Identification and prioritization of factors affecting the adoption of electric vehicles using analytic network process

Given the spread of environmental pollution, the development of strategies and changes in lifestyle to reduce urban environmental pollution is of great importance. One of the existing solutions is to develop and promote the use of electric vehicles. An electric vehicle is a car that uses a battery pack to store the electrical energy that powers the motor. EV batteries are charged by plugging the vehicle in to an electric power source. These days, electric vehicles are considered as one of the most modern innovations in the car business. However, the use of these vehicles has not been particularly widespread in third world countries, and this may be due to several reasons. Despite the high level of air pollution in Iran, People and the government still do not accept electric vehicles as an important way to reduce air pollution, and electric vehicles are rarely seen on the streets. The main factors that can affect the reception of electric vehicles by the Iranian people are economic, technical, access, laws, marketing, and personal.

The factors extracted from literature and identified factors were prioritized using the analytic network process technique. After data gathering with help of questionnaire which its relaiablity and validity was confirmed through inconsistency index and content validity ratio consecutively, the data analysis was performed using SuperDecision software.

The results showed that Economic with 23%, Technical with 17.8%, Accessibility with 16.4%, laws with 15.3%, marketing with 15.1% and personal with 12.2% had the most significant effect in acceptance of the Iranian people of electric vehicles.

The results showed that economic factors had the most significant effect in comparison with other criteria. depreciation time, producer company, fuel subsidy, availability of repair shop, car and importance to the environment were the most significant factor in each criteria.  The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.