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Both China’s national subsidy policies for plug-in electric vehicles (PEVs) purchasers and passenger cars corporate average fuel consumption and new vehicle credit regulation (dual-credit policy) favor long-range 300+ km battery electric vehicles (BEVs) and 80+ km plug-in hybrid electric vehicles (PHEVs). However, these electric vehicles tend to have lower energy efficiency and higher purchase and operation costs. Vehicle with larger batteries can also be less equitable because the subsidies are often provided to more expensive vehicles and wealthier owners. This study takes advantage of a novel dataset of daily driving data from 39,854 conventional gasoline vehicles in Beijing and 4999 PHEVs in Shanghai to determine the optimal range of BEVs and PHEVs within their respective cities. We simulate a model to explore ranges with which PEVs emit less GHGs than that of a baseline hybrid and conventional gasoline vehicle while ensuring that all daily travel demands are met. Our findings indicate that in both cities, the optimal ranges to balance cost and travel demand for BEVs are 350 km or less and for PHEVs are 60 km or less in Beijing and 80 km or less in Shanghai. We also find that to minimize carbon dioxide (CO2) emissions, the ranges are even lower 10 km in Beijing and 30 km in Shanghai. Our study suggests that instead of encouraging long-range PEVs, governments should subsidize PEV models with shorter ranges. Parallel efforts should also be made to both increase renewable energy over fossil fuels and expand charging facilities. Although individual mobility demand varies, the government could reduce occasional long-distance driving by subsidizing alternative transportation choices. Providing week-long driving trials to consumers before their purchases may help decrease the demand of very long range PEVs by alleviating the range anxiety through a learning process.
相似文献The combination of concentrated solar power–chemical looping air separation (CSP-CLAS) with an oxy-fuel combustion process for carbon dioxide (CO2) capture is a novel system to generate electricity from solar power and biomass while being able to store solar power efficiently. In this study, the computer program Advanced System for Process Engineering Plus (ASPEN Plus) was used to develop models to assess the process performance of such a process with manganese (Mn)-based oxygen carriers on alumina (Al2O3) support for a location in the region of Seville in Spain, using real solar beam irradiance and electricity demand data. It was shown that the utilisation of olive tree prunings (Olea europaea) as the fuel—an agricultural residue produced locally—results in negative CO2 emissions (a net removal of CO2 from the atmosphere). Furthermore, it was found that the process with an annual average electricity output of 18 MW would utilise 2.43% of Andalusia’s olive tree prunings, thereby capturing 260.5 k-tonnes of CO2, annually. Drawbacks of the system are its relatively high complexity, a significant energy penalty in the CLAS process associated with the steam requirements for the loop-seal fluidisation, and the gas storage requirements. Nevertheless, the utilisation of agricultural residues is highly promising, and given the large quantities produced globally (~?4 billion tonnes/year), it is suggested that other novel processes tailored to these fuels should be investigated, under consideration of a future price on CO2 emissions, integration potential with a likely electricity grid system, and based on the local conditions and real data.
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