iSThAT Transport Tool Overview

iSThAT: An Integrated Sustainable Transport health Assessment Tool 1

Health and Environmental Benefits from Reduced Transport-Related Emissions

(Version 2.5, 1-Jan-2018)

Joseph V. Spadaro, Ph.D. [email protected]

Outline  Introduction to iSThAT  Baseline and Alternate scenario definition  Air pollution health impact assessment  Health benefits of active travel and Carbon costs  Comparison of Baseline and Alternate scenarios iSThAT: An Integrated Sustainable Transport health Assessment Tool 2

 Case study: Application of iSThAT in Kaunas, Lithuania  Annex: Transportation intervention measures

Scope and intended users

Objectives  To develop a simplified methodological framework and accompanying software tool for evaluation of health and economic benefits of carbon measures in the context of urban transportation  To identify the merits of alternative choices based on sound, reproducible, science-based reasoning


 To develop a user-friendly interactive tool evaluating carbon mitigation alternatives in surface transportation for informational and educational purposes

Intended users Local authorities (their advisors and technical staff), regulators, urban planners, private/public enterprises, NGOs and educators


iSThAT Transport Tool Concept


iSThAT Transport Tool at a glance

iSThAT Transport Tool description

▪ Definition and comparison of future scenarios are carried out in worksheets labeled "Baseline_Scenario", "Alternate_Scenario", and "Summary of Results". ▪ Data on business-as-usual are entered in "Baseline_Scenario", while an alternative evolution of the transportation fleet is specified in "Alternate_Scenario". Only two scenarios are compared at any one time. Choose from the drop-down menu box to select a country, and follow the on-screen directions.


▪ Country name and location (city) information is entered in "Baseline_Scenario", which is then shared with "Alternate_Scenario".

▪ Atmospheric dispersion characteristics are entered once in worksheet "Baseline_Scenario". These inputs are linked to the calculations carried out in worksheet "Alternate_Scenario". The current version of the tool assumes city characteristics and atmospheric properties remain constant over time. Concentrations are related to changes in air emissions. ▪ Output for each scenario is saved in its respective worksheet, while comparison results are reported in tabular format and visualized graphically in "Summary of Results". The worksheet “Summary of Results−Data” reports annual results between 2015 and 2050. These data can be exported to Excel for further analysis. ▪ In addition to saving this workbook, scenarios can be individually loaded from a text file or saved to disk in ".csv" file format (comma separated values).

iSThAT Transport Tool structure and flow diagram Background concentration

Health benefits physical activity

Carbon values Charts Summary tables Key outputs

Key user inputs

• Socioeconomic data • Modal share (private, public, active travel)

Scenario definition

• Carbon emissions

• Health benefits of reduced air pollution and physical activity

• Fleet specifics and emission factors


Default data given

Air pollution health impact assessment Epi data

• Economic valuation

Life tables

Scope: Informed decision-making


Baseline and Alternate scenario definition Background concentration


Carbon values

Key user inputs

Charts Summary tables Key outputs


Scenario definition

• Carbon emissions • Health effects of reduced air pollution and physical activity


• Fleet specifics and emission factors Default data given



Life tables

Scope: Informed decision-making iSThAT: An Integrated Sustainable Transport health Assessment Tool 8

Scenario definition

What does user need to input?  Exogenous data (population)  Travel modes and shares for cars, mass transit (bus, rail, ...), walking, cycling

 Vehicle fleet specifics (fuel economy, occupancy rate (pkm/vkm), travel distance)  Vehicle fleet technology (conventional, HEV, PHEV, BEV ..., petrol, biofuel blends ...)  Direct emission factors (PM, NOx); SO2 & CO2 emissions are fuel-based


 Indirect CO2 emissions (lifecycle, including LUC from biofuel production) Current and projected input data are specified at 5-year intervals from 2015 (base year) out to 2050. For in-between years, the tool interpolates linearly. Abbreviations: pkm – passenger-kilometers; vkm – vehicle-kilometers; HEV – hybrid electric vehicle PHEV – plug-in HEV; BEV – battery electric vehicle; LUC – carbon emissions from land use changes

Model provides default background information and lookup tables


Help - Assistance is available through message boxes, Tips, individual cell comments (look for red corner at the top-right of the cell), access to background documents (these are located in the directory "\Resources" and are accessed internally through hyperlinks), and weblinks to resources on the Internet (look for the navigation panel at the top of the worksheet, when provided).


Scenario definition


What does model deliver?  Transport activity (passenger-km (pkm) per year)

[congestion]

 Energy demand and intensity (TJ/yr, MJ/pkm, MJ/capita)

[efficiency]

 Transport-related air pollutant emissions (tonnes per year)

[air quality, climate]

 Carbon intensity (gCO2 per pkm and gCO2 per MJ energy)

[footprint]

Model provides time series results from 2015 (base year) and out to 2050 in 5-year increments, while charts show trends at 1-yr resolution. Annual results may be printed, or exported to an Excel file for further processing.


Scenario definition – Exogenous data


Scenario definition – Vehicle technology/fuel/emissions


Scenario definition – Vehicle technology/fuel/emissions

Click here to explore interim results


Transport activity, energy demand and emissions assessment



Transport activity, energy demand and pollutant emissions (sample charts)





Air pollution health impact assessment Background concentration


Carbon values Charts Summary tables Key outputs

Key user inputs


Scenario definition

• Carbon emissions • Health benefits of reduced air pollution and physical activity





Life tables



Impact Pathway: Source  Dispersion  Effect  Cost

Assessing burdens of air pollution requires a multi-disciplinary approach

Emissions

Pollutant dispersion, chemical transformation, and removal

Changes in environmental concentrations

Impacts from environmental exposure: • human and animal health • agricultural products, and forests • natural and man-made structures, • visibility, soil and water quality leads to

Healthcare costs, loss of economic productivity (GDP), and welfare loss (pain and suffering) Losses in crop yield, etc.


Impact Pathway analysis applied to air emissions – 1/2  Health risks are calculated using an "impact pathway" analysis, which explicitly traces the fate of pollutants from the moment they are released into the environment, followed by atmospheric dispersion, and removal by deposition and chemical transformation (characterized by a removal or depletion velocity).  Vulnerable population subgroups, such as the sick, children and the elderly, who are exposed to atmospheric contaminants via inhalation and/or ingestion pathways are at a higher risk of suffering from adverse health symptoms, ranging from mild discomfort to


more serious life threatening conditions.  Quantified health benefits of reduced emissions include avoided cases of illness (health morbidity), fewer premature deaths, and gained life years from an extension in life expectancy among the exposed population.  Physical benefits are calculated using epidemiological associations linking exposure to specific adverse health outcomes (exposure-response functions, ERFs).

Impact Pathway analysis applied to air emissions – 2/2  The health benefits are valued economically taking into consideration the prevented healthcare expenditures and productivity losses (market costs) plus social benefits from avoided premature deaths, or life years gained (welfare benefit).

 Avoided deaths are valued economically using the statistical value of life (VSL), while the statistical value of a life year (VOLY) is applied to life years gained.  Averted health effects are projected over time (2015-50), and future costs are


discounted. Net present values (NPV) are reported in International dollars (2011 prices).  With the exception of cancer risks, the tool assumes there is no time delay between exposure and the health effect. Whereas for cancer mortality, future costs are discounted assuming a 20-year latency period. The burden of cancer includes the morbidity costs while alive.

Population exposure assessment

Exposure calculations are carried out using the methodological framework developed within the RiskPoll model† Change in mean ambient concentration of ground-level source pollutants


𝑐𝑝ҧ

10∙𝑘𝑝 ∙𝑅𝑜 1.009 ∙ 𝜎𝑝 ∙ 𝑚ሶ 𝑝 − 𝐷𝑅 𝜇𝑔/𝑚3 = 1−𝑒 𝑘𝑝 ∙ 𝑅𝑜2

Change in mean ambient concentration of secondary pollutants (SO4 , NO3) 𝑐𝑠ҧ [𝜇𝑔/𝑚3 ] =

3.171 ∙ 𝜎𝑠 ∙ 𝑚ሶ 𝑝 𝑘𝑠 ∙ 𝐴

𝑚ሶ 𝑝 [tonnes/yr]

Pollutant emission rate

𝑚ሶ 𝑝 [tonnes/yr]

Precursor emission rate

Ro [km]

Urban footprint (as circle)

A [km2]

DR [m2/s]

Dilution ratio

Circle of radius 1,600 km (8.042 × 106 km2 )

kp [cm/s]

Pollutant removal velocity

kS [cm/s]

Pollutant removal velocity

𝜎𝑝 [-]

Factor adjusts concentration for population proximity to source (range is 3 - 10 based on local population density)

𝜎𝑠 [-]

Concentration multiplier  sulphates: 7.7 coastal, 12.1 inland source  nitrates: 6.6 coastal, 8.9 inland source

Total change in ambient particulate matter concentration is PM2.5 + secondary aerosols † Rabl, A., Spadaro, J. V. & Holland, M., 2014. How Much Is Clean Air Worth? Calculating the Benefits of Pollution Control, Cambridge University Press.

Effects of fine particulate matter on human health Not all particles are created equal. Toxicity varies with: • Particle number, size, surface area • Chemical composition • Pollution mixture (O3, metals, organics, endotoxins) • Mechanism of action (oxidative stress, inflammation, lung function)


Credit: US EPA

Lung function, inflammation

Exposure may lead to premature death, acute and chronic responses affecting the following systems:

• Respiratory • Cardio-Vascular • Immunologic • Neurological, and • other organs Individual susceptibility varies widely among people, depending on:

• Exposure pathway • Proximity to sources • Single vs. multipollutant exposure • Exposure timing, intensity, frequency • Person's age and gender • Underlying health status, and • Genetic, social, behavioural factors

Cardiovascular diseases Credit: Guariero et al. (2013), InTech, doi:10.5772/52513

Epidemiological associations for quantifying health effects Relative Risk (RR) is defined as the ratio of health events in a risk group exposed to air pollution to a comparison group that is unexposed. RR of unity signifies no difference between the two populations. WHO, Health Risks of Air Pollution In Europe – HRAPIE


Pollutant

Health outcome

ERF shape

Cutoff concentration

PM2.5

• Long-term all-cause mortality (adults) Linear • Hospitalisations, restricted activity and lost working days, respiratory illnesses (children, adults)

Ozone

• All-cause mortality (short/long term) • Hospitalisation (elderly), restricted activity days

Hockey stick

70 μg/m3

NO2

• Long-term all-cause mortality (adults) Hockey stick

20 μg/m3

NO2

• Hospitalisations, respiratory illnesses (children)

0

Linear

0

Mortality integrated ERFs for exposure to PM2.5 ▪Relationships are non-linear with exposure, 5.8 𝛍g/m3 cutoff concentration (below cutoff no health effects calculated) ▪RR vary by gender, age, and specific health burden

𝐸𝑥𝑐𝑒𝑠𝑠 𝑏𝑢𝑟𝑑𝑒𝑛 1 = 1− 𝑇𝑜𝑡𝑎𝑙 𝑏𝑢𝑟𝑑𝑒𝑛 𝑜𝑓 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑅𝑅

RR

≥ 0

1.0 Cutoff

Concentration

Source apportionment of premature deaths of ambient PM2.5 and ozone pollution (2010) CHN+IND 61% total Top 5 countries 70% total

EU: 180,000 deaths (5.5%)

Tool 26 Assessment Tool health Assessment Transport health Sustainable Transport Integrated Sustainable An Integrated iSThAT: An 26 iSThAT:

20.1% from transport  Transport deaths = 5,550

Surprisingly, agriculture and residential energy use cause 50% of all pollution-related deaths (62% excluding Natural emissions)

Source: Lelieveld et al., Nature 2015; 525:367-71

17.7% from transport Transport deaths = 5,565


Incremental concentration and mortality attributable to surface transport (2005)

Source of raw data: Chambliss et al., Environmental Research Letters 2014; 9-104009


Surface transport attributable fraction TAF by world region in 2005 Globally, 8.5% of PM2.5 health burden is attributable to transport emissions

Source of raw data: Chambliss et al., Environmental Research Letters 2014; 9-104009

Cost of illness or death (unit health costs) – 1/2 Cost adjustment from Country A to Country B (benefit transfer method) Ideally, national or regional studies should be used to value economic losses due to exposure to ambient air pollution. In the absence of such data, however, the equation below may be used to transfer unit health costs from another study to the policy location

using the methodology proposed by OECD (2011). The adjustment takes into account differences in income levels between the two places, all other socioeconomic conditions are assumed to be similar (ceteris paribus). Here, Y is the GDP per capita (at PPP prices), and β is the elasticity factor (elasticities 0.8, 0.9 and 1.0, iSThAT: An Integrated Sustainable Transport health Assessment Tool 29

respectively, for high, upper middle and lower middle income countries, have been recommended by the OECD).

𝐶𝑜𝑠𝑡𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐵 = 𝐶𝑜𝑠𝑡𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐴

OECD 2011. Valuing Mortality Risk Reductions in Regulatory Analysis of Environmental, Health and Transport Policies: Policy Implications. OECD, Paris (www.oecd.org/env/policies/vsl)

𝑌𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐵 ∙ 𝑌𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐴

𝛽

Cost of illness or death (unit health costs) – 2/2 Cost adjustment over time (income growth effect) Future costs are calculated using the following expression, where tref and t represent, respectively, the reference and future times. Future GDP/capita growth is based on the real income growth rate (constant currency, no inflation). When calculating cumulative results over a prolonged period of time, future economic costs (or benefits) must be discounted. The discount rate is a reflection of local decision-maker's


time preference regarding future costs, or benefits, in relation to the present time.

𝐶𝑜𝑠𝑡 (𝑡) = 𝐶𝑜𝑠𝑡 𝑡𝑟𝑒𝑓

𝑌𝑡 ∙ 𝑌𝑡𝑟𝑒𝑓

𝛿

Oftentimes, the elasticity of marginal utility of consumption δ is assumed to be equal to the income elasticity 𝛽

EU-27 unit health costs (Int$ at 2011 prices)

Definitions ASD – Asthma symptom days RAD – Restricted activity days RHA – Respiratory hospital admissions CHA – Circulatory hospital admissions VSL – Value of a statistical life


VOLY – Value of a life year

Values include healthcare costs, labor productivity losses, and welfare loss due to personal pain and suffering.


Two future economic development scenarios – SSP2 and SSP5

Source: Boesch et al., 2014. Deliverable 2.3 for ToPDad Project, EC FP7 (www.topdad.eu/publications)

Cost of illness or death (unit health costs, undiscounted)


2010

2030

Source: Own elaboration

Abbreviations HA – Hospital admissions RAD – Restricted activity days WDL – Work loss days (absenteeism) CB – Chronic bronchitis VSL – Value of a statistical life VOLY – Value of a life year

Economic cost per unit of exposure to PM2.5 (€/yr per person−μg/m3)

VOLY – Value Of a Life Year, or equivalent economic cost for valuing the loss of 1 year of life expectancy VSL – Value of a Statistical Life, or the equivalent economic cost for valuing one statistical death (social value to prevent an anonymous fatality)


Source: Own elaboration

𝐻𝑒𝑎𝑙𝑡ℎ 𝑐𝑜𝑠𝑡 = 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑐𝑜𝑠𝑡

Abbreviations

𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 = ෍ 𝐶𝑖 ∙ 𝑃𝑜𝑝𝑖

•C • Pop • CRF

𝑖

𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑐𝑜𝑠𝑡 = ෍ 𝐶𝑅𝐹𝑗 ∙ 𝑈𝑛𝑖𝑡 𝑐𝑜𝑠𝑡𝑗

Concentration at location i (μg/m3) Population at location i (persons) Concentration response function for burden j (excess incidences per yr - person - μg/m3) • Unit cost Cost per illness or premature mortality

𝑗

The summation over index i extends to all persons exposed to air pollution.

Air pollution health impact assessment

What does user need to input?  PM2.5 background population-weighted concentration  Anthropogenic share of air emissions (regional default values provided)

 Life table data (EUROSTAT, WHO, UN World Population Prospects 2015 ...)  Natural mortality rates in deaths and life years lost (WHO-GBD, national databases)  Morbidity prevalence rates (EUROSTAT, WHO-HFA/HMdb, country databases)


 Health costs per illness case (medical/healthcare + productivity loss + dis-welfare)  Mortality valuation: statistical value of life (VSL), value of life year (VOLY)

Model provides default lookup databases

Attributable burdens of air pollution on health mortality and morbidity Mortality: All-cause and Cause-specific endpoints (ALRI, IHD, COPD, CeVD, LC)† Morbidity: Hospitalisations (HMdb, ISHTM 0900, 1000), RAD, WDL, asthma, bronchitis (recurring in children and new incidences in adults)

Exposure-Response functions, ERFs

(quantifying deaths)

(i) linear with and without cutoff (based on WHO-HRAPIE relative risks)


(ii) integrated exposure-response functions iERF (Burnett et al., 2014)

Exposure-Response functions, ERFs (quantifying YLLs) (i) linear functions based on life table calculations (“LifeTable Calculator” worksheet) (ii) iERF based on YLL/death derived from GBD studies (WHO, IHME in USA)

† ALRI – Acute lower respiratory infections (< 5 yr); in adults (≥ 30 yr): IHD – Ischemic heart disease, COPD – Chronic obstructive pulmonary disease, CeVD – Stroke, and LC – Lung cancer Abbreviations: GBD – Global Burden of Disease IHME – Institute for Health Metrics Evaluation (downloabable data at http://ghdx.healthdata.org/gbd-results-tool)



What does model deliver?  Mortality measured as deaths and Years of Life Lost (YLL)

[health burdens]

 Health costs (mortality + morbidity effects)

[health costs]




“WHO Ambient air pollution data” worksheet

continues


“City default data” worksheet – 1/2

continues



“City default data” worksheet – 2/2



“Country database” worksheet – 1/3









“Exposure Cost” worksheet Input data for a particular year


“Exposure Cost” worksheet – Results for a particular year

𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑐𝑜𝑠𝑡 =

෍ 𝑘𝑡ℎ ℎ𝑒𝑎𝑙𝑡ℎ 𝑏𝑢𝑟𝑑𝑒𝑛

𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛

× 𝑘

𝐶𝑜𝑠𝑡 𝑝𝑒𝑟 𝑐𝑎𝑠𝑒 𝑜𝑓 𝑖𝑙𝑙𝑛𝑒𝑠𝑠 𝑜𝑟 𝑑𝑒𝑎𝑡ℎ

𝑖𝑛 𝑘

$2011 𝑝𝑒𝑟𝑠𝑜𝑛 ∙

𝜇𝑔 𝑚3



Health burdens and costs of ambient air pollution


Health burdens and costs of ambient air pollution (sample charts)

See previous slide for definitions of abbreviations

Health benefits of active travel and Carbon costs Background concentration


Carbon values

Key user inputs

Charts Summary tables Key outputs


Scenario definition

• Carbon emissions • Health benefits of reduced air pollution and physical activity





Life tables


Health benefit of increased physical activity on mortality ▪ Health benefits of active travel are quantified using the Health Economic Assessment (HEAT) model developed by WHO. Transport Tool calculates the future health benefit on mortality in the reference population relative to activity in the reference year 2015.

▪ The chart shows the relative risk as a function of time spent walking or cycling. The reference population for each mode is identified in the curve label. Activity time is calculated assuming a reference speed of 4.8 km/h when walking, and 14 km/h during cycling. Benefits are capped at 65 min/day (≈ 400 hours per year). ▪ RRo is the relative risk for reference activity time to. The lag time between increased physical activity and benefits on health from reduced mortality is 5-years.


▪ The averted deaths are valued using VSL. 𝑅𝑒𝑑𝑢𝑐𝑒𝑑 𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 𝑓𝑟𝑜𝑚 𝑊𝑎𝑙𝑘𝑖𝑛𝑔 𝑡 𝜇20−74 ∙ 1 − 0.89 ∙ ∙ 𝑃𝑜𝑝20−74 168 𝑅𝑒𝑑𝑢𝑐𝑒𝑑 𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 𝑓𝑟𝑜𝑚 𝐶𝑦𝑐𝑙𝑖𝑛𝑔 𝑡 𝜇20−64 ∙ 1 − 0.90 ∙ ∙ 𝑃𝑜𝑝20−64 100 μx−y

cohort mortality rate between ages x and y

Popx−y

population between ages x and y

t

activity time in minutes


Carbon valuation Carbon emissions are valued economically using a carbon price, or avoidance cost ($ per tonne of carbon saved). In an alternatively approach, the benefits are monetised using the social cost of carbon (SCC), which is related to the expected environmental consequences of climate change. Future costs are discounted.


Abatement cost range in 2030

* Department of Energy & Climate Change (DECC) 2009. Carbon Valuation in UK Policy Appraisal: A Revised Approach. * Korzhenevych et al. 2014. Update of the Handbook on External Costs of Transport (Final report). Report for European Commission, DG MOVE. Ricardo-AEA/R/ED57769, Issue No.1, 8th January 2014.

Health benefits of physical activity and Carbon emissions

What does user need to input?  Percentage of total transport activity (measured in pkm) that is attributable to walking and cycling for the relevant benefit groups: •

Walking – people between ages 20 and 74 years

•

Cycling – 20 to 64 years old


 Electricity CO2 emission factors (fuel-cycle emissions, incl. production and use)  Indirect CO2 emissions from production of fossil fuels (“upstream emissions”); carbon emissions from combustion of petrol/diesel and other fossil fuels (tailpipe emissions) are calculated endogenously based on vehicle fuel consumption, and fuel properties (e.g., carbon content)

Model provides default indirect carbon emission factors in worksheet “Fuel properties & GHG EFs”

Health benefit of increased physical activity and Carbon costs

What does model deliver?

[health, economic implications]

 Health co-benefits of active travel (avoided premature deaths plus costs)


 Carbon emissions and equivalent costs


Health benefit of increased physical activity and Carbon costs

Comparison of Baseline and Alternate scenarios Transport Tool Output Charts Summary tables Key outputs

• Carbon emissions

• Health benefits of reduced air pollution and physical activity iSThAT: An Integrated Sustainable Transport health Assessment Tool 55


Transport activity and energy demand (results for selected years) Baseline scenario defined in previous slides.


Alternate scenario (modal shift)  reduce private car use, increase demand for urban buses and trolley-buses, and encourage cycling.

Transport activity and energy demand (footnote) ▪ Only final energy demand from vehicle use is calculated (fuel combustion + electricity). ▪ In a more complete analysis, energy supply would also include energy demand for production and delivery of fuel (i.e., entire fuel-chain) plus rejected energy during

electricity generation. Furthermore, total energy consumption should include energy supply for vehicle production and delivery. ▪ Increased physical activity may potentially lead to additional energy use, and corresponding environmental emissions, from increased food consumption. The tool iSThAT: An Integrated Sustainable Transport health Assessment Tool 57

assumes that this energy contribution is negligible compared to that from direct fuel use.

Emissions, Health benefits, and Carbon costs (results for selected years) Baseline scenario defined in previous slides.


Alternate scenario (modal shift)  reduce private car use, increase demand for urban buses and trolley-buses, and encourage cycling.

Baseline versus Alternate scenarios – Aggregate results 2015-2050

Baseline scenario defined in previous slides. Alternate scenario (modal shift to buses/cycling)


 Reduce private cars (40% fewer pkm in 2050 compared to 2015 vs. 25% reduction in BASELINE)  Increase demand for urban buses (from 6.2% to 36.8% of pkm in 2050 vs. 33.6% in BASELINE for that same year)  Increase demand for trolley-buses (from 6.2% to 16% of pkm in 2050 vs. 9.8% in BASELINE for that same year)  Promote cycling activity (150% higher in 2050 compared to 2015 vs. 50% higher in BASELINE scenario); under BASELINE scenario, walking has increased by 50% in 2050 relative to 2015.





Transport activity by mode and technology (sample charts)



Energy demand and fuel consumption (sample charts)



Mortality and health costs of air pollution (sample charts)



Benefits of physical activity and carbon costs (sample charts)



Cumulative results, 2015-50 (sample charts)


Calculating health co-benefits of transport interventions in Kaunas (LT) using iSThAT ….

Development of Kaunas transport sector – BASELINE scenario


Development of Kaunas transport sector – ALTERNATE 1 scenario ALTERNATE 1 : [MODAL SHIFT] ▪ Reduce private cars (40% fewer pkm in 2050 compared to 2015 vs. 25% reduction in BASELINE) ▪ Increase demand for urban buses (from 6.2% to 36.8% of pkm in 2050 vs. 33.6% in BASELINE for that same year) ▪ Increase demand for trolley-buses (from 6.2% to 16% of pkm in 2050 vs. 9.8% in BASELINE for that same year)


▪ Promote cycling activity (150% higher in 2050 compared to 2015 vs. 50% higher in BASELINE scenario); under BASELINE scenario, walking has increased by 50% in 2050 relative to 2015.

Development of Kaunas transport sector – ALTERNATE 2 scenario ALTERNATE 2 : [MODAL SHIFT, FUEL EFFICIENCY] In addition to interventions already considered in ALTERNATE 1, ▪ Scrap older passenger cars at a faster rate and Improve overall fuel efficiency. Fuel consumption decreases by 14% in Petrol and Diesel cars; 11% in Petrol HEVs, and fuel efficiency improves by 24% for both Petrol PHEV (electricity mode only) and battery EV.


▪ Reduce fuel consumption by 20% for conventional buses, 22% for trolley buses over BASELINE assumptions in year 2050.

BENEFIT over ALTERNATE 1 Saved 721 kt of CO2

Development of Kaunas transport sector – ALTERNATE 3 scenario ALTERNATE 3 : [MODAL SHIFT, FUEL EFFICIENCY, TRANSPORT DEMAND] In addition to interventions already considered in ALTERNATE 2,


▪ Reduce transport activity by 23% relative to BASELINE in 2050.

Benefit above BENEFIT over ALTERNATE 2 Averted (LNT) 57 deaths Gained (LNT) 347 years Saved 719 kt of CO2 Loss over ALTERNATE 2 Additional 275 deaths

Development of Kaunas transport sector – ALTERNATE 4 scenario ALTERNATE 4 : [MODAL SHIFT, FUEL EFFICIENCY, TRANSPORT DEMAND, DECARBONIZE POWER SUPPLY] In addition to interventions already considered in ALTERNATE 3, ▪ Reduce GHG emissions from electricity supply by 50% in 2050 compared to reference year 2015. ▪ Passenger car fuel/technology mix: 10% petrol, 10% diesel, 5% petrol HEV, 35% petrol PHEV, and 40% BEV.


▪ Increase utilization of trolley buses to contribute to 24.2% pkm by 2050 vs. 9.8% in BASELINE scenario for the same year (standard diesel buses contribute 28.6% pkm, down from 33.6% in BASELINE in 2050).

BENEFIT over ALTERNATE 3 Averted (LNT) 66 deaths Gained (LNT) 404 years Saved 1,492 kt of CO2

Development of Kaunas transport sector – ALTERNATE 5 scenario ALTERNATE 5 : [MODAL SHIFT, FUEL EFFICIENCY, TRANSPORT DEMAND, DECARBONIZE POWER SUPPLY, ACTIVE TRAVEL] In addition to interventions already considered in ALTERNATE 4,


▪ Encourage more people to shift from private cars to active travel than assumed under ALTERNATE 4 scenario: (i) combined cycling and walking increases to 8% of pkm by 2050 vs. 5.5% in ALTERNATE 4, and (ii) passenger cars contribute 39.3% pkm in that same year vs. 41.7% pkm under ALTERNATE 4 scenario.

BENEFIT over ALTERNATE 4 Averted (LNT) 7 deaths Gained (LNT) 45 years Active travel 566 lives saved Saved 70 kt of CO2

Development of Kaunas transport sector – ALTERNATE 5 scenario ALTERNATE 5 : [MODAL SHIFT, FUEL EFFICIENCY, TRANSPORT DEMAND, DECARBONIZE POWER SUPPLY, ACTIVE TRAVEL] In addition to interventions already considered in ALTERNATE 4, ▪ Encourage more people to shift from private cars to active travel than assumed under ALTERNATE 4 scenario: (i) combined cycling and walking increases to 8% of pkm by 2050 vs. 5.5% in ALTERNATE 4, and (ii) passenger cars contribute 39.3% pkm in that same year vs. 41.7% pkm under ALTERNATE 4 scenario.

Mean annual benefit ≈ 0.6% of Kaunas GDP


5 deaths averted per year, which represents 15-23% of annual mortality in Kaunas due to transport pollution

15 lives saved per year – three times the reduced air pollution benefit 34% reduction over Baseline scenario, or 20% of current transport emissions in Kaunas


Annex: Transportation intervention measures

Sustainable human development ▪ A definition: Living in a sustainable world where consumption demands less of the ecosystem services that Earth can deliver without compromising the needs of future generations. ▪ Demand for energy services is straining natural reserves, polluting the environment and damaging health and ecosystems.


▪ The way forward requires consideration of development planning that is based on economic analysis, environmental protection and social acceptance, pillars of sustainable development. ▪ Risks of different economic development strategies need to be assessed and the results communicated to decision-makers in a transparent and concise way in which socioeconomic trade-offs, and uncertainties are considered on present and future generations.

Role of stakeholders in sustainable transportation−An integrated approach, 1/2 Policymakers and regulators must balance actions that bring quick results against the dangers of market disruptions and economic hardship. • Fuel taxes, carbon pricing (user/polluter pays principle); • Land use planning (access to public transit, non-motorized modes); • Regulatory instruments (LEZ, speed control);

• Fuel efficiency standards and emission targets; • Car maintenance and inspection programs; • Promote low-carbon energy sources;


• Consumer incentive programs (rebates, feebates and financing options); but discourage 'rebound' effect that undermines policy (fuel/vehicle taxes, road/park pricing schemes−impact depends on household income); • Information campaigns empower consumers and foster changes in purchase and vehicle operation behavior (ownership vs. social cost).

Automakers and suppliers must actively participate in the evolution of future mobility through technological innovation of fuel efficient and low-carbon systems. • Coordinate with policymakers, energy suppliers, financial institutions, and other key actors to define and develop sound transport systems/networks that are economically- and environmentally-based, and meet consumer demands; • Build business partnerships that lead to economies of scale (risk- and benefit-sharing); • Develop products that appeal to consumer's performance demands, but are environmentally sustainable; improvement of battery performance and reduction of battery cost will spur shift to electrified transport; • Information campaigns empower consumers and foster behavioral changes.

Role of stakeholders in sustainable transportation−An integrated approach, 2/2 Biofuel and electricity companies, like automakers, have a role to play in the medium to long-term evolution of the transport sector and its decarbonization. • Biofuel companies should continue efforts to expand sustainable production of current biofuels; develop and commercialize new biofuels that have lower carbon lifecycle emissions, compete less for croplands, and have higher yields per land use;


• Electricity providers should invest in capacity build-up to provide adequate and reliable supply of renewables and other low carbon energy to facilitate transition to electricity-based transportation; establish infrastructure to support EV expansion; coordinating projects with regulatory efforts, and seeking ways to finance capacity expansion will be crucial.

Consumer choices and preferences impact automotive technology uptake and start-up of new business models, which in turn affect economies of scale and carbon abatement potential. • Avoid travel (telecommuting, teleconferences, virtual travel, e-commerce); • Shift to less polluting technologies and fuels, greater use of public transport, and electric vehicles; • Improve fuel efficiency through changes in vehicle operation and maintenance practices; • Choose alternative transport modes that are more environmentally friendly, such as active travel for short trips, high-speed rail transport for intermediate distance travel; • Co-sharing schemes and car-pooling. Further reading  IPCC, 2014. Transport [https://www.ipcc.ch/pdf/assessment-report/ar5/wg3/ipcc_wg3_ar5_chapter8.pdf]  Litman, T., 2017. Understanding transport demand and elasticities [http://www.vtpi.org/elasticities.pdf]  Sustainable Urban Transport Project, 2014 [http://www.sutp.org/en/resources/publications-by-topic/climate-environment-andhealth.html]

Shift

Improve

Reduced carbon emissions


Light-duty vehicle LDV fuel efficiency could increase 75% by 2040

Source: The Outlook for Energy: A View to 2040. ExxonMobil 2013

Factoid A 10% decrease in vehicle weight reduces fuel consumption by 7%

Lifecycle carbon emissions in the European context (excl. biofuels LUC emissions) gCO2 per litre of fuel, except electricity in gCO2 per kWh


Significant potential if decarbonised

Source: Own elaboration using various resources LUC = land use changes

Globally, fuel efficiency plays key role in reducing transportation carbon emissions

“ iSThAT: An Integrated Sustainable Transport health Assessment Tool 80

The benefit from abatement would outweigh the incremental upfront investment

Source: McKinsey & Co., 2009. Roads toward a low carbon future: Reducing CO2 emissions from passenger vehicles in the global road transportation system. http://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/sustainability/pdfs/roads_toward_low_carbon_future_new.ashx

Transport demand varies with personal income and fuel costs Transport demand. Drivers of transport demand consist of personal income (household expenditures), operational costs and prices (fuel), travel time (urban form, infrastructure, accessibility, safety), social and cultural factors (population size, ownership), and consumer awareness, attitude, lifestyle, and behaviour. Transport elasticity. Transport choices and decisions are quantified by elasticities, which measure a consumers’ response to a change in income or price.

If real income increases 10%


 Vehicle ownership and fuel consumption will increase nearly 4% within a year, and over 10% in the longer run,  Travel distance will increase 2% within a year and about 5% in the longer run, indicating that the additional vehicles are driven less than average mileage. At higher income levels, incremental changes eventually saturate.

If real fuel price increases 10%  Fuel consumption will fall about 2.5% within a year and 6% over the longer run,

 Traffic distance will fall about 1% within a year and 3% over the longer run (five years),  Vehicle ownership will fall less than 1% in the short run and 2.5% in the longer run,  Vehicle fuel economy will increase about 1.5% within a year and 4% over the longer run. Source: Goodwin et al., 2003. Elasticities of road traffic and fuel consumption with respect to price and income: A review. ESRC Transport Studies Unit, UCL (www.transport.ucl.ac.uk)


An Integrated Transport Carbon-Health-Economic-Assessment Tool

(Version 2.5, 1-Jan-2018)

Joseph V. Spadaro, Ph.D. [email protected]