Modeling the diffusion of residential solar photovoltaic (PV) systems in their social, political and economic context is crucial to help policymakers assess which policies may best support adoption. Current models of renewable energy adoption [1, 2, 3, 4] assume regulatory and incentive frameworks that do not apply to Gulf Cooperation Council (GCC) states, where energy tariffs are strongly subsidized, tax credits are not viable due to the lack of personal income tax, and support for distributed generation through grid access policies such as the Feed-in Tariff and Net Metering is not available. The goal of this study is to address this gap by analyzing the impact of home ownership, the falling cost of PV, the reduction of electricity subsidies, the introduction of a carbon tax, and the diffusion of innovation on the residential adoption of solar PV technologies in Qatar. Our objective is to develop a social simulation platform that helps policymakers and other stakeholders assess the optimal regulatory framework to promote the adoption of building-integrated PV systems in Qatar and other countries which share a similar geographical, political, economic and social context. We present an agent-based model for residential adoption of solar photovoltaic (PV) systems in the state of Qatar as a case study for the Arabian Gulf Region. Agents in the model are defined as households. Each household corresponds to a dwelling in the Al Rayyan municipality of Qatar that is either owned (by citizens) or rented (by expatriates). The objective of the model is to evaluate PV adoption in terms of these two household cohorts under diverse regulatory and incentive scenarios. In the present state of affairs, only Qatari citizens can own property in Al Rayyan and Qatari households are exempt from electricity charges. Therefore, home owners are Qataris who have free electricity, while renters tend to be expatriates who pay for electricity. The more competitive the cost of electricity from residential PV systems is as compared to the electricity tariff, the more likely are household agents to adopt solar PV. Several factors can contribute to make the cost of electricity from residential PV more competitive, including:the falling cost of PV due to increasing technology maturitythe reduction of subsidies for electricity and the gas used for electricity productionthe introduction of a carbon taxthe extension of the electricity tariff to Qatari householdsthe neighborhood effect, which implements peer effects on the diffusion of PV innovation as a percent discount on residential PV cost.We compute solar PV adoption as resource limited exponential growth. Households adopt solar PV with a probability established by the logistic function in (1), where L is a scaling constant, e is the natural logarithm, x is the cost of electricity from residential PV systems and k is a parameter which determines the slope of the adoption curve. We set L = 1 to normalize the output of the logistic function as a probability. For the k parameter, we select a value (k = 0.59) that in the null-hypothesis scenario yields a PV market share that is equivalent to the innovator cohort of adopters in Rogers’ adoption/innovation curve (2.5%) [5]. According to Rogers, “innovators are active information seekers about new ideas”, who are close to the scientific community and other innovators, have financial liquidity, and are willing to take high risks to pursue their vision. The rationale for restricting adopters to innovators in the null hypothesis scenario is that only eco-warriors with financial means and high technology awareness would adopt in the absence of incentives, with high PV costs.(1) f (x) = L / 1+e^( − k∗x) At each simulation tick, each household agent that has not adopted yet, is presented with the opportunity of doing so. Adoption is determined randomly according to the output of the logistic function in (2): a random probability p is generated, and if the probability of adoption as calculated by (1) is greater or equal to p, adoption occurs.We analyze three alternative simulation scenarios:Scenario 1 — Business as usual: no measures are introduced to incentivize PV, the neighborhood effect is active, and the price of PV falls due to increasing technology maturityScenario 2 — 40% of gas and electricity subsidies are curtailed, the neighborhood effect is active, a carbon tax of $8/tCO2e is introduced, the price of PV falls due to increasing technology maturity, and citizens continue to have free electricityScenario 3 — same as scenario 2, with the variant that citizens too pay for electricity. As baseline, we establish a null hypothesis scenario, which is the same as scenario 1, except that the price of PV does not fall due to increasing technology maturity. Details of the simulation results are provided in the attached file to this submission. Our study suggests that Qatar's residential PV adoption is strongly promoted by the falling cost of PV and can be further facilitated through the reduction of electricity subsidies and the extension of the electricity tariff to Qatari households, which are currently exempt. The introduction of a carbon tax can also play a role in accelerating residential PV adoption, if above $8 per metric ton of carbon dioxide equivalent. The ensuing PV adoption rates would help facilitate the national targets of 2% electricity production from solar energy by 2020 and 20% by 2030.


[1] Zhao, J., E. Mazhari, N. Celik, Y.-J. Son. “Hybrid agent-based simula- tion for policy evaluation of solar power generation systems”. Simulation Modeling Practice and Theory 19, (2011): 2189–2205.

[2] Paidipati, J., L. Frantzis, H. Sawyer, A. Kurrasch. “Rooftop photovoltaics market penetration scenarios”. Navigant Consulting, Inc., for NREL: February, (2008).

[3] Drury, E., P. Denholm, R. Margolis. “Modeling the US rooftop photo- voltaics market”. In National Solar Conference 2010, 17–22 May 2010, Phoenix, USA, (2010).

[4] Graziano, M. and K. Gillingham. “Spatial patterns of solar photovoltaic system adoption: the influence of neighbors and the built environment”. Journal of Economic Geography 15, (2015): 815–839.

[5] Rogers, E. M.. Diffusion of innovations. New York, Free Press of Glencoe, RS(N), (2010).


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