Academic literature on the topic 'Input-Output Analysi'

This research use relationship marketing teory which applied 4 factor of input process understanding customer expectations, building service partnerships, empowiring employers, and total quality management, and output relationship marketing process: customer satisfaction and customer layalty. The objective of this research is to find out wich factors of input process that has more impact on the output process in relationship marketing. There are 60 trusted correspondencea from Grand Inna Malioboro Hotel customer whom at least have been stayed at the hotel three times. The method of collecting data in these research use questioners and Likert scale measuring instrument 5 points that will be tested by the instrument and analyzed by using regressision analysi the simultaneous test of variabel for the relationship marketing input s. The result of the instrument shows the items used are valid and reliable. It displays the outpout process passed the assumtions test, while the signifikacant relationship marketingto the output of relationtionship marketing, For the passive test of vareable input process relationship marketing with the ouput process relationship marketing shows all significant variables unless vareabel understands customer expectations does not have a significant effect on the process of output relationship marketing

This research use relationship marketing teory which applied 4 factor of input process understanding customer expectations, building service partnerships,empowiring employers, and total quality management, and output relationship marketing process: customer satisfaction and customer layalty. The objective of this research is to find out wich factors of input process that has more impact on the output process in relationship marketing. There are 60 trusted correspondencea from Grand Inna Malioboro Hotel customer whom at least have been stayed at the hotel three times. The method of collecting data in these research use questioners and Likert scale measuring instrument 5 points that will be tested by the instrument and analyzed by using regressision analysi the simultaneous test of variabel for the relationship marketing input s. The result of the instrument shows the items used are valid and reliable. It displays the outpout process passed the assumtions test, while the signifikacant relationship marketingto the output of relationtionship marketing, For the passive test of vareable input process relationship marketing with the ouput process relationship marketing shows all significant variables unless vareabel understands customer expectations does not have a significant effect on the process of output relationship marketing.

Energy production and consumption contribute to 76% of the European greenhouse gas (GHG) emissions in 2018, and 90% of global GHG emissions with land use, land use change and forestation (LULUCF) in the same year. By applying energy efficiency (EE) and renewable energy (RE) technologies, the GHG emission intensity of the energy sector reduced by 1.3% in 2018 compared to the previous year. The current climate change policy aims at decarbonization, sustainable environment, economic prosperity and social equity. It requires the deep decarbonisation of the economies, meaning that the energy and power systems as well as other emission intensive sectors need to transform into zero-emission ones. It also requires the minimization of the environmental impacts while ensuring the economic development and meeting the need of the population growth. This thesis quantifies and evaluates the life cycle environmental impacts with focus on GHG emissions of the power sector, as consequences of changes in the environmental policy. Specifically, the thesis will answer five research questions: 1. What are climate change and energy/ power development policies in Italy? 2. What are changes in the energy/ power systems as consequences of energy climate policies? 3. What are the methods and approach for quantifying and evaluating life cycle environmental impacts as consequences of changes? 4. What are the life cycle environmental impacts of the Italian energy/ power system, with focus on GHG emissions, as consequences of changes in environmental and power policies? 5. The interactions between the energy climate policies and the environmental impacts/ GHG emissions of the Italian power system? The thesis is structured into six chapters, including two chapters of introduction and conclusion, and four chapters of answering five above-mentioned research questions. Chapter 2 provides the answers for two questions (Question 1 and Question 2) on climate and energy policies and changes in the Italian energy/power system due to climate and energy policies. Climate change and energy/ power development policy in Italy is presented in five main documents: FIT for 55, Integrated national energy and climate plan (NECP), national energy strategy (SEN), national energy efficiency action plan (PAEE), and national renewable energy action plan (NREAP). The four national documents set out the targets for EE and RE. Specifically, the targets of energy savings by 2030 include 43% reduction in primary energy consumption, 0.8% reduction in annually final energy consumption without transportation sector and 10 MTOE final energy consumption reduction. For RE, by 2030, the target is 28% ~ 30% of share of RE in total energy consumption, 55% of RE share in electricity consumption and 21% ~ 22% of RE share in transportation sector. It is expected that the electricity generation technology mix will change in order to meet the requirement on RE and EE targets set out in the Italian energy and climate policies. In this thesis, the energy scenarios called National Trend Italia (NT Italia) will be used. The NT Italia was developed by Terna and Snam, for the horizon years 2025, 2030 and 2040, using modelling tools for electricity demand, gas demand and market simulation. In these scenarios, the installed capacity of electricity by natural gas, which is slightly increased by 2040. The installed capacity of coal-based electricity and other fossil fuels-based electricity reduce from 7GW currently to 2GW by 2025, and will not change then. The scenarios also see a constant growth of electricity by RE, reaching 64 GW for solar and 25 GW for wind power (including 4.2 GW offshore) by 2040, while the installed capacity of hydropower and other renewable electricity will be stable. Chapter 3 and Chapter 4 of this thesis will deal with the research question 3, in which Chapter 3 is about the methodology and Chapter 4 focuses on the applied framework. In Chapter 3, the state of the art of consequential life cycle assessment (C-LCA) in the energy and power sectors has been reviewed. The review was conducted on 43 case studies of C-LCA in energy sector and 31 C-LCA papers in power sector. It was identified that economic models are frequently applied in combination with life cycle assessment (LCA) to conduct a C-LCA study in energy and power sectors. The identified economic models include equilibrium (partial and general equilibrium), input-output, and dynamic (agent based and system dynamic) models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability. The review indicates that the most suitable approach to conduct the study is combining one or several economic models and LCA to assess the consequential life cycle impacts of the power system. As each economic model has their own strengths and limitations, the choice of the applied models in combination with LCA largely depends on the goal of the study, the nature of the changes due to market mechanisms, economic or social origins, and the availability of data. In Chapter 4, a framework of combining Input Output Analysis (IOA) and process-based LCA for conducting the study was proposed. Moreover, this chapter provides detailed information on data collected for the model. There are several weighting points for proposing this framework. Firstly, the goal of the study is to assessing the consequential life cycle impacts of energy/ power systems. It requires the comprehensive overview of all economic sectors, as energy is connected all economic activities. The comprehensiveness will be ensured by applying IOA. At the same time, the process-based LCA will provide the detail of a sector/ a product system, which is normally a limitation of economic-wide tool such as IOA. Secondly, the change in the power system originates from economic activities (supply and demand of energy) as well as the environmental requirement to GHG emission reduction and zero carbon emissions. This change can be well modelled with an economic analysis tool (IOA) in combination with an environmental management tool (processed-based LCA). Finally, data for these tools is publicly available. The IOA depends on the input output tables (IOT), which is published every five years by the Italian Statistics (Istat). Data on energy sector is collected from Energy Balance Table, published annually by Ministry of Economic Development, the data from Terna and Snam, the database of the International Energy Agency (IEA), International Renewable Energy Agency (IRENA) and European Commission. Data on environmental aspects includes the National Accounting Matrix with Environmental Accounts (NAMEA), being collected from Istat. Data for process-based LCA is taken from ecoInvent 3. Some global database for IOA are available such as World Input Output Database (WIOD), EXIOBASE, and ect. Followings is the general framework for combining IOA and processed-based LCA to conduct a C-LCA. Consequential life cycle impact is the subtraction of the life cycle impact ‘after change’ and the life cycle impact ‘before change’. The life cycle impact ‘before change’ is quantified by applying IOA. The life cycle impact ‘after change’ depends on the change of pollutant amount, technological coefficient and the final demand due to the inclusion of renewable energy into the Italian energy system. In this thesis, multiregional input output (MRIO), a variant of IOA is used to cover several regions or countries. The application of hybrid MRIO and process-based LCA (hereinafter being called as H-MRIO) is described as followings: • First, two types of data, including MRIO and hybridization data are collected. MRIO data such as the Italian and multiregional IOTs and air emissions accounts are collected from Istat and EXIOBASE. Hybridization data is collected from Italian power/energy suppliers for power development scenarios, and from the ecoinvent database for direct air emissions of power generation technologies • From MRIO data, the MRIO model with two regions of Italy and Rest of the World (RoW) and 36 economic sectors will be constructed. • In combination with the power development scenarios, the Italian electricity sector is disaggregated into seven power generation technologies, for both intermediate flow matrices and final demand vectors in Italian IOT. Similarly, in the environmental burden matrices, the air emissions of electricity sector are disaggregated into those of seven power generation technologies, with data taken from ecoinvent. At this time, the H-MRIO model composes of 42 sectors (36 economic sectors - 1 electricity sector + 7 power technologies). • The model is calculated with historical data of 2010 and 2017 (reference scenario) and replicated for the future scenarios of 2025, 2030 and 2040. Chapter 5 focuses on applying the proposed H-MRIO framework on the Italian context, to obtained the answers for the last two research questions (Question 4 and 5). The total GHG emissions to meet global final demand in 2017 calculated in the study is at 47.69 GtCO2e, which is slightly higher than the global GHG emissions estimated by Climate Watch, at 47 GtCO2e excluding Land use change and forestation (LUCF). The difference in the obtained results of this model and other models is caused by the difference in scope of air emissions being studied. This model quantified actual anthropogenic emissions of CO2, CH4 and N2O, excluding emissions from LULUCF and biomass burning as a fuel. Meanwhile the Climate Watch’s model takes into account all GHGs (CO2, CH4, N2O, and F-gases such as HFCs, PFCs, and SF6), excluding LUCF. This causes a difference of around 1 GtCO2eq of F-gases and 2.8 Gt CO2eq of CH4. The exclusions of emissions from land use (mostly CH4), biogenic CO2 and F-gases in this model leads to an insignificant difference of around 0.69 GtCO2e (less than 1.5%). In order to look into details of the sources of the change in the air emission, a decomposition analysis has been conducted. With the change in final demand and electricity sector composition of Italy, consumption-based GHG emissions appear to decrease in the period 2010-2040. Specifically, due to changes in production structure, emission coefficients, and final demand, the annual CO2 emission reduction embodied in production activities during the period 2017- 2025 will be up to 7.1 MtCO2, which makes up 57.1 MtCO2 emission reduction in the whole period. The increased final demand of Italy causes an annual increase of 4.8 MtCO2. While the change in production structure, including electricity sector and corresponding change in other economic sectors, helps to reduce 6.1 MtCO2 annually. The change in emission flow coefficients brings an annual reduction credit of about 5.8 MtCO2. During the period of 2025-2030 and 2030-2040, the annual change in emission reduction will be much smaller, at 2.3 MtCO2 and 33.9 ktCO2 respectively. Due to the change in power supply technologies and power consumption, the future air emissions dramatically reduce in electricity sector. Most of the emissions of the domestic electricity production come from fossil fuel based electricity, e.g. electricity by coal and natural gas. A smaller part comes from other renewable electricity, including geothermal and biomass based electricity. The productions of solar and wind power do not generate any air-borne emission, and that of hydropower emits an amount of N2O. The reduction in electricity from fossil fuels such as coal and natural gas help to reduce the emissions of the domestic electricity production nearly four times from 97.5 MtCO2 in 2017 to 25.9 MtCO2 by 2040. Besides, the CO2 emission of final consumption of electricity is 34.9 MtCO2 in 2017, which reduces by more than half, at 13.7 MtCO2 by 2040. The CO2 emission of final electricity consumption is divided among technologies by their production structure. As it can be observed, low-carbon technologies such as solar and wind power technologies contribute to emissions, because of the manufacturing of their infrastructures. The emissions of final electricity consumption are smaller than that of domestic electricity production, as they are shared by other economic sectors as intermediates for production activities. The changes in electricity consumption induce changes in other economic sectors, which are clearly shown in coke and petroleum, pharmaceuticals, water transportation, education, and healthcare, either increase or decrease their emissions. Particularly, electricity sector accounts for 11.6% of the total CO2 emissions in 2017, which reduces to 5.9% by 2040. The CO2 emission shares of some other economic sectors also decrease during the period 2017-2040, such as construction and healthcare (reducing around 1 percent point). Meanwhile, the CO2 emission shares of some sectors increases, such as food and beverage (increasing less than 1 percent point). It should be noted that the CO2 emission contributions of these sectors to the national final consumption emissions do not show the correspondingly absolute increase (or decrease). Instead, they relatively present the changes in the identified ‘hotspot’ sectors over years. The absolute values of the CO2 emissions decrease in all economic sectors between 2017 and 2040. The decrease is clearly presented in economic sectors such as construction, decreasing from 20.99 MtCO2 in 2017 to 13.4 MtCO2 by 2040, at about 0.33 MtCO2 annually; or food and beverage, decreasing from 15 MtCO2 to 12.5 MtCO2, or 0.1 MtCO2 annually; or healthcare, decreasing from 17.7 MtCO2 to 11.43 MtCO2 or 0.27 MtCO2 annually in the same period. Five economic sectors holding larges shares out of total CO2 emission of final consumption includes: wholesale and retail, healthcare, food and beverage, electricity and construction (‘hotspot’ sectors). In 2017, wholesale and retail contribute to more than 12% of the total CO2 emission of the Italian final consumption. The four remaining sectors account for an average CO2 emission, from 6% to 10% of the total CO2 emissions. By 2040, the shares of emissions of these sectors remain in the same range. This emission pattern suggests that between 2017 and 2040, in order to reduce the national CO2 emissions, effort should be focused on these ‘hotspot’ sectors. Besides, the different contributions of domestic and import emissions to the total emissions suggest that Italy should have proper strategies to reduce its emissions in term of geographical effort. CO2 emissions of Italian trade partners for food and beverage, health, construction, and wholesale and retail should be taken into account because their emissions largely depends on import. The effort should be taken either to reduce their trade partners’ emission intensity, or to move away from trade partners that having high emission intensities. Meanwhile equal effort should be shared between local manufacturers and trade partners being relevant to renewable power technologies such as solar, wind and other renewable.
Energy production and consumption contribute to 76% of the European greenhouse gas (GHG) emissions in 2018, and 90% of global GHG emissions with land use, land use change and forestation (LULUCF) in the same year. By applying energy efficiency (EE) and renewable energy (RE) technologies, the GHG emission intensity of the energy sector reduced by 1.3% in 2018 compared to the previous year. The current climate change policy aims at decarbonization, sustainable environment, economic prosperity and social equity. It requires the deep decarbonisation of the economies, meaning that the energy and power systems as well as other emission intensive sectors need to transform into zero-emission ones. It also requires the minimization of the environmental impacts while ensuring the economic development and meeting the need of the population growth. This thesis quantifies and evaluates the life cycle environmental impacts with focus on GHG emissions of the power sector, as consequences of changes in the environmental policy. Specifically, the thesis will answer five research questions: 1. What are climate change and energy/ power development policies in Italy? 2. What are changes in the energy/ power systems as consequences of energy climate policies? 3. What are the methods and approach for quantifying and evaluating life cycle environmental impacts as consequences of changes? 4. What are the life cycle environmental impacts of the Italian energy/ power system, with focus on GHG emissions, as consequences of changes in environmental and power policies? 5. The interactions between the energy climate policies and the environmental impacts/ GHG emissions of the Italian power system? The thesis is structured into six chapters, including two chapters of introduction and conclusion, and four chapters of answering five above-mentioned research questions. Chapter 2 provides the answers for two questions (Question 1 and Question 2) on climate and energy policies and changes in the Italian energy/power system due to climate and energy policies. Climate change and energy/ power development policy in Italy is presented in five main documents: FIT for 55, Integrated national energy and climate plan (NECP), national energy strategy (SEN), national energy efficiency action plan (PAEE), and national renewable energy action plan (NREAP). The four national documents set out the targets for EE and RE. Specifically, the targets of energy savings by 2030 include 43% reduction in primary energy consumption, 0.8% reduction in annually final energy consumption without transportation sector and 10 MTOE final energy consumption reduction. For RE, by 2030, the target is 28% ~ 30% of share of RE in total energy consumption, 55% of RE share in electricity consumption and 21% ~ 22% of RE share in transportation sector. It is expected that the electricity generation technology mix will change in order to meet the requirement on RE and EE targets set out in the Italian energy and climate policies. In this thesis, the energy scenarios called National Trend Italia (NT Italia) will be used. The NT Italia was developed by Terna and Snam, for the horizon years 2025, 2030 and 2040, using modelling tools for electricity demand, gas demand and market simulation. In these scenarios, the installed capacity of electricity by natural gas, which is slightly increased by 2040. The installed capacity of coal-based electricity and other fossil fuels-based electricity reduce from 7GW currently to 2GW by 2025, and will not change then. The scenarios also see a constant growth of electricity by RE, reaching 64 GW for solar and 25 GW for wind power (including 4.2 GW offshore) by 2040, while the installed capacity of hydropower and other renewable electricity will be stable. Chapter 3 and Chapter 4 of this thesis will deal with the research question 3, in which Chapter 3 is about the methodology and Chapter 4 focuses on the applied framework. In Chapter 3, the state of the art of consequential life cycle assessment (C-LCA) in the energy and power sectors has been reviewed. The review was conducted on 43 case studies of C-LCA in energy sector and 31 C-LCA papers in power sector. It was identified that economic models are frequently applied in combination with life cycle assessment (LCA) to conduct a C-LCA study in energy and power sectors. The identified economic models include equilibrium (partial and general equilibrium), input-output, and dynamic (agent based and system dynamic) models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability. The review indicates that the most suitable approach to conduct the study is combining one or several economic models and LCA to assess the consequential life cycle impacts of the power system. As each economic model has their own strengths and limitations, the choice of the applied models in combination with LCA largely depends on the goal of the study, the nature of the changes due to market mechanisms, economic or social origins, and the availability of data. In Chapter 4, a framework of combining Input Output Analysis (IOA) and process-based LCA for conducting the study was proposed. Moreover, this chapter provides detailed information on data collected for the model. There are several weighting points for proposing this framework. Firstly, the goal of the study is to assessing the consequential life cycle impacts of energy/ power systems. It requires the comprehensive overview of all economic sectors, as energy is connected all economic activities. The comprehensiveness will be ensured by applying IOA. At the same time, the process-based LCA will provide the detail of a sector/ a product system, which is normally a limitation of economic-wide tool such as IOA. Secondly, the change in the power system originates from economic activities (supply and demand of energy) as well as the environmental requirement to GHG emission reduction and zero carbon emissions. This change can be well modelled with an economic analysis tool (IOA) in combination with an environmental management tool (processed-based LCA). Finally, data for these tools is publicly available. The IOA depends on the input output tables (IOT), which is published every five years by the Italian Statistics (Istat). Data on energy sector is collected from Energy Balance Table, published annually by Ministry of Economic Development, the data from Terna and Snam, the database of the International Energy Agency (IEA), International Renewable Energy Agency (IRENA) and European Commission. Data on environmental aspects includes the National Accounting Matrix with Environmental Accounts (NAMEA), being collected from Istat. Data for process-based LCA is taken from ecoInvent 3. Some global database for IOA are available such as World Input Output Database (WIOD), EXIOBASE, and ect. Followings is the general framework for combining IOA and processed-based LCA to conduct a C-LCA. Consequential life cycle impact is the subtraction of the life cycle impact ‘after change’ and the life cycle impact ‘before change’. The life cycle impact ‘before change’ is quantified by applying IOA. The life cycle impact ‘after change’ depends on the change of pollutant amount, technological coefficient and the final demand due to the inclusion of renewable energy into the Italian energy system. In this thesis, multiregional input output (MRIO), a variant of IOA is used to cover several regions or countries. The application of hybrid MRIO and process-based LCA (hereinafter being called as H-MRIO) is described as followings: • First, two types of data, including MRIO and hybridization data are collected. MRIO data such as the Italian and multiregional IOTs and air emissions accounts are collected from Istat and EXIOBASE. Hybridization data is collected from Italian power/energy suppliers for power development scenarios, and from the ecoinvent database for direct air emissions of power generation technologies • From MRIO data, the MRIO model with two regions of Italy and Rest of the World (RoW) and 36 economic sectors will be constructed. • In combination with the power development scenarios, the Italian electricity sector is disaggregated into seven power generation technologies, for both intermediate flow matrices and final demand vectors in Italian IOT. Similarly, in the environmental burden matrices, the air emissions of electricity sector are disaggregated into those of seven power generation technologies, with data taken from ecoinvent. At this time, the H-MRIO model composes of 42 sectors (36 economic sectors - 1 electricity sector + 7 power technologies). • The model is calculated with historical data of 2010 and 2017 (reference scenario) and replicated for the future scenarios of 2025, 2030 and 2040. Chapter 5 focuses on applying the proposed H-MRIO framework on the Italian context, to obtained the answers for the last two research questions (Question 4 and 5). The total GHG emissions to meet global final demand in 2017 calculated in the study is at 47.69 GtCO2e, which is slightly higher than the global GHG emissions estimated by Climate Watch, at 47 GtCO2e excluding Land use change and forestation (LUCF). The difference in the obtained results of this model and other models is caused by the difference in scope of air emissions being studied. This model quantified actual anthropogenic emissions of CO2, CH4 and N2O, excluding emissions from LULUCF and biomass burning as a fuel. Meanwhile the Climate Watch’s model takes into account all GHGs (CO2, CH4, N2O, and F-gases such as HFCs, PFCs, and SF6), excluding LUCF. This causes a difference of around 1 GtCO2eq of F-gases and 2.8 Gt CO2eq of CH4. The exclusions of emissions from land use (mostly CH4), biogenic CO2 and F-gases in this model leads to an insignificant difference of around 0.69 GtCO2e (less than 1.5%). In order to look into details of the sources of the change in the air emission, a decomposition analysis has been conducted. With the change in final demand and electricity sector composition of Italy, consumption-based GHG emissions appear to decrease in the period 2010-2040. Specifically, due to changes in production structure, emission coefficients, and final demand, the annual CO2 emission reduction embodied in production activities during the period 2017- 2025 will be up to 7.1 MtCO2, which makes up 57.1 MtCO2 emission reduction in the whole period. The increased final demand of Italy causes an annual increase of 4.8 MtCO2. While the change in production structure, including electricity sector and corresponding change in other economic sectors, helps to reduce 6.1 MtCO2 annually. The change in emission flow coefficients brings an annual reduction credit of about 5.8 MtCO2. During the period of 2025-2030 and 2030-2040, the annual change in emission reduction will be much smaller, at 2.3 MtCO2 and 33.9 ktCO2 respectively. Due to the change in power supply technologies and power consumption, the future air emissions dramatically reduce in electricity sector. Most of the emissions of the domestic electricity production come from fossil fuel based electricity, e.g. electricity by coal and natural gas. A smaller part comes from other renewable electricity, including geothermal and biomass based electricity. The productions of solar and wind power do not generate any air-borne emission, and that of hydropower emits an amount of N2O. The reduction in electricity from fossil fuels such as coal and natural gas help to reduce the emissions of the domestic electricity production nearly four times from 97.5 MtCO2 in 2017 to 25.9 MtCO2 by 2040. Besides, the CO2 emission of final consumption of electricity is 34.9 MtCO2 in 2017, which reduces by more than half, at 13.7 MtCO2 by 2040. The CO2 emission of final electricity consumption is divided among technologies by their production structure. As it can be observed, low-carbon technologies such as solar and wind power technologies contribute to emissions, because of the manufacturing of their infrastructures. The emissions of final electricity consumption are smaller than that of domestic electricity production, as they are shared by other economic sectors as intermediates for production activities. The changes in electricity consumption induce changes in other economic sectors, which are clearly shown in coke and petroleum, pharmaceuticals, water transportation, education, and healthcare, either increase or decrease their emissions. Particularly, electricity sector accounts for 11.6% of the total CO2 emissions in 2017, which reduces to 5.9% by 2040. The CO2 emission shares of some other economic sectors also decrease during the period 2017-2040, such as construction and healthcare (reducing around 1 percent point). Meanwhile, the CO2 emission shares of some sectors increases, such as food and beverage (increasing less than 1 percent point). It should be noted that the CO2 emission contributions of these sectors to the national final consumption emissions do not show the correspondingly absolute increase (or decrease). Instead, they relatively present the changes in the identified ‘hotspot’ sectors over years. The absolute values of the CO2 emissions decrease in all economic sectors between 2017 and 2040. The decrease is clearly presented in economic sectors such as construction, decreasing from 20.99 MtCO2 in 2017 to 13.4 MtCO2 by 2040, at about 0.33 MtCO2 annually; or food and beverage, decreasing from 15 MtCO2 to 12.5 MtCO2, or 0.1 MtCO2 annually; or healthcare, decreasing from 17.7 MtCO2 to 11.43 MtCO2 or 0.27 MtCO2 annually in the same period. Five economic sectors holding larges shares out of total CO2 emission of final consumption includes: wholesale and retail, healthcare, food and beverage, electricity and construction (‘hotspot’ sectors). In 2017, wholesale and retail contribute to more than 12% of the total CO2 emission of the Italian final consumption. The four remaining sectors account for an average CO2 emission, from 6% to 10% of the total CO2 emissions. By 2040, the shares of emissions of these sectors remain in the same range. This emission pattern suggests that between 2017 and 2040, in order to reduce the national CO2 emissions, effort should be focused on these ‘hotspot’ sectors. Besides, the different contributions of domestic and import emissions to the total emissions suggest that Italy should have proper strategies to reduce its emissions in term of geographical effort. CO2 emissions of Italian trade partners for food and beverage, health, construction, and wholesale and retail should be taken into account because their emissions largely depends on import. The effort should be taken either to reduce their trade partners’ emission intensity, or to move away from trade partners that having high emission intensities. Meanwhile equal effort should be shared between local manufacturers and trade partners being relevant to renewable power technologies such as solar, wind and other renewable.

This thesis studies a theory for the amplification of technological improvement by the production network structure of the economy. The theory is motivated by the idea that, to the extent that inputs and outputs of industries form a chain, improvements are passed down the chain and accumulate multiplicatively. Under a simple model for technological improvement it is possible to compute the overall improvement factor for the general case where the production network has a complicated structure containing cycles. We call this the trophic depth by analogy with ecology. This leads to testable predictions about GDP growth and its variance. We analyse data for 40 countries and 35 industries from 1995 to 2009 and demonstrate that trophic depths are strongly correlated with economic growth. A regression of GDP growth of countries against their trophic depths has a highly statistically significant R-squared equal to 0.38, and when other standard explanatory variables are added to the regression, the trophic depth remains a robust and statistically significant contributor. We perform some statistical analysis to understand the evolution of trophic depths at different stage of the economic development. We identify two growth paths. Along the first growth path, countries are catching up frontier economies while along the second growth path countries are falling behind. This approach allows us to make some forecasts about the evolution of trophic depths and of the wealth of countries. This provides a comprehensive framework to understand the acceleration and deceleration of economic growth. Then, we study another type of technological progress that corresponds to the adoption of new goods in the production chain. This mechanism is related to the dynamic of the production network and for this purpose we perform a link prediction analysis to determine some key factors for new adoptions. Finally, we analyse the relation between stock return comovement and institutional preferences across stocks of various size. A growing literature highlights the role of investors' common asset holdings on market dynamics. While previous studies focused on large stocks we also include small stocks in the sample in order to acknowledge the shift in institutional preferences towards small stocks over the last decades. Moreover, we add the input-output linkages between firms from different industries to our set of explanatory variables.

The aim of the dissertation thesis is to develop a methodology of the compilation of symmetric Time Input-Output tables under the conditions of the Czech Republic. The following aim is to create an input-output model which is based on the compiled symmetric Time Input-Output tables. For the practical applications of this model it is crucial to link the created Input-Output model with the Semi-Dynamic Input-Output model. Semi-Dynamic Input-Output model in the conception of the submitted dissertation thesis takes into account several multiplier effects and presents more comprehensive tool for the use of the Input-Output analyses in this way. The first chapter focuses on the development of the issues linked to the Input-Output tables and analyses on the territory of the Czech Republic and in the world as well. The second chapter which is also theoretical is focused on mapping of different kinds of Input-Output analyses which are done in the world using Physical, Time or Hybrid Input-Output tables. The third chapter is a purely methodological because here it is described the methodology of the compilation of symmetric Time Input-Output tables as well as methodological approach to the various sensitivity analyses. The fourth chapter focuses on the creation Semi-Dynamic Input-Output model and on the formal linking with the Input-Output model based on the Time Input-Output tables. The last fifth chapter is focused analytically. Methods described in the third chapter are applied on the official published data on the Czech economy. The analytical chapter is perceived in the submitted dissertation thesis as a tool for the sensitivity analysis in the sense of validation of the quality of the compiled Time Input-Output tables.

First, the thesis investigates the interrelationships between the forestry sector and other components of the rural economy, through the application of the Generation of Regional Input-Output Tables (GRIT) technique to the estimation of an input-output table for the rural areas of Scotland. This is followed by a forestry-centred multiplier analysis. Second, the thesis considers the implications of alternative forestry development scenarios in Scotland over the next several decades for land use, timber production and processing, agriculture, and rural employment. Six alternative afforestation scenarios ranging from 'no further planting' to 'accelerated expansion' are defined, including 'lowland' and 'green forestry', and a 'most-likely' scenario. This analysis is carried out through a simulation model which is built on a spreadsheet, and consists of base-period data and parameters, followed by successive projected decade blocks. Taking account of labour productivity trends in both forestry and agriculture, scenario-specific calculations produce future values of forest area, wood output, transfer of farmland, displaced agricultural employment, and forest employment created. A distinction is drawn between current (decade-specific) and accumulated (rotation-specific) forest jobs created on transferred agricultural land and existing forest areas. In this way, the future implications of different assumptions as to future forestry policy are produced. Finally, scenario-specific projections for the year 2050 concerning new planting area and total wood production are converted into gross input value estimates for the Forestry Planting and Harvesting sectors. Alternative assumptions which represent extremes of correspondence between the domestic Forestry and Wood Processing sectors yield new levels of gross national (Scottish) output. The adjustment of the national input-output tables is then followed by their regionalisation through the application of GRIT and the estimation of scenario-specific regional direct, indirect and induced output, income and employment effects.

The main focus of the thesis is to derive stability criteria for networked control system (NCS) models featuring imperfections such as time-varying and constant delays, quantization, packet dropouts, and non-uniform sampling intervals. The main method of proof is based on matrix algebra, as opposed to methods using Lyapunov functions or integral quadratic constraints (IQC). This work puts a particular focus on handling systems with a single integrator. This framework is elaborated in different specific directions as motivated by practical realizations of NCSs, as well as through numerical examples. A novel proof of the discrete time multivariate circle criterion and the Tsypkin criterion for systems including a single integrator is presented, as well as a stability criterion for linear systems with a single integrator subject to variable sampling periods and sector-bounded nonlinear feedback. Four stability criteria for different classes of systems subject to packet loss and time-varying delay are given. Stability criteria for a closed loop system switching between a set of linear time-invariant systems (LTIs) are proved. This result is applied to a single-link NCS with feedback subject to packet loss. Finally, necessary and sufficient conditions for delay-independent stability of an LTI system subject to nonlinear feedback are derived.

This thesis involves construction of input-output models measuring the economic impact of a farming innovation organisation (The Birchip Cropping Group) on the Victorian regional economy of Buloke Shire. The input-output modeling undertaken is of two forms; the first being a simple naïve top-down model, and the second a more sophisticated hybrid model. The naïve top-down model is based on input-output coefficients drawn from the Australian national input-output tables, and is regarded as naïve because these input-output coefficients are not adjusted to take account of local economic factors. The hybrid model uses the same national input-output coefficients as a base, and then modifies these coefficients to better reflect industrial conditions in the Shire using a location quotients-adjustment technique, as well as using original survey data collected from entities operating in Buloke Shire. One of the aims of the thesis is to determine whether the simpler naïve top-down approach produces results consistent with the theoretically more accurate hybrid methodology, and thus whether the naïve top-down approach represents a reliable method of conducting regional economic impact analysis. That is, can such studies be undertaken accurately using a naïve top down approach, or is it necessary to adopt the more resource intensive methodology of a hybrid model. The results of the analysis suggest construction of a hybrid model is advisable, as generally the naïve top-down approach produces over-estimates of the economic effects of the Birchip Cropping Group. That is, it appears the economic impact multipliers estimated with the naïve top-down model are too large, suggesting the time and effort involved in constructing the hybrid model was worthwhile. Using the hybrid model, the conclusion is that the Birchip Cropping Group has a significant affect on the regional economy of Buloke Shire, with the economic impact being estimated at close to $600,000 in additional output, $61,000 in additional income, and 3.5 additional jobs per year.

The Duncan Hunter National Defense Authorization Act for Fiscal Year 2009 mandates that the Fully Burdened Cost of Fuel, including the total cost of procuring and transporting fuel, infrastructure operating costs, and the cost of force protection for the logistics tail, be applied in trade-off analyses for all Defense systems that create a demand for energy. Using data from the Defense Logistics Agency Energy, this thesis builds a model of its worldwide supply chain for bulk fuels, and uses the principles of input-output analysis to calculate the total cost to deliver three fuel types to each destination in the supply chain. Although the Defense Logistics Agency Energy charges a standard price to each service for bulk fuels, these results show that they incur very different costs, ranging from less than a penny per gallon to over 70 cents per gallon, to deliver to different locations. Given the appropriate data on services' fuel distribution networks, a Defense-wide extension of the Bulk Fuels Distribution Model could be used to replace the current seven-step Fully Burdened Cost of Fuel process with a single step, allowing for less complex and more accurate Fully Burdened Cost of Fuel calculations.

More and more modern transport modalities are equipped with complex human-machine interfaces (HMI). HMI aim to narrow the information gap between the complex automation system and their human operator to ensure fast, effective interaction and decision making. We see HMI in the traffic controllers' rooms, the ADAS-equipped vehicles, the public transport drivers' rooms, and many other modern transport modes. Designers create HMIs to effectively draw the operator’s attention to the most necessary and critical information and to facilitate accurate and fast decision making. Whether these systems adequately support human operators and achieve the intention of their designer is difficult to test objectively. [Hamilton and Grabowki 2013] showed that visual, manual and cognitive distractions of ADAS-equipped vehicles tend to distract drivers, who in turn behave less safe on the roads. There is, however, no comprehensive overview about the typical cognitive challenges operators facing in different domains of HMI applications and how these challenges can be objectively assessed. We conducted a series of interviews on difficulties of operators’ Human-Machine interface experience with human factors experts working with in railway and ADAS systems and investigated Endsley's situation awareness theory in dynamic systems [Endsley 1995]. Our interviewees reported several typical issues from their HMI studies, including missing events on the HMI displays, information overload of operators, lack of contextual and situational awareness and, as a resulting mismatch in expected and performed operator actions. We aim to develop and objective approach based on mobile eye tracking technology that can be used to characterize operator situation awareness, decision making and task performance and validate HMI designs in specific mobility and industry applications. The first step of our method is HAZOP analysis of the Human-Machine events and operator tasks, which results in a set of use cases for the eye-tracking experiments. In the experiments, we use wearable eye-tracking glasses combined with AI based computer vision algorithms. Wearable eyetracking enables us to conduct studies in real world scenarios, while AI based computer vision helps use to automatically identify relevant events and streamline the eye tracking data analysis workflow. With the use of glasses, we collect hotspot analysis, sequence of eye movement analysis, time to capture alarms and other parameters. Finally, we use an AI (and open AI) component in the glasses to mark the event of interest and track when the eye interacts with an area or an event of interest. We process gained data to conclude the events engagement, mistakes in responses, and missed out information and explain the root causes. In the past period, we conducted a pilot study to validate the quality of data collected with the openeye eye-tracking equipment (https://kexxu.com/ ). In the next step, we will use validate our method in a full-size experiment. We are convinced that our insights will help to bring significant improvements in current research approaches for human factor studies about comfort, safety and effectiveness of the human-machine interaction. We also aim to apply our method in training and upskilling operators."

This paper introduces a novel computational method for mapping indoor luminance values to the facade of an open workplace to improve its daylight performance. 180-degree fisheye renderings from different locations, view positions, and times of the year are created. These renderings are then transformed from two-dimensional (2D) images into three-dimensional (3D) hemispheres. High luminance values are filtered and projected from the hemisphere to the facade design. This framework will highlight the areas of the facade that allow too much light penetration into the interior environment. This study introduces a flexible framework that allows for an occupant-centric lighting analysis to compute multiple design parameters and synthesize results based on luminance values mapped on the facade design for localized performance optimization to improve facade performance.

The lecture outlines the past five years of a research-based design practice with an interest how technology, craft, and materials come together in ways that explore the boundaries between design, architecture, and other disciplines. Specifically, the pedagogy of material based computational strategies supporting the integration of form, material, and structure by incorporating physical form-finding strategies with digital analysis and fabrication processes. In this approach material often comes before shape, with material explorations as the premise for making and fabricating, and design decisions that emerge from the results of the material experiments and testing. The work produced by my students and myself seeks to challenge digital technology and fabrication to further the relationship of material to machine and material to design. With the intent to develop and employ novel software techniques that aid in the translation from the virtual world to the physical medias we engage through craft and technology to hybridize design and making. The work presented varies in scale, technique, method, intent, and fabrication processes but is fascinated with thinking though material based computational design strategies.

The outbreak of the Coronavirus in 2020 impacted social behaviors and urban daily activities greatly. Activities involving city path-finding and navigation have been impacted particularly because the new virus is air transmissible, meaning that crowding should be avoided. There have been numerous social distancing measures defined for daily activities in cities. However, there have not been sufficient virus safety measures for pathfinding. There is thus a need for a pathfinding method that can produce paths that could be perceived as safe from the virus by navigators. Related studies include the mobile app “Safe Paths”, a 2020 research by MIT Media Lab which uses Bluetooth to track the number of people in locations and find paths that can be the safest from the virus. This is a time-based approach as it deals with the live tracking of pedestrians. A second study by Space Syntax Limited, employed a probability-based approach, based on street network analysis, aiming to propose cycling and walking plans. Rather than only using a macroscale method for pathfinding, this research aims to use both a macroscale and microscale method, as both spatial configuration and human experience matter for navigation in paths. Additionally, based on the related work, as a time-based approach is not cost-efficient, a probability-based approach is chosen as the methodology.

The existing methods for solution space navigation require numerical values to score solutions. The authors introduce a method of quantitative aesthetic evaluation utilizing Computer Vision (CV) as a criterion to navigate solution spaces. Therefore, aesthetics can complement structural, environmental, and other quantitative criteria. The work stands in the extended history of quantifying the visual aesthetic experience. Some precedents are: Birkhoff [1933] and Max Bense [1965] built an approach with experiments to empirically support a measure, whereas Birkin [2010], Ostwald, and Vaughan [2016] devised the first computational methods working on vector drawings. Our research automates and accelerates aesthetic quantification by utilizing CV to extract computable datasets from images. We are especially keen on architectural images as a shorthand to assign an aesthetic value to design, aiming to navigate the solution space in architecture. This work devises a method for rearranging parts in architectural images focusing on formal aspects, in opposition to semantic segmentation where objects unrelated to architectural design (cars, persons, sky…) are quantified to score images [Verma and Jana and Ramamritham 2018]. It uses Maximally Stable Extremal Regions (MSER) [Matas 2004] to recognize architectural parts because it is superior to similar methods such as SimpleBlobDetector in this task. Our method disassembles the parts in a diagram of scaled parts (Fig. 2) to analyze them in isolation, and a diagram of connectivity graph (Fig. 3), to evaluate relationships. These diagrams are examined to compare photos of buildings, cars, and trees to assess the applicability of such a method to a range of objects. Parts and connections are thus quantified, and these values are inputted in a refined version of Birkhoff’s formula to calculate an aesthetic score for each image for navigating the solution space. Finally, it tests the method to draw comparisons between the discrete and continuous paradigms (Fig. 1) in the contemporary discourse of architecture, comparing Zaha Hadid Architects` Heydar Aliyev Centre and Gilles Retsin´s Diamonds House to argue that there is a difference between the aesthetic effects of continuous and discrete designs, besides their distinction in tectonic logic. The method proved to be an efficient procedure for comparatively quantifying the aesthetic judgment of architectural images, enabling designers to incorporate aesthetics as a complementary criterion for solution space navigation in computational design. The method of computational aesthetic measure for solution space navigation and its calibrations via crowdsourced evaluation of images is further detailed in a paper by the authors being published at the 2022 eCAADe conference.

This paper extends to different indicators of labor underutilization the Updated Okun Method (UOM) for estimation of potential output proposed in Fontanari et al (2020), which, from a demand-led growth perspective, regards potential output as an empirical approximation to full-employment output, as in A.M.Okun’s (1962) original method. Based on the apparent incapability of the official rate of unemployment to fully account for labor underutilization, in this paper we offer estimates of Okun’s law both with broad unemployment indicators and with an indicator of ‘standardized hours worked’ which we propose as a novel measure of the labor input. The paper reflects on the possible different empirical measures of full employment. The various measures of potential output that we extract from our analysis show greater output gaps than those produced by standard methods, thus highlighting a systematic tendency of the latter to underestimate potential output. Output gaps that underestimate the size of the output loss or that tend to close too soon during recovery, may produce a bias towards untimely restriction.

Gridded elevation models of the earth’s surface derived from airborne lidar data or other sources can provide qualitative and quantitative information about the terrain and its surface features through analysis of the local spatial variation in elevation. The DEM Breakline and Differencing Analysis Tool was developed to extract and display micro-terrain features and vegetative cover based on the numerical modeling of elevation discontinuities or breaklines (breaks-in-slope), slope, terrain ruggedness, local surface optima, and the local elevation difference between first surface and bare earth input models. Using numerical algorithms developed in-house at the U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, various parameters are calculated for each cell in the model matrix in an initial processing phase. The results are combined and thresholded by the user in different ways for display and analysis. A graphical user interface provides control of input models, processing, and display as color-mapped overlays. Output displays can be saved as images, and the overlay data can be saved as raster layers for input into geographic information systems for further analysis.

Nonlinear propagation of shock waves through periodic structures have the potential to exhibit interesting phenomena. Frequency content of the shock that lies within a bandgap of the periodic structure is strongly attenuated, but nonlinear frequency-frequency interactions pumps energy back into those bands. To investigate the relative importance of these propagation phenomena, numerical experiments using the Khokhlov-Zabolotskaya-Kuznetsov (KZK) equation are carried out. Two-dimensional propagation through a periodic array of rectangular waveguides is per-formed by iteratively using the output of one waveguide as the input for the next waveguide. Comparison of the evolution of the initial shock wave for both the linear and nonlinear cases is presented.

The present paper sets out trends in the functional income distribution implied by countries’ integration in Global Value Chains (GVCs), taking account also of interregional interactions (South-South and North-South). Through the application of an innovative input-output methodology, it quantifies inter-country differences in functional income distribution by means of a novel indicator to estimate the distributive profile associated with domestic vis-à-vis international specialization. The focus is on trade flows, and the analysis carried out allows us to single out the distributive implications of alternative regional integration projects, in view of a more inclusive multilateral trade system.

The Agricultural Policy Research in Africa study in Ghana consists of three work streams. This report contains results of the analyses of Work Stream 1 (WS1) baseline and endline survey datasets for Ghana. Oil palm commercialisation arrangements and outcomes are the focus of WS1 in Ghana. Case studies have been carried out in two districts – Ahanta West and Mpohor – in Western Region. This report highlights the changes between 2017 and 2019 for five APRA indicators, including agricultural commercialisation (input and output), employment, poverty (income, subjective poverty and household asset ownership), food security and women empowerment.