A stated preference analysis of smart meters, photovoltaic generation, and electric vehicles in Japan: Implications for penetration and GHG reduction

Abstract Smart grids have recently received considerable attention in many countries. One emerging issue is the diffusion process of smart equipment, such as smart meters, photovoltaic generation, and electric/hybrid electric vehicles. However, since the revealed preference data have not been accumulated for smart equipment diffusion, this paper conducts a conjoint analysis to examine consumers’ stated preferences on the basis of a large-scale survey administered in Japan. A mixed logit model that allows for individual heterogeneity is used for estimation, and willingness-to-pay (WTP) values are calculated for the attributes of smart equipment. Furthermore, the expected penetration rates of smart equipment and potential for reductions in greenhouse gas (GHG) emissions are investigated.

[1]  Kathryn B. Janda,et al.  From “if only” to “social potential” in schemes to reduce building energy use , 2014 .

[2]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[3]  C. Clastres.  Smart grids: Another step towards competition, energy security and climate change objectives , 2011 .

[4]  Yeonbae Kim,et al.  A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach , 2008 .

[5]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[6]  Seung-Jun Kwak,et al.  Valuing energy-saving measures in residential buildings: A choice experiment study , 2010 .

[7]  Martin Achtnicht,et al.  Do environmental benefits matter? Evidence from a choice experiment among house owners in Germany , 2011 .

[8]  Jerry A. Hausman,et al.  Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables , 1979 .

[9]  Anna Alberini,et al.  Energy Efficiency Investments in the Home: Swiss Homeowners and Expectations about Future Energy Prices , 2013 .

[10]  Richard D. Duke,et al.  Accelerating residential PV expansion: supply analysis for competitive electricity markets , 2001 .

[11]  John K. Dagsvik,et al.  Potential demand for alternative fuel vehicles , 2002 .

[12]  Calvin Lee Kwan,et al.  Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States , 2012 .

[13]  Gordon Walker,et al.  The dynamics of energy demand: Change, rhythm and synchronicity , 2014 .

[14]  J. Halton.  On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[15]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[16]  Margo Sadler,et al.  HOME ENERGY PREFERENCES & POLICY: APPLYING STATED CHOICE MODELING TO A HYBRID ENERGY ECONOMY MODEL , 2003 .

[17]  C. Bhat.  Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model , 2001 .

[18]  B. Sovacool.  What Are We Doing Here? Analyzing Fifteen Years of Energy Scholarship and Proposing a Social Science Research Agenda , 2014 .

[19]  Nobuhiro Hosoe,et al.  Regional electric power demand elasticities of Japan's industrial and commercial sectors , 2009 .

[20]  W. Poortinga,et al.  Household preferences for energy-saving measures: A conjoint analysis , 2003 .

[21]  Robin Segal,et al.  Forecasting the Market for Electric Vehicles in California Using Conjoint Analysis , 1995 .

[22]  C. Woo,et al.  Now that California has AMI, what can the state do with it? , 2008 .

[23]  Martin A. Green,et al.  Photovoltaics: technology overview , 2000 .

[24]  Jerry A. Hausman,et al.  Assessing the potential demand for electric cars , 1981 .

[25]  John E. Calfee,et al.  ESTIMATING THE DEMAND FOR ELECTRIC AUTOMOBILES USING FULLY DISAGGREGATED PROBABILISTIC CHOICE ANALYSIS , 1985 .

[26]  Meryl P. Gardner,et al.  Willingness to pay for electric vehicles and their attributes , 2011 .

[27]  Ryuichi Kitamura,et al.  Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project , 1993 .

[28]  Mark Jaccard,et al.  The ‘neighbor effect’: Simulating dynamics in consumer preferences for new vehicle technologies , 2008 .

[29]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[30]  Paul C. Stern,et al.  Individual and household interactions with energy systems: Toward integrated understanding , 2014 .

[31]  Pavlos S. Kanaroglou,et al.  Household demand and willingness to pay for clean vehicles , 2007 .

[32]  Jonn Axsen,et al.  Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles , 2009 .

[33]  S. Banfi,et al.  Willingness to pay for energy-saving measures in residential buildings , 2007 .

[34]  Colin Vance,et al.  Willingness to Pay for Energy Conservation and Free-Ridership on Subsidization: Evidence from Germany , 2008 .

[35]  N. Meade,et al.  The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation , 2013 .

[36]  K. Train,et al.  Forecasting new product penetration with flexible substitution patterns , 1998 .

[37]  K. Train,et al.  Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles , 1999, Controlling Automobile Air Pollution.

[38]  G. Ewing,et al.  Assessing Consumer Preferences for Clean-Fuel Vehicles: A Discrete Choice Experiment , 2000 .