För att nå internationella såväl som nationella miljömål står samhället inför en omfattande omställning av fordonsflottan. Utfasningsutredningen (2021) redogör i sin rapport för hur samtliga aktörer i samhället behöver vidta åtgärder för att bidra till måluppfyllnad. En central aktör som i dagsläget vidtar åtgärder är fordonstillverkare som med ett varierande tidsspann har förmedlat ambitionen om att enbart producera fordon med nollnettoutsläpp inom spannet 2030 – 2035. Fordonstillverkarnas omställning kommer att få konsekvenser för andra samhälleliga aktörer som är beroende av en tillförlitlig fordonsflotta i syfte att genomföra sitt uppdrag, en av dessa aktörer är polismyndigheten. Syftet med denna uppsats är att kartlägga vilka främjande samt hindrande faktorer som finns för implementering av en fossilfri fordonsflotta i dagsläget. Ett särskilt fokus kommer i uppsatsen att vara polisregion Nord som med sina geografiska förutsättningar upptar 55% av Sveriges yta, samtidigt som 26 av Sveriges 30 mest glesbefolkade kommuner återfinns inom regionen. För att kartlägga förutsättningarna ska uppsatsen utifrån implementeringsteoretiska utgångspunkterna förstå, vilja och kunna studera dokument och genomföra intervjuer med relevanta aktörer från nationell, regional samt polisområdesnivå. I dagsläget framgår det av kartläggningen att en fossilfri fordonsflotta inom polismyndigheten snarare är en utopi än en realistisk framtid. Ingen av de valda teoretiska utgångspunkterna förstå, vilja och kunna kan i dagsläget betraktas som helt uppfyllda. I de fall åtgärder inte vidtas kan myndighetens operativa förmåga komma att bli begränsad, vilket kommer särskilt hårt mot polisregion Nord och dess invånare. / To achieve international as well as national environmental targets, society is facing a major shift in the vehicle fleet. Utfasningsutredningen (2021) sets out how all actors in society need to take action to contribute to meet the targets. A key actor currently taking action is vehicle manufacturers. With varying timeframes, several manufacturers have communicated the ambition to produce only vehicles with zero net emissions within the range 2030 - 2035. The transition of vehicle manufacturers will have implications for other societal actors that depend on a reliable vehicle fleet to carry out their mission, one of these actors being the police authorities. The aim of this paper is to identify the facilitators and barriers to the implementation of a fossil-free vehicle fleet in the present time. A special focus will be the police region North which with its geographical conditions occupies 55% of Sweden's surface, where 26 of Sweden's 30 most sparsely populated municipalities are found in the region. To map the conditions, the paper will study documents and conduct interviews with relevant actors from national, regional and police area level based on implementation theory regarding the concepts understanding, will and being able to. At present, the thesis shows that a fossil-free vehicle fleet in the police authority is more of a utopia than a realistic future. None of the implementation theoretical starting points can be considered as fully met at present. If no action is taken, the operational capability of the authority may be limited, which will have a particular impact on the Northern police region and its inhabitants.

As usinas sucroalcooleiras utilizam grandes áreas para o cultivo da cana-de-açúcar, conhecidas como fundos agrícolas. Nesses fundos agrícolas, as atividades são realizadas por uma grande quantidade de máquinas agrícolas diferentes. As necessidades dessas máquinas, como combustível, óleo lubrificante e outras, são supridas pelos comboios de manutenção. Este trabalho visa propor um método para alocação e dimensionamento da frota de comboios de manutenção para o atendimento às máquinas. Para tanto foi utilizado um Sistema de Informações Geográficas aplicado a transportes (SIG-T), denominado comercialmente por TransCAD. Um estudo de caso foi realizado na Usina da· Barra S.A., localizada no município de Barra Bonita, centro oeste do Estado de São Paulo. Diversas simulações de atendimento, com configurações diferentes de distribuição das máquinas, foram realizadas. O atendimento às situações reais de distribuição das máquinas também foi analisado, com o objetivo de confrontar os dados reais de atendimento com os resultados obtidos através da aplicação da metodologia proposta. Foram obtidas as rotas e, conseqüentemente, o número de comboios necessários para o atendimento no período de safra e de entressafra. Com relação à aplicação do TransCAD na roteirização, pode-se constatar uma redução de até 19,6% na distância total percorrida pela frota de comboios no primeiro turno de trabalho em um dos dias analisados. / The sugar-cane industries use large areas for the cultivation of the sugar-cane. In these areas, different agricultural machines carry out several activities. The basic needs of these machines, such as fuel, lubrication and others, are supplied by a fleet of vehicles. The objective of this work is to propose a method to analyze the number of vehicles to compose the fleet already mentioned, based on the application of a Geographical lnformation System (GIS), commercially denominated by TransCAD. A case study was done in the Usina da Barra S.A. company, located at Barra Bonita city, State of São Paulo. Several situations of attendance were simulated with different distribution of machines, based in the initial annual planning of the company. We also analyzed the attendance of real distribution of machines with the objective of comparing the results obtained through the application of the proposed method and the real operation. The routes were obtained and, consequently, the necessary number of vehicles for the attendance. In relation to the application of TransCAD in the routing, a reduction of 19,6% was verified in the total distance traveled by the fleet of vehicles.

As usinas sucroalcooleiras utilizam grandes áreas para o cultivo da cana-de-açúcar, conhecidas como fundos agrícolas. Nesses fundos agrícolas, as atividades são realizadas por uma grande quantidade de máquinas agrícolas diferentes. As necessidades dessas máquinas, como combustível, óleo lubrificante e outras, são supridas pelos comboios de manutenção. Este trabalho visa propor um método para alocação e dimensionamento da frota de comboios de manutenção para o atendimento às máquinas. Para tanto foi utilizado um Sistema de Informações Geográficas aplicado a transportes (SIG-T), denominado comercialmente por TransCAD. Um estudo de caso foi realizado na Usina da· Barra S.A., localizada no município de Barra Bonita, centro oeste do Estado de São Paulo. Diversas simulações de atendimento, com configurações diferentes de distribuição das máquinas, foram realizadas. O atendimento às situações reais de distribuição das máquinas também foi analisado, com o objetivo de confrontar os dados reais de atendimento com os resultados obtidos através da aplicação da metodologia proposta. Foram obtidas as rotas e, conseqüentemente, o número de comboios necessários para o atendimento no período de safra e de entressafra. Com relação à aplicação do TransCAD na roteirização, pode-se constatar uma redução de até 19,6% na distância total percorrida pela frota de comboios no primeiro turno de trabalho em um dos dias analisados. / The sugar-cane industries use large areas for the cultivation of the sugar-cane. In these areas, different agricultural machines carry out several activities. The basic needs of these machines, such as fuel, lubrication and others, are supplied by a fleet of vehicles. The objective of this work is to propose a method to analyze the number of vehicles to compose the fleet already mentioned, based on the application of a Geographical lnformation System (GIS), commercially denominated by TransCAD. A case study was done in the Usina da Barra S.A. company, located at Barra Bonita city, State of São Paulo. Several situations of attendance were simulated with different distribution of machines, based in the initial annual planning of the company. We also analyzed the attendance of real distribution of machines with the objective of comparing the results obtained through the application of the proposed method and the real operation. The routes were obtained and, consequently, the necessary number of vehicles for the attendance. In relation to the application of TransCAD in the routing, a reduction of 19,6% was verified in the total distance traveled by the fleet of vehicles.

The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

Made available in DSpace on 2016-06-02T19:06:14Z (GMT). No. of bitstreams: 1 6183.pdf: 2878708 bytes, checksum: f9bc336337651cbba67af52d8acb7ec2 (MD5) Previous issue date: 2014-06-10 / Financiadora de Estudos e Projetos / Vehicle fleets are an important component in several applications, moving materials and people. Examples include material handling in warehouses, factories and port terminals, people transportation as in taxi fleets and emergency services, such as medical assistance, fire-fighters and police. Fleet operation is crucial for these applications: it can mean loss of money and commercial partners in case of industry, os loss of lives in case of emergency services. Controlling the fleet to achieve efficient levels of performance is a difficult problem, though, and becomes even harder as the fleet grows. Research in the area has been linking vehicle fleet operation to Multi-Agent Systems, because vehicle fleets are naturally distributed and Multi-agent System is a convenient abstraction to cope with distributed Artificial Intelligence problems. Therefore, it is proposed a Multi-Agent System to control vehicle fleets, focusing on material handling application in warehouses. The proposed system has three types of agents: Vehicle Agent, Loading Point Agent and Storage Point Agent. Agents interact amongst themselves through messages, trying to efficiently realize the material handling in a warehouse. System implementation is done through a simulation of a warehouse operation, built on top of MASON multi-agent system simulation platform. Task assignment strategies is also an important problem, therefore four strategies are shown and tested using the simulation: CNET, Fuzzy, DynCNET and FiTA. To enable comparison among these strategies, a Genetic Algorithm is employed to systematically search good parameters for each strategy. The proposed system, as well as the simulation, are offered as a framework for development of other vehicle fleets controlling multi-agent systems and/or task assignment strategies. / Em várias aplicações, frotas de veículos são um componente importante, transportando materiais e pessoas. Exemplos incluem o manejo de materiais em depósitos, fabricas e terminais portuários, o transporte de pessoas como em frotas de taxis e serviços de emergência, como socorro medico, bombeiros e polícia. A operacao da frota e crucial para essas aplicações: pode significar perda de dinheiro e parceiros comerciais no caso dos exemplos na indústria, ou perda de vidas, no caso de serviços de emergência. Porem, controlar a frota de modo que ela opere eficientemente e um problema difícil, que se torna ainda mais custoso com o aumento da frota. Pesquisas na área tem ligado a operação de frotas de veículos a Sistema Multiagente, notando os fatos de que frotas de veículos são naturalmente distribuídas e que o conceito de Agentes (e, consequentemente, Sistemas Multiagentes) e uma abstração conveniente para lidar com problemas de Inteligencia Artificial de forma distribuída. Com base nisto, e proposto um Sistema Multiagente para controle de frotas de veículos, focando a aplicação dessa frota no manejo de materiais em um depósito. O sistema proposto possui três tipos agentes: Agente de Veículo, Agente de Ponto de Carga e Agente de Ponto de Armazenamento. Os agentes interagem entre si, trocando mensagens a fim de realizar o manejo dos materiais no deposito de forma eficiente. O sistema e implementado na forma de uma simulação de operação de um deposito, construída na plataforma de simulação de sistemas multiagentes MASON. Como a estrategia de associação de tarefas também e um problema importante, quatro estratégias são mostradas e testadas através da simulação: CNET, Fuzzy, DynCNET e FiTA. Para possibilitar comparações entre as estrategias, um Algoritmo Genetico foi utilizado para sistematicamente encontrar bons parâmetros para as quatro estrategias. O sistema proposto, bem como a simulação, são oferecidos como framework para construção de outros sistemas multiagentes para frotas de veículos e/ou estrategias de associação de tarefas.

Ocorrem anualmente aproximadamente 600.000 mortes de crianças com até cinco anos, no mundo. Pneumonia é a principal causa e mais de 50 por cento destas mortes são atribuídas à poluição do ar. Ela ainda é responsável pelo aumento do risco de infecções respiratórias, asma, condições neonatais adversas e anomalias congênitas. A poluição do ar também afeta o desenvolvimento cognitivo de crianças e induz o desenvolvimento de doenças crônicas na idade adulta. Entre 70 e 80 por cento da poluição do ar em nações em desenvolvimento são de origem veicular. Objetivando definir critérios baseados em medições com sensoriamento remoto para identificação de veículos automotores leves do ciclo Otto com elevadas emissões de monóxido de carbono, hidrocarbonetos ou óxido nítrico, foram utilizados os dados secundários gerados pela Remote Sensing do Brasil Ltda dos quais foram selecionados 179.142 veículos em uso da frota circulante da cidade de São Paulo com medições completas dos índices de emissão dos poluentes monóxido de carbono (CO), hidrocarbonetos (HC) e óxido nítrico (NO) e ainda velocidade e aceleração do veículo quando da medição e inclinação da pista no local escolhido para as medições. Foram ajustados modelos estatísticos da classe Generalised Additive Models for Location, Scale and Shape (GAMLSS) visando testar a influência do Tipo de Combustível, da Potência Específica do Veículo (VSP) e das Fases do Programa de Controle da Poluição do Ar por Veículos Automotores (Proconve) sobre as emissões de CO, HC e NO, medidos usando o Remote Sensing Device (RSD). As emissões foram então conceitualmente subdivididas em dois grupos: veículos com emissões normais e com emissões anormais, isso para os diversos poluentes em veículos das Fases L3, L4 e L5 que são as fases de interesse para o gerenciamento da qualidade do ar. Variáveis latentes foram definidas para indicarem as distribuições dos veículos em relação a esses grupos e Fases. O algoritmo Expectation-Maximization (EM) foi empregado para identificação dos parâmetros das distribuições. Para determinação dos valores associados aos veículos com elevadas emissões de determinado poluente e fase do Proconve, foi empregado o percentil 98 por cento da distribuição ajustada para os veículos dos grupos com emissões normais. Assim sendo, o Erro de Tipo I foi fixado em 2 por cento sendo que esse percentual foi estabelecido considerando o Erro de Tipo II, de apontar o veículo como tendo emissão normal quando na realidade trata-se de um high emitter. Através desta abordagem foram determinados os valores indicativos de veículos com elevadas emissões segundo o poluente e a Fase do Proconve. Os resultados apontaram decréscimo nas emissões de CO e de HC segundo as Fases do Proconve. Para o NO, o comportamento das emissões não acompanhou as reduções impostas pelas Fases do Proconve. Foi constatado que os veículos de 2005 a 2009, movidos exclusivamente a gasool, foram os que apresentaram as maiores emissões de NO. Diversos possíveis fatores causadores deste comportamento diferenciado do NO foram discutidos neste trabalho. Os dados de qualidade do ar detectaram aumento significativo nas concentrações ambientais de Óxidos de Nitrogênio (NOx) em 2007, quando foi monitorado este parâmetro no período de inverno, o que pode indicar a influência dos high emitters, mas necessita de estudos mais aprofundados para confirmação da causa deste comportamento. / Approximately 600,000 deaths occur worldwide annually for children up to five years of age. Pneumonia is the leading cause and more than 50 per cent of these deaths are attributed to air pollution. It is still responsible for increased risk of respiratory infections, asthma, adverse neonatal conditions and congenital anomalies. Air pollution also affects the cognitive development of children and induces future development of chronic diseases in adulthood. In order to define criteria based on remote sensing measurements to identify Otto cycle light duty vehicles (LDV) with high emissions of carbon monoxide, hydrocarbons or nitric oxide it was used secondary data produced by Remote Sensing do Brasil Ltda, from which 179,142 inuse vehicles were selected, that belongs to the city of São Paulos current fleet. All those vehicles had complete measurements of emission of carbon monoxide (CO), hydrocarbons (HC) and nitric oxide (NO), and also speed and acceleration of the vehicle during measurements, and slope of the track at the place chosen for the measurements. Statistical models of the Generalized Additive Models for Location, Scale and Shape (GAMLSS) class were adjusted to test the influence of fuel type, Vehicle Specific Power (VSP) and of the Brazilian Vehicle Emission Control Program [Proconve] phases on CO, HC and NO emissions, measured using Remote Sensing Device (RSD). The emissions were then conceptually subdivided into two groups: vehicles with normal and abnormal emission, for the various pollutants in vehicles of L3, L4 and L5 phases of Proconve, which were of interest for the air quality management. Latent variables were defined to indicate the distribution of vehicles in relation to those groups and phases. The algorithm Expectation Maximization (EM) was employed to identify all parameters of the distributions. We use the 98 per cent percentiles of the statistical distribution set, for vehicles of groups with normal emissions to determine the limit values for vehicles with high emissions of pollutants and Proconve Phase. Therefore, the Type I Error was set at 2 per cent and this percentage was established considering the Type II Error to point the vehicle as having normal emission when in fact it is a high emitter. Through this approach, the indicative values of vehicles with high emissions according to the pollutant and the Proconve Phase were determined. Results of emissions measured with the RSD technique indicated a decrease in CO and HC emissions according to the Proconve Phase. For the NO, the emissions behavior did not follow the reductions imposed by the Proconve Phases. It was found that newer vehicles year model from 2005 to 2009 exclusively gasohol-powered vehicles, were the ones that presented the highest NO emissions. Several possible causative factors of this differential behavior of NO were discussed in this study. A significant increase in the environmental concentrations of Nitrogen Oxides (NOx) was detected in 2007, when this parameter was monitored in the winter period. This may indicate the influence of the high emitter vehicles, but it requires a more in-depth cause-effect study for confirmation of this behavior.

Ocorrem anualmente aproximadamente 600.000 mortes de crianças com até cinco anos, no mundo. Pneumonia é a principal causa e mais de 50 por cento destas mortes são atribuídas à poluição do ar. Ela ainda é responsável pelo aumento do risco de infecções respiratórias, asma, condições neonatais adversas e anomalias congênitas. A poluição do ar também afeta o desenvolvimento cognitivo de crianças e induz o desenvolvimento de doenças crônicas na idade adulta. Entre 70 e 80 por cento da poluição do ar em nações em desenvolvimento são de origem veicular. Objetivando definir critérios baseados em medições com sensoriamento remoto para identificação de veículos automotores leves do ciclo Otto com elevadas emissões de monóxido de carbono, hidrocarbonetos ou óxido nítrico, foram utilizados os dados secundários gerados pela Remote Sensing do Brasil Ltda dos quais foram selecionados 179.142 veículos em uso da frota circulante da cidade de São Paulo com medições completas dos índices de emissão dos poluentes monóxido de carbono (CO), hidrocarbonetos (HC) e óxido nítrico (NO) e ainda velocidade e aceleração do veículo quando da medição e inclinação da pista no local escolhido para as medições. Foram ajustados modelos estatísticos da classe Generalised Additive Models for Location, Scale and Shape (GAMLSS) visando testar a influência do Tipo de Combustível, da Potência Específica do Veículo (VSP) e das Fases do Programa de Controle da Poluição do Ar por Veículos Automotores (Proconve) sobre as emissões de CO, HC e NO, medidos usando o Remote Sensing Device (RSD). As emissões foram então conceitualmente subdivididas em dois grupos: veículos com emissões normais e com emissões anormais, isso para os diversos poluentes em veículos das Fases L3, L4 e L5 que são as fases de interesse para o gerenciamento da qualidade do ar. Variáveis latentes foram definidas para indicarem as distribuições dos veículos em relação a esses grupos e Fases. O algoritmo Expectation-Maximization (EM) foi empregado para identificação dos parâmetros das distribuições. Para determinação dos valores associados aos veículos com elevadas emissões de determinado poluente e fase do Proconve, foi empregado o percentil 98 por cento da distribuição ajustada para os veículos dos grupos com emissões normais. Assim sendo, o Erro de Tipo I foi fixado em 2 por cento sendo que esse percentual foi estabelecido considerando o Erro de Tipo II, de apontar o veículo como tendo emissão normal quando na realidade trata-se de um high emitter. Através desta abordagem foram determinados os valores indicativos de veículos com elevadas emissões segundo o poluente e a Fase do Proconve. Os resultados apontaram decréscimo nas emissões de CO e de HC segundo as Fases do Proconve. Para o NO, o comportamento das emissões não acompanhou as reduções impostas pelas Fases do Proconve. Foi constatado que os veículos de 2005 a 2009, movidos exclusivamente a gasool, foram os que apresentaram as maiores emissões de NO. Diversos possíveis fatores causadores deste comportamento diferenciado do NO foram discutidos neste trabalho. Os dados de qualidade do ar detectaram aumento significativo nas concentrações ambientais de Óxidos de Nitrogênio (NOx) em 2007, quando foi monitorado este parâmetro no período de inverno, o que pode indicar a influência dos high emitters, mas necessita de estudos mais aprofundados para confirmação da causa deste comportamento. / Approximately 600,000 deaths occur worldwide annually for children up to five years of age. Pneumonia is the leading cause and more than 50 per cent of these deaths are attributed to air pollution. It is still responsible for increased risk of respiratory infections, asthma, adverse neonatal conditions and congenital anomalies. Air pollution also affects the cognitive development of children and induces future development of chronic diseases in adulthood. In order to define criteria based on remote sensing measurements to identify Otto cycle light duty vehicles (LDV) with high emissions of carbon monoxide, hydrocarbons or nitric oxide it was used secondary data produced by Remote Sensing do Brasil Ltda, from which 179,142 inuse vehicles were selected, that belongs to the city of São Paulos current fleet. All those vehicles had complete measurements of emission of carbon monoxide (CO), hydrocarbons (HC) and nitric oxide (NO), and also speed and acceleration of the vehicle during measurements, and slope of the track at the place chosen for the measurements. Statistical models of the Generalized Additive Models for Location, Scale and Shape (GAMLSS) class were adjusted to test the influence of fuel type, Vehicle Specific Power (VSP) and of the Brazilian Vehicle Emission Control Program [Proconve] phases on CO, HC and NO emissions, measured using Remote Sensing Device (RSD). The emissions were then conceptually subdivided into two groups: vehicles with normal and abnormal emission, for the various pollutants in vehicles of L3, L4 and L5 phases of Proconve, which were of interest for the air quality management. Latent variables were defined to indicate the distribution of vehicles in relation to those groups and phases. The algorithm Expectation Maximization (EM) was employed to identify all parameters of the distributions. We use the 98 per cent percentiles of the statistical distribution set, for vehicles of groups with normal emissions to determine the limit values for vehicles with high emissions of pollutants and Proconve Phase. Therefore, the Type I Error was set at 2 per cent and this percentage was established considering the Type II Error to point the vehicle as having normal emission when in fact it is a high emitter. Through this approach, the indicative values of vehicles with high emissions according to the pollutant and the Proconve Phase were determined. Results of emissions measured with the RSD technique indicated a decrease in CO and HC emissions according to the Proconve Phase. For the NO, the emissions behavior did not follow the reductions imposed by the Proconve Phases. It was found that newer vehicles year model from 2005 to 2009 exclusively gasohol-powered vehicles, were the ones that presented the highest NO emissions. Several possible causative factors of this differential behavior of NO were discussed in this study. A significant increase in the environmental concentrations of Nitrogen Oxides (NOx) was detected in 2007, when this parameter was monitored in the winter period. This may indicate the influence of the high emitter vehicles, but it requires a more in-depth cause-effect study for confirmation of this behavior.

Energy efficiency within the transport sector - A case study on the vehicle fleet of a companyIsak EklövThe environmental objective of zero net emissions of greenhouse gases by 2045 asdecided by the Swedish parliament establishes a framework for a standard thatimplies a demand for considerable changes within many sectors at both technical and political level. The need for long term efficiency solutions with respect tosustainability to be able to reach this goal is great and one step towards this couldpotentially be an adaption to an increased amount of vehicles with alternative fuelsin the vehicle fleet of Sweden. This thesis examined the potential for companiesto reduce their life-cycle emissions of greenhouse gases as well as the total cost ofownership (TCO) for their vehicles by changing the composition of their vehiclefleet.The project started with a literature review of a general character where data forlife-cycle emissions of greenhouse gases as well as TCO for different vehicle typeswas examined and collected. Then the life-cycle emissions of greenhouse gases andTCO were calculated for the different vehicle types through a case study on thevehicle fleet of a company. Finally a programming script was developed to increasethe efficiency of the process which was then used to create scenarios with differentcompositions of the vehicle fleet. A sensitivity analysis was also carried out to evaluate the robustness of the life cycle calculations where the parameters individuallywere altered and the effect on the final result was examined.The result of the case study showed that alternative fueled vehicles are expected tolead to lower life-cycle emissions of greenhouse gases compared to the conventionalalternatives for all vehicle types where alternative fuels are commercially available.The only exception for this was the electric fringe benefit vehicle with a 100 kWhbattery which was expected to lead to higher life-cycle emissions than its fossilalternatives. The result of the cost analysis showed a similar pattern but in thiscase the service vehicle fueled with gas was expected to lead to a higher value ofTCO than its fossil alternatives. The sensitivity analysis for life-cycle emissionsof greenhouse gases showed that production of lithium-ion batteries, vehicle base production and tailpipe emissions were the most contributing parameters forfringe benefit vehicles. The purchase cost was found to be the most contributingparameter for TCO.The result of the scenario analysis showed that there is a potential to decreaseiiilife-cycle emissions of greenhouse gases by 22 % of the total life-cycle emissionsfor the vehicle fleet according to the Base-case scenario. The potential to decreaseTCO was found to be 1,1 %. The other scenarios showed a potential decrease forlife-cycle emissions of 37 % and a cost decrease of 7 % individually.Key words: greenhouse gas emissions, alternative fuels, electric vehicles, totalcost of ownership, life cycle assessment, sustainable vehicle fleet