Assessing greenhouse gas emissions from electric vehicle operation in Australia using temporal vehicle charging and electricity emission characteristics

ABSTRACT

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required. A key issue in this regard is the extent to which GHG emissions are a function of where and when EV charging will be enabled (or disabled) by the provision of recharging infrastructure and implementation of charging management strategies by the electricity industry. This article presents an investigation of the GHG emissions arising from electricity and gasoline consumption by plug-in hybrid EVs under a range of standard EV-power system integration scenarios. An assessment framework is presented, and GHG emissions from EV use are assessed for the New South Wales (NSW) and South Australian (SA) pools of the Australian National Electricity Market (NEM) using retrospective electricity system generation data for 2011. Results highlight that there is a range of possible outcomes depending on the integration scenario and emissions accounting approach used. This range illustrates value of a temporally explicit assessment approach in capturing the temporal alignment of electricity sector emission intensity and EV charging. Results also show the importance of a clean electricity generation mix in order for EVs to provide a GHG abatement benefit beyond what would be achieved by a hybrid (but non-plug-in) vehicle. The extent to which overnight charging in NSW is observed to produce higher emissions relative to unmanaged charging also illustrates a possible trade-off between GHG emissions and benefits for electricity industry from EV charging at times of low demand.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.     

Compensating Wage Differentials with Unemployment: Evidence from China

We estimate the economic value of mortality risk in China using the compensating-wage-differential method. We find a positive and statistically significant correlation between wages and occupational fatality risk. The estimated effect is largest for unskilled workers. Unemployment reduces compensation for risk, which suggests that some of the assumptions under which compensating wage differentials can be interpreted as measures of workers’ preferences for risk and income are invalid when unemployment is high. Workers may be unwilling to quit high-risk jobs when alternative employment is difficult to obtain, violating the assumption of perfect mobility, or some workers (e.g., new migrants) may be poorly informed about between-job differences in risk, violating the assumption of perfect information. These factors suggest our estimates of the value per statistical life (VSL) in China, which range from approximately US$30,000 to US$100,000, may be biased downward. Alternative estimates adjust for heterogeneity of risk within industry by assuming that risk is concentrated among low-skill workers. These estimates, which are likely to be biased downward, range from US$7,000 to US$20,000.