Common use of LIST OF TABLES Clause in Contracts

LIST OF TABLES. Table 1 Summary of Literature in AVs 2 Table 2 Summary of Literature on Adoption 6 Table 3 Summary of Literature on WTP 8 Table 4 Summary of Literature on Mode Choice 9 Table 5 Summary of Literature on Benefit and Concerns 10 Table 6 Summary of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.

LIST OF TABLES. Table 1 Page Table 1-1. Continuous Parameters Monitored at the NAEMS Lagoon Sites 1-4 Table 1-2. Monitoring Sites Under the NAEMS 1-5 Table 2 1. Summary of NAEMS Open-Source Sites 2-11 Table 3 1. NAEMS Emissions and Process Parameter Data Received 3-3 Table 3 2. Reported Emission Rates for NAEMS Lagoon Sites 3-5 Table 3 3. Review of Swine Lagoon Articles Received in Response to EPA’s CFI 3-9 Table 3 4. Review of Dairy Lagoon Articles Received in Response to EPA’s CFI 3-12 Table 3 5. Review of Swine Lagoon Articles Obtained by Previous EPA Literature in AVs 2 Searches 3-15 Table 2 3 6. Review of Dairy Lagoon Articles Obtained by Previous EPA Literature Searches 3-19 Table 4 1. Reported Number of Valid Emissions Days by Site 4-5 Table 4 2. Summary of Literature on Adoption 6 Intermittent Data 4-7 Table 3 Summary 4 3. Dates of Literature on WTP Lagoon Coverage Observations 4-8 Table 4 4. Dates or Animal Inventory Records 4-10 Table 4 5. Negative Emissions Values by Xxxxxxxxx and Measurement Method 4-12 Table 4 6. Number of Valid Emissions Days Available in the Spring and Summer 4-13 Table 4 7. Number of Valid Emissions Days Available in the Fall and Winter 4-13 Table 4 8. NAEMS Data for Swine and Dairy Lagoon Confinement Operations 4-14 Table 4 9. Design and Operating Parameters of the NAEMS Swine and Dairy Lagoon Sites 4-15 Table 4 10. Site-Specific Ambient and Lagoon Conditions 4-16 Table 4 11. Average Daily Emissions for by Site 4-17 Table 5 1. Summary of Literature on Mode Choice Symbols and Terms Used in Equation 5 1 5-4 Table 5 2. Number of 30-Minute NH3 Emissions Values by Site 5-7 Table 5 3. Number of 30-Minute Data Values for Continuous Variables 5-8 Table 5 4. Number of 30-Minute Values Available for Intermittent Data by Site 5-9 Table Page Table 5 5. Farm and Lagoon Information by Site 5-10 Table 5 6. Selected Candidate Predictor Variables 5-13 Table 5 7. Meteorological Variable Bin Cut-Offs 5-21 Table 5 8. Summary Statistics for NH3 and Meteorological Variables 5-25 Table 5 9. Summary of Literature on Benefit and Concerns 10 Main Effect Mean Trend Variables 5-26 Table 6 5 10. Summary of Literature on Perception of Technology and Operations 12 Farm-Based Predictor Variables 5-44 Table 7 5 11. Summary of Literature on Travel Demand 14 Table 8 Classification of Studies Data Available by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle Month and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model Site 5-52 Table 17 5 12. Fit Statistics for Subsets of Mean Trend Variables 5-53 Table 5 13. Fit Statistics for Identity, Log and Reciprocal Link Functions 5-55 Table 5 14. Fit Statistics With and Without a Random Effect of Site 5-57 Table 5 15. Fit Statistics for Three Combinations of Two Farm-Based Variables 5-58 Table 5 16. Variables Values Used In Example Calculations 5-58 Table 5 17. Results of Factor Analysis the animal/sa EEM Examples 5-60 Table 5 18. Values of Mean Trend Variables for Mode Dependency 56 the animal/sa EEM Examples 5-61 Table 18 Linear SVM Model Performances 57 5 19. Results of the animal/size EEM Examples 5-63 Table 19 Linear SVM Model Coefficients 59 5 20. Values of Mean Trend Variables for animal/size EEM Examples 5-64 Table 20 Mode Choice Model 5 21. Results of the sa/size EEM Examples 5-66 Table 5 22. Values of Mean Trend Variables for Regular Trips (tthe sa/size EEM Examples 5-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.67

LIST OF TABLES. Table 1 Summary of Literature in AVs 2 2.1 Multi-resource objectives 3 Table 2 Summary of Literature on Adoption 3.1 Okanagan Shuswap LRMP issues 6 Table 3 Summary of Literature on WTP 8 Table 4 Summary of Literature on Mode Choice 4.1 Sensitivity analyses 9 Table 5 Summary of Literature on Benefit and Concerns 10 4.2 Incremental silviculture scenarios 9 Table 6 Summary of Literature on Perception of Technology and Operations 12 6.1 Data sources 13 Table 7 Summary of Literature on Travel Demand 14 7.1 Timber harvesting land base determination 15 Table 8 Classification of Studies by Detailed Approach 7.2 Road removals 16 Table 9 PCA result for AT1 7.3 Definition of operable areas 17 Table 7.4 Description of sites with low timber growing potential 17 Table 7.5 Unmerchantable type criteria 17 TABLE 7.6 TIMBER LICENCE REVERSIONS, AREA (preferences for lifestyle HA) 18 Table 7.7 NSR restocking rules 19 Table 7.8 Future road removals 19 Table 8.1 Wet/dry belt classification 21 Table 8.2 Sub-unit area summary 21 Table 8.3 Landscape units and mobility options) BEC zones 22 Table 10 PCA result 8.4 Resource emphasis areas 27 TABLE 8.5 ANALYSIS UNITS AS PER TSR 28 Table 8.6 Analysis units for AT2 incremental silviculture 29 Table 9.1 Utilization levels 32 Table 9.2 Volume exclusions for mixed species types 33 Table 9.3 Inputs into VDYP for determining natural (perceived benefits and concerns existing) stand yields 33 Table 9.4 Inputs into TIPSY to determine managed stand yields 35 TABLE 9.5 GENETIC GAINS IN THE ISBC 36 Table 10.1 Unsalvaged losses 37 Table 11.1 Resource emphasis zone forest cover requirements 39 Table 11.2 Forest cover requirements for visually sensitive areas 41 Table 11.3 Forest cover requirements for lakeshore management zones 41 TABLE 11.4 WILDLIFE TREE PATCH CALCULATION (EXAMPLE) 42 Table 11.5 OSLRMP OGMA constraints 43 Timberline Forest Inventory Consultants Limited (Timberline) has been retained by Tolko Industries Ltd. on behalf of shared mobility) 23 Table 11 PCA result for AT3 the Okanagan Innovative Forestry Society to undertake an Incremental Silviculture Type 2 analysis in support of the Okanagan Innovative Forest Practices Agreement (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACESIFPA). The nationwide survey engaged in carefully members of the Society have entered into Innovative Forestry Practices Agreements (IFPAs) with the Ministry of Forests under Section 59.1 of the British Columbia Forest Act. IFPAs are designed choice experiments to measure test and pilot alternative and new approaches to forest resource management. This Information Package documents the likelihood inventory and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods forest management issues which will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations incorporated into the modeling framework are presented various analyses described in Section 3.0, as well as the next chapter. The last chapter summarizes sources of information used to model the study with major findings and conclusionstimber supply.

LIST OF TABLES. Table 1 Summary Contribution of Literature in AVs 2 partners 10 Table 2 Summary of Literature on Adoption 6 Data Models Developed 30 Table 3 Summary of Literature on WTP 8 ANTALYA Interventions and FIWARE Data Model Dependency 31 Table 4 Summary EU level legislation for MAtchUP Antalya lighthouse project 44 Figure 1 Vertical Interoperability 12 Figure 2 Horizontal Interoperability 12 Figure 3 Antalya Smart City Framework 15 Figure 4 Antalya Urban Platform 16 Figure 5 Instrumented Layer 17 Figure 6 Conceptual Big Data Visualization Architecture for Antalya 18 Figure 7 AUP Components and Actions 19 Figure 8 General System Architecture for the Urban Platform 21 Figure 9 - All communication links in ELK cluster are encrypted 22 Figure 10 Xxxxx Connectors with Elasticsearch 23 Figure 11 Confluent Schema Registry for storing and retrieving schemas 25 Figure 12 NGSI Context Information Model 27 Figure 13 Antalya Open Data Portal 38 Figure 14 Antalya Open Data Portal-Organization List 39 Figure 15 Antalya Open Data Portal-Datasets 40 Figure 16 Open Data Portal Graph View 41 Figure 17 Antalya Open Data Portal-Map View 42 Figure 18 Antalya Open Data Portal-API 42 Figure 19 Big Data Ingestion & Data flow 46 Figure 20 Data Pseudonymization 47 Figure 21 Data anonymization 48 Figure 22 Antalya Interoperability & Integration Action Plans for M38-M48 52 API Application Programming Interface AUP Antalya Urban Platform BI Business Intelligence CKAN Comprehensive Knowledge Archive Network CSW Catalogue Services for the Web DAQ Data Acquisition DCAT-AP Data Catalogue vocabulary – Application Profile EIP European Innovation Partnership EMT Municipal Transport Company (Empresa Municipal de Transporte) ESB Enterprise Service Bus EV Electric Vehicle GDPR EU General Data Protection Regulation HTTP HyperText Transfer Protocol ICT Information and Communications Technology IDABC Interoperable Delivery of Literature European eGovernment Services to public Administrations, Businesses and Citizens IoT Internet of Things JSON JavaScript Object Notation KML Keyhole Markup Language KPI Key Performance Indicator LOPD Organic Law on Mode Choice 9 Table 5 Summary the Protection of Literature Personal Data LTS Long Term Support NGSI Next Generation Service Interfaces OMA Open Mobile Alliance POI Point of Interest REST Representational State Transfer SQL Structured Query Language STH Short-Term Historic VM Virtual Machine WMS Warehouse Management System XML Extensible Markup Language IPG Interoperability Principles Guide KDEP Short Term Action Plan for Digital Transformation of Turkey This deliverable is reporting on Benefit the current state of development of the Urban Platform integration and Concerns 10 Table 6 Summary interoperability in Antalya in the third year of Literature on Perception MAtchUP Project (M25 – M38). This task includes the new projects and services to improve city’s operations. Also another important task is to increase the connection between the City of Technology Antalya and Operations 12 Table 7 Summary its citizens. The developments during MAtchUP follow the same principles: ensuring open data, interoperability through open APIs developments and assessing the evaluation process by considering the requirements of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 Antalya’s monitoring plan. Therefore, new operations and services must guarantee interoperability between the different components involved. Moreover, it is needed to take into consideration the new European General Data Protection Regulation (preferences for lifestyle GDPR). Security and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobilityprivacy aspects should be taken into account. It is essential important to incorporate ridesourcing publish non-sensible and automated vehicle (AV) considerations into current long-range transportation planning effortsanonymized data for learned by citizens. Also developers want to make use when they will start creating innovative services for the city. Integration architecture is one of the most critical aspects of the urban platform to sustain consistency and communication between several internal and 3rd party components. Integration architecture and methodologies are introduced in this version of the platform integration and interoperability. As far as the cities in the project share a common objective, this deliverable D4.24 shares a common structure with the analogous deliverables of WP2, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulationsis D2.24, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society WP3, which is D3.24. Furthermore, these deliverables are due in M38 as a whole. To address these challengesthird and final version of the documents DX.10 and DX.23, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusionsrespectively.

LIST OF TABLES. Table 1 1: List of Abbreviations and Nomenclature 11 Table 2-Annual mileages 34 Table 3 Main technical parameters of the two baseline vehicles and demonstrators 41 Table 4 Main characteristics of the baseline vehicles under study 46 Table 5 Main characteristics of the EMPOWER demonstrators under study. 47 Table 6 Main assumptions for the LCA model of the EoL phase of the four vehicles under study. 50 Table 7 LCI for the LCA model of the hydrogen tanks 52 Table 8 Main assumptions adopted for the LCA of the FC system 54 Table 9 Material composition of the FC system 55 Table 10 Main assumptions adopted for the LCA of the Li-ion battery pack. 55 Table 11 Material composition of the Li-ion battery pack 56 Table 12-Main TCO assumptions 57 Table 13-Energy Consumption (kWh/km) 57 Table 14-Taxes 59 Table 15-Highways Tolls 60 Table 16-Driver Cost 60 Table 17-Breakdown of maintenance cost for each truck type 61 Table 18-Automotive Gas Oil Prices 61 Table 19-Countries Electricity Price 62 Table 20-Hydrogen Cost at Pump EURO/kgH2 62 Figure 1: Life cycle of a product 15 Figure 2: Phases of an LCA 16 Figure 3: LCA Literature review workflow 18 Figure 4: LCA Time coverage of LCA investigated studies 18 Figure 5: Geographical coverage in LCA investigated studies 19 Figure 6: Powertrain technology in LCA investigated studies 19 Figure 7: LCI available in LCA investigated studies 20 Figure 8: Functional unit in LCA investigated studies 21 Figure 9: System boundary in LCA investigated studies 21 Figure 10: Annual mileage (km/y) in LCA investigated studies 22 Figure 11: Vehicle lifetime (km) in LCA investigated studies 22 Figure 12: Vehicle lifetime (years) in LCA investigated studies 23 Figure 13: Software used in LCA investigated studies 23 Figure 14: Database used in LCA investigated studies 24 Figure 15: Impact categories in LCA investigated studies 25 Figure 16: LCIA methods in LCA investigated studies 25 Figure 17: Carbon footprint (t CO2eq) for different system boundaries 26 Figure 18-Total Cost of Ownership 28 Figure 19 - Literature Review Workflow 31 Figure 20-Year of Publication 32 Figure 21-Functional Unit 32 Figure 22-Vehicle Lifetime (years) 33 Figure 23-Yearly Mileage 34 Figure 24-Technologies Considered 35 Figure 25-EU Truck Group (accordingly VECTO) 36 Figure 26-Delivery Mission 37 Figure 27-Discount Rate Chosen (%) 38 Figure 28-Is the inflation rate considered? 38 Figure 29-Battery replacement throughout the vehicle's life 39 Figure 30-Fuel Cell replacement throughout the vehicle's life 40 Figure 31 IVG specific VPs identified as the state-of-the-art baseline diesel trucks 42 Figure 32 System boundary of the LCA study in terms of life cycle phases 43 Figure 33 System boundary of the LCA study in terms of vehicle components 44 Figure 34 Summary of Literature the main assumptions adopted in AVs 2 Table 2 Summary the LCA assessment 44 Figure 35 Scheme of Literature on Adoption 6 Table 3 Summary the data collection management 47 Figure 36 Material composition of Literature on WTP 8 Table 4 Summary the four vehicles under study: a) DIE-LH; b) DIE-R; c) FCEV; d) XXX 50 Figure 37 System boundary of Literature on Mode Choice 9 Table 5 Summary the hydrogen production routes 54 Figure 38-Purchase Cost Estimation 58 Figure 39: GWP results from cradle-to-grave 65 Figure 40: LCA comprehensive results from cradle-to-grave boundary for trucks 66 Figure 41: Cradle-to-gate and EoL results for one battery pack. 67 Figure 42: Comprehensive comparison of Literature on Benefit and Concerns 10 Table 6 Summary the environmental impacts of Literature on Perception of Technology and Operations 12 Table 7 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 hydrogen production (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in bracketsWTT contribution) 68 Figure 43: Cradle-to-gate results of the PEMFC system 69 Figure 44-Regional TCO results of baseline diesel 69 Figure 45-Long-haul TCO results of baseline diesel truck 70 Figure 46-Long-haul TCO results with granularity 70 Figure 47-Long-haul sensitivity analysis over countries 71 Figure 48-Long-haul sensitivity analysis with granularity 72 Figure 49-Long-haul baseline taxes and road cost breakdown 72 Figure 50-Regional baseline taxes and road cost breakdown 73 Figure 51-Long-haul sensitivity analysis over taxes and road use costs 74 Figure 52-Driver cost breakdown Average EU scenario 75 Figure 53- Long-haul geographical sensitivity analysis over driver cost 75 Figure 54-Maintenance Breakdown 76 Figure 55-Diesel cost breakdown 77 Figure 56-Preliminary evaluation XXX demonstrator 78 Figure 57-Preliminary evaluation XXX demonstrator with granularity 79 Figure 58-XXX future projection scenarios 80 Figure 59-XXX future projection scenarios with granularity 81 Figure 60-FCEV truck preliminary evaluation for an average European scenario 82 Figure 61-FCEV truck preliminary evaluation for an average European scenario with granularity 82 Figure 62-Projection scenarios for FCEV truck over the evaluated pathways 83 Table 21 Mode Choice Model Results for Occasional Trips (t1: List of Abbreviations and Nomenclature ZE HDV Zero-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary Emission Heavy-Duty Vehicles LCA Life Cycle Assessment TCO Total Cost of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 Today’s world is deeply influenced by the way new technology evolves. Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of car sharing, ridesourcing, and various other onOwnership GHG Greenhouse Gas FC Fuel Cell VECTO Vehicle Energy Consumption Calculation Tool DIE-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current R 2020 diesel baseline truck with regional distribution mission profile DIE-LH 2020 diesel baseline truck with long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot haul distribution mission profile XXX Battery Electric Vehicle FCEV Fuel Cell Electric Vehicle EoL End of uncertainties with respect to technology development, regulations, Life SMR Steam Methane Reforming CCS Carbon Capture and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the first phase of this research effort, to examine travelers’ mode choice behavior in the upcoming age of automated, connected, electric, and shared vehicles (ACES). The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for advanced vehicle technologies, views and concerns of vehicle automation, and attitudes and perceptions toward mobility options. Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the survey data and attitude analysis. The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and WTP, shared mobility adoption, and mode choice behavior. Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. The last chapter summarizes the study with major findings and conclusions.Storage GWP Global Warming Potential AE Alkaline Electrolysis HDV Heavy-Duty Vehicles LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment method