. A survey of energy demand elasticities in support of the development of theNEMS. Work. Pap., Colorado Sch. Mines, US Dep. Energy
Wade S. 2003. Price Responsiveness in the AEO2003 NEMS Residential and CommercialBuildings Sector Models. Washington, DC: US Dep. Energy/EIA
Kok, Nils, Marquise McGraw, and John M. Quigley. 2011. The Diffusion of Energy Efficiency in Building. American Economic Review, 101(3): 77–82.
McFadden, D., C. Puig, and D. Kirschner. 1977. Determinants of the Long-Run Demand for Electricity.American Statistical Association, Proceedings of the Business and Economic Statistics Section, Part 2:109-14.
Denton, F. T., D. C. Mountain, and B.G. Spencer. 2000. Energy Use in the Commercial Sector: Estimated Intensities and Costs for Canada Based on US Survey Data. Energy Studies Review 9:23-45.
Frank T. Denton, DeanC.Mountain, Byron G. Spencer, 2003. Energy Demand with Declining Rate Schedules: An Econometric Model for the U.S.Commercial Sector. Land Economics, Vol. 79, No. 1 (Feb., 2003), pp. 86-105
Richard G. Newell, William A. Pizer,2008. Carbon mitigation costs for the commercial building sector: Discrete–continuous choice analysis of multifuel energy demand.Resource and Energy Economics 30 (2008) 527–539
Clinton J. Andrews and Uta Krogmann, 2009 a. Explaining the adoption of energy-efficient technologies in U.S. commercial buildings. Energy and Buildings 41 (2009) 287–294
Clinton J.Andrews and Uta Krogmann, 2009 b. Technology diffusion and energy intensity in US commercial buildings. Energy Policy 37(2009)541–553