Quantifying the Energy & carbon emissions implications of a 10% electric vehicles target

EU Directive 2009/28/EC on Renewable Energy requires each Member State to ensure 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020 (10% RES-T target). In addition to the anticipated growth in biofuels, this target is expected to be met by the increased electrification of transport coupled with a growing contribution from renewable energy to electricity generation. Energy use in transport accounted for nearly half of Ireland’s total final energy demand and about a third of energy-related carbon dioxide emissions in 2007. Energy use in transport has grown by 6.3% per annum on average in the period 1990 – 2007. This high share and fast growth relative to other countries highlights the challenges Ireland faces in meeting ambitious renewable energy targets. The Irish Government has set a specific target for Electric Vehicles (EV) as part of its strategy to deliver the 10% RES-T target. By 2020, 10% of all vehicles in its transport fleet are to be powered by electricity. This paper quantifies the impacts on energy and carbon dioxide emissions of this 10% EV target by 2020. In order to do this an ‘EV Car Stock’ model was developed to analyse the historical and future make-up of the passenger car portion of the fleet to 2025. Three scenarios for possible take-up in EVs were examined and the associated energy and emissions impacts are quantified. These impacts are then compared to Ireland’s 10% RES-T target and greenhouse gas (GHG) emissions reduction targets for 2020. Two key findings of the study are that the 10% EV target contributes 1.7% to the 10% RES-T target by 2020 and 1.4% to the 20% reduction in Non-ETS emissions by 2020 relative to 2005.

The molecular form of mercury in biota:identification of novel mercury peptide complexes in plants

The molecular structure of a variety of novel mercury-phytochelatin complexes was evidenced in rice plants exposed to inorganic mercury (Hg2+) using RP-HPLC with simultaneous detection via ICP-MS and ES-MS.

Investigation into mercury bound to biothiols:structural identification using ESI-ion-trap MS and introduction of a method for their HPLC separation with simultaneous detection by ICP-MS and ESI-MS

Mercury in plants or animal tissue is supposed to occur in the form of complexes formed with biologically relevant thiols (biothiols), rather than as free cation. We describe a technique for the separation and molecular identification of mercury and methylmercury complexes derived from their reactions with cysteine (Cys) and glutathione (GS): Hg(Cys)(2), Hg(GS)(2), MeHgCys, MeHgGS. Complexes were characterised by electrospray mass spectrometry (MS) equipped with an ion trap and the fragmentation pattern of MeHgCys was explained by using MP2 and B3LYP calculations, showing the importance of mercury-amine interactions in the gas phase. Chromatographic baseline separation was performed within 10 min with formic acid as the mobile phase on a reversed-phase column. Detection was done by online simultaneous coupling of ES-MS and inductively coupled plasma MS. When the mercury complexes were spiked in real samples (plant extracts), no perturbation of the separation and detection conditions was observed, suggesting that this method is capable of detecting mercury biothiol complexes in plants.

Food miles or carbon emissions? Exploring labeling preference for food transport footprint with a stated choice study

The ecological footprint of food transport can be communicated using carbon dioxide emissions (CO2 label) or by providing information about both the length of time and the mileage travelled (food miles label). We use stated choice data to estimate conventional unobserved taste heterogeneity models and extend them to a specification that also addresses attribute nonattendance. The implied posterior distributions of the marginal willingness to pay values are compared graphically and are used in validation regressions. We find strong bimodality of taste distribution as the emerging feature, with different groups of subjects having low and high valuations for these labels. The best fitting model shows that CO2 and food miles valuations are much correlated. CO2 valuations can be high even for those respondents expressing low valuations for food miles. However, the reverse is not true. Taken together, the results suggest that consumers tend to value the CO2 label at least as much and sometimes more than the food miles label.