The Market for Electric Vehicles in New Zealand: Using stated choice methods to evaluate the implications for electricity demand and carbon emissions to 2030

<p>Anthropogenic global climate change caused by the emissions of greenhouse gases (GHGs) from the combustion of fossil fuels is one of the greatest environmental threats faced by society. Electric vehicles (EVs), which use lithium-ion battery technology, have been proposed as a means of reducing GHG emissions produced by light passenger vehicles (LPVs). The ability of this vehicle technology to assist in reducing GHG emissions will depend on the market uptake and the effect that a growing EV fleet has on the GHG emissions produced by the electricity sector. This thesis is the first use of stated choice methods in New Zealand to develop a vehicle demand model that takes detailed account of car buyers’ preferences for EV purchase price, driving range, performance, fuel and battery costs, and charging network availability. A nationwide stated choice survey of New Zealand car buyers was undertaken in 2010 (n=281). The data from the survey was used to estimate a mixed multinomial logit discrete choice model, which was linked to a vehicle stock model of the New Zealand LPV fleet developed for this research. These two models were then used to simulate the New Zealand vehicle stock and energy demand, and the LPV fleet’s GHG emissions over a twenty year period. The Electricity Commission’s mixed integer programming ‘generation expansion model’ (GEM) was used to take account of the additional GHG emissions produced by the electricity sector in response to meeting the electricity demand estimates from the vehicle stock model. The results of this study indicate that, assuming the current state of EV technology and only modest reductions in EV prices over the modelling period, there would be sufficient demand for EVs to reduce, by 2030, the annual GHG emissions produced by the LPV fleet to approximately 80% of levels emitted in 2010. Changes in technology or vehicle design that reduce the cost of batteries and the purchase price of EVs would have the greatest impact in increasing the demand for these vehicles, and would further reduce the GHG emissions produced by the LPV fleet. The electricity sector modelling indicates that less than 730 MW of additional generation capacity will be required to be built if network operators can prevent EVs from charging during periods of peak demand, but without this capability, up to 4,400 MW of additional generation capacity could be required. The modelling also indicates that a policy environment where the use of coal-fuelled electricity generation is permitted and the price of carbon limited to $25 per tonne, the increased electricity sector GHG emissions that would result offset 88% of the cumulative GHG emission reductions achieved by the introduction of EVs into the LPV fleet. A policy raising the price of carbon to $100 per tonne would reduce the offsetting effect to 30%. EVs are an emerging technology with considerable potential for further development. The results of this study indicate that even at current prices and levels of technological performance, EVs have the capacity to make a significant contribution to New Zealand’s efforts to reduce GHG emissions. However, the ability to realise this potential is dependent on vehicle manufacturers’ willingness to produce EVs in sufficient quantities and models so that they can fully compete in the market with internal combustion engine vehicles; and on policies that discourage the future use of coal-fuelled electricity generation.</p>

The Market for Electric Vehicles in New Zealand: Using stated choice methods to evaluate the implications for electricity demand and carbon emissions to 2030

<p>Anthropogenic global climate change caused by the emissions of greenhouse gases (GHGs) from the combustion of fossil fuels is one of the greatest environmental threats faced by society. Electric vehicles (EVs), which use lithium-ion battery technology, have been proposed as a means of reducing GHG emissions produced by light passenger vehicles (LPVs). The ability of this vehicle technology to assist in reducing GHG emissions will depend on the market uptake and the effect that a growing EV fleet has on the GHG emissions produced by the electricity sector. This thesis is the first use of stated choice methods in New Zealand to develop a vehicle demand model that takes detailed account of car buyers’ preferences for EV purchase price, driving range, performance, fuel and battery costs, and charging network availability. A nationwide stated choice survey of New Zealand car buyers was undertaken in 2010 (n=281). The data from the survey was used to estimate a mixed multinomial logit discrete choice model, which was linked to a vehicle stock model of the New Zealand LPV fleet developed for this research. These two models were then used to simulate the New Zealand vehicle stock and energy demand, and the LPV fleet’s GHG emissions over a twenty year period. The Electricity Commission’s mixed integer programming ‘generation expansion model’ (GEM) was used to take account of the additional GHG emissions produced by the electricity sector in response to meeting the electricity demand estimates from the vehicle stock model. The results of this study indicate that, assuming the current state of EV technology and only modest reductions in EV prices over the modelling period, there would be sufficient demand for EVs to reduce, by 2030, the annual GHG emissions produced by the LPV fleet to approximately 80% of levels emitted in 2010. Changes in technology or vehicle design that reduce the cost of batteries and the purchase price of EVs would have the greatest impact in increasing the demand for these vehicles, and would further reduce the GHG emissions produced by the LPV fleet. The electricity sector modelling indicates that less than 730 MW of additional generation capacity will be required to be built if network operators can prevent EVs from charging during periods of peak demand, but without this capability, up to 4,400 MW of additional generation capacity could be required. The modelling also indicates that a policy environment where the use of coal-fuelled electricity generation is permitted and the price of carbon limited to $25 per tonne, the increased electricity sector GHG emissions that would result offset 88% of the cumulative GHG emission reductions achieved by the introduction of EVs into the LPV fleet. A policy raising the price of carbon to $100 per tonne would reduce the offsetting effect to 30%. EVs are an emerging technology with considerable potential for further development. The results of this study indicate that even at current prices and levels of technological performance, EVs have the capacity to make a significant contribution to New Zealand’s efforts to reduce GHG emissions. However, the ability to realise this potential is dependent on vehicle manufacturers’ willingness to produce EVs in sufficient quantities and models so that they can fully compete in the market with internal combustion engine vehicles; and on policies that discourage the future use of coal-fuelled electricity generation.</p>

Lookback Option Pricing Problems of Uncertain Mean-Reverting Stock Model

A lookback option is a path-dependent option, offering a payoff that depends on the maximum or minimum value of the underlying asset price over the life of the option. This paper presents a new mean-reverting uncertain stock model with a floating interest rate to study the lookback option price, in which the processing of the interest rate is assumed to be the uncertain counterpart of the Cox–Ingersoll–Ross (CIR) model. The CIR model can reflect the fluctuations in the interest rate and ensure that such rate is positive. Subsequently, lookback option pricing formulas are derived through the α-path method and some mathematical properties of the uncertain option pricing formulas are discussed. In addition, several numerical examples are given to illustrate the effectiveness of the proposed model.

The Self-Constrained Hand-to-Mouth

Many studies have shown that consumption responds to the arrival of predictable income (excess sensitivity). This paper uses a buffer stock model of consumption to understand what causes excess sensitivity and to test which parametrization is consistent with empirical excess sensitivity estimates. Using high frequency granular data from a personal finance app, it finds that while liquidity constraints are a proximate cause, preferences are the ultimate cause of excess sensitivity. Furthermore, it finds that for feasible parameters, a quasi hyperbolic version of the model is more consistent with the level of excess sensitivity relative to a standard exponential model.

Building schematic of Vienna in the late 1920s

Urban archives provide rich information on historical data. To a large extent, these data are not available in machine-readable format and therefore not linkable with other datasets. The “Häuser-Kataster der Bundeshauptstadt Wien” is a building schematic for the city of Vienna for the end of the 1920s. While this schematic was used as a knowledge base for real estate and finance business about 100 years ago, it has been used in the 2000s to manually map the historic building periods by property. We use the analog version and produced a machine-readable version to assign the historic addresses, building periods and number of floors to a building stock model down the road. The dataset has been complemented with codes of cadastral communities from the late 2010s to enable geotagging of the historic building data. To avoid unnecessary duplication of efforts by others and to share the dataset with urban historians and the public, we provide the dataset under creative common license.