Comprehensive impact assessment of road transport sector transition scenarios : application to Italy

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POLITECNICO DI MILANO

Scuola di Ingegneria Industriale e dell’Informazione

Dipartimento di Energia

Master of Science in Energy Engineering

Comprehensive Impact Assessment of

Road Transport Sector Transition

Scenarios: Application to Italy

Supervisor: Matteo Vincenzo Rocco

Co-Supervisor: Lorenzo Rinaldi

Master Thesis Candidate:

Di Leo Giulia ID. 898196

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Sommario

Nonostante i risultati raggiunti nelle conferenze internazionali, le emissioni di CO2 a livello globale continuano ad aumentare. Una profonda attenzione viene rivolta al settore dei trasporti, tra i più responsabili emettitori di gas serra e inquinanti e il maggior consumatore di petrolio, con particolare riferimento al trasporto automobilistico. Risulta quindi necessario attuare strategie di decarbonizzazione del settore, le quali includono non solo il miglioramento dell’efficienza di veicoli già in uso e lo spostamento verso modalità di trasporto più efficienti, ma anche l’introduzione e la diffusione di nuove tecnologie, quali le auto ibride, le auto elettriche (BEV), le auto ibride plug-in (PHEV) e le auto a idrogeno (FCEV).

Sono molteplici gli scenari di penetrazione di nuove tecnologie di veicoli disponibili in letteratura, e altrettanto numerosi sono gli studi che tentano di valutare l’impatto conseguente alla potenziale realizzazione di tali scenari. Tra le principali metodologie analizzate, spiccano gli approcci WTW (Well-to-Wheel) e LCA (Life-Cycle Assessment). Tuttavia, si evidenzia l’assenza di un approccio in grado di valutare un impatto multidimensionale, in particolare dal punto di vista energetico, ambientale, ed economico, senza perdere il dettaglio tecnologico che caratterizza i nuovi veicoli e le reazioni di filiera legate ai settori ad essi connessi. In questo lavoro viene proposta pertanto una metodologia alternativa, basata su un modello Multi-Layer, generato a partire dalle tavole Input-Output di tipo Supply and Use fornite da Eurostat in versione sia energetica che economica. Il modello è stato applicato per valutare gli impatti dell’introduzione delle nuove tecnologie nel parco auto italiano, secondo gli scenari proposti dall’International Energy Agency (IEA) per il 2060. Da questo studio è risultato che un cambiamento del trasporto auto, ridurrebbe sicuramente le emissioni ambientali durante la vita del veicolo, ma ciò avrebbe un impatto apprezzabile se coordinato con un cambiamento di mix nel settore di generazione dell’elettricità, a causa della forte penetrazione dei veicoli a motorizzazione elettrica. Inoltre, l’analisi di sensitività ha mostrato che l’introduzione della tecnologia a idrogeno aiuterebbe l’Italia non solo a ridurre il proprio impatto ambientale, ma anche, in caso di produzione da elettrolisi, a sfruttare l’alto potenziale italiano di fonti rinnovabili variabili (VRE), contribuendo dunque al loro sfruttamento ed alla stabilità italiana in tema di elettricità e diminuendo la dipendenza energetica dall’estero.

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Abstract

Despite the results achieved during the international conferences, global CO2 emissions continue to increase. The transport sector, which is one of the most responsible emitters of greenhouse gases and pollutants, and the largest oil consumer, with particular reference to motor vehicle transport, receives the greatest attention. It is therefore necessary to implement decarbonization strategies in this sector, which include not only improving the efficiency of vehicles already in use and shifting to more efficient transport modes, but also introducing and deploying new technologies, such as hybrid cars, electric vehicles (BEV), plug-in hybrid cars (PHEV) and fuel cell electric vehicles (FCEV).

There are several scenarios of penetration of new vehicle technologies available in the literature, and there are also numerous studies that attempt to assess the impact resulting from the potential realization of such scenarios. Among the main methodologies analyzed, stand out the WTW (Well-to-Wheel) and the LCA (Life-Cycle Assessment) approaches. However, it is clear that an approach capable of assessing a multidimensional impact is lacking, in particular from the energy, environmental, and economic points of view, without losing the technological detail that characterizes the new vehicles and the chain reactions related to the sectors connected to them.

In this work, therefore, an alternative methodology is proposed, based on a Multi-layer model, generated from the supply and use input-output tables provided by Eurostat in both energy and economic versions. The model was applied to assess the impact of the introduction of new technologies in the Italian car fleet, according to the scenarios proposed by the International Energy Agency (IEA) for 2060.

This study showed that a change in car transport would certainly reduce environmental emissions during the life of the vehicle, but this would have an appreciable impact if coordinated with a change in the electricity generation technology mix, due to the strong penetration of electric motor vehicles. In addition, the sensitivity analysis showed that the introduction of the hydrogen technology would help Italy not only to reduce its environmental impact, but also, in case of production by electrolysis, to exploit the high variable renewable sources (VRE) potential of the country, thus contributing to the Italian stability and reducing the energy dependence on foreign countries.

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Chapter 1 1 The Energy Transition in the Transport

Sector

In recent decades the economic growth and the increased human well-being around the globe have come at the cost of fast-growing natural resource use (including materials and energy) and carbon emissions, leading to converging pressures of declining resource security, rising and increasingly volatile natural resource prices, and climate change. It is now widely accepted by academics, policy-makers, industry leaders and civil society that economic growth and human well-being need to be decoupled from escalating resource use and negative environmental impacts in order to secure long-term sustainability for humankind [1].

In this landscape, The United Nations (UN) included some objectives in their Sustainable Goals Agenda, aimed at improving the current environmental situation, including both controlling the exploitation of natural resources and decreasing the pollution release, without stopping the economic growth or reducing the quality of life [2].

In this chapter an overview on the current situation concerning worldwide CO2 emissions

is provided, in order to understand the reasons that are leading society to think about shifting from a fossil fuel-based economy to a low carbon one, considering also the new environmental policies entered into force after the Paris Agreement [3].

Particular attention has been paid by the scientific and political world on the transport sector, which is one of the most carbon intensive sectors. An overall picture of the Italian situation is presented, followed by a literature review which shows the difficulties of current models to represent the regional economies and account for their carbon footprint. Finally, objectives and expected results are exposed.

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Chapter 1. The Energy Transition in the Transport Sector

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1.1 CO2 Emissions

Climate scientists have observed that carbon dioxide (CO2) concentrations in the atmosphere have been increasing significantly over the past century compared to the preindustrial level. While emissions from fossil fuels started before the industrial era, they only became the dominant source of anthropogenic emissions to the atmosphere around 1950 and their relative share has continued to increase until present [4].

Among the many human activities that produce greenhouse gasses, the use of energy

represents by far the largest source of emissions, and CO2 emissions from energy account

for the largest share of global anthropogenic GHG emissions [5].

As we can see from the figure below, since 1970 the global energy demand as measured by Total Primary Energy Supply (TPES) has been continuously increasing and in 2017 more than doubled, reaching 14 Gtoe. Despite its structure changed, with a deeper penetration of natural gas (from 16% to 22.2%), a 14% drop of the share of oil (from 46.3% to 32%) and an increase of nuclear from 0.5% to 4.9%, TPES still mainly relies on fossil fuels.

Figure 1. World1_{TPES from 1971 to 1917 by source (Mtoe). IEA}

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13 Even though there has been an important increase of low-carbon resources use, it is not

enough to contrast the rise of fossil fuels TPES in the CO2 emission accounting [6, 7].

Figure 2. Energy-related CO2 emissions, 1990-2019. IEA

In fact, global energy-related CO2 emissions have constantly increased and flattened in

2019 at around 33 gigatons (Gt), after a peak reached in 2018 (Figure 2). This was the

result of the combination of a sharp decline in CO2 emissions in advanced economies2

power sector, and the increase of energy-related emissions in the rest of the world. The trend shown by the former group of countries is due to the expanding role of renewable sources (mainly wind and solar PV), the fuel switching from coal to natural gas, and the higher nuclear power output [8].

On the other hand, developing countries have shown a stable and continuous growth of emissions since 2000, excluding the period 2013-2016. The main drivers of this pace can be found in the increases in economic output and population, together with the high reliance on fossil fuels.

2_{Here IEA refers to: Australia, Canada, Chile, European Union, Iceland, Israel, Japan,}

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Figure 3. Drivers of the annual changes in CO2 emissions in non-OECD countries. IEA

From 2010, progressive improvements of energy efficiency and energy mix carbon intensity, in particular in China, contributed to flatten the growth, which anyway remain considerable [5].

In 2019 global CO2 emissions from coal use drop by 1.2% from 2018 levels,

counterbalancing the rise in emissions due to oil and natural gas use. In these last two years, advanced economies showed a 3.2% drop in their emission, equal to 370 Mt, with the power sector responsible for 85% of the decrease. The main causes are the milder weather in many regions compared to the one of the previous years, the weaker global economic growth, that affected in particular the emerging economies, and the underway clean energy transition.

The global power sector emissions declined by 1.2% (170 Mt) with the biggest falls taking

place in the advanced economies where CO2 emissions are now at levels not seen since

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Figure 4. Change in energy related CO2 emissions by region, 2018-2019. IEA

1.2 Decarbonization Measures

Since climate change caused by a rise in carbon dioxide is irreversible for 1000 years, it is paramount that efforts are directed toward decreasing the concentration of these emissions in the atmosphere [9].

The first binding commitments to reduce greenhouse gas emissions were set in 1997 under the Kyoto Protocol’s first commitment period (from 2008 to 2012). Over this period, the developed countries which took part in the COP3 were asked to rein in the emissions by about 5% compared to the 1990 levels [10].

During the agreement of a second Kyoto Protocol commitment period, developed and developing countries submitted voluntary emission reduction pledges for 2020 under the Copenhagen Accord and Cancún Agreements, with the participating Parties producing over 80% of global GHG emissions. This represented an important improvement compared to the Kyoto Protocol, in terms of number of countries taking measures in to curb emissions, and paved the way for the Paris Agreement [5].

The Paris Agreement is the first international climate agreement to embrace both developed and developing countries in mitigation obligation. It was adopted in December 2015 and was founded on Nationally Determined Contributions (NDC), made by countries in order to outline their “highest possible ambition” to constrain climate change and contain GHG emissions. The long-term aims of Paris Agreement are ambitious:

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holding the global average temperature increase to “well below 2°C above pre-industrial levels […] and pursuing efforts to limit [it] to 1.5°C”. In order to reach these goals, it is important that countries aim to achieve the global peak of greenhouse gas emissions “as soon as possible” and to undertake a rapid reduction thereafter; only in this way they will be able to “achieve a balance between anthropogenic emissions by sources and removals by sinks of GHGs in the second half of this century”, or, in other words, to reach net-zero emissions by this time [3].

The breath of the world’s energy needs means that there are no simple or single solutions. Sharp emission cuts are achieved across the board thanks to multiple fuels and technologies providing efficient and cost-effective energy services for all.

Some of the energy sector measures focus on the short term, as carbon pricing, while others aim to achieve long-term goals, such as carbon capture and storage (CCS) and alternative vehicle fuels. The new policies aim to achieve the transition to a low-carbon economy, moving to a deeper economic and societal transformation, embracing all the sectors. In particular, the most involved sectors are the Energy and the Transportation ones.

The pursuit of all the economically viable opportunities for efficiency improvement can reduce global energy intensity by more than 3% each year. This includes efforts to promote the efficient design and the use and recycling of materials such as steel, aluminium, cement and plastics. This increased “material efficiency” could be enough in itself to halt the growth in emissions from these sectors. Innovative approaches also include the use of digital tools to shift electricity demand to cheaper and less emissions-intensive daily hours, reducing consumers electricity bills and helping with system balancing, while also helping to reduce emissions [11].

The “Report of the Intergovernmental Panel on Climate Change (IPCC) on the Impacts of Global Warming of 1.50°C above pre-Industrial Levels” (2018) [12], stated that we must limit global warming to 1.5°C by the end of this century to avoid irreversible and catastrophic impacts. This means that carbon dioxide emissions need to decrease by about 45% by 2030 and to reach net zero in 2050. Even if according to IPCC this goal is within reach, achieving it would require an urgent transformation in both social and economic fields.

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17 Against this backdrop, the initial national climate pledges (Nationally Determined Contributions, NDC) made under the Paris Agreement are inadequate. Pathways reflecting countries’ current climate plans imply global warming of about 3°C by 2100, with warming continuing afterwards.

The Climate Action Summit was convened in September 2019, in order to focus the global attention on the worsening of climate crisis and devise new strategies to accomplish the target of the Paris Agreement and the 2030 Agenda for Sustainable Development. A table summarising the main results of the Climate Action Summit is shown below [13].

Table 1-1 Major results of the Climate Action Summit

Plans for a carbon neutral world

The Summit reinforced on a global stage the critical need for countries to define and implement more ambitious national climate plans (NDCs) and long-term strategies consistent with the objective of net zero emissions by 2050.

Climate finance

The Summit confirmed finance as key for the transition to net zero emissions climate resilient economies. Public and private financial flows need to align with the objectives of the Paris Agreement and be accessible to actors on the ground, especially in developing countries.

Powering the future from coal to clean

The Summit squarely put the issue of ending the building of new coal-fired plants beyond 2020 at the centre of discussions on the necessary decarbonization of economies.

Unlocking potential of nature in climate actions

The Summit delivered new initiatives that demonstrated that nature-based solutions are a realistic and economically viable option for climate action, providing over 30% of mitigation potential and offering scalable solutions to increase resilience and adaptation.

Toward a reliance future

Climate risks cannot be ignored and must be integrated very early on in decision making systems, long-term planning and into investment decision making and business planning.

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Live, work and move green

The Summit delivered potentially far-reaching new measures that highlight the critical role of subnational actors and especially cities to secure our climate future and successfully implement national climate plans. The Summit also demonstrated that investing in sustainable cities yields enormous social and economic benefits for all.

Cutting GHG emissions now with cooling and energy efficient

The Summit delivered extensive new measures that recognize the need to increase energy efficiency and support climate friendly cooling solutions to ensure that populations can live, work and breathe while dramatically reducing greenhouse gas emissions.

The economy, moving from grey to green

The Summit delivered new partnerships and concrete measures that exemplify how transition toward zero net GHG emissions by 2050 is possible, even in the highest emitting industries.

1.3 The Transport Sector

Being responsible for nearly one quarter of direct CO2 emissions from fuel combustion

worldwide, the transport sector needs to play a main role in the decarbonization efforts that countries must make in order to achieve the Paris agreement goals. Road transport, that is cars, trucks, buses and two- and three-wheelers, represents almost the 75% of transport carbon emissions, but on the other hand, aviation and shipping emissions are continuously increasing, meaning that these hard to-abate-subsectors need more attention [14].

In 2017 the transport sector represented the 29% of the world total final consumption of energy. In fact, the oil consumption in this sector has been continuously increasing since 1970, in particular in the road transport modes, which count for almost the 50% of the oil consumption [11].

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Figure 5. Oil Total Final Consumption from 1971 to 2017 by sector (Mtoe). IEA3

Given the current trends, the transport energy use and the CO2 emissions are expected

to increase by more than 80% by 2050. It is evident that this sector must play an important role in the decarbonization of the human activity worldwide, according to the aims of the international agreements. Combined efforts across all the transport modes, accompanied by the decarbonization of the power sector, will play a crucial role in achieving the Paris Agreement goals [15].

Road transport is in a critical transition, because the already existing measures implemented to increase vehicles efficiency and decrease energy demand, must comply with the new decarbonization measures.

Road transport emissions have increased despite the progress in electrification: the global share of electric car sales rose to more than 2.5% in 2018 and fleets of electric buses and trucks are being deployed in more and more cities around the world, exceeding 5.1 millions of total electric vehicles up by 2 million in 2017 [16].

Therefore, the growth in emissions is due largely to:

• Car buyers continuing to purchase larger, heavier vehicles, not only in the United States but increasingly in Europe and Asia. In Europe, the preference for larger cars, together with plummeting shares of more efficient diesel cars, offsets and even outweighs the impact of higher shares of electric car sales and caused the

average new car CO2 emissions to rise in 2017 and 2018.

3_{“Other” includes agriculture, commercial and public services, non-specified other,}

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• Rising global GDP, coupled with the spread of online shopping, including the home delivery, which keeps on raise the road freight demand.

In order to meet the projected mobility and the freight demand, while overthrowing the

CO2 emission growth, the energy efficiency measure in the transport sector will need to

be deployed to maximum effect [15]. These measures can be multifarious, but we can summarize them in 3 key-actions:

• Avoid-approach: avoiding private transport, for example thanks to a better urban planning or a significant increase in smart-working;

• Shift-approach: shifting the travel demand to the most efficient modes, such as railways or public transport;

• Improving transport technologies by

o Increasing the efficiencies of already existing technologies

o Promoting the deployment of alternative vehicles, as full electric vehicles

(BEVs), plug-in hybrid electric vehicles (PHEVs) and fuel cell electric vehicles (FCEVs) [17].

In this landscape, the European Union has been working to ensure the mobility in the European area, while minimizing the environmental impact of transport. With this in mind, in 2011 the EU adopted the White paper, a roadmap of 40 concrete initiatives aiming at building a competitive transport system, increase the mobility and remove the major barriers in key areas and the fuel growth and employment. Among other targets, particular attention must be given to the dramatic reduction of Europe's dependence on imported oil and the cut carbon emissions in transport by 60% by 2050. In 2017, the “Europe on the Move” was launched, completing the European agenda for safe, clean and connected mobility [18].

In 2013, through the communication “Clean Power for Transport: a European alternative fuels strategy”, the European Commission identified electricity, hydrogen, biofuels, natural gas and liquefied petroleum gas as the principal alternative fuels, currently and in the long-term perspective, in order to replace oil.

As we can see in Figure 6, among all transport modes passenger cars are the most carbon-intensive ones.

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Figure 6. CO2 emissions in the EU transport sector, 2016. European Environment Agency (EEA)

This better explains the adoption of the Regulation (EU) 2019/631 [19] on 17 April 2019,

through which the European Parliament and Council introduced new CO2 emission

standards for new passenger cars and light commercial vehicles in the European Union.

This regulation set reduction targets of -15% and -37.5% for the tailpipe CO2 emissions

of newly-registered passenger cars for the years 2025 and 2030 respectively. In 2023, the European Commission will review the Regulation, reporting back to the European Parliament and Council on the progress made towards reaching the car CO2 targets. Amongst other things, this ‘mid-term review’ will take stock of the roll-out of charging and refuelling infrastructure for alternatively-powered vehicles, their market uptake, as

well as CO2 reductions from the car fleet [20].

1.4 The Italian Situation

In 2017 the energy Total Final Consumption (TFC) in Italy was 120 Mtoe (millions of tons of oil equivalent), 18.3% of which was from renewable sources, exceeding the target

imposed to Italy by the Directive 2009/28/CE [21] for 20204_.

4_{The Directive required Italy to achieve by 2020: a 13% GHG emission reduction in all}

sectors non-regulated by the ETS Directive, with respect to 2005 levels, a 17% renewable energy share in gross TFC and 10% of renewable energy in transport total final consumption.

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Regarding the electric power sector, in 2017 the 35% of total national production was from renewables, among which the most important role was played by hydroelectric (35% of the total renewable electricity production), followed by solar electricity (23%), bioenergy (19%), wind (17%) and geothermic (6%).

In the heat sector, slightly less than 20% of energy consumption derived from renewable sources; the most widespread renewable source was the solid biomass (7.9 Mtoe out of 11.2 Mtoe), used in particular by households in the form of firewood and pellets. Heat pumps assume importance (2.65 Mtoe), while biogas, bioliquids, geothermal and solar contributions are still quite limited.

For what concerns the transport sector, in 2017 almost 1.2 million of tons of biofuels entered into consumption, mostly in the form of biodiesel.

During the last years, the progressive incidence of renewables and the energy intensity reduction have contributed to the decrease of the Italian energy dependence on imports; the net import share in the national energy requirements is still very high (77,7%), but almost 5% lower than in 2010 [22].

Figure 7. Energy dependence of European countries in 2014. EUROSTAT

The development of renewable sources is functional not only to the reduction of GHG emissions, but also to the restraint of the energy dependence and, in the future, to the decrease of the electricity price gap with respect to the European average [23].

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23 According to Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA) in 2016 GHG emissions major emitters were the power sector and the transport sector with a share of more than 24% each.

Moreover in 2017 almost 30% of the TFC was represented by the transport sector, which is the largest consumer of oil among all sectors (71% of total oil consumption in 2016).

Figure 8. TFC by sector in Italy from 1990 to 2017. IEA

In fact, the transport sector strongly relies on oil (91% more or less), with only a renewable share of 6.4% in 2015. Increasing the share of renewables in this sector is one of the objectives of the Italian energy strategy [23].

In the transport sector, the road transport is particularly important, as already said above; in 2012 GHG emissions from this sub-sector represented the 92.6% of total

national transport emissions, the 25.8% of the CO2 release by energy sector and the

21.3% of the GHG national total [24].

The Italian car fleet is very particular. In fact, in 2016 5 out of the 10 European regions with the highest number of passenger cars per inhabitant were located in Italy; on the other hand, along with Denmark and Portugal, the country has one of the lowest average vehicle mass and engine power values. In addition, Italy has a high share of diesel cars (almost 43% in 2016) and one of the major shares of liquefied petroleum gas (LPG)-fuelled cars and natural gas-(LPG)-fuelled cars among Europe countries [18, 24].

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The Italian walk to sustainability after 2020 will follow the pathway recommended by the Energy Union Strategy [25] – that relies on five cornerstones: decarbonization (including renewables), energy efficiency, energy security, integrated energy market, research, innovation and competitivity- and the Climate and Energy Framework 2030 [26], approved by the European Commission in October 2014 and then revised.

The document “Elements for a Sustainable Mobility Roadmap” [27], elaborated in 2017 with contributions from the Ministry of the Environment, Land and Sea Protection, Ministry of Economic Development, Ministry of Infrastructures and Transport, research institutes, economic operators in this field and consumers associations, provides the current context of mobility in Italy and the environmental impacts, as well as a deepening of the opportunities offered by the technological evolution of the means of transport. According to this document, the construction of an industrial vehicle chain based on innovative technologies, is a key element in the development of a large infrastructure for alternative fuels. Moreover, the Roadmap highlights the role of support measures, among which considerable emphasis is placed on local policies for sustainable mobility. In fact, cities must face the problem of traffic congestion, pollutant emissions and road safety. It is therefore important to promote in cities the cultural change towards the use of bicycles, public transport and modal shift, the electric and shared mobility and vehicles powered by alternative fuels.

The National Strategic Framework for the Development of the Alternative Fuels Market in the Transport Field and the Construction of the Relative Infrastructures (Legislative Decree of 16/12/2016, n.257 [28]), promotes the use of alternative fuels, in particular electricity, natural gas and hydrogen. For what concerns electricity, the decree has laid down some measures for the realization of an adequate number of charging points. In particular, there is the obligation to prepare the conditions for the installation of electric charging infrastructures in the new buildings. Moreover, local authorities are required to equip their car fleet, buses and public utility means of transport, when it is time to renew it, with at least 25% of electric vehicles or LNG or CNG (compressed natural gas) fuelled. The standard includes the installation of refuelling points in ports for the LNG for inland and sea navigation. A path is also established for the perspective use of hydrogen in the transport field [22].

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1.5 Literature Review

The achievement of the long-term temperature goal of the Paris Agreement requires efforts by all the Parties involved. However, the NDCs submitted to the agreement are insufficient to meet the target. To assess the probability to attaining such stringent goals and to identify how they might be achieved, different scenarios have been created, giving particular attention to the changes needed in the passenger cars transportation. In fact, particular focus is put on the impacts that new technology vehicles have, opening the way for the creation of models and the publication of several studies for the evaluation of the new technologies’ effects.

The studies in this matter may be divided into two main groups: Life Cycle Assessment (LCA) and Well-to-Wheel (WTW) studies, according to the model they use and the objects of their analyses. In fact, LCA studies are generally focused on the manufacture of the fuel cell or the battery and the vehicle, representing in detail all the materials and the energy requirements of the processes. On the other hand, the WTW stages cover the life cycle steps from energy resource extraction to energy conversion in the vehicle (i.e. driving). In other words, shifting from WTW to LCA analyses, the origin of the environmental impact seems to move from the fuel usage to the manufacture of the vehicle and its powertrain.

Table 1-2 provides a short description of the most recent relevant studies analysed in this thesis, in chronological order.

Table 1-2 WTW and LCA studies on different technologies vehicles.

Authors Year Ref. Method Analysis’

object Time period Region

Penth 2001 [29] LCA FC stack

(methanol, hydrogen)

Contemporary

years Germany

Sorensen 2004 [30] LCA FC stack

(hydrogen) Contemporary years EU

Hussain et al. 2007 [31] LCA FCEV Contemporary

years USA

Garraín, et al. 2011 [32] LCA FC stack Contemporary

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Shen et al. 2012 [33] WTW ICEV, HEV,

PHEV, BEV, FCEV

2010-2020 China

Bauer et al. 2015 [34] LCA ICEV, HEV,

PHEV, BEV, FCEV

until 2030 EU

Li, Zhang, Li 2015 [35] WTW ICEV, BEV,

FCEV 2012-2050 China

Simons, Bauer 2015 [36] LCA PEMFC 2020 EU

Ramachandran

Stimming 2015 [37] WTW BEV, FCEV (hydrogen,

bio-ethanol)

Contemporary

years EU

Bicer, Dincer 2016 [38] LCA BEV, ICE

(fuelled with methanol and hydrogen)

Contemporary

years Not specified

Evangelisti et

al. 2016 [39] LCA ICEV, BEV, FCEV Contemporary years EU

Yazdanie et al. 2016 [40] WTW ICEV, BEV,

HEV, PHEV, FCEV

2012-2030 Switzerland

Miotti, et al. 2017 [41] LCA ICEV, BEV,

FCEV 2030 EU

Sharma,

Strezov 2017 [42] WTW ICEV, BEV, FCEV Contemporary years Australia

Lombardi et al. 2017 [43] LCA ICEV, PHEV,

BEV, FCEV Contemporary years EU

Ke, Zhang, et

al. 2017 [44] WTW ICEV, HEV, PHEV, BEV 2015-2030 Beijing

Rocco et al. 2018 [45] LCA FCEV 2050 Germany

Regarding Life Cycle Assessment studies, the work of Penth [29] is the most cited one on fuel cells and used as a reference for further studies, such as the ones of Sorensen [30] and Garraín [32]. Pehnt’s study analyzed automotive fuel cell stack manufacture but not the other associated components on the vehicle. Furthermore, the study was published almost 20 years ago and did not show any inventory data used for the LCA model, because of commercial data protection. Hussain, Dincer and Li [31] studied the environmental impact of a FCEV from cradle to grave, and showed that the total GHG emissions of the FCEV are 13 times lower compared to a traditional vehicle (ICEV). However, the inventory data used in the LCA model are presented at a very high level of aggregation

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27 study evaluated the environmental impacts of current and future proton exchange membrane fuel cell (PEMFC) systems for automotive applications, including manufacturing, use and end-of-life phases. The inventory presented was comprehensive and a sensitivity analysis was performed on some key parameters of the fuel cell system. In the same year, the study published by Bauer et al. [34] considered a complete set of vehicle technologies and fuel pathways and highlighted the importance of taking into account the differences among technologies’ stages of maturity. Moreover, the Authors emphasized the importance of the manufacturing stage of a vehicle life-cycle, criticizing WTW analyses that disregard it, but at the same time recognized the limitation of their work, which does not allow for conclusion concerning the potential effects of large-scale adoption of new technology vehicles due to the assumed functional unit. Both Evangelisti et al. [39] and Miotti et al. [41], in 2016 and 2017 respectively, applied LCA analysis for assessing the environmental impact of a PEMFC life cycle considering future technical development scenarios and comparing results to ICEV and BEV.

In 2016 Bicer and Dincer [38] evaluated the impact on human health and environment of both fuel and vehicle cycle for each of the options of hydrogen, electric and methanol driven vehicles. In the following year, Lombardi et al. [43] performed a detailed LCA analysis of different electric powertrains, focusing on the production and use phases. Finally, Rocco et al. [45] assessed the economic and environmental impact on the entire German economy related to the prospect diffusion of FCEVs in the country in 2050, via LCA methodology. The Authors, then, compared the results with the ones obtain from well-established WTW literature studies, reaching the conclusion that WTW analysis is not appropriate for the evaluation of the overall impact of new technologies vehicles on the entire economy.

For what concern Well-To-Wheel analysis, Shen et al. [33] conducted a WTW analysis on more than 140 combinations of alternative fuel, matching powertrain systems and choosing China 2020 as the target framework. In 2015 Ramachandran and Stimming [37] performed a WTW analysis to compare the use of alternative fuels (such as hydrogen, bio-ethanol and electricity) in combination with BEV and FCEV. Besides, Li et al. [35] conducted a detailed hypothetical analysis with comprehensive comparison of the WTW performances of different hydrogen and electricity pathways of FCEVs and BEVs in

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China. Yazdanie et al. [40] and Sharma and Strezov [42], instead, applied WTW analysis for the economic and environmental comparative assessment of conventional ICE-based vehicles and alternative vehicles, including BEV, PHEV, HEV and FCEV. Ultimately, Ke et al. [44] calculated the WTW energy consumption and emissions for diverse vehicle propulsions and fuel combinations for Beijing’s passenger cars, considering 2015 and 2030 as reference years for the analysis.

From the literature review emerges that a hybrid model between the two discussed approaches is missing. In fact, while WTW models provide an accurate but non-comprehensive picture of the effects of new technologies vehicles penetration, considering the energy aspect of the changes, LCA models based on Input-Output Analysis permit to have a good perspective on the entire framework, providing an economic insight but lacking accuracy of the results. A model comprehensive of both energy and economic levels, could help to fill the gap shown by the literature review, taking into account, together with the environmental effects, also the economy and energy impacts on the entire framework.

1.6 Objectives and Expected Results

In response to the always heavier pressures on the transport sector and in particular on the car transport sub-sector, due to its strong impact on the environment and on human health, many studies have been focusing on the effects that new technologies vehicles actually have on the economy and on the environment balance of a region.

These studies, however, analyse the problem concentrating their attention on the environmental impact in either an energy or an economic framework, through WTW or LCA models.

The ones having a Well-To-Wheel approach, do not consider the economic effects that the new technologies cause on the regional layout, while the ones using the Life Cycle Analysis, lose the fuel technology characterization typical of WTW studies. It seems that a hybrid model is needed in order to obtain a complete overview of the new vehicles impacts, trying not to lose the technology details nor the whole national framework sight.

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29 The main purpose of this thesis is to fill this gap by creating a novel hybrid model, which studies the impact of new technologies on the national framework not only from an environmental and economic point of view, but also considering the analysed region from an energy perspective.

For this reason, the study does not focus only on one technology, but analyse the new framework that would derive from the penetration of a combination of different new technologies vehicles in the car fleet.

In particular, the starting point were the scenarios proposed by the International Energy Agency (IEA) in its Energy Technology Perspective (2017) [46], which included in the new global car fleet, together with gasoline and diesel internal combustion engine vehicles (ICE) and methane- and LPG-fuelled vehicles, also hybrid electric vehicles (HEV), full electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV) and fuel cell electric vehicles (FCEV). The IEA presented these scenarios at a global level, considering in some cases the differences among the principal regions of the world.

On the other hand, the country studied in this thesis work is Italy, which is characterized by the highest number of premature deaths due to pollution in Europe [17]. Moreover, Italy has a particular car fleet compared to the global and the European averages. In fact, the country presents a high share of diesel ICE vehicles and one of the most important penetration of LPG- and CNG-fuelled vehicles, while the penetration of BEVs is very low and the ones of PHEVs and FCEVs are almost null.

The methodology used is a novel model, generated from the Supply and Use tables, consisting of two different layers for the energy and the monetary framework and including the satellite matrices concerning the environmental impact. In fact, the presence of two different layers is the real novelty of this work, which adds to the traditional economic Input-Output tables (IOT) framework used for LCA studies, a new perfectly superimposable layer where energy flows within the entire studied region may be accounted.

This peculiarity of the model permits to obtain results showing the impacts of the new vehicles penetration on the entire national economy and energy frameworks, taking into account also the environmental effects deriving from the new layout.

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30

The main objective of this thesis is to propose a model able to comprehensively evaluate the economic-energy-environmental impact of different energy transition scenarios. Therefore, the principal steps may be summarized in the following points:

- Create a multilayer Input-Output model, starting from the energy and economic Supply and Use tables, coupled with the environmental impacts tables for Italy in 2016; - Well characterize the electricity production sector for both the layers, taking into

account the most important technologies used in the case-study;

- Introduce the automotive fuel cell manufacturing industry, considering the necessary materials, energy and services flows and the pollution release of the process;

- Evaluate the potential effects of a change in the national car fleet composition, combined with a variation of the electricity production technologies mix;

The implementation of steps regarding the model preparation will be better explained in chapter 0.

The thesis is structured as follows: first, the steps and the theory behind the model created are explained. Subsequently, the case study and the considered scenarios are introduced together with the employed data and assumptions. Later the results are discussed, and a sensitivity analysis is performed. In the end, the limit of the model, further developments and the main conclusions are proposed.

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Chapter 2 2 Methodology

This chapter aims at describing the theoretical background of the model that was built for this study, contextualizing it in the framework of current Input-Output models.

Then, Supply and Use tables are introduced and described and the multi-layer model approach is briefly described. Finally, the model preparation is presented.

2.1 Input-Output Analysis

The so-called Input-Output analysis (IOA) is the analytical framework developed by Wassily Leontief in the late 1930s, that became one of the most widely applied methods in Macroeconomics. The fundamental purpose of the input-output framework is analysing the interdependencies that coexist among the several industries of an economy [47]. In the most basic form, the core of an input-output model is composed of a system of linear equations, each one describing the distribution of an industry’s product throughout the economy. A fairly exhaustive history of input-output analysis applications is provided by Rose and Miernyk [48].

The adaptability of IOA offers the opportunity to develop analysis at many geographical levels, from local up to international, looking at the world economy as a system composed of many interrelated parts. Input-output has been also extended to be part of an integrated framework of employment and social accounting metrics associated with industrial production and other economic activities, as well as to account for energy consumption and environmental pollution associated with interindustry activity.

The model may be used to describe a single production process, but it can also be easily extended in order to represent an entire productive system, composed by n industries,

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Chapter 2. Methodology

32

representing as many productive processes, interconnected to each other by endogenous transactions of both goods and services. In addition, in any system there are sales to purchasers who are more external or exogenous to the industrial sectors that constitute the

producers in the economy – for example, households, government, and foreign trade. The

demand of these external units, since it tends to be much more for goods to be used as such and not to be used as an input to an industrial production process, is generally referred to as final demand [49] .

Each productive process provides a certain share of its production to the other processes

(xi) as endogenous transactions and a determined quantity outside the boundaries, in order

to satisfy the final demand (fi). In input it clearly receives the shares of the other processes

output and exogenous resources (ri).

A graphical representation of a productive system is shown in Figure 9 [50].

Figure 9. Graphical representation of a productive system composed by n productive processes

We may write a simple equation accounting for the way in which sector i distributes its product through sales to other sectors and to final demand:

x" = z"%+ ⋯ + z")+ ⋯ + z"*+ f"= , z") *

)-%

+ f" (1)

The zij terms represent the interindustry flows by sector i (also known as intermediate flows) to all sectors j (including itself, when j = i). Equation (1) represents the distribution of sector i output. We can write n equations like the one above, as many as the number of

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33 productive processes [49]. In fact, the mathematical structure of an input–output system consists of a set of n linear equations with n unknowns and therefore it can be represented in a compact matrix form by properly defining the production vector 𝐱(n × 1), the endogenous transaction matrix 𝐙(n × n) and the final demand vector 𝐟(n × 1):

𝐱 = 𝐙𝐢 + 𝐟 (2)

where 𝐢(n × 1) represents the summation vector.

At this point the technical coefficients matrix 𝐀(n × n) can be introduced:

𝐀 = 𝐙 ∙ 𝐱9:% (3)

The element aij of the matrix A represents the direct amount of the i-th product necessary

to the process j for the production of one unit of its output.

Finally, we can derive the Leontief Production Method (LPM), through which we can

compute the quantities of output, x, required by the system to satisfy a specified vector of

final demand, f, knowing the technical coefficients of the system:

𝐱 = (𝐈 − 𝐀):%_𝐟 (4)

where 𝑰(𝑛 × 𝑛) is the identity matrix.

Equation (4) is known as Leontief output (or production) model and highlights the dependence of the outputs on the values of the final demand.

The matrix 𝐋 = (𝐈 − 𝐀):%_{is call the Leontief inverse matrix, whose elements represents the}

embodied (direct and indirect) amount of the i-th product required from the j-th process in order to deliver one unit of its product as final demand.

Each process of the system, in order to produce a commodity, must use, transform and dispose the inputs and the outputs of its production. These activities are connected with the consumption of m natural resources (such as raw material, working hours and so on) and with environmental burdens. An evaluation of the environmental impact caused by the production of goods and services is one of the most important potentials in IOA. As can be seen in Figure 9, the total production of each sector involves the extraction or release of natural resources or wastes. The Leontief impact model can be represented by means of the equations (6), (7), (8) below:

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Chapter 2. Methodology 34 𝐁 = 𝐑 ∙ 𝐱9:% ₍₅₎ 𝐞 = (𝐈 − 𝐀):%C_𝐁C_{= (𝐁}_D𝐋)C (6) 𝐄 = 𝐟F ∙ 𝐞 = (𝐁D𝐋)C_{∙ 𝒆} ₍₇₎ Where:

§ _{𝐑(m × n) is the exogenous transaction matrix and represents the quantity of exogenous}

resources directly extracted from the environment by the j-th sector and needed for its production activities;

§ 𝐁D(m × n) is the exogenous input coefficients matrix, whose element b_mj_{represents the}

amount of the m-th exogenous resource directly required for the production of one unit of j-th product;

§ _{𝐞(n × m) is the specific embodied exogenous resource matrix;}

§ _{𝐄(n × m) is the total embodied exogenous resource matrix, representing the allocation}

of exogenous resources among final demand products.

Note that such impact model evaluates the direct resources consumptions and the embodied resources requirements, considering both the resources directly consumed by each process j-th and j-the indirect resources, consumed by j-the oj-ther processes j-that contribute to j-j-th total

production. Each productive activity has direct consumptions 𝐑 as well as embodied

consumptions 𝐄 : they provide different information, but the sum of the exogenous resources directly absorbed by all productive activities equals the sum of the resources embodied in

their products (𝐑IJI= 𝐄IJI) and represents the impact of the overall productive system (also

called “footprint” in literature studies).

Assumption of the Leontief Input-Output Model

The literature [52, 53] shows that the Leontief model is valid under following assumptions: • Constant return to scale: the model assumes that the same relative mix of inputs will

be used by an industry to create its output, regardless to the quantity produced. This implies that:

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35

o Technical coefficients are assumed to be constant: the amount of input necessary to

produce one unit of a certain output is assumed to be constant. Hence, the amount of input purchased by a sector is exclusively based on the level of output desired; no consideration regarding the price effects, changes in technology or economies of scale is developed.

o Input-output analysis assumes linear production functions: the input-output process

assumes that if the output level of an industry changes, the input requirements will change in a proportional way.

These assumptions have been widely criticized due to the fixity they give the model; indeed, several studies tried to incorporate technical coefficients variations [53], [54].

• Each sector produces only one product, measured with one specific unit.

• There are not resource constrains: supply is assumed infinite and perfectly elastic. This assumption is needed in a perspective of a demand driven model, which means that the production activity is invoked to meet the final demand, disregarding the resources availability.

• Local resources are efficiently employed: there is no underemployment of resources. Moreover, there are other important limitations of the IOA, such as: the actuality of the data, which means there is a long-time lag between the collection of data and the availability of the input-output tables; and the degree of aggregation, required in order to represent a very large and complex system, at the expense of, for example, a good characterization of a certain technology.

All these gaps of the Leontief model have led researchers to investigate other modelling approach. In particular, Faye Duchin proposed the so-called World Trade Model (WTM), a linear programming IO model of the world economy based on comparative advantage subject to resource constraints [55]. Then, Duchin developed also the Rectangular Choice of Technology (RCOT) to include the possibility that some sectors may not produce certain products while other may use different technologies [56]–[58]. However, this modelling approaches contains an optimization process and so a logic that goes beyond a traditional Input-Output analysis.

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36

An important disadvantage of the IOTs is represented by the second in the assumptions list above. In fact, considering only one product supplied by each industry is a mere simplification of the economy and risks to aggregate too many products or disregard secondary products, which in some cases are really important. For instance, according to the input-output tables, the industry “Manufacture of coke and petroleum products” would produce one single commodity: “Coke and refined petroleum products”; clearly this is a too strong simplification of this industry, whose products are, to name a few, gasoline, transport

diesel, naphtha, kerosene, coke oven products, and so on.

The problem is even more evident for the industry “Crop and animal production, hunting

and related service activities” whose “Products of agriculture, hunting and related service services” include a wide variety of crop products, animal products and other activities.

Of course, this is not always a problem, but depends on the focus of the research. Surely, when an energy impact analysis has to be done, the symmetry of IOTs becomes a real issue due to the multiplicity of the products involved and the relative few industries producing them.

2.2 Supply and Use Tables

Leontief’s input–output model features a one-to-one correspondence between industries and products. The matrix of inter-industry flows is square and the resulting input–output table is homogeneous; it can be interpreted as commodity-by-commodity or industry-by-industry table. A first complication comes with the presence of secondary products (by-products, joint products or subsidiary products). During the 1950s and 1960s, rapid industrial diversification caused further problems since input–output tables were being constructed on a single product industrial basis [59]. To cope with these problems, the United Nations System of National Accounts (SNA) [60] created two new tables: the so-called use and make tables (recently renamed as supply and use in the SNA-93). Although this new framework solved many problems, new problems arose, such as the construction of a technical coefficients matrix on the basis of supply and use matrices [61] (see below).

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37 Using a “commodity–industry” format, we are able to account for the fact that an industry may produce more than one commodity (product). This was a major reason for the introduction of the commodity–industry accounting system – to explicitly account for “non-characteristic” production such as secondary products and by-products. [49]

The supply and use tables can be seen as the output mix of industries and the industries’ use of inputs, respectively. On the one hand, the supply table comprises an intermediate matrix of goods and services (rows) produced by industries (columns), plus additional column vectors including imports, distribution margins (trade and transport) and net taxes on products, all of which make the total supply of products of an economy. On the other hand, the use table represents domestically produced and imported intermediate and final uses. They may be valued at basic and at purchasers’ prices. There are several additional column vectors that show the usual final demand categories, i.e. final consumption, investment and exports; and additional rows, which eventually represent the different components of the gross value added, e.g. labour costs, capital use, other net taxes on production and net operating surplus [62]. The tables show links between components of GVA (Gross value added), industry inputs and outputs, and product supply and use [63]. The figures below [64] well represent the two tables.

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38

Figure 11. Simplified overview of a use table

Between supply and use tables, two types of identities hold. In the supply table and the use table total output by industries is equal to total input by industries and total supply by products is equal to total uses by products.

The supply and use framework enables detailed analysis of industries and products through a breakdown of the production account, the goods and services account and the generation of income account. These tables show the structure of the costs of production and income generated in the production process, the flow of goods and services produced within the national economy, and the flows of goods and services with the rest of the world. [65] The underlying observation is that industries use commodities to make commodities. It is commodities that are the inputs to industrial processes and that are used to satisfy final demands.

Use can occur under any of several scenarios, for example, the products can be:

a) Used by other industries to produce different products (intermediate consumption); b) Consumed by households (household final consumption);

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39 c) Consumed by governments (government final consumption);

d) Sold to the rest of the world (exports);

e) Held as inventories for later use5_.[66]

The classification used for industries is the ‘General Industrial Classification of Economic Activities within the European Communities’ (NACE) [67] and the classification employed for products is the ‘Classification of Products by Activity’ (CPA) [68]. These classifications are fully aligned to each other. At each level of aggregation, the CPA shows the principal products of the industries according to the NACE1.

This accounting framework serves also as a basis for various interconnections with satellite accounts, such as Social Accounting Matrix (SAM), employment statistics, linkages with physical flows (land use, energy), linkages with other physical flows related to environmental issues (emissions, waste, sewage) and other forms of satellite systems for tourism, transport, health and education. [65]

Use Matrix

A use table shows the use of goods and services by product and by type of use. The table of intermediate use shows the intermediate consumption by products and by industry, the table of final uses shows the uses of products for final consumption, gross capital formation and exports, and the table of value added shows the components of value added by industry. Totals over the columns of intermediate and final uses show total use by products, totals over the rows of the intermediate table and the value-added table identify total inputs by industries. [65]

5_{When products are withdrawn from inventories in subsequent accounting periods, they are}

effectively resupplied to the economy at that time. By accounting convention, the net change in inventories during an accounting period (additions to inventories less withdrawals) is recorded as “use” of products.

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40

The Use matrix _{U = [uij] is the parallel to the IOT interindustry transactions matrix, Z,}

where uij is the value of purchases of commodity i by industry j. In conjunction with total

industry output, _{g, the parallel to ordinary technical coefficients, aij, would appear to be}

b")= u")N g₎

or

𝐁 = 𝐔 ∙ 𝐠9:% ₍₈₎

in which column j represents the value of inputs of each commodity per dollars’ worth of

industry j’s output. The dimensions of B are therefore commodities-by-industries.

Supply Matrix

In the supply table, primary (main) activities of industries are reported on the diagonal of the production matrix while secondary activities of industries are reported off the diagonal. In order to distinguish between primary and secondary output of an industry, a relation between industries and products has to be defined based of the criteria of industrial origin. Each product is related to one industry which by definition is the primary producer of that product. Thus, each industry can be defined by the list of primary products that are attributed to that industry. The applied European classifications (NACE and CPA) are already structured upon that principle. Furthermore, the classifications show this relationship directly in their coding system.

The share of secondary outputs varies across industries. Some industries may only have primary outputs, while others will have a considerable amount of secondary outputs. Secondary outputs are usually smaller than primary outputs as units are classified according to their main activity, but the size of secondary outputs also depends on the level of aggregation. A greater disaggregating level of the supply table shows a higher degree of secondary output, and vice versa.

The transposed of the supply matrix is called the make matrix and is usually denoted V.

In the commodity-industry framework, both total industry output (g) and total commodity

output (q) are accounted for. They can be found starting from the make matrix, applying

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41

𝑔_R= 𝑣_R%+ ⋯ + 𝑣_RT or 𝐠 = 𝐕𝐢 (9)

𝑄R= 𝑣%R+ ⋯ + 𝑣WR or 𝐪 = (𝐕′)𝐢 (10)

Alternatively, the commodity output vector may be expressed as:

𝐪 = 𝐔𝐢 + 𝐲 ₍₁₁₎

being y the final demand vector.

As we can notice, the commodity-industry approach uses (12) and (9) in the same way as (2) and (3) respectively. From (9) and (12) we can obtain:

𝐪 = 𝐁𝐠 + 𝐲 (12)

It is clear that, contrary to what seen for the IOA, (13) cannot derive a total requirements matrix, due to the fact that the equation contains commodity output to the left and industry output to the right. [49]

Technology and Total Requirement Matrices in SUT Approach

One solution to this problem is to find an expression transforming industry outputs, g, to

commodity outputs, q or, alternatively, to transform commodity outputs (and commodity

final demand, y) into industry terms.

Amongst other textbooks, the United Nations Handbook on Input-Output Table Compilation [69] distinguishes two basic technology assumptions for the construction of symmetric product-by-product input-output tables:

• the industry technology assumption, in which the production recipe is unique to an industry, while products’ input recipe is a weighted sum over industries’ production recipes;

• the commodity technology assumption, according to which the input recipe is unique to a product, while industries’ production recipes are a weighted sum over their primary and secondary outputs.

The industry technology assumption has been shown to be implausible; on the other side, even if the commodity technology assumption has proven to be theoretical superior, it can lead to negative elements during table construction and requires the supply matrix to be

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42

square, which would mean loss of detail in rectangular account. Therefore, in practice both the assumptions have drawbacks.[70]

On the other hand, for the construction of symmetric industry-by-industry IOTs, two new main assumption are needed, stating that when the product output is translated into the industry output, the pattern of sales remains the same. This is the so-called sale structure approach, which admits only two options: the fixed sales structure, where industry supply is not dependent on commodity delivery, and the fixed product sales structure, which means that industry supply does not depend on the producing industry. As for the product-by-product IOTs, also in this case the choice could be discussed at length, and even if Ten Raa and Rueda-Cantuche [59] demonstrated that the fixed industry sale approach is theoretically superior, both the approaches have gaps.

A solution to this problem was proposed by Lenzen and Rueda-Cantuche in 2012 [71], using the supply-use blocks simultaneously to generate multipliers both for industries and for products.

Lenzen and Rueda-Cantuche’s approach

In their work “A note on the use of supply-use tables in impact analyses” [71], the Authors demonstrated that the industry technology and the fixed product sales structure assumptions can be jointly formulated in a common framework, which allows carrying out impact analyses simultaneously in terms of products and industries; the same is valid for the commodity-technology assumption and the fixed industry sales structure one.

Calling T the supply-use transaction block, represented by:

In order to satisfy the national accounting identity, we can write:

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43

Equation (14) includes the product balance 𝐔𝐢 + 𝐲𝐜= 𝐪 (12) and the industry balance 𝐕𝐢 =

𝐠 (10).

Therefore, it can be transformed into:

And so:

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Where B is expressed in (9) and D is called the market share matrix and is calculated as

𝐃 = 𝐕𝐪]:%_.

With some other calculations we can obtain:

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Where LI,ii is the Leontief inverse of the industry-by-industry type of a technical coefficient

matrix constructed on the basis of the fixed product sales structure and LI,cc is the series

expansion of the Leontief inverse of a product-by-product type technical coefficient matrix constructed with the industry technology model.

𝐋_{𝐈,𝐢𝐢}= (𝐈 − 𝐃𝐁):% ₍₁₇₎

𝐈 + 𝐁𝐋_,""𝐃 = 𝐈 + 𝐁𝐃 + (𝐁𝐃)(𝐁𝐃) + ⋯ = (𝐈 − 𝐃𝐁):%= 𝐋_,`` (18)

Hence, we can conclude that when supply and use matrices are handled under an integrated supply-use framework, the compound Leontief inverse elegantly reproduces the product-by-product type model assuming the industry technology assumption and the industry-by-industry model assuming the fixed product sales structure assumption.

Comprehensive impact assessment of road transport sector transition scenarios : application to Italy

POLITECNICO DI MILANO

Scuola di Ingegneria Industriale e dell’Informazione

Dipartimento di Energia

Master of Science in Energy Engineering

Comprehensive Impact Assessment of

Road Transport Sector Transition

Scenarios: Application to Italy

Supervisor: Matteo Vincenzo Rocco

Co-Supervisor: Lorenzo Rinaldi

Master Thesis Candidate:

Di Leo Giulia ID. 898196

Table of Contents

Sommario

Abstract

Chapter 1

1 The Energy Transition in the Transport

Sector

1.1 CO2 Emissions

1.2 Decarbonization Measures

1.3 The Transport Sector

1.4 The Italian Situation

1.5 Literature Review

1.6 Objectives and Expected Results

Chapter 2

2 Methodology

2.1 Input-Output Analysis

Assumption of the Leontief Input-Output Model

2.2 Supply and Use Tables

Use Matrix

Supply Matrix

Technology and Total Requirement Matrices in SUT Approach

Lenzen and Rueda-Cantuche’s approach