Sustainability assessment of electric mobility

The transport sector is a main contributor to GHG emissions: in 2015 transport CO2 represented 50% of all man-made carbon emissions. Every person on this planet produces on average 1.5 tons of CO2 just from being on the move. In OECD countries every citizen is responsible for 3 tons of CO2 as a result of his mobility. In non-OECD it is 0.5 tons. Reasons behind this difference are lower km travelled per person, and non-motorized means of transport. Non-OECD countries also consume fewer goods, which means less freight. However, figures are expected to change with the development of non OECD countries and the gap between poor and developed countries is expected to collapse. Demand for passenger transport will double between 2015 and 2050 and 80% of this growth will come from developing economies. Cities will be particularly hit by the increase of transport demand. As urbanisation in Africa and Asia will continue and more and more people will move into even larger agglomeration, many cities will face congestion and pollution. Trade will also do its share: as global goods exchange intensifies, the volume of goods moved around the planet will triple between 2015 and 2050. Those goods will likely be carried by trucks, with a potentially huge impact on CO2 emissions. According to the International Transport Forum (ITF) projections, the global passenger demand will more than double between 2015 and 2050, from 50,000 to 120,000 billion passenger-kilometres. It is increasing in all regions and for all modes, but the growth is not uniformly distributed. Most of the growth will occur in Asia (International Transport Forum, 2017). Transportation is the backbone of capitalistic society, and transport of goods and people absolves to several functions. Our wellness depends on goods and people movements, and in particular on private transport (Steren et al. 2016). The demand for transport is directly related to the households’ income: the higher the income, the higher the vehicle kilometre travelled (VKT). And vice versa. There has been a close statistical correlation between the growth of Gross Domestic Product (GDP) and growth in transport, both passenger and freight (Banister & Stead, 2002). Growth in per-capita income levels has had a positive effect on the ownership and use of private vehicles, tending to increase reliance on private vehicles to meet mobility demand, particularly in emerging economies. If present transport policies do not undergo significant changes, CO2 emission from transport will increase around 2/3 between 2015 and 2050. Electric mobility has the ability to detach transportation and fuel consumption if policies in matter of transportation and electricity generation are taken, thus solving the main problem of the transportation sectors. Policy makers must take decisive action now to put transport on a sustainable path. The transition to a sustainable transport sector requires a framework able to quantify and define metrics to achieve this sustainability. In order to do so, a combination of Life Cycle Assessment (LCA) tool and Energy System Analysis (ESA) has been built to define a framework to assess transition to electric mobility. Life Cycle Assessment has proved to be the right tool to have a comprehensive picture of environmental problems. The system boundary of the electric mobility has been detailed in order to include all the aspects of the transport sector in the analysis: the vehicle production and use, the infrastructure and the energy carrier used to propel the vehicle (Figure 1.1). The target of the thesis is to develop a method for a comprehensive assessment of the mobility, which includes vehicles, infrastructure and electricity generation. It also wants to apply this method to extract useful insights in this topic and present some relevant interconnectedness between the three aforementioned axes. The outcomes of such an analysis are intended to provide relevant information at the policy level for a transition towards electric mobility and generation systems based on renewable sources. The thesis is structured following the three axes of mobility presented in Figure 1.1. In Chapter 2 the Life Cycle Assessment of three identical vehicles equipped with different powertrains is analysed. The analysis presented in this chapter stemmed from the limits of the comparisons between electric vehicles and traditional mobility found in the literature: differences in system boundaries, vehicle properties and functions, make the comparison unfair and not clear. Thanks to the collaboration with an Italian car manufacturer, it was possible to address the gap found in the literature studying the production system of a light commercial vehicle (LCV) with three different powertrains (CNG –Compressed Natural Gas, Electric engine, Diesel). The vehicles are produced in the same plant and the differences between them are due only to the different powertrain and related components. The peculiarity of this system allowed us to even out comparison inequalities that usually originate in LCA studies comparing traditional and electric vehicles. The results of this analysis show higher impacts in the production phase of the electric version of the LCV, with a relevant contribution from the battery. However, the analysis indicates that the lower impacts in the use phase compensate for the higher impact of the production. The analysis of the use phase was made in collaboration with Politecnico di Torino – Energy department: in a Matlab environment the vehicles were modelled in order to obtain fuel consumption and emissions per km, in various load conditions and driving cycles (NEDC, WHTC, WLTC). Figure 1.1 System boundary of the electric mobility In chapter 3 the hurdles of the infrastructure required for the shift to a widespread adoption of electric mobility are addressed. In particular, the effects of the introduction of a cutting-edge technology like the on-road dynamic charging are analysed. The on-road dynamic charging technology could be seen as a possible solution to push the widespread adoption of the electric vehicles. Due to the greater complexity of this system the analysis cannot be limited to the vehicle itself, but requires an evaluation of the entire system and the definition of scenarios in which this technology could be profitable. The first part of the work (done under the European Fabric project (FP7)) was the definition of scenarios for the implementation of this technology. The second part was the drafting of guidelines for the application of the LCA methodology to such a complex system. Based on the definition of scenarios set with the partners and on the data collected by the partners of the project, the impact related to construction and implementation of the infrastructure for the Dynamic charging adoption has been assessed. Since it is a pioneering technology and only data from pilot test is available, assumptions have been made to scale up to the infrastructure scale in the scenario definition. In chapter 4 the role played by electricity generation in Life cycle assessment of electric mobility is addressed, through an extensive literature search, and a meta-analysis of the data available is performed in order to get meaningful insights from pervious researches. The analysis shows that Life Cycle Assessments on electric mobility are providing a plethora of diverging results. 44 articles published from 2008 to 2018 have been investigated in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly regarding the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables in the assessments, the electricity mix’s carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results. The results of this analysis lead to a framework that is reported in Table 1.1. Hereafter the scale of the policy analysis and the electricity mix that has to be taken into account and the way to calculate it are linked. Table 1.1 Framework for the main application found in the literature Policy Level Goal Time Horizon Scale and Potential Effect from The Grid Electricity Mix Calculation Method Electricity Mix Transitioning to Electric Mobility Effects of adding significant number of EVs/displacing ICEVs with EVs Future Nation-wide Fleet based LCA Mutual effects between transport and energy sector to be investigated Simple long term marginal Energy system analysis Long term marginal Charging Strategies development Effects of load balance from EVs (considering smart charging, V2G, etc.) Both present and future local (handling bottleneck) and nationwide. Potential grid optimisation due to flexible load both bottom up approach (only for near present and small changes on the grid) and top down approach Short term marginal Incentives for Household Purchasing of Electric Vehicles Effects of introducing a small number of electric vehicles in the present market Present (EVs expected to self-sustain in the future) small changes on the grid =(total load)/(total electricity production) Average When it comes to evaluating the effect of a transition to electric mobility drawing the attention to the vehicle level can be misleading. In order to have a deep understanding of the phenomenon stopping at the vehicle level it is not enough to evaluate electric mobility. As mentioned in chapter 3, the dimension of the analysis should be at fleet level, when the aim is to inform the decision makers on the effect of shifting to electric mobility. In this case the entangled system transport-power generation has to be included in the analysis. In order to do so, explorative scenarios, evaluated with the use of ESA tools have been identified as the correct solution. In chapter 5, as a proof of concept, the Italian energy and transportation systems at 2030 have been analysed under different EV and Renewable Energy sources (RES) penetration scenarios, using the ESA tool EnergyPLAN. The results obtained showed environmental benefit of shifting to electric mobility - compared with the business as usual - that a classical LCA structure at vehicle scale would not have detected. In particular, the role played by the introduction of a massive amount of EVs in the power system allows to reduce GHG emissions not only because of the use of more efficient motors (compared to traditional engines) but also because of the synergies of electricity demand from EVs and intermittent sources. The main outcomes of this thesis are reported below, structured in following the three axes we have selected to describe the electric mobility sector: The vehicle side: In the literature, assessment of EV production suffered from data inequalities in comparative studies with ICEV. Old and oversimplified datasets for ICEV are compared with detailed data for EVs, thus resulting in a higher impact for EVs, equipped with more sophisticated and impacting components, while the overall trend shows an increased sophistication of the vehicles, regardless of the powertrain option. Notwithstanding that, EV production still represents an issue for electric mobility, having more than double the impacts compared to ICEV in resource depletion, photochemical oxidation, acidification, and eutrophication. Our study represents an unprecedented level of component detail in the vehicles and fairness in the comparison, thanks to a case study that allowed us to compare three identical vehicles differing only for the powertrain, and a detail composition. Expressing the impact of electric mobility in general as an average emission per km might be an over-generalization. Specific applications should be compared instead. In the case of this study, the situation of urban delivery has been tested. Results from chapter 2 show how emissions per km might vary depending on load and driving cycle. Thus, when comparing vehicles, a specific function has to be defined. According to our research, in the case of transport of goods in the urban environment the use phase is particularly advantageous for electric vehicles, as the energy savings compared to ICEVs are emphasized in urban driving cycles and as their loads increase. Part of these savings might get lost as the Just-in-Time (JIT) deliveries reduce the load. The results showed a predominance of the use phase in Climate Change led the research to the role of the electricity mix. Electricity side Despite the increasing role of the production, due to the sophistication of the vehicles, electricity mix remains the turning point of EV performance. The electricity mix can be accounted for in various way in an LCA study, depending on the approach, the calculation method, the granularity and the time horizon. Most of the time the selection of the electricity mix is not inserted in a precise framework or clearly justified as a consequence of the goal of the study. Especially when it comes to marginal mix, the choice can lead to completely different results. The meta-analysis of the literature review showed that the choice of the electricity mix explains the 70% of the variability in the results in the climate change impact category.

Il settore dei trasporti è il principale contributore alle emissioni di anidride carbonica: nel 2015 le emissioni di CO2 causate da trasporti hanno rappresentato il 50% delle emissioni antropiche totali. Le emissioni medie pro-capite dovute agli spostamenti sono di 1.5 tonnellate. Nei paesi dell’OCSE ogni Cittadino emette circa 3 tonnellate di CO2 per I suoi spostamenti, mentre nei paesi non-OCSE le emissioni medie si attestano sulle 0.5 tonnellate. Questo divario è dovuto a un kilometraggio più basso nei paesi non-OCSE e all’uso di mezzi di trasporto non motorizzati. Nei paesi non-OCSE inoltre il consumo di beni è minore, e questo significa minor trasporto merci. Questi valori però sono destinati a cambiare, con lo sviluppo dei paesi non-OCSE, e il divario verrà colmato. La richiesta di trasporto di persone raddoppierà dal 2015 al 2050 e l’80% di questo aumento sarà dato dai paesi in via di sviluppo. I centri urbani saranno particolarmente colpiti dall’aumento di domanda. Con il procedure dell’inurbamento, molte città in Asia e in Africa dovranno fronteggiare problemi di traffico e inquinamento. Anche gli scambi di beni avranno la loro parte in questo gioco: il volume di merci trasportate per il pianeta triplicherà tra il 2015 e il 2050. Il principale mezzo di trasporto sarà costituito da camion, con un potenziale notevole impatto sulle emissioni di CO2 (International Transport Forum, 2017). Secondo le previsioni dell’Interational Transport Forum (ITF), la domanda di trasporto passeggeri passerà da 50,000 a 120,000 miliardi di passeggeri per kilometro. L’aumento si verificherà in tutte le zone del pianeta, ma non sarà distribuito in maniera uniforme. Gran parte di questo aumento si verificherà in Asia (International Transport Forum, 2017). Gli spostamenti sono indispensabili nelle società capitalistiche, dove gli spostamenti di merci e persone assolvono molte funzioni. Il nostro benessere dipende da questi spostamenti: la richiesta di mobilità delle famiglie è direttamente correlata al reddito. E viceversa (Steren et al. 2016). Vi è sempre stata una stretta correlazione tra la crescita del Prodotto Interno Lordo (PIL) e l’aumento della mobilità, sia di merci sia di passeggeri (Banister & Stead, 2002). All’aumentare del reddito si accompagna spesso l’acquisto di un veicolo e l’aumento del suo uso e aumenta la tendenza ad utilizzare mezzi privati invece che pubblici, specialmente nelle economie emergenti. In assenza di un cambio significativo nelle politiche di gestione dei trasporti, l’aumento delle emissioni di CO2 che ci sia attende al 2050 sarà di 2/3 rispetto al valore del 2015. La mobilità elettrica può permetterci di disinnescare la correlazione trasporto-emissioni tramite l’uso di fonti rinnovabili, se verranno intraprese misure in materia di trasporti e generazione di potenza, permettendo così di risolvere il principale problema del settore dei trasporti. La transizione verso una mobilità sostenibile richiede di definire una struttura in grado di quantificare e definire metodi di misura della sostenibilità del sistema dei trasporti. Con questo obiettivo in mente è stata ideata una combinazione di due strumenti, l’Analisi del Ciclo di Vita (LCA nell’acronimo inglese) e un software per le simulazioni del sistema energetico (ESA nell’acronimo inglese), col fine di quantificare la sostenibilità della transizione elettrica. L’Analisi del Ciclo di Vita ha dimostrato di essere lo strumento giusto per valutare in maniera omnicomprensiva questioni ambientali. I confini del sistema da analizzare in materia di mobilità elettrica hanno incluso tutti gli aspetti rilevanti nel settore dei traporti: la produzione e l’uso del veicolo, l’infrastruttura e il vettore energetico utilizzato per far muovere il veicolo (Figure 1.1). La tesi è strutturata secondo i tre assi che compongono il settore della mobilità, come mostrato in Figure 1.1. Nel capitolo 1 viene presentata l’Analisi del Ciclo di Vita di tre veicoli identici ma equipaggiati con motori differenti. Questo studio è nato per dare una risposta ai limiti intrinseci degli studi comparativi tra veicoli tradizionali e veicoli elettrici: veicoli diversi, con funzioni differenti rendevano la comparazione iniqua. Grazie alla collaborazione con un’azienda automobilistica italiana è stato possibile far fronte a parte di questi limiti: l’azienda infatti produce un modello di van in tre diverse motorizzazioni : Diesel, a gas naturale compresso (CNG) ed elettrico. La produzione inoltre avviene nello stesso stabilimento per le tre tipologie. La peculiarità dell’oggetto di studio ha fatto sì che gran parte dei limiti presenti in letteratura potessero essere superati (per l’analisi dettaglaita della letteratura si faccia riferimento al Capitolo 2). I risultati dell’analisi hanno mostrato che il veicolo elettrico presenta impatti maggiori in fase di produzione per tutte le categorie d’impatto analizzate, con un contributo rilevante dato dalla produzione delle batterie. Parte di questi impatti maggiori in fase di produzione vengono compensati durante l’intero ciclo di vita del veicolo, grazie ai vantaggi che il veicolo elettrico mostra durante la fase d’uso. La valutazione della fase d’uso è stata condotta in collaborazione con il dipartimento di Energia del Politecnico di Torino, che ha sviluppato un modello in Matlab in grado di riprodurre consumi ed emissione al kilometro dei tre veicoli in varie condizioni di carico del mezzo e durante diversi cicli guida. Nel capitolo 2 è stata presentata la questione degli ostacoli dati dall’infrastruttura all’adozione di veicoli elettrici. in particolare sono stati valutati gli impatti ambientali di un metodo di ricarica innovativo: la ricarica dinamica su strade elettrificate. La ricarica dinamica può essere vista come una soluzione che spinga il passaggio alla mobilità elettrica. La questione della ricarica non può essere analizzata come elemento a sé stante, ma va analizzata nel quadro dell’intero settore della mobilità elettrica, con la definizione di scenari. Nel capitolo 3 il ruolo determinante del mix energetico è stato analizzato, tramite un’estensiva analisi di letteratura e una meta-analisi dei risultati presenti nel mondo scientifico. Il risultato mostra che la scelta del mix energetico negli studi di LCA spiega il 70% della variabilità dei risultati. Questo ha portato a definire l’importanza di una struttura che possa guidare LCA practictioner e decisori politici nello svolgimento dell’analisi e nell’interpretazione dei risultati. Un primo abbozzo di struttura è stato presentato in Table 1.1. Quando si tratta di valutare la transizione verso la mobilità elettrica, limitare l’analisi al livello del singolo veicolo può portare a risultati fuorvianti. Come menzionato nel capitolo 3, il livello d’indagine deve essere alla scala di flotta quando l’obiettivo è di fornire indicazioni per il decisore politico in materia di una transizione verso la mobilità elettrica. In questo caso gli effetti che il sistema dei trasporti può avere sul settore energetico nazionale vanno considerati. Nel capitolo 4, come caso di studio è stata analizzata la situazione italiana al 2030, dove il Sistema energetico è stato modellato con un software di simulazione energetica (energyPLAN) e diversi scenari di penetrazione dei veicoli elettrici sono stati testati. I risultati ottenuti mostrano un beneficio quando ci si muove verso la mobilità elettrica, che non sarebbe stato rilevato limitando l’analisi a livello di veicolo. Infatti oltre alla riduzione di emissioni dirette date dalla sostituzione di auto a combustione interna, I veicoli elettrici aumentano la penetrazione delle rinnovabili grazie alla loro flessibilità intrinseca.