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.
Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The
New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects)
and the extent these parameters interact with each other (second-order effects). This method requires the specification of
two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various
inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and
Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex
processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness
of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition
System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the
sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss
the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring
the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing
these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method
estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown
that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different
metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock. 相似文献