Abstract
This paper addresses two key decisions by households to adopt rooftop solar photovoltaic (PV) systems and the length of time until the adoption. It is hypothesized that these decisions are controlled by different mechanisms and should be modeled independently. This is the first attempt to formally estimate the length of time until the adoption to the authors’ knowledge. Two models are presented in this paper. The first is a mixed logit to model the respondents’ intention to adopt a solar PV system, and the second is a random parameters ordered probit to estimate the length of time until the adoption. Estimation results show that the number of electrical appliances, the households’ interest to harness economic benefits, and the type and characteristics of the dwelling motivate households to select a shorter duration until the adoption. Results also show that the majority (77.80%) of respondents with electric vehicles are highly likely to adopt a rooftop system and select a shorter time duration until adoption. In addition, a significant proportion (83.23%) of respondents with high monthly electricity bills is more likely to adopt a rooftop PV system and select a shorter time duration. Results show that the average monthly electricity bill for households with a PV system has decreased by 74.04%. Reducing monthly electricity bills is a key instigator for adopting a rooftop PV system. Our results confirm the hypothesis that even if there is an intent to adopt a rooftop solar PV system, the length of time until the adoption is controlled by another mechanism.
References
1.
Amado
, M.
, and Poggi
, F.
, 2012
, “Towards Solar Urban Planning: A New Step for Better Energy Performance
,” Energy Procedia
, 30
(1
), pp. 1261
–1273
. 2.
Gautam
, B. R.
, Li
, F.
, and Ru
, G.
, 2015
, “Assessment of Urban Roof Top Solar Photovoltaic Potential to Solve Power Shortage Problem in Nepal
,” Energy Build.
, 86
(1
), pp. 735
–744
. 3.
Janda
, K. B.
, and Parag
, Y.
, 2013
, “A Middle-out Approach for Improving Energy Performance in Buildings
,” Build. Res. Inf.
, 41
(1
), pp. 39
–50
. 4.
Kim
, D.
, Cho
, H.
, and Luck
, R.
, 2019
, “Potential Impacts of Net-Zero Energy Buildings With Distributed Photovoltaic Power Generation on the U.S. Electrical Grid
,” ASME J. Energy Resour. Technol.
, 141
(6
), p. 062005
. 5.
Abu-Rayash
, A.
, and Dincer
, I.
, 2021
, “A Sustainable Trigeneration System for Residential Applications
,” ASME J. Energy Resour. Technol.
, 143
(1
), p. 012101
. 6.
Meraj
, M.
, Mahmood
, S. M.
, Khan
, M. E.
, Azhar
, M.
, and Tiwari
, G. N.
, 2020
, “Effect of N-Photovoltaic Thermal Integrated Parabolic Concentrator on Milk Temperature for Pasteurization: A Simulation Study
,” Renewable Energy
, 163
(1
), pp. 2153
–2164
. 7.
De Groote
, O.
, Pepermans
, G.
, and Verboven
, F.
, 2016
, “Heterogeneity in the Adoption of Photovoltaic Systems in Flanders
,” Energy Econ.
, 59
(1
), pp. 45
–57
. 8.
Ammar
, M. B.
, Ammar
, R. B.
, and Oualha
, A.
, 2021
, “Photovoltaic Power Prediction for Solar Car Park Lighting Office Energy Management
,” ASME J. Energy Resour. Technol.
, 143
(3
), p. 031303
. 9.
López Vega
, A.
, Rubio-Maya
, C.
, Kowalski
, G. J.
, and Pacheco Ibarra
, J. J.
, 2020
, “Analysis of the Design and Operation of a Hybrid Trigeneration-Photovoltaic System Installed in a Shopping Mall
,” ASME J. Energy Resour. Technol.
, 142
(1
), p. 012101
. 10.
Best
, R.
, Burke
, P. J.
, and Nishitateno
, S.
, 2019
, “Understanding the Determinants of Rooftop Solar Installation: Evidence From Household Surveys in Australia
,” Aust. J. Agric. Resour. Econ.
, 63
(4
), pp. 922
–939
. 11.
Alipour
, M.
, Salim
, H.
, Stewart
, R. A.
, and Sahin
, O.
, 2020
, “Predictors, Taxonomy of Predictors, and Correlations of Predictors With the Decision Behaviour of Residential Solar Photovoltaics Adoption: A Review
,” Renewable Sustainable Energy Rev.
, 123
(1
), p. 109749
. 12.
Fina
, B.
, Fleischhacker
, A.
, Auer
, H.
, and Lettner
, G.
, 2018
, “Economic Assessment and Business Models of Rooftop Photovoltaic Systems in Multi Apartment Buildings: Case Studies for Austria and Germany
,” Renewable Energy
, 2018
(1
), p. 9759680
.13.
Zhai
, P.
, and Williams
, E. D.
, 2012
, “Analysing Consumer Acceptance of Photovoltaics (PV) Using Fuzzy Logic Model
,” Renewable Energy
, 41
(1
), pp. 350
–357
. 14.
Coffman
, M.
, Bernstein
, P.
, and Wee
, S.
, 2017
, “Integrating Electric Vehicles and Residential Solar PV
,” Transp. Policy
, 53
(1
), pp. 30
–38
. 15.
Kesari
, B.
, Atulkar
, S.
, and Pandey
, S.
, 2018
, “Consumer Purchasing Behaviour Towards Eco-Environment Residential Photovoltaic Solar Lighting Systems
,” Global Bus. Rev.
, 3
(1
), pp. 1
–19
.16.
Balta-Ozkan
, N.
, Yildirim
, J.
, and Connor
, P. M.
, 2015
, “Regional Distribution of Photovoltaic Deployment in the UK and Its Determinants: A Spatial Econometric Approach
,” Energy Econ.
, 51
(1
), pp. 417
–429
. 17.
Dato
, P.
, 2018
, “Investment in Energy Efficiency, Adoption of Renewable Energy and Household Behaviour: Evidence From OECD Countries
,” Energy J.
, 39
(3
), p. 213
. 18.
Zorić
, J.
, and Hrovatin
, N.
, 2012
, “Household Willingness to Pay for Green Electricity in Slovenia
,” Energy Policy
, 47
(1
), pp. 180
–187
. 19.
Qandil
, M. D.
, Abbas
, A. I.
, Qandil
, H. D.
, Al-Haddad
, M. R.
, and Amano
, R. S.
, 2019
, “A Stand-Alone Hybrid Photovoltaic, Fuel Cell, and Battery System: Case Studies in Jordan
,” ASME J. Energy Resour. Technol.
, 141
(11
), p. 111201
. 20.
Vesterberg
, M.
, Zhou
, W.
, and Lundgren
, T.
, 2021
, “Wind of Change: Small-Scale Electricity Production and Distribution-Grid Efficiency in Sweden
,” Util. Policy
, 69
(4
), p. 101175
. 21.
Ek
, K.
, 2005
, “Public and Private Attitudes Towards “Green” Electricity: The Case of Swedish Wind Power
,” Energy Policy
, 33
(13
), pp. 1677
–1689
. 22.
Goett
, A. A.
, Hudson
, K.
, and Train
, K. E.
, 2000
, “Customers’ Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes
,” Energy J.
, 4
(1
), pp. 1
–28
.23.
Borchers
, A. M.
, Duke
, J. M.
, and Parsons
, G. R.
, 2007
, “Does Willingness to Pay for Green Energy Differ by Source?
,” Energy Policy
, 35
(6
), pp. 3327
–3334
. 24.
Kotchen
, M. J.
, and Moore
, M. R.
, 2007
, “Private Provision of Environmental Public Goods: Household Participation in Green-Electricity Programs
,” J. Environ. Econ. Manage.
, 53
(1
), pp. 1
–16
. 25.
Hansla
, A.
, Gamble
, A.
, Juliusson
, A.
, and Gärling
, T.
, 2008
, “Psychological Determinants of Attitude Towards and Willingness to Pay for Green Electricity
,” Energy Policy
, 36
(2
), pp. 768
–774
. 26.
Küfeoğlu
, S.
, and Pollitt
, M. G.
, 2019
, “The Impact of PVs and EVs on Domestic Electricity Network Charges: A Case Study From Great Britain
,” Energy Policy
, 127
(1
), pp. 412
–424
. 27.
Cabral
, J.
, Cabral
, M.
, and Pereira
, A.
, 2020
, “Elasticity Estimation and Forecasting: An Analysis of Residential Electricity Demand in Brazil
,” Util. Policy
, 66
(1
), p. 101108
.28.
Al Masri
, A.
, 2020
, “Household Demand for Rooftops Solar Panels in Jordan
,” A thesis submitted in partial fulfilment of the requirements for the Master's Degree of Science at the German Jordanian University.29.
Anastasopoulos
, P. C.
, and Mannering
, F. L.
, 2011
, “An Empirical Assessment of Fixed and Random Parameter Logit Models Using Crash- and Non-Crash-Specific Injury Data
,” Accid. Anal. Prev.
, 43
(3
), pp. 1140
–1147
. 30.
Mannering
, F. L.
, 2018
, “Temporal Instability and the Analysis of Highway Accident Data
,” Anal. Methods Accid. Res.
, 17
(1
), pp. 1
–13
. 31.
Hamed
, M. M.
, and Al-Eideh
, B. M.
, 2020
, “An Exploratory Analysis of Traffic Accidents and Vehicle Ownership Decisions Using a Random Parameters Logit Model With Heterogeneity in Means
,” Anal. Methods Accid. Res.
, 25
(1
), p. 100116
. 32.
Washington
, S.
, Karlaftis
, M. G.
, and Mannering
, F. L.
, 2011
, Statistical and Econometric Methods for Transportation Data Analysis
, CRC Press
, Thames, Oxfordshire, UK
.33.
Hensher
, D.
, and Greene
, W. H.
, 2003
, “The Mixed Logit Model: The State of Practice
,” Transportation
, 30
(2
), pp. 133
–176
. 34.
McFadden
, D.
, 1981
, “Econometric Models of Probabilistic Choice,” Structural Analysis of Discrete Data With Econometric Applications
, C
Manski
, and D
McFadden
, eds., M.I.T. Press
, Cambridge
, p. 198272
.35.
Seraneeprakarn
, P.
, Huang
, S.
, Shankar
, V.
, Mannering
, F. L.
, Venkataraman
, N.
, and Milton
, J.
, 2017
, “Occupant Injury Severities in Hybrid-Vehicle Involved Crashes: A Random Parameters Approach With Heterogeneity in Means and Variances
,” Anal. Methods Accid. Res.
, 15
(1
), pp. 41
–55
. 36.
Harris
, M. N.
, and Zhao
, X.
, 2007
, “A Zero-Inflated Ordered Probit Model, With an Application to Modelling Tobacco Consumption
,” J. Econometrics
, 141
(2
), pp. 1073
–1099
. 37.
McKelvey
, R. D.
, and Zavoina
, W.
, 1975
, “A Statistical Model for the Analysis of Ordinal Level Dependent Variables
,” J. Math. Sociol.
, 4
(1
), pp. 103
–120
. 38.
Hamed
, M. M.
, and Easa
, S.
, 1998
, “Integrated Modeling of Urban Shopping Activities
,” J. Urban Plann. Dev.
, 124
(3
), pp. 115
–131
. 39.
Hamed
, M. M.
, and Easa
, S.
, 1998
, “Ordered Probability Modeling of Seat Belt Usage
,” J. Transp. Eng.
, 124
(3
), pp. 271
–276
. 40.
Hamed
, M. M.
, 1990
, “Modelling the Demand for Taxicab Services
,” Road Transp. Res.
, 8
(3
), pp. 22
–33
.41.
Hamed
, M. M.
, and Abdul-Hussain
, A.
, 2001
, “Drivers’ Familiarity With Urban Route Network Layout in Amman, Jordan
,” Cities
, 18
(2
), pp. 93
–101
. 42.
Duncan
, C. S.
, Khattak
, A. J.
, and Council
, F. M.
, 1998
, “Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions
,” Transp. Res. Rec.
, 1635
(1
), pp. 63
–71
. 43.
Anastasopoulos
, P. C.
, Haddock
, J. E.
, Karlaftis
, M. G.
, and Mannering
, F. L.
, 2012
, “Analysis of Urban Travel Times: Hazard-Based Approach to Random Parameters
,” Transp. Res. Rec.
, 2302
(1
), pp. 121
–129
. 44.
Mannering
, F. L.
, Shankar
, V.
, and Bhat
, C. R.
, 2016
, “Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data
,” Anal. Methods Accid. Res.
, 11
(1
), pp. 1
–16
. 45.
Fountas
, G.
, Anastasopoulos
, P. C.
, and Abdel-Aty
, M.
, 2018
, “Analysis of Accident Injury Severities Using a Correlated Random Parameters Ordered Probit Approach With Time Variant Covariates
,” Anal. Methods Accid. Res.
, 18
(1
), pp. 57
–68
. 46.
Bondio
, S.
, Shahnazari
, M.
, and McHugh
, A.
, 2018
, “The Technology of the Middle Class: Understanding the Fulfilment of Adoption Intentions in Queensland’s Rapid Uptake Residential Solar Photovoltaics Market
,” Renewable Sustainable Energy Rev.
, 93
(1
), pp. 642
–651
. 47.
Hardman
, S.
, Shiu
, E.
, and Steinberger-Wilckens
, R.
, 2016
, “Comparing High-End and Low End Early Adopters of Battery Electric Vehicles
,” Transp. Res. Part A: Policy Pract.
, 88
(1
), pp. 40
–57
. 48.
Shi
, L.
, Zhou
, W.
, and Kriström
, B.
, 2013
, “Residential Demand for Green Electricity
,” Environ. Econ.
, 4
(1
), pp. 39
–50
.49.
Walters
, J. P.
, Kaminsky
, J.
, and Huepe
, C.
, 2018
, “Factors Influencing Household Solar Adoption in Santiago, Chile
,” J. Constr. Eng. Manage.
, 144
(6
), p. 05018004
. 50.
Gu
, G.
, and Feng
, T.
, 2020
, “Heterogeneous Choice of Home Renewable Energy Equipment Conditioning on the Choice of Electric Vehicles
,” Renewable Energy
, 154
(1
), pp. 394
–403
. 51.
Wang
, N.
, Tang
, L.
, and Pan
, H.
, 2019
, “A Global Comparison and Assessment of Incentive Policy on Electric Vehicle Promotion
,” Sustainable Cities Soc.
, 44
(1
), pp. 597
–603
. 52.
Hamed
, M. M.
, and Abdelwahab
, W.
, 1996
, “Effect of Government Policies and Vehicle Marketing Strategies on Household Vehicle Demand and Fuel Consumption
,” Can. J. Civil Eng.
, 23
(3
), pp. 587
–594
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