T he transportation sector's carbon footprint and dependence on oil are of deep concern to policy makers in many countries. Use of all-electric drive trains is arguably the most realistic medium-term solution to address these concerns. However, motorist anxiety induced by an electric vehicle's limited range and high battery cost have constrained consumer adoption. A novel switching-station-based solution is touted as a promising remedy. Vehicles use standardized batteries that, when depleted, can be switched for fully charged batteries at switching stations, and motorists only pay for battery use. We build a model that highlights the key mechanisms driving adoption and use of electric vehicles in this new switching-station-based electric vehicle system and contrast it with conventional electric vehicles. Our model employs results from repairable item inventory theory to capture switching-station operation; we embed this model in a behavioral model of motorist use and adoption. Switching-station systems effectively transfer range risk from motorists to the station operator, who, through statistical economies of scale, can better manage it. We find that this transfer of risk can lead to higher electric vehicle adoption than in a conventional system, but it also encourages more driving than a conventional system does. We calibrate our models with motorist behavior data, electric vehicle technology data, operation costs, and emissions data to estimate the relative effectiveness of the two systems under the status quo and other plausible future scenarios. We find that the system that is more effective at reducing emissions is often less effective at reducing oil dependence, and the misalignment between the two objectives is most severe when the energy mix is coal heavy and has advanced battery technology. Increases in gasoline prices (by imposition of taxes, for instance) are much more effective in reducing carbon emissions, whereas battery-price-reducing policy interventions are more effective for reducing oil dependence. Taken together, our results help a policy maker identify the superior system for achieving the desired objectives. They also highlight that policy makers should not conflate the dual objectives of oil dependence and emissions reductions as the preferred system, and the policy interventions that further that system may be different for the two objectives.
Explicit formal mechanisms dominate the discussion about incentives in Operations Management, yet many other mechanisms exist. Social comparison between peers may provide strong implicit incentives for individuals. Social comparison arises naturally in all social settings and may thus be unintended; however, many companies deliberately use it to motivate employees. In this study, we model a social context in which purchasers evaluate their performance relative to their peers; a feeling of inferiority results in a negative contribution to utility, whereas a feeling of superiority results in a positive contribution. We find that social comparison induces characteristic deviations from the newsvendor optimum ordering decision: if fear of inferiority outweighs anticipation of superiority, then purchasers herd together; the converse scenario incites actors to polarize away from each other. In both cases, actors will deviate from ordering the newsvendor optimum in order to satisfy social goals. Demand correlation and profit margins moderate the extent of the deviation.
Motivated by the agricultural industries, this paper studies the economic and environmental implications of biomass commercialization; that is, converting organic waste into a saleable product from the perspective of a processor that uses a commodity input to produce both a commodity output and biomass. We characterize the economic value of biomass commercialization and examine how input and output spot price uncertainties affect this value. Using a model calibration, we find that lower input spot price variability or higher output spot price variability or correlation between the two spot prices increases this value for a typical palm oil mill. To measure the environmental impact, we use total expected carbon emissions resulting from profit-maximizing decisions and characterize the change in total expected emissions after commercialization. Our analysis reveals that, although higher biomass demand or biomass price always increases the value of biomass commercialization, these changes are not necessarily environmentally beneficial as they may increase the emissions associated with biomass commercialization. We also characterize conditions under which biomass commercialization is environmentally beneficial or harmful; that is, it leads to a reduction or an increase in the total expected emissions, respectively. In comparison with the existing understanding which does not take into account optimization of operational decisions, our analysis highlights two types of misconceptions (and characterizes the specific conditions under which they appear): (i) we would mistakenly think that biomass commercialization is environmentally beneficial when it is not, and (ii) we would mistakenly think that biomass commercialization is environmentally harmful when it is not. This paper was accepted by Victor Martínez-de-Albéniz, operations management.
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