The Adoption of Electric Vehicles in Qatar Can Contribute to Net Carbon Emission Reduction but Requires Strong Government Incentives

Abstract

Electric mobility is at the forefront of innovation. Cutting down greenhouse gases when low-carbon electricity sources are maintained has answered the concerns of skeptics when switching to electric mobility. This paper presents a life-cycle-based comparative study between the electric and conventional gasoline vehicles with respect to their environmental performance, taking the case of Qatar. A well-to-wheel life cycle assessment is used to understand the carbon footprint associated with the use of alternative mobility when powered by non-renewable energy sources such as natural gas for electricity production. A survey was also conducted to evaluate the economic and practical feasibility of the use of electric vehicles in Qatar. The analysis showed that electric vehicles (EVs) have passed conventional gasoline vehicles with a minimum difference between them of 12,000 gCO2eq/100 km traveled. This difference can roughly accommodate two additional subcompact electric vehicles on the roads of Qatar. Even though Qatar is producing all of its electricity from natural gas, EVs are still producing much less carbon footprint into the atmosphere with the results showing that almost identical alternatives produce triple the amount of GHG emissions. The results of the survey showed that, despite promising results shown in switching to carbon-neutral mobility solutions, a lack of willingness prevails within the State of Qatar to incline towards electric mobility among users. This implies that Qatar has to spend a lot of time and resources to achieve its ambitious goal to decarbonize mobility on roads with 10% electric vehicles by 2030. This research highlights the need for more practical incentives and generous subsidies by the government of Qatar on e-mobility solutions to switch the transportation system into an eco-friendly one.

Idealized analysis of relative values of bidirectional versus unidirectional electric vehicle charging in deeply decarbonized electricity systems

“…In this regard, we assume the electricity mix remains as it is today and that EVs replace the entire vehicle stock. We consider Qatar, a proxy for countries with 100% dispatchable generation assets (Qatar has 100% natural gas electricity generation assets ( Al-Buenain et al., 2021 )). Using 196 Wh/km as the average energy consumption of EVs ( Electric Vehicle Database, 2021 ); 16,229 km as Qatar’s approximate average annual vehicle kilometers traveled ( Al-Thani et al., 2020 ), and 1,655,700 units as Qatar’s vehicle stock ( CEIC, 2021 ); we estimate the annual EV load to be 5.3 TWh.…”

Possible Future Risks of Pollution Consequent to the Expansion of Oil and Gas Operations in Qatar

“…A low contribution of exhaust PM10 emissions occurs because most modern vehicles are equipped with efficient systems to reduce it in the exhaust. Therefore, the State of Qatar has pledged to achieve a promising objective of using 10% electric vehicles by 2030 (Al-Buenain et al, 2021) as a practical measure to reduce air pollution. These studies have placed much pressure on decision-makers to establish more encouraging, promising, and incentivizing measures to achieve the objective of increasing the number of electric vehicles as replacement for conventional gasoline vehicles.…”

A Contribution to Sustainable Product Development Using the Example of Battery Electric Vehicles

“…Many studies from academia, non-profit research organizations, state governments, and independent consulting firms conclude that BEVs can reduce the carbon footprint of today's individual mobility, although many of the studies arrive at different potential savings due to regional differences, see [3][4][5][6][7][8]. In 2018, the transport sector was responsible for 29% of the total greenhouse gas emissions in the EU [9].…”

“…For the simulations in this study, the WLTC simulation model has been verified with WLTC measurements of the VW e-up (detailed description of the measurements in [4]). With the help of the parameters used from Table 3, the required power demand at the wheel P R for the WLTC is determined, see Equation (3). Using the required wheel power, the efficiency map of the drivetrain, see Figure 8, can be computed via the use of an evolutionary optimizer so that the objective function from Equation ( 5) is minimized.…”