APPENDIX A REFERENCE CASE EMISSIONS: METHOD AND …

TASMANIAN CLIMATE CHANGE OFFICE, DEPARTMENT OF PREMIER AND CABINET

Ref: J1685 Final Report, 30 November 2009 109 McLennan Magasanik Associates

APPENDIX A REFERENCE CASE EMISSIONS: METHOD AND ASSUMPTIONS

A.1 Energy sector

In Tasmania in 2007, the energy sector accounted for 2.9 Mt CO2e or 32% of greenhouse gas emissions. Assuming emissions created from energy imported from the mainland are counted, energy emissions in Tasmania have the potential to increase substantially by 2050 as a result of increasing electricity demand (assuming Tasmania continues to maintain energy imports in preference to increasing state based generation).

A.1.1 Electricity Generation

Modelling of electricity generation emissions for the reference case was based on established MMA market modelling and using analysis of source data from MMA’s in-house Strategist database. Where possible the MMA in-house database has been populated with data that is consistent with NEMMCO’s Statement of Opportunities.

Rather than explicitly modelling the NEM for this work, MMA used extracts from modelling done for the Garnaut Review, a report that was used to advise the Federal Government on electricity market issues related to implementing a CPRS. The demand forecast was based on the demand forecast used in modelling for the Reference case of the Garnaut Review, and Emission Intensity Factors were derived from analysis of modelled data in the same study. Tasmanian generation was also based on the Garnaut work, with slight modification. The hydro generation projection was restricted to 9000 GWh p.a. (in line with projections from Hydro Tas) and the recently committed 210 MW Tamar Valley Combined Cycle Gas Generation plant was projected to run at a 54% capacity factor for 30 years. After this time both forms of generation were projected to continue, though the gas plant was assumed to be subject to market forces after 30 years and therefore had a slight drop in generation with increased variability of load from year to year.

Reference case emissions estimates were calculated using this data as follows:

E = EFg x G + EFi x I

Where E = Emissions, measured in kt CO2e

EFg = Emissions Factor (Gas), measured in kt CO2e / GWh

G = Gas generation, measured in GWh

EFi = Emissions Factor (Imports), measured in kt CO2e / GWh

I = Imports, measured in GWh (calculated as Tasmanian Demand less Tasmanian Sent Out Generation)

The emissions factor assumed for gas generation was 51.33 kt CO2e/PJ which is consistent with the NGI Factors and Methods Workbook.



The emissions factor for imported power is assumed to decline over time (refer to Figure A-1). The emission factors in the initial years are high as they are based on a mixture of generation on the mainland sourced partially from brown coal generation and partially from black coal generation in NSW (the emission intensity for which is based on combustion intensity plus an allowance of 15% for losses during transmission). The proportion of brown coal generation is small and falls to zero over time as the brown coal plant are fully utilised supplying mainland demand. Thus, the emissions intensity of imported electricity is projected to go down over time due to the increasing predominance of black coal generation as the form of generation operating at the margin (and so any increase in imports to Tasmania will increasingly be met from a black-coal fired generation).

Figure A-1: Assumed mainland electricity emissions factors

0.90

1.00

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2005

2007

2009

2011

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kg

CO

2e /

kW

h

Source: MMA Analysis

A.1.2 Direct fuel combustion

Modelling of direct fuel combustion for the reference case was based on analysis of NGI historical emissions and historical fuel use data obtained from various sources. Data available on coal, natural gas and liquid fuels were sourced from ABARE and Australian Petroleum Statistics39.

Data from some industry participants was also sourced to validate coal usage statistics. Projections for industry were obtained by applying growth indices from Garnaut modelling under the reference case. MMA adjusted the 2007 value of the foundation fuel consumption dataset in light of advice from industry participants. This precaution was

39 February 2009 issue.



taken to avoid overinflating estimates of emissions in the reference case. Industry participants who provided advice included Australian Paper, Norske Skog, Rio Tinto, Cement Australia, TEMCO, Simplot, Fonterra, Unimin, Grange Resources, Cadbury and Nyrstar. MMA assumed that 25% of smaller industry users (i.e. consuming 10 kt of coal per annum or less) would switch to gas by 2012.

The projection methodology for other sectors is described in Table A-1. Figure A-2 shows historical and projected fuel use for the entire energy sector in the MMA reference case.

Figure A-2: Fuel consumption under the reference case

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10

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1990

1994

1998

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PJ

Gas for electricity generation

Gas for direct combustion inmanufacturing

LPG

Fuel Oil

MMA projection of coalconsumption for directcombustionCoal - Direct Combustion

Coal - Industrial Processes

Coal - Power Generation

ProjectedActual

Source: MMA Analysis. Excludes petrol, diesel, lubricants and wood.

With regard to non industrial gaseous and liquid fuel consumption, MMA estimated from NGI data that this component is significant comprising around 6% of all Tasmanian emissions. As information on this sector is difficult to obtain, it was infeasible to define suitable abatement measures beyond simple substitution of bio-fuels. Projected emissions from direct combustion of fuel are shown in Figure A-3.

Table A-1: Projection methodology for non industrial sectors

Fuel Projection methodology

LPG Assumed to remain constant Diesel and fuel oil in the agriculture, forestry and fishing industry

Trended with GSP

Diesel and fuel oil Assumed to remain constant Lubricants and greases Assumed to remain constant Emissions from use of petrol based lawnmowers

Time trend

Wood and wood waste (non industrial use) Declines with time reflecting expectation that natural gas is the replacement for this fuel as the gas network expands over time.

Wood and wood waste (industrial use) Increasing trend reflecting low levels of replacement of coal with wood waste



Figure A-3: Comparison of MMA direct fuel combustion emission projections with Garnaut Review projections (Reference Case)

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O2e

MMA projection of Direct Combustion Emissions

NGI estimates and Garnaut Projection - Direct Combustion (adjusted for mismatch with history)

ProjectedActual


A.2 Transport

The transport sector currently accounts for around 21% or 1.9 Mt CO2e of 2007 Tasmanian green house gas emissions. The transport sector can be divided into road, rail, sea and air transport. Road transport is the most significant sector and can further be divided into broad vehicle categories including private vehicles, light commercial vehicles, motorbikes, rigid trucks, articulated trucks and buses.

MMA’s reference case for rail and sea transport is based on time based linear extrapolation to 2050. GSP was used to extrapolate air transport emissions to 2050.

For the road sector, MMA has built a detailed reference case based on data collected from Australian Bureau of Statistics (ABS), the Commonwealth Scientific and Industrial Research Organisation (CSIRO), the National Greenhouse Gas Methods and Factors Workbook from the Department of Climate Change (DCC), the Tasmanian Government and the Bureau of Transport and Regional Economics (BTRE).

A.2.1 Road usage and vehicle numbers

MMA obtained average annual kilometres travelled from the ABS40 and vehicle numbers for Tasmania2 for the period 1998 to 2007 for each vehicle category. Vehicle numbers per person were projected forward using saturation models and then converted to forecasts of

40 ABS Survey of Motor Vehicle Use, 9208.0.



total vehicle numbers. Forecasts of average annual kilometres were projected forward using GSP. These projections are shown in Figure A-4 and Figure A-5.

A.2.2 Calculation of Emissions

The reference case was built on the following simple model where emissions projections are the product of vehicle numbers, average kilometres and emissions factors:

Emissions (kt CO2e) = Σi Ei x Vi x Ai

Where Ei = emissions factor for vehicle type i (kt CO2e / km),

Vi = number of vehicles of type i, and

Ai = average kilometres per vehicle for vehicle type i.

Emissions factors depend on the emissions intensity of the type of fuel used in a vehicle and the efficiency of the vehicle and are based on the following formula:

Emissions Factor (kt CO2e / 100 km) = Σi Fi x Ei

where Fi = efficiency factor for vehicle type i (L / 100 km), and

Ei = emissions value for fuel type i (ktCO2e / L).

Emissions values were derived from the Greenhouse Gas Factors and Methods Workbook (DCC, Nov 2008). Selected values are shown in Table A-2 below.

Table A-2: Emissions values of various fuels

Emission factor, g CO2e/L, (relevant oxidation factors incorporated)

Fuel combusted

CO2 CH4 N2O Sum

Gasoline (other than for use as fuel in an aircraft)

2281.1 20.5 78.7 2380.3

Diesel oil 2671.1 7.7 19.3 2698.1

Gasoline for use as fuel in an aircraft 2194.5 1.3 23.2 2219.0

Kerosene for use as fuel in an aircraft 2535.5 0.4 25.8 2561.6

Liquefied petroleum gas 1561.5 15.7 15.7 1593.0

Biodiesel 0.0 41.5 76.1 117.6

Ethanol for use as fuel in an internal combustion engine

0.0 28.1 51.5 79.6

Biofuels other than those mentioned in items above

0.0 28.1 51.5 79.6

Natural gas (light duty vehicles) 2012160 216150 11790 2240100

Natural gas (heavy duty vehicles) 2012160 82530 11790 2106480

Source: Greenhouse Gas Factors and Methods workbook, Nov 2008, DCC.



Figure A-4: Forecast of Tasmanian vehicle numbers to 2050, by vehicle type

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Passenger Vehicles Light commercial vehicles Motor cycles

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Buses Rigid Trucks

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Articulated Trucks




Figure A-5: Forecast of Tasmanian average kilometres travelled to 2050

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/yr/

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Passenger Vehicles Light commercial vehicles Motor cycles

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Buses Rigid Trucks

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Articulated Trucks




Calculation of average efficiency factors for a group of vehicles in a given year depends on several attributes of the cohort of vehicles in use in that year, including the age distribution of the vehicles, the size distribution of the vehicles and the mix of vehicle technologies.

The age distribution is considered important because the efficiency of a vehicle deteriorates as it ages. It is assumed that vehicle efficiency deteriorates by 0.5% per annum for vehicles under 20 years of age and by 1% per annum beyond that.

Private and light vehicles are split into small, medium and large size categories. For the reference case it is assumed that the share of small, medium and large vehicles remains constant over the forecast period. Shares of small, medium and large private vehicles are assumed to have shares of 35%, 34% and 31% respectively, and shares of small, medium and large light commercial vehicles are assumed to have shares of 8%, 34% and 59% respectively. These values were extracted from the CSIRO report completed as an input to the modelling for The Garnaut Review.

Baseline market shares of each technology were estimated from historical emissions data on the basis of fuel type. These are shown in Table A-3.

MMA estimated average historical efficiency factors using the methodology shown in Exhibit A-1 and calibrated vehicle efficiency factors so that calculated emissions values corresponded with historical NGI estimates of emissions. The calibration value for private vehicles is close to 1 indicating that the model yields vehicle efficiency values close to the CSIRO estimates. The calibration value of light commercial vehicles is the lowest of all values at 0.68. Possible reasons for the lower value in MMA’s model include (i) loading of these vehicles is not as high on average as those on the mainland and (ii) it is possible that light commercial vehicles travel a greater proportion of the time in non-peak periods, reducing the effect of congestion on these vehicles. The relatively large adjustments for trucks may also be due to differences between sizes or capacity loadings in Tasmania relative to mainland states.

Table A-3: Baseline market shares of different vehicle technologies

ICE Hybrid Electricity Diesel LPG Total

Private 90% 0% 0% 8% 2% 100%

Commercial 59% 0% 0% 38% 2% 100%

ICE Hybrid Diesel LPG/LNG H2 Total

Buses 6% 0% 92% 2% 0% 100%

Rigid Trucks 1% 0% 99% 0% 0% 100%

Articulated Trucks 0% 0% 100% 0% 0% 100% Source: MMA analysis of NGI emissions data. Note: ICE = ‘Internal Combustion Engine’.



Table A-4: Calibration values required to match NGI and MMA emissions estimates Vehicle Type Efficiency Calibration Factors

Private Vehicle 0.96

Light Commercial Vehicle 0.68

Bus 0.7

Rigid Trucks 0.75

Articulated Trucks 0.75 Source: MMA Analysis.

A.2.3 Projected Efficiencies of vehicle technologies

New vehicle efficiencies were initially obtained using CSIRO41 estimates which allow for improved efficiency over time to 2050. It was found however, that after calibrating the model to historical emissions data that these efficiencies were too low (that is, litres required per km of travel are too high). Higher efficiencies can be expected in Tasmania since Tasmanian cities are generally smaller than mainland cities and hence associated congestion is not as severe. Reduced congestion is likely to increase fuel efficiency and therefore reduce emissions per kilometre travelled. Calibrated vehicle efficiencies are shown in Table A-5.

41 http://www.csiro.au/files/files/plm3.pdf



Exhibit A-1: Estimation of average efficiency factors for a cohort of vehicles

To estimate the age distribution of a given cohort of vehicles (in a given year) of a given technology type and size, a probability distribution describing the probability of a vehicle of a given age to still be in the stock is used. This distribution is assumed to have a quadratic form and have estimated parameters using least squares based on Tasmanian registration data42. Where available, registration data from the ABS was used to extend the data set back to 1946 (passenger and light commercial vehicles only). The resulting probability of a vehicle remaining in the fleet at a given age is shown below. The average efficiency is then the weighted average of the vehicles in the cohort:

Average efficiencyiy (L/100 km) = (Σn Fin x V*in x Piy x riy) / (Σn V*in x Pin x rin)

where Fin = efficiency factor for vehicle type i in registration year n (L / 100 km), V*in = number of new vehicles of type i registered in year n, Rin = ratio of decline of efficiency of vehicle I by year y, and Pi = probability of vehicle type i registered in year n still being around in year y (ktCO2e/L).

Figure A-6: Probability of a vehicle remaining in the fleet after x years

0%10%20%30%40%50%60%70%80%90%

100%

0 10 20 30 40 50 60 70

Age of vehicle

Pro

bab

ility

of ve

hic

le s

till b

ei

flee

t

Private and Light Commercial vehicles(Average age = 11 years)

Trucks and Buses (Average age = 12.3 years)


Data from the 2002 ABS Motor Vehicle Census43 indicate that the average age of the motor vehicle fleet as at 31 March 2002 was 12.5 years compared to the Australian average of 10.5 years. In Australia commercial vehicles are on average 1-4 years older than private vehicles, depending on the type of vehicle. MMA have assumed that the gap between Tasmanian and Australian vehicle ages will get smaller.

42 http://www.transport.tas.gov.au/publications/statistics_-_historical 43

http://www.abs.gov.au/ausstats/[email protected]/ProductsbyReleaseDate/4D5098BC9257F222CA256DEA007F2BBA?OpenDocument



Table A-5: Vehicle efficiencies for MMA modelling of Tasmanian reference case

Passenger Vehicle Efficiencies (L/100 km)

2006 2050

Technology

Fuel Light Medium Heavy Light Medium Heavy Internal combustion engine Petrol 8.7 9.8 13.4 6.5 7.3 10.0

LPG LPG 11.6 13.1 17.9 8.3 9.2 12.7

Hybrid (with regenerative braking) Petrol 8.3 9.3 12.8 4.6 5.1 7.0

Plug-in hybrid Petrol and electricity 4.5 5.1 7.0 0.7 0.8 1.1

Diesel Diesel 6.0 6.5 8.8 5.2 5.6 7.6

E85 BioFuelE85 12.3 13.7 18.8 8.3 9.2 12.7

H2 Hydrogen 35.2 39.5 54.0 22.4 25.1 34.3

Electric Electricity (kWh/100 km) 19.2 19.2

Light Commercial Vehicle Efficiencies (L/100 km)

2006 2050

Technology

Fuel Light Medium Heavy Light Medium Heavy

Internal combustion engine Petrol 7.072 7.888 10.812 5.30 5.92 8.09

LPG LPG 9.384 10.54 14.416 6.664 7.48 10.2

Hybrid (with regenerative braking) Petrol 6.72 7.49 10.27 3.71 4.14 5.66

Plug-in hybrid Petrol and electricity 5.2 5.8 7.8 0.8 0.9 1.2

Diesel Diesel 4.896 5.508 7.548 4.216 4.76 6.46

E85 BioFuelE85 9.928 11.152 15.232 6.664 7.48 10.2

H2 Hydrogen 28.424 31.892 43.656 18.088 20.196 27.676

Electric Electricity (kWh/100km) 13.6 13.6

Other vehicle Efficiencies (L/100 km)

Buses Rigid Trucks Articulated Trucks

Technology

Fuel 2006 2050 2006 2050 2006 2050

Internal combustion Engine

Petrol 29.684

22.14

32.144

24.026

59.942

44.772

LNG LNG 39.442

27.962

42.804

30.34

69.864

57.154

Hybrid (with Regenerative Braking)

Petrol 28.1998

15.498

30.5368

16.8182

56.9449

40.2948



Other vehicle Efficiencies (L/100 km)

Buses Rigid Trucks Articulated Trucks

Technology

Fuel 2006 2050 2006 2050 2006 2050

Plug-in Hybrid (Rigid Trucks only)

Petrol and Electricity

19.6 5.9

Diesel Diesel 21.894

18.86

23.698

20.418

44.28

38.048

E85 BioFuelE85 41.656 27.962 45.182 30.34 73.718 57.072

H2 Hydrogen 119.392 75.768 129.396 82.082 211.232 163.508

Source: CSIRO, http://www.csiro.au/files/files/plm3.pdf

A.2.4 Market penetration of new technologies

To determine market penetration of new technologies, MMA developed a simple market penetration model. This model evaluates the net present value of road vehicle technology choices (within small, medium and large categories in the case of passenger and Light Commercial vehicles) in any given year. The technologies evaluated include the internal combustion engine, diesel engines, hydrogen (which are not available until after 2020), LPG, hybrid vehicles (i.e. using regenerative braking as an additional power source to the petrol driven motor), plug in hybrid vehicles, electric vehicles and E85 vehicles (which are not available until after 2020). LNG was evaluated instead of LPG for articulated trucks and buses.

The technologies available in a given year are ranked from the cheapest to the most expensive and the market penetration of the cheapest vehicle technology is increased by a fixed percentage for that year based on a sigmoid curve (as a means of simulating the take-up rates of new technologies exhibited in the real world), while the market penetration of existing technologies is reduced by a fixed percentage.

Fuel prices were taken from projections provided by the International Energy Agency, while the (delivered) electricity price was taken from the MMA reference case. Hydrogen prices were taken to be around $5/m3 based on 2006 reported values44, and assumed to move with electricity prices. LNG prices were based on MMA gas market modelling and assumed that around 10% of the energy was required in the form of electricity for liquefaction.

Assumed book life for each vehicle type is shown in Table A-6. These differ among vehicle types because purchasers of new vehicles are likely to consider different vehicle lives for vehicles with differing purposes. Vehicle costs were obtained using CSIRO estimates45 (see Table A-7).

44 $AUD/m3, based on "Hydrogen Fuel Cell Buses: An Economic Assessment", Colin J CockCroft and Anthony D Owen,

2006. 45 http://www.csiro.au/files/files/plm3.pdf



Under these assumptions, and assuming that availability and performance of alternative technologies is equivalent to the traditional internal combustion engine (ICE), market shares of vehicle technologies are expected to increase markedly for diesel technologies initially and later on for hybrid vehicles once the efficiencies of these start to overtake diesel technology. Hybrid vehicles start to make a significant entry to new market share in the mid 2030’s. Market shares of each technology by vehicle type are shown in Figure A-8.

The MMA model does not include external factors such as improved cash flow under more predictable fuel pricing (as would be the case for trucks using LNG in place of diesel fuel), and inclusion of such factors may improve the viability of technology change for individual trucking businesses. To account for the advantage of stable fuel prices, LNG vehicles were included as a replacement for diesel based articulated trucks based on linear trend growth rate. LNG Refuellers Pty Ltd has begun a project establishing LNG supply infrastructure with the expectation that around 120 heavy duty vehicles will replace existing vehicles by 2011 growing to around 240 by 2014. These are initially expected to improve the emissions intensity of the vehicle fleet in the forestry sector but should eventually improve the emissions intensity in other parts of the articulated truck fleet once market share improves.

Another limitation of the MMA model is that is does not adequately segregate the articulated truck fleet by level of duty and the number of containers carried but uses an average efficiency measure over the entire fleet. These factors significantly influence the viability of any technology change calculation. Not allowing for a distribution of travel kilometres for each vehicle may make some technologies seem uneconomic whereas they may be economic for a select percentage of the population.

Figure A-7: Fuel cost Projection assumptions (c/L)

0

100

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900

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c/l (

c/kW

h f

or

elec

, c/m

3 fo

r

Petrol

LNG

LPG

Diesel

H2

Electric

Source: MMA analysis based on EIA fuel price projections.



Table A-6: Assumed travel distances and book life for each vehicle type for market penetration model

Private Commercial Bus Rigid Truck

Articulated Truck

Book Life 10 12 25 15 25

Source: MMA assumptions

Table A-7: CSIRO vehicle cost estimates and efficiencies

Passenger Vehicles Light Commercial Vehicles

Trucks Bus

Light Med Heavy Light Med Heavy Rigid Artic

2006

ICE 14 25 41 14 25 41 61 300 180

Hybrid 28 44 28 44 100 370 260

Plug-in Hybrid

48 64 48 64 160

All Electric 24 24 121

2025

ICE 14 25 41 14 25 41 61 300 180

Hybrid 26 42 26 42 61 300 180

Plug-in Hybrid

34 50 34 50 87

All Electric 17 17 76

Source: CSIRO, http://www.csiro.au/files/files/plm3.pdf

Figure A-8: Market share of each technology under the reference case by vehicle type

Number of Private Vehicles

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Plug-in hybrid

Elec

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E85

Diesel

Hybrid

LPG

ICE



Number of Light Commercial Vehicles

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160,000

1990

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Plug-in Hybrid

Elec

H2

E85

Diesel

Hybrid

LPG

ICE

Number of Rigid Trucks

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Plug-in Hybrid

H2

E85

Diesel

Hybrid

LNG

ICE

Number of Articulated Trucks

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ICE



Number of Buses

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E85

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Hybrid

LNG

ICE


A.2.5 Reference case emissions projections

The calculated transport emissions under the described methodology are displayed in Figure A-9. The forecasts are generally fairly similar except that the MMA model has predicted a less rapid increase in emissions due to uptake of diesel based technologies and LNG and, in the longer term, uptake of hybrid vehicles. This uptake to some extent counteracts any growth in vehicle km travelled over the study period.

Figure A-9: Comparison of historical and projected transport emissions

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Year ending June

MMA Projected Total Emissions Garnaut Projections



A.3 Industrial Processes

Industries in Tasmania that emit process emissions include the Aluminium industry (Rio Tinto in Bell Bay), the Cement Industry (Cement Australia in Railton), the Ferroalloy industry (TEMCO in Bell Bay), and to a lesser extent the lime industry (Unimin) and the zinc industry (Nyrstar). In addition, MMA understand that industrial process emissions from the consumption of halocarbons and SF6, the use of solvents and an allocation of nitric acid production are included in the Tasmanian estimates of greenhouse emissions in the National Greenhouse Inventory.

MMA has estimated process emissions for cement production, aluminium smelting, production of ferroalloys, lime production, zinc production and consumption of products containing synthetic gases (i.e. air-conditioners, refrigeration, etc) using emissions factors from the National Greenhouse Accounts (November 2008) and estimated production statistics sourced from Mineral Resources Tasmania Annual Reviews (1998 through to 2008), validating where possible with industry supplied data. Process emissions were dominated by the cement industry (40% of Tasmanian industrial process emissions in 2006), followed by ferroalloys (28%), aluminium (19%), synthetic gases (9%) and lime and zinc production (3%).

Figure A-10: Breakdown of Tasmanian industrial process emissions, (kt CO2e, 2006)

Cement process emissions, 603 , 40%

Aluminium process emissions, 292 , 19%

Ferromanganese process emissions, 218 , 15%

Silicamanganese process emissions, 196 , 13%

Synthetic gases (including solvents), 123, 8%

Lime production process emissions, 39 , 3%

Zinc production process emissions, 27 , 2%

Source: MMA analysis of NGI data for 2006

Synthetic gases are used in air conditioning and are released during usage of air conditioners over time. While emission due to release of synthetic gas are currently relatively small, it is expected to be the fastest growing component. It grew from 4% of all Tasmanian industrial process emissions in 1998 to around 9% in 2006. This is primarily because of the increased use of air-conditioning in the last decade. However, total



synthetic gas emissions are estimated nationally in the NGI and are allocated between states on the basis of population. While Tasmanians are less likely to install air-conditioners than mainland Australians, there is likely to be an increased use of refrigeration facilities in Tasmania due to increasing food production. MMA suggest further investigation be undertaken of the synthetic gas emissions as this component has the potential to grow to around 17% of all Tasmanian industrial process emissions by 2050 (on the basis of population), and the Tasmanian government will want to ensure that the allocation to the state of national synthetic gas emissions has been done fairly.

Production statistics for each industry were projected forward using industry projections from the Garnaut Review work46. Emissions factors are shown in Table A-8.

Table A-8: Greenhouse gas emissions factors for industrial processes (t CO2e/tonne produced)

Industry Emissions Factor Notes

Cement 0.534 To be applied to clinker production only

Aluminium Ranged from 3.71 in 1996, and dropped steadily to 1.74 in 2008.

Based on advice from Rio Tinto. As Rio Tinto has now reached technology limits this has been projected to stay constant.

Ferro-alloys 1.7 Based on TEMCO data for the year ending June 2008. This factor includes emissions from use of coal, coking coal and coke as a reductant, from use of lime and dolomite as a flux, and use of anthracite and coal tar pitch as carbon electrodes.

Lime 0.675 Sourced from NGI Methods and Factors workbook (November 2008) and end result emissions in broad agreement with Unimin estimates.

Nyrstar 0.123 Based on Nyrstar average estimate for years ending June 2007 and June 2006.

Source: MMA analysis based on DCC Factors and Methods Workbook and industry advice.

A.4 Fugitive Emissions

In Tasmania fugitive emissions occur as a result of extraction of black coal and distribution of natural gas. Fugitive emissions contribute around 0.2% of Tasmania’s emissions at present.

The level of fugitive emissions from black coal depends on the amount of black coal produced and it varies by the type of mine (underground or open-cut). Production of raw coal for 2007-2008 totalled 725,490 tonnes of which about 72% was mined from underground and the rest from open-cut. Greenhouse gas emissions from the production of black coal are calculated as the product of raw coal production and an emission factor less recovered emissions. Emission factors were taken as 0.008 for non-gassy underground

46 Note that industry process emissions estimated by MMA are larger than the NGI values. The NGI estimates include zinc

and emissions from use of limestone and dolomite and carbon electrodes in ferro-alloy industry – even though these are not mentioned in the factors and methods workbook of NGI.



mines and 0.014 for open cut-mines in Tasmania (based on the DCC Factors and Methods Workbook). Raw coal production data is reported in Table A-9.

Black coal emissions were derived from the projection of black coal consumption using an energy content factor of 22.1 GJ/t. It was assumed that all black coal consumed after 2007 was mined in Tasmania and that sufficient coal reserves were available to meet demand until 2050. The use of the higher emission factor of 0.014 for all production (i.e. assuming all coal produced are from open cut mines) is more consistent with historical NGI estimates and hence this has been used for all coal production.

Table A-9: Black coal production by mine type in Tasmania

FY ending 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Under-ground 456336 441398 529555 417813 507740 502341 489482 474332 528049 477388 524900

Open-cut 75624 113011 30121 48372 32080 20377 15192 101081 107418 61214 200500

Total 531960 554409 559676 466185 539820 522718 504674 575413 635467 538602 725400

Source: Mineral Resources Tasmania, Annual Review (1998-99 to 2007-08).

Fugitive emissions for natural gas are based on total gas utility sales from the pipeline system. Greenhouse gas emissions from the pipeline system are calculated as the product of gas sales, the percentage of unaccounted for gas (0.4%), the proportion of unaccounted for gas allocated as leakage (0.55) and the natural gas composition factor for natural gas supplied in the pipeline system (0.9 for CO2 and 326 for CH4).

A.5 Waste Emissions

The reference projection of emissions from the waste-sector is projected using the IPCC First Order Decay (FOD) model. Degradable organic carbon (DOC) stocks in landfill were estimated using historical waste data for Australia and the projection of total waste generation was done using statistical modelling. Estimated total waste generation and emissions from waste sector for the reference case are presented in Table A-10. There is a 2%, 21% and 58% increase over the 1990 level for 2020, 2030 and 2050 respectively in the reference case.

Table A-10: Waste stream percentages 2006 Australia47 Tasmania48

Municipal Solid Waste 28.4% 57%

Commercial and Industrial 34.4% 33%

Construction and Demolition 37.2% 10%

Source: MMA Analysis.

47 Waste Recycling in Australia (final report: Nov 2008) prepared for the Department of Environment Water, Heritage and

the Arts by Hyder Consulting (percentage are calculated from the tabel, sum to 100%, may not be exact). 48 Australian Methodology for the Estimation of Greenhouse Gas Emission and sinks 2006, (Waste); Department of Climate

Change ( MSW – 57%, C&I – 33% and C&D – 10%). (data source reported as Hobart City council).



Table A-11: Total waste generation and total emissions for reference case

2020 2030 2050

Total Waste Generation (kt) 978 1,119 1,359

Per Capita Waste Generation (tonnes) 1.83 2.00 2.35

Total Waste Emission (Gg) 442 526 685


A.5.1 Emission calculations for solid waste

Emissions from Solid Waste are predominantly methane generated at the landfill sites, which is produced when degradable organic carbon found in the waste stream decays. The amount of methane generated each year depends on the amount and type of waste at the landfill accumulated over time as well as the landfill itself. The amount of methane generated each year is estimated using the IPCC 2006 First order decay (FOD) Model and the concept of the carbon stock model approach is illustrated in Figure A-11. The projection of total waste generation was done by separately projecting the municipal solid waste (MSW), commercial and industrial (C&I) and construction and demolition (C&D) waste.

Figure A-11: Carbon Stock Model Flow Chart

Source: Department of Climate Change.

To estimate emissions from solid waste, the current degradable organic carbon (DOC) stock that is present in the landfill as well as yearly deposits of waste to landfill are estimated. A time series of waste deposited in the landfill sites since 1940 was estimated using the volumes of total waste generated in Australia that were reported by the DCC in the National Greenhouse Inventory Report 2006. These are presented in Table A-12 along with estimates for Tasmania.

Historic Total Waste Generated at time t is estimated using historic population, per capita waste (PCWt) and a ratio of per capita waste generated for 2006 for both Australia and Tasmania.



The calculation is as follows:

2006

2006

)(

)(*)(*

NationalWPC

TasmaniaWPCNationalWPCPopulationTasmaniaWasteTotal ttt

Total waste for each decade since the 1940’s is listed in Table A-12. Values for the intervening years are estimated by linear interpolation. These values were used in the FOD model to estimate the DOC.

Table A-12: Total Waste Generation

Year 1940 1950 1960 1970 1980 1990 2000 2006

Australia 9,637 10,065 15,185 17,748 17,098 16,408 19,560 20,867

Tasmania 222 230 344 372 333 312 427 658


The mix of MSW, C&I and C&D streams varies over time. Since the mixture of waste by source is only available for 200649, these values were used for all periods. These are reported in Table A-13. Table A-14 lists all other model parameters.

The methane correction factor (MCF) for a given landfill varies from 0.4 to 1.0 and depends on the type of landfill sites. MMA use a constant MCF of 0.9 for all years until 2020 and increase this value to 1.0 thereafter, assuming all the landfill sites will be well managed and the landfill sites that were closed in 1990’s will be completely stabilized.

Table A-13: Waste mix percentage by stream for 2006 and other key model parameters

Waste Type MSW C&I C&D DOC Half Life “k” Value

Food 26.20% 8.00% 0.00% 15% 12 0.06

Paper and Textiles 26.20% 48.00% 3.00% 40% 17 0.04

Garden and Green 10.20% 4.00% 2.00% 20% 14 0.05

Wood 2.20% 12.00% 6.00% 43% 35 0.02

Other 35.20% 28.00% 89.00% - - -

Total (%) 100% 100% 100% - - -

Table A-14: Other Assumptions

Fraction of Degradable Organic Carbon (DOCf) 0.5

Fraction to CH4 (F) 0.5

Oxidation Factor (Ox) 0

Landfill Methane Correction Factor (MCF) 0.9 & 1

49 Australian Methodology for The Estimation of Greenhouse Gas Emissions and Sinks 2006.



MMA assume a recycling rate of 4, 8 and 12% for year 1995, 2003 and 2006 respectively, with intervening values calculated using linear interpolation. Furthermore it is assumed that no recycling was done prior to 1995 and a constant 12% divergence rate for food organics, garden and greens, and wood is applied post 2006 for the reference case. The recycling rate for paper is expected to reach 40% by 2015 due to increase kerbside recycling in Tasmania and thereafter stay constant.

MMA assume that no methane was captured prior to 2005. The methane recovered for flaring of 3.4 tonnes and for power generation of 4.250 tonnes for 2005 and 2006 respectively were considered as carbon offsets, reducing future emissions from these sites. A reduction of 10,000 tonnes of CO2e emissions per annum from effluent in the Boyer paper mill is assumed and manually deducted from the total emissions. For each of these a constant rate of 30% capture was assumed. Reported emissions and MMA estimates are presented in Figure A-12.

Figure A-12: Tasmania emission from solid waste

Solid Waste Emission (Tasmania)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

500.00

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Year

CO

2e (

Gg

)

MMA Estimates Actual Reported


A.5.2 Solid waste generation projection

From Figure A-13 it is clear that the rate of Australian waste generated per million dollars of GDP is increasing over time but appears to have stabilised since 2003.

A logistic model using GSP as an independent variable is used in this study.

50 Review of Methane Recovery and Flaring from Landfills, (Hyder Consulting, October 2007) AGO.



Figure A-13: Waste generation rate by sources in Australia

5

7

9

11

13

15

17

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Was

te t

/$(M

) G

DP

0.70

0.90

1.10

1.30

1.50

1.70

1.90

MS

W, t

/ho

us

eho

ld

C&I Waste t/$(M) GDP C&D Waste t/$(M) GDP MSW t/household

Source: MMA Analysis of NGI data.

Total waste generation from the municipal waste stream is assumed to be driven by household consumption. Household consumption, in turn is influenced by GSP. A logistic trend model was built using the estimated waste generation per household and GSP from the period 1990 to 2006. The MSW stream is projected on the basis of the projected number of households and income growth per household. Figure A-14 shows the relationship between MSW and GSP. The projection of number of households to 2025 was taken from ABS projections51 and extended to 2050 by holding the persons per household constant after 2026 and using population forecasts provided by the Tasmanian Treasury.

51 3236 ABS. Household and Family Projections 2001 to 2026.



Figure A-14: MSW generation projections

Projection of MSW Generated (Tasmania)

100000.00

200000.00

300000.00

400000.00

500000.00

600000.00

700000.00

12500 17500 22500 27500 32500 37500 42500

GSP

tonnes MSW

Generated

Fitted Estimated from Nation Level


The amount of commercial and industrial waste produced is estimated with a logistic model using GSP. Figure A-15 illustrates actual and projected waste generated from C&I stream.

Figure A-15: Projection of C&I waste generation as GSP increases

Projection of C&I Waste Generation (Tasmania)

0

100000

200000

300000

400000

500000

600000

12000 17000 22000 27000 32000 37000 42000 47000

GSP

tonnes of C&I W

aste Generated

Estimated from the National Level Fitted


The level of waste generated from C&D is linked to building activity expenditure, which in turn is assumed to increase in line with GSP. C&D waste generation was projected by building a logistic model of waste generated per million dollars of GSP. Figure A-16 below presents the projection of total waste generated as GSP increases.



Figure A-16: Projection of C&D waste generation as GSP increases

Projection of C&D Waste Generation (Tasmania)

10000

30000

50000

70000

90000

110000

130000

150000

170000

190000

12000 17000 22000 27000 32000 37000 42000 47000

GSP

C&D W

aste (tonnes)

Estimated form National Level Fitted


Figure A-17: Projection of total waste generated

Total Waste Generated (Tasmania)

0.00

200000.00

400000.00

600000.00

800000.00

1000000.00

1200000.00

1400000.00

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Total W

aste (tonnes)

Fitted Estimated from the National Level


Projected total waste generated for Tasmania is the sum of total waste generated from the three streams: MSW, C&I and C&D. This is shown in Figure A-17.

Using statistical models to estimate the total waste generated is limited technique since extrapolation was done for all three models beyond the data regions that were used to build the model. The observed values were also estimated from national data and the percentage of the stream was kept at 2006 levels for all years.

A.6 Agriculture and LUCF

Agricultural emissions are calculated using the NGI methods and activity data used to compile the NGI. Not all emissions sources have been included in the model as some are insignificant in Tasmania. On average over the period 1990 to 2006, the emissions included account for 99% of total agricultural emissions. The largest sources of emissions which are not included are from animal waste applied to soils, manure management of poultry for



meat and prescribed burning of savannas. Through the period 1990 to 2006, these produced annual average emissions of 7,414t, 3,950t and 4,070t CO2e emissions respectively. On average they account for approximately two thirds of the emissions not included over the period 1990 to 2006.

There are many difficulties in forecasting emissions from agriculture. First is the difficulty of measuring emissions from agriculture. Soil emissions (both direct and indirect) are governed by complex interactions that take place to produce these emissions. For example, N2O production may result from three separate microbial mediated processes that take place at varying soil moisture contents, temperatures, soil carbon levels, soil carbon state, nitrogen levels, soil structures, salinity, availability of other nutrients and pH levels52. Further, uncertainties in emissions factors, activity data, lack of coverage of measurements, spatial and temporal aggregations, and lack of information on specific on-farm practices lead to uncertainties in direct N2O emissions and emissions factors that are not representative of specific locations53. Another problem is measurement accuracy. Different measurement techniques will give different results at a specific location, the accuracy of different techniques will vary depending on local conditions and results for a specific technique at a specific location will be affected by the implementation and application of the technique. Measurement takes specialised scientific skills, requires access to specialised and expensive scientific equipment, and is generally labour intensive. This high degree of labour intensity will lead to measurement errors due to inconsistencies in the collection, handling and processing of samples.

Another major challenge in making long-term predictions of emissions in the agriculture sector is predicting production levels. Factors which may have a strong effect on agricultural production include:

Direct effects on the productive capacity of Tasmanian agriculture

Changes in water availability

Market effects induced by changes in productivity elsewhere in Australia and globally

Potentially large impacts on productivity through scientific innovation

Attitudes toward scientific innovation

Changes in the structure of agricultural production

Changes in cultural preferences in agricultural markets

In this work it is assumed that markets are competitive. Demand elasticity for agricultural products is generally quite low and here it is implicitly assumed to be zero by assuming that production levels will be maintained. Under this assumption any changes in prices will have no effect on demand and will be borne by consumers.

52 Ugalde, Brungs, Kaebernick, Mcgregor, & Slatery (2007); Dalal, Wang, Robertson, & Parton, (2003). 53 Ugalde, Brungs, Kaebernick, Mcgregor, & Slatery, (2007).



A.6.1 Reference case forecasts

MMA has chosen simple methods of developing the reference case forecasts as any forecast will be subject to large uncertainty.

Livestock

The reference case has been developed using figures from the Department of Climate Change. Figure A-18 through Figure A-20 show the numbers of animals reported to be in Tasmania between 1989 and 2007. The beef cattle herd has remained similar in size and structure through this period. Sheep numbers have declined significantly for all animal types. There has been a decline in world demand for wool and extensive periods of drought that have lead to this decline. The dairy cattle herd increased in size consistently up to 1999 and has remained roughly constant since then.

Figure A-18: Beef cattle numbers by animal type

Figure A-19: Sheep numbers by animal type



Figure A-20: Dairy cattle numbers by animal type

The reference case livestock numbers have been estimated as follows:

For beef cattle, the average of the years 1989 to 2007 has been used for each animal type for the period 2008 to 2051.

For sheep, the number of each animal type converges toward the average of the years 1989 to 2007 over the period 2008 to 2051.

For dairy cattle, the average of the years 2000 to 2007 has been used for each animal type for the period 2008 to 2051.

The resulting forecasts are shown in Figure A-21 to Figure A-23.

Figure A-21: Beef cattle numbers from 1989 to 2051



Figure A-22: Sheep numbers from 1989 to 2051

Figure A-23: Dairy cattle numbers from 1989 to 2051

Comments from the Tasmanian Department of Primary Industries, Parks, Water and Environment (DPIPWE) are that the structure of the sheep flock might change into the future, as farmers move from production of sheep for wool toward production of lambs for meat. In particular, lambs and hoggets may constitute a large proportion of the flock. As the emissions factors for these animals are similar, this does not have a significant effect on the results presented herein.

The emissions calculations for livestock are as described in Department of Climate Change methodology documents54. The main source of discrepancy between the NGI estimates and MMA estimates for the emissions sources modelled are between the enteric fermentation emissions from milking cows. These discrepancies expressed as a percentage of the NGI estimates of enteric fermentation emissions from milking cows are shown in Figure A-24. These discrepancies are likely to be due to estimates of milk production, where annual numbers are used rather than 3 yearly averages.

54 Australian Methodology for the Estima tion of Greenhouse Gas Emissions and Sinks 2006



Figure A-24: Enteric fermentation discrepancies for milking cows expressed as percentage of NGI estimates

A very slight discrepancy is also apparent for breeding ewes and beef cows older than two years in the years 1990 to 1996. For breeding ewes: from 1990 to 1994 this is approximately 0.14%, in 1995 it is 0.09% and in 1996 it is 0.05%. For beef cows older than two years: from 1990 to 1994 this is approximately 0.15%, in 1995 it is 0.1% and in 1996 it is 0.05%. These discrepancies are probably also due to milk production for rearing young. For all other animal types MMA calculations agree with the NGI to within at least 0.001%.

Other emissions sources from livestock are not disaggregated in the NGI so direct comparisons of model calculations and the NGI on a source by source basis are not possible. However, discrepancies between total emissions estimates for agriculture (adjusted for the emissions not being modelled) and the NGI are small. These are shown in Figure A-25. These discrepancies are likely to be due largely to enteric fermentation of milking cows as discussed above.

Figure A-25: Discrepancies between total emissions estimates and NGI



Synthetic fertiliser

For each crop type, the average use over the period 2003 to 2006 as reported in the NGI activity tables55 has been used to estimate fertiliser use for the period 2007 to 2051. The averages for these years were chosen and are shown in Figure A-26.

Figure A-26: Mass of N applied by crop type (‘000 t)

Crop Production

Emissions arising from crop production are modelled using NGI methodology and factors. Production of each crop is given in activity tables available from the Department of Climate Change. In this data, production of legume pastures in the years 2001 and 2003 appears to be aberrant, as does the production of “other crops” in 1989. This data has been replaced with estimates based on the observed trend in previous years.

There are some notable trends in this data. Wheat production has increased steadily through most of the period; legume pastures has increased since 1998; oats have decreased through most of the period; pulses increased dramatically from 1994 to 1995.

The production of pulses through the period 2008 to 2051 is based on the average production through the period 1995 through 2007. The production of legume pastures is based on the average through the period 2004 to 2007. The production of wheat is based on the average through the period 2007 through 2007. For all other crop types, the future production is assumed to be the average from 1989 to 2007. The historical data and forecasts are shown in Figure A-27.

55 Source: http://www.ageis.greenhouse.gov.au/GGIDMUserFunc/QueryAppendixTable/QueryAppendixTable.asp.



Figure A-27: Historical and forecast crop production (‘000t)

LUCF

Advice on the future of the Tasmanian forest estate was provided by Forestry Tasmania (FT) and Private Forests Tasmania (PFT). In recent years there has been an expansion of the private forest estate, driven largely by favourable tax incentives provided by managed investment schemes. While the future of these schemes is uncertain, both FT and PFT assert that it is reasonable to assume that forest coming up to harvesting will be replanted.

FT is under a legislated obligation to produce a fixed quantity of saw log annually, and their forest management is driven largely by this requirement. This requirement would be unlikely to change under the reference case.

Following this advice MMA assume that both the estate managed by Forestry Tasmania and the private forest estate will stay remain as they currently is through the study period under the reference case.



APPENDIX B ENERGY SECTOR ABATEMENT OPTIONS

B.1 Introduction

For the purpose of this study, MMA has assigned five sub-sectors within energy:

1. Energy efficiency (which is further divided into residential, commercial, and industrial).

2. Renewable energy.

3. Cogeneration.

4. Fuel switching (coal to gas or bio-fuel such as wood/biomass pellets or biochar).

5. Fossil fuel generation improvements with carbon capture and storage (CCS).

B.2 Energy efficiency model: residential

The housing stock, energy consumption and appliance characteristics used in this model are derived from Energy Efficient Strategies56, which will be referred to hereafter as the ‘EES report’. The EES report is a detailed baseline study of energy use in residential dwellings that incorporates a wide range of information, including housing stock (by dwelling size, construction methods, insulation characteristics and thermal properties), occupancy rates, equipment characteristics and existing energy efficiency programs in Australia.

This study is primarily interested in what affects changes in electricity prices and other government interventions will have on energy consumption. This requires a model of how dwelling owners and builders will respond to these changes. The main difficulty with building such a model is in capturing the barriers to uptake. In the economic literature, the reasons for these barriers include hidden transaction costs, search costs, capital constraints and principal/agent effects.57 However, it is worth noting that there is no one set of assumptions that will effectively address all possible barriers simultaneously. The modelling conducted in this study assumes that dwelling owners and builders use net present value (NPV) when making decisions about equipment purchases or building insulation. The model was also run using a fixed payback period as the decision making criteria.

B.2.1 Baseline energy consumption estimates

The dwelling classes in the model are categorised as per the EES report and additions to the housing stock and energy consumption in each year are the same between 2007 and 2020. Around 0.18% of the previous building stock was assumed to be demolished in each

56 http://www.environment.gov.au/settlements/energyefficiency/buildings/publications/energyuse.html 57 See, for instance Sorrell, S., O'Malley, E., Schleich, J., & Scott, S. (2004). The Economics of Energy Efficiency: Barriers to Cost-

Effective Investment. Cheltenham, Gloucestershire, United Kingdom: Edward Elgar Publishing, Inc.



year. Over the period of this analysis this would amount to approximately 7.7% of dwellings.

Both the housing stock and energy consumption have been extrapolated beyond 2020 based on the relationship between population estimates provided by the Tasmanian Treasury and trends from the EES report.

B.2.2 Model Description

The residential energy efficiency model is a simulation model. Each simulated unit represents a number of dwellings which are assumed to have the same characteristics. All simulation units use the same criteria to make investment decisions and they do not interact with each other in any way.

The model is designed such that the reference case energy figures are exactly those provided in the EES report up to 2020. Forecasts were derived from 2021 to 2050 for each usage type (space heating, lighting, refrigeration, water heating and consumer electronics). The energy consumed in each dwelling for each usage type is estimated in the model and these are aggregated to form an estimate of total energy use by residential dwellings. These estimates are then benchmarked against the forecasts from EES report providing calibration factors. Changes in electricity prices and other interventions change the energy use of a dwelling by changing the technologies adopted in the dwelling and modified using these calibration factors.

A formal definition of the model is as follows:

Let:

be the energy use in a dwelling through technology under the reference case.

be the energy use in a dwelling through technology when the abatement options

are applied.

denote the energy use within a dwelling when the dwelling has the upgraded

form of technology improvement.

denote the energy use within a dwelling when the dwelling does not have the

upgraded form of technology improvement.

be the total energy use reported in Energy Efficient Strategies (2008) for a given

energy usage (space heating, lighting, water heating, refrigeration and electronic equipment) or as extrapolated (beyond 2020).

Then:

and,



Where is the set of all dwellings.

Improvements to the heating performance of a dwelling can be achieved by a combination of improving its insulation and/or improving the efficiency of heating equipment. Improvement in either of these will reduce returns to improving the other. Dwellings choose the set of heating performance based opportunities they will exploit by comparing all possible combinations of insulation improvements and heating efficiency improvements using the decision making criteria under which the scenario is being run. The insulation efficiency improvements considered are: installation of roof insulation (RI), wall space insulation (WI) and double glazing (DG). Floor insulation is excluded as it is not considered in the EES report and hence would cause problems when benchmarking back to the energy use forecasts.58 The heating efficiency improvements considered are: more efficient space heaters (AC) and reducing the thermostat setting (TC).

The overall change to energy consumption and costs associated with the adoption of a given set of thermal efficiency improvements is calculated as follows.

Let:

denote an indicator function which returns one if dwelling is to be a

candidate for an upgrade of technology type and , and zero otherwise.

denote an indicator function which returns one if dwelling already has

technology and zero otherwise.

denote a function which returns one if either argument or is non-zero and

zero otherwise. be the set of insulation related measures as defined above.

be the set of space conditioning performance measures .

denote the initial cost of installing or upgrading a dwelling with technology .

denote the relative energy use of a new technology compared to an old

technology of type .

The cost of installing upgrades of technologies , , in dwelling is:

That is, the cost for of candidate measures is only incurred if the dwelling does not already have this measure in place. The energy that escapes from a dwelling before any (additional) upgrades are considered is:

58 Double glazing is also not considered in the EES report, but it is fairly uncommon in existing dwellings and its uptake

would be expected to be quite low under the BAU due to its high cost.



The energy that escapes from a dwelling when a set of upgrades are considered

is:

That is, the energy that escapes from a dwelling depends on the existing insulation already installed in the dwelling and any the upgrades being considered. The relative thermal efficiency of a dwelling given a set of candidate insulation improvements is then

The proportional reduction in energy used to heat a dwelling given candidate insulation improvements and heating technologies is then calculated as:

For space conditioning related measures, the reduction in energy use was calculated using a simple model of the level of air conditioning required based on specified r-values for windows, walls and ceilings. The ceiling height, the relative area of walls compared to floor, the relative upper and lower floor areas in two storey dwellings, the desired internal temperature and the assumed external temperature are also parameters in the model. The values assumed for these parameters are shown in Table B-1.

Table B-1: Model parameters and values Parameter Value Units Ceiling height 2.40 square meters Proportion of window area 0.30 NA Outside temperature59 10.00 degrees centigrade Desired inside temperature 22.00 degrees centigrade Standard ceiling r-value 1.00 r-value Ceiling insulation r-value 3.50 r-value Standard wall r-value 0.76 r-value Wall insulation r-value 1.50 r-value Standard floor r-value 0.75 r-value Floor insulation r-value 1.50 r-value Standard window r-value 0.91 r-value Double glazed window r-value 2.20 r-value Light wall r-value 0.50 r-value Brick wall r-value 0.80 r-value Heavy wall r-value 1.00 r-value Proportion of floor area in lower floor in two storey dwellings

0.66 NA

Number of external walls - flats 1.50 NA

59 Note that the it is the difference between the inside and outside temperature that is used by the model.



Parameter Value Units Number of external walls- semi-detached

2.00 NA

Number of external walls -other 4.00 NA

Let:

denote the r-value of a given material

denote the desired inside temperature

denote the assumed outside temperature

denote the surface area of an external wall, window or ceiling.

The energy loss through every square meter of external wall, window or ceiling is given by60

The floor area of a given dwelling type is taken from the EES report. The calculations for the roof and wall areas assume that:

The upper floor area of two storey dwellings is proportional to the lower floor The area of the ceiling exposed to the roof cavity is the same as the floor area The internal temperature of the dwelling is the same throughout the dwelling.

Once equipment is purchased its lifespan is assumed to have a normal distribution with mean and variance specified by the user.

Let:

denote a given year

denote the year in which the equipment is installed

denote the mean life time of technology

denote the standard deviation of the lifetime of technology

denote the cumulative normal distribution with mean and standard

deviation .

For a given dwelling, the probability of a piece of equipment retiring in a given year conditional on it not retiring before this year is hence:

The in the first term of the numerator of the above expression assumes that upgrades

occur at the end of a given year (after the energy consumed in that year is accounted for). This is the only source of randomness in the model. For lighting and electric appliances, not all the equipment is replaced at once. MMA has assumed for these equipment types

60 Note that only heating is considered in Tasmania.



that a fixed proportion is replaced each year. The assumed means, standard deviations decision horizons used when making decisions and the proportion replaced are shown in Table B-2. Price and relative efficiency assumptions are shown in Table B-3.

Table B-2: Lifetime and decision assumptions for deteriorating equipment

Equipment type Average lifetime Standard deviation

Decision Horizon

Proportion replaced

Lighting NA NA 5.00 0.20

Water heaters 15.00 4.00 15.00 1.00

Refrigerators 12.00 2.00 12.00 1.00

Appliances NA NA 6.00 0.17

Table B-3: Price and efficiency assumptions

Costs

New Home Replace/Retrofit Item Units Efficient

technology Inefficient technology

Efficient technology

Inefficient technology

Relative efficiency

Roof insulation $/m2 16.50 0.00 16.50 0.00 NA

Wall insulation $/m2 13.50 0.00 27.00 0.00 NA

Double glazing $/m2 400.00 200.00 400.00 0.00 NA

Space heaters $ 700.00 500.00 700.00 500.00 0.10

Lighting $ 140.00 57.11 26.67 10.88 0.75

Water heaters $ 1800.00 1000.00 1800.00 1000.00 0.25

Refrigerators $ 1300.00 1000.00 1500.00 1000.00 0.60

Appliances $ 4800.00 4000.00 800.00 666.67 0.50

B.2.3 Assumptions

Some important assumptions to note about these calculations are:

Maintenance costs are ignored. The only cash flows arising from the adoption of a given technology are the up-front capital costs, changes in expenditures on energy consumed through the life of the product and replacement costs.

The performance of equipment and insulation do not degrade through time.

The efficiency of all equipment is constant through time.

All heating technologies are assumed to be able to raise the inside temperature to the user specified level.



There may be differences between the effective lifetime of efficient and inefficient equipment which would imply different decision horizon lengths. For lighting, the cost of inefficient lighting has been inflated relative to efficient lighting to account for this. There is unlikely to be a great difference between efficient and inefficient appliances.

B.2.4 Abatement options included

The following abatement options have been included in this model. Each is discussed in more detail in following sections.

The energy efficiency options that have been modelled are:

Increasing the required energy star rating of new dwellings.

Mandating the installation of roof insulation in new dwellings.

Mandating the installation of wall insulation in new dwellings.

Mandating the installation of floor insulation in new dwellings.

Mandating the installation of double glazing in new dwellings.

Mandating the installation of high efficiency lighting in new dwellings.

Mandating the installation of high efficiency heaters in new dwellings.

Mandating that new lighting must be high efficiency.

Mandating that new consumer appliances must be high efficiency.

Mandating that all new water heaters must be high efficiency.

Mandating that all new refrigerators must be high efficiency.

Encouraging residents to reduce the thermostat setting on heaters.

Encouraging residents to be more willing to take up energy efficiency improvements.

In the abatement options modelled for this sector, investment decisions are based on net present value calculations. NPV is generally considered to be economically rational in that investments will be undertaken when they are profitable. While there are many energy efficiency improvements available for residential dwellings which are economically efficient in this sense, even in the absence of a carbon price, the degree of uptake of improvements does not reflect this. It has been observed that generally only investments with payback periods of less than three years are adopted. Assuming a constant stream of payments accruing to a given capital expenditure, this equates to a hurdle rate of approximately 33%, which has been assumed for the analysis herein.

Increasing the required energy star rating of new dwellings

For this option, the user specifies the star rating that a new dwelling must comply with in each year and the relationship between the star rating and the efficiency improvement for



that star rating. The assumed relationship between star ratings and efficiency gains is shown in Table B-4. The net benefits and efficiency gains from each possible combination of measures is then calculated for each new dwelling, and the combination of measures with the highest net present value amongst those with efficiency gains which are greater than or equal to the required efficiency gain is chosen for that dwelling. If other mandatory measures are also specified then only combinations which include the mandated measures are evaluated.

Table B-4: Relationship between star ratings and efficiency gains

Star ratings Efficiency gain

0.00 0.00

3.50 0.00

5.00 0.30

7.50 0.50

10.00 0.70

The efficiency of a dwelling has been assumed to include both the thermal properties of the dwelling (i.e. things that affect the energy flux between the interior of the dwelling and the outside environment) and the heating equipment installed in the dwelling.

New dwellings: mandating insulation, double glazing, or high efficiency lighting/heating

For these abatement options, the user specifies when the adoption of the given measure will be mandated for new dwellings. All dwellings built after that time must comply with this mandate. Dwellings will take up these measures without the mandate, if the net present value of doing so is higher than not doing so.

Mandating that new lighting, consumer appliances, water heaters, refrigerators and space heaters must be high efficiency

For each equipment type, the user specifies the year in which all new equipment of the specified type must be of high efficiency. Before the specified time, consumers will choose the more efficient equipment if the net present value of doing so exceeds that of purchasing the less efficient equipment. After the specified time, only the efficient equipment is available.

The mean and standard deviation of the lifetime of the equipment, the investment horizon, capital and replacement costs and, for lighting and electrical equipment, the proportion of existing equipment that is replaced each year are specified.

Reduce the thermostat setting on heaters

The rate that heat is lost from a dwelling is proportional to the difference between the internal and external temperature. Reducing the internal temperature therefore reduces the rate that heat is lost.



While there is no actual cost associated with reducing the thermostat setting, it is modelled based on the assumption that the occupant of the dwelling would be prepared to sacrifice their comfort if it saved them enough money (or alternatively, if they were appropriately compensated). This cost has been modelled based on the relationship:

where is the compensation required to reduce the temperature by degrees.

The same holds for the rate at which a dwelling heats up in summer and the same could arguments hold to encouraging householders to increase the thermostat setting on air conditioners. MMA has only considered space heating, as cooling a relatively minor consumer of energy in Tasmania.

Encouraging residents to be more willing to take up energy efficiency improvements

A high rate of interest of 33% is assumed in this modelling, reflecting the high hurdle rate that appears to exist for the uptake of energy efficiency improvements. Reducing this hurdle rate considerably increases the uptake of these opportunities. This could potentially be achieved, for example, through awareness campaigns, subsidies or grants on energy efficiency improvements. This is modelled by simply specifying different interest rates under the reference case and with measures scenarios.

B.3 Energy efficiency – commercial

ABARE’s historical energy statistics and forecasts are the only temporal source of energy consumption data available for the commercial sector. Figure B-1 shows these data and the reference case energy forecast. Extrapolation was done using a smoothing spline and is fitted to ABARE’s predictions (not the historical data). It is clear that beyond 2015 the ABARE forecasts are essentially linear with respect to time and hence so are the reference case forecasts.

Figure B-1: Commercial sector energy use forecast (PJ)



These data aggregate ANZSIC divisions F, G, H, J, K, L, M, N, O, P and Q which cover a wide range of facility and operation types which have quite different energy use profiles61.

Two modelling approaches were considered for commercial energy efficiency for this project. The first approach was:

Estimate energy usage by building class and energy use category. Such data is available, for instance in the United States, from the Commercial Building Energy Consumption Survey (CBECS)62.

Estimate the existing level of efficiency of existing equipment for each energy use category.

Determine what efficiency gains could be achieved within each equipment category and build abatement options around these improvements.

There were several problems identified with this approach. Firstly, neither Tasmanian nor Australian specific data is available for step 1. Commercial energy use data for Tasmania has been hard to obtain. George Wilkenfeld & Associates, (2002) gives estimates of various end energy uses (air handling, cooling, pumping, heating, lighting and other) for the commercial sector nationally and for individual states for the year 1999.

Use of CBECS data, or at least a subset thereof coming from regions with a similar climate to Tasmania, was considered, but this would require assuming that the energy use between commercial subsectors are similar in Tasmania to those in the US. Further, energy efficiency improvement has been historically slow in Australia63, 64. Hence, even if commercial subsectors were similar in relative size, the energy use profiles may be significantly different.

Secondly, no data could be found on estimating the energy efficiency of existing equipment in Tasmania.

A third problem is that it is very hard to generalise what the most cost effective means of improving energy efficiency within a given building is. This makes it hard to determine what the uptake of improvements of a specific equipment type might be65.

The second modelling approach drew on energy efficiency audits the Tasmanian Government have recently completed for 25 government occupied buildings in Tasmania.

61 ANZSIC divisions: F. Wholesale Trade; G. Retail Trade; H. Accommodation and Food Services; J. Information Media and

Telecommunications; K Financial and Insurance Services; L. Rental, Hiring and Real Estate Services; M. Professional, Scientific and Technical Services; N. Administrative and Support Services; O. Public Administration and Safety; P. Education and Training; and Q. Health Care and Social Assistance.

62 http://www.eia.doe.gov/emeu/cbecs 63 See: International Energy Agency. (2007). Energy Use in the New Millennium: Trends in IEA Countries. Paris: International

Energy Agency. 64 The Climate Institute. (2007). National Energy Efficiency Target. Retrieved March 23, 2009, from

http://www.climateinstitute.org.au/index.php?option=com_content&view=article&catid=83:r1&id=114:briefingnational-energy-efficiency-target&Itemid=26#sdfootnote2sym

65 Unlike residential dwellings, the energy consumption of commercial buildings of comparable size and use differs greatly with respect to a given energy consumption type. Further, the costs and efficiency gains that might be achieved through retrofitting new equipment will also vary greatly and it is hard to determine what the relative costs and benefits of adopting a given energy efficiency improvement might be without reference to a specific building.



These reports identified energy efficiency improvements in and characterised them into three groups; investments with payback periods of less than three years, less than 5 years and more than 5 years. For each group of investments (in each building) the annual energy savings and capital costs are given. The current annual energy consumption is also given. The majority of these buildings are office space, though the sample also included buildings which housed laboratories, Parliament House and the state library. One office building contained some retail space and another some accommodation. Most of the efficiency improvements relate to lighting and HVAC. These data can be used as follows:

Estimate the internal rate of return (IRR) for each class of investments for each building in each year between now and 205066.

For a given hurdle rate, determine which year (if any) each investment would be taken up.

For each year between the present and 2050, determine what proportion of energy efficiency improvements have been taken up to that time. The capital expenditure in a given year is the sum of the capital expenditure on investments made in that year. The energy savings in a given year is the sum of the annual energy savings from all investments taken up prior to or in that year.

Assume that buildings, capital costs and energy efficiency improvements are representative of all commercial buildings in Tasmania, and weight the capital costs and energy savings by the ratio of total commercial energy consumption in 2009 to the total energy consumption of buildings in the sample.

This approach makes the strong assumption that the buildings in the sample are representative of all commercial buildings in Tasmania, but the buildings in the sample are predominantly office space and clearly the composition of energy use of different building types will differ. MMA has made adjustments to various building categories in cases that there will clearly be differences between the energy use profiles of that building compared to offices. Specifically:

Food service, inpatient and lodging building types will have commercial hot water heaters.

Food service will have commercial stoves and ovens.

Food service and food sales will have commercial refrigerators.

Figure B-2 shows energy use by building type data from United States, from the Commercial Building Energy Consumption Survey (CBECS)67. It is assumed that energy use profiles within a given commercial subsector are similar in Tasmania to the US, and

66 The IRR will change through time as energy prices change. MMA has used an investment of horizon of 25 years in IRR

calculations. 67 http://www.eia.doe.gov/emeu/cbecs



adjust for the above equipment differences. The resulting energy consumption profiles are shown in Figure B-3.

Figure B-2: Energy use for different building types from the CBECS

Figure B-3: Adjusted energy use for different building types

Some points worth noting about these profiles are:

While public assembly is quite different to other building types, the inclusion of Parliament House in the sample should cater for this to some extent.



Vacant buildings are also different to other building types, but are likely to only account for a small fraction of total energy use68.

For the two dominant energy uses (space heating and lighting), office buildings are approximately average when compared to the other building types (with the exception vacant buildings and public assembly).

Even with these adjustments, there is still a strong assumption being made about the representativeness of offices for other building types. For example, it could be expected that the lighting technology used in warehouses and storage facilities is predominantly fluorescent and, in particular, high intensity discharge. These are relatively efficient forms of lighting and there may be limited potential to improve on their efficiency. It could also be expected that buildings like hospitals would get larger efficiency gains from similar levels of capital expenditure. If, for example, the same lighting technology upgrade were applied to both a hospital and an office, the reduction annual reduction in energy consumption may greater in the hospital since the lights are likely to be left on for a larger amount of time69.

The energy consumption that has been removed as described above could be dealt with in other abatement options: improvements to commercial refrigeration, improvements to commercial hot water heaters and improvements to commercial cooking equipment. This has not been pursued further.

Currently, various government departments are currently investigating their energy usage and compiling similar energy efficiency audits. Once these are completed this analysis can be extended and potentially done for individual commercial subsectors.

The above modelling approach is only applicable to existing buildings. The same energy audits used above also gave indicative NABERS ratings and the floor area of each building audited70. These data are shown in Figure B-4. The average star rating for the buildings for which NABERS ratings were given was 3.33. Based on the regression shown in Figure B-5, this corresponds to an energy usage of 0.6 MJ/m2/year, while buildings of 5 have an energy use of 0.28 MJ/m2/year; an energy use reduction of 53%. Increasing the star rating of new buildings has been implemented as an abatement option.

Energy use has been divided into three categories:

1. Energy use in existing buildings that is represented by the 25 buildings audited, 2. Energy use in existing buildings that is not represented by the 25 buildings

audited, 3. Energy use in new buildings

68 This is the case in the CBECS data. 69 This is likely to be only true of capital costs. Maintenance costs (e.g. replacement bulbs) are likely to be proportional to

operation levels. 70 Six (non-office) buildings did have NABERS ratings and were excluded from the plot.



The division between these three categories under the reference case is shown in Figure B-5.

Figure B-4: NABERS star rating Vs energy use (MJ/m2/year)

Figure B-5: Division of commercial energy use, represented additively

B.3.1 Abatement options

The following abatement options have been developed for the commercial sector:

Increase uptake of energy efficiency opportunities with paybacks of less than three years.

Increase uptake of energy efficiency opportunities with paybacks of less than five years (but greater than three).



Increase uptake of energy efficiency opportunities with paybacks of greater than five years.

Increase the required star required for new buildings.

The IRRs for investments with payback periods of less than three years for twenty of the twenty-five audited buildings are shown in Figure B-6. The following exclusions were made:

1. The TGIO building was excluded because it had no investments with payback periods of less than three years,

2. The Anne O’Byrne building, Marine Board building, Mt Pleasant labs and Lands building were excluded because they had IRRs of 274%, 296%, 447% and 2394% respectively and including them in the plot would reduce interpretability.

Figure B-6: IRRs for energy efficiency improvements for audited buildings using 2009 and 2050 (CPRS) electricity prices

The minimum internal rate of return for efficiency improvements with payback periods of three years or less is 31% at current electricity prices. This seemingly high hurdle rate implies that there are non-cost barriers to the uptake of energy efficiency opportunities. Clearly increases in energy prices will increase the IRRs, but a tripling of electricity price typically doubles the IRR amongst the twenty buildings represented in Figure B-6. As MMA has no information about energy efficiency opportunities that have been exploited, there is no guide as to what magnitude of IRR might be required for a given opportunity to be exploited or evidence to hypothesise as to whether these specific improvements are not exploited for specific reasons.

Assuming that these buildings are representative of commercial buildings in general, the total energy use reduction potential for investments with payback periods of less than three years is 0.124 PJ. Any hurdle rate under 31% would achieve this potential in 2009.



With regard to energy efficiency improvements with payback periods of less than five years, analysis of IRRs show that they still appear high enough to indicate that there are non-price barriers to the uptake of these investments. These investments generally involve larger capital expenditure and, by definition, take longer to pay back.

With regard to energy efficiency improvements with payback periods of more than five years, analysis of IRRs for the majority of these investments show that IRRs are below the required hurdle rate for energy efficiency improvements. However, it is likely that the barriers to uptake in this class of investments would be greater than for investments with lower payback periods.

B.4 Analysis of options to improve energy efficiency

B.4.1 Increasing the required energy star rating of new dwellings

The effect of increasing the star ratings is shown in Figure B-7. Increasing the required star ratings of new dwellings increases the energy saved and the NPV of benefits up to 9 stars. Increasing it to 10 stars further increases the energy saved, but the net benefits are negative. This is because at a 10 star rating implies that double glazing must be installed in most dwelling classes, although it is not cost effective. The capital expenditures, energy savings and net benefits are shown in Table B-5.

Figure B-7: Difference in energy consumption for new dwellings under various star ratings



Table B-5: Changes in energy consumption and expenditure accruing under various star ratings if instated in 2010, $

Scenario Total

expenditure NPV

expenditure Energy savings

Energy cost savings

NPV energy savings Net benefit

6 Stars 214,613,036 28,531,738 7.62 515,447,521 30,582,737 2,051,000

7 Stars 243,000,072 36,642,383 9.39 628,004,851 41,986,396 5,344,013

8 Stars 202,462,234 35,399,640 9.51 632,177,013 44,860,238 9,460,598

9 Stars 207,833,245 36,527,833 9.96 661,685,922 47,215,805 10,687,972

10 Stars 475,487,189 86,843,563 11.10 736,145,664 53,282,577 -33,560,986

CPRS Only 173,958,259 18,457,719 5.69 390,472,706 19,944,072 1,486,353

B.4.2 Installation of ceiling or wall insulation, double glazing or high efficiency heaters in new dwellings

Five scenarios were considered:

1. Mandate the installation of ceiling insulation in new dwellings.

2. Mandate the installation of wall insulation in new dwellings.

3. Mandate the installation of double glazing in new dwellings.

4. Only allow efficient space heaters (in new dwellings or as replacements).

5. Mandate the installation of ceiling insulation, wall insulation and double glazing and only allow efficient space heaters.

The differences in energy consumption are shown in Figure B-8. Total expenditure on equipment, energy savings and present values are shown in Table B-6.

Of these scenarios, mandating the installation of wall insulation is the only one which gives rise to a positive net present value. This is because wall insulation is installed early in the study period and those dwellings then reap the rewards later in the study period. This effect is partially due to the (arbitrary) decision horizon of twenty years and the high hurdle rate.

Mandating the installation of double glazing alone or efficient heaters alone leads to higher energy consumption at the end of the study period. This occurs because installation of double glazing is sufficient to meet the 5 star rating standard, but once it is installed, it is not cost effective to install any other insulation under the prevailing energy prices toward the end of the study period. The same is true of efficient heaters. In both these cases higher levels of efficiency can be achieved using other sets of thermal measures.



Figure B-8: Changes in energy consumption from mandating energy efficiency options in new dwellings

Table B-6: Changes in energy consumption and expenditure from mandating energy efficiency options in new dwellings, $

Scenario Total

Expenditure NPV Energy cost

savings NPV Net Benefit

All Together 566,234,394 118,660,854 737,944,763 54,616,490 -64,044,364

Ceiling Insulation 248,279,364 39,841,187 617,377,159 39,682,470 -158,717

Wall Insulation 182,705,783 25,492,879 528,088,033 31,858,219 6,365,339

Double Glazing 404,588,291 81,009,689 349,263,000 20,151,228 -60,858,462

Efficient Heaters 221,857,961 31,379,118 398,274,930 22,469,974 -8,909,144

B.4.3 Mandating new lighting, consumer appliances, water heaters and refrigerators to be high efficiency

As lighting is fully taken up under the reference case, mandating all new lighting to have high efficiency makes no difference. Forcing consumer appliances to be efficient beyond 2010 brings forward the increase in their take up by a few years as shown in Figure B-9. This is also the case for refrigerators and water heaters which are shown in Figure B-10 and Figure B-11 respectively. Later in the study period all these appliances become efficient to all dwellings and they get fully taken up under the reference case. Table B-7 shows the changes in capital expenditure, electricity savings and the resulting net benefits when each of the appliances are forced to be efficient from 2010.



Table B-7: Change in expenditure when all lighting, consumer appliances, water heaters, refrigerators and space heaters beyond 2010 are high efficiency

Energy Use Capital ($) Electricity Savings ($) NPV (2008 $)

Lighting 0 0 0

Water Heating 7,504,200 73,767,597 8,177,886

Refrigeration 41,805,000 302,544,411 23,958,722

Consumer Appliances 1,740,800 898,487 -549,933

Figure B-9: Comparison of emissions reductions when all consumer appliances are mandated to be high efficiency

Figure B-10: Comparison of emissions reductions when all refrigerators are mandated to have high efficiency



Figure B-11: Comparison of emissions reductions when all water heaters are mandated to have high efficiency

B.4.4 Encouraging residents to take up energy efficiency improvements

Halving the hurdle rate (to 16.5%) decreases energy consumption over the study period by around 44PJ. The changes in actual energy consumption are shown in Figure B-11 and the differences in expenditure in Table B-8. The sudden drop (relative to the CPRS alone) in energy use by consumer appliances is caused by the uptake of efficient appliances which increase sharply around 2014. This change occurs because consumers observe the higher electricity prices induced by the CPRS, and are more sensitive to these under the lower hurdle rate.

Figure B-12: Percentage reductions in energy consumption when the hurdle rate is halved (from 33% to 16.5%)



Table B-8: Change in expenditure when the investment hurdle rate is halved (from 33% to 16.5%)

Energy Use Capital ($) Electricity Savings ($) NPV (2008 $)

Space Conditioning 399,788,081 1,828,834,750 14,874,665

Lighting 0 0 0

Water Heating 33,456,400 250,096,994 70,099,882

Refrigeration 55,523,500 336,131,942 32,255,871

Consumer Appliances 5,774,400 168,357,456 125,033,914

Total 494,542,381 2,583,421,141 242,264,332

B.4.5 Increase the required star required for new buildings.

As noted above, increasing the star rating of new buildings has the potential to dramatically reduce the energy consumption of those buildings. Under the reference case and with the division of energy between new and existing buildings described above, this option has the potential to reduce energy consumption by 1.7 PJ in 2050.

B.5 Renewable energy

Uptake of renewable energy sources induces emissions reductions through the reductions in imported generation which is likely to have a significant share of fossil fuel generation. Renewable energy can also delay the requirement for new fossil fuel power stations and can replace aged fossil fuel power stations.

Modelling of the renewable energy generation potential is based on MMA’s internal database of existing, committed, and proposed renewable generation projects. This data has been collected over many years and includes published and derived data on the capital and operating costs, capacity, fuel costs, location and capacity factors. In some cases this data has been updated with Tasmania specific information.

Each form of renewable generation has been assigned limits to the absolute quantity of the generation that may be installed. Individual limits are assigned for the period to 2020 and the period from 2020 to 2050. The limits are there to allow for issues that cannot easily be modelled, such as:

Resource availability.

Economic constraints.

Social issues.

A combination of more the one of the above.

The total quantity of renewable generation that may be introduced into the electricity market is used in the model to reduce the quantity of imports from the mainland. This total therefore, is only limited by the amount of demand in Tasmania plus the export



capability of Basslink. A limit on the wind energy that can be installed without upgrade of the transmission system and without upgrade of ancillary services has been imposed.

The renewable energy sub-sector comprises generating technologies that derive their power from renewable resources and therefore, have zero (or very low in the case of biomass) net carbon dioxide emissions. Renewable energy generation options are described below.

Wind

In the case of wind generation, the total capacity that may be installed is subject to the availability of suitable wind resources in regions where access to transmission or distribution assets are available. As more wind farms are commissioned they will utilise the best available sites with the highest average wind speeds. Subsequent installations will be installed in regions with lower quality wind resources resulting in lower capacity factors and therefore lower generation. This impact has been incorporated in the analysis by reducing the capacity factor of newly installed wind farms based on the cumulative capacity of all wind farms installed to that date. These calculations are based on the capacity factor reducing from 40% to 35% after more than 1,000 MW of wind generation is installed in Tasmania, to 27% after more than 2,000 MW of wind generation is installed in Tasmania. MMA also understands that the transmission system and ancillary services infrastructure need to be upgraded should any significant wind generation resources be located in the north of Tasmania. MMA has assumed transmission costs of $100/kW, which increase to $500/kW after 1,000 MW of wind generation is installed in Tasmania. Transend has advised MMA that ancillary services need immediate upgrade with any new wind generation and thus MMA has assumed ancillary costs of $10/MWh for any new generation, $15/MWh after 1,000MW of wind is installed, and $20/MWh after 2,000MW of wind is installed.

Capital costs for wind farm developments have been assumed to be $2,500/kW, and are projected to decline by 2.5% per annum in real terms up to 2020, declining by 1% per annum after 2020. Although costs have risen in the past few years due to increasing steel prices it is assumed that economies of scale in manufacturing and more efficient turbines will reduce the cost in the long-term. Operating costs consist primarily of monitoring and maintenance costs and are assessed to be $5/MWh. Grid connection costs are included with the capital cost.

Small scale wind projects are assumed to cost $4,624 per installation, based on IPCC data. Other operating data is the same as for centralised wind projects. Total installations have been limited to 30% of households.

Photovoltaic / Solar

The available resource for photovoltaic generation is large and Australia’s climate is ideally suited to its widespread adoption. This is also true in Tasmania in spite of shorter daylight hours than elsewhere in Australia. Two scales of photovoltaic installation have been considered:



Small scale of 1 to 5 kW, installed on the roofs of houses and commercial buildings and feeding excess electricity into the local electricity grid.

Large scale, installed as centralised power stations in high solar insolation regions that will connect directly into the transmission grid.

The applicable limits for each scale are determined on different bases. Small scale limits are limited by the percentage of rooftops that are suitable or available for installation of PV systems. MMA has assigned a limit of 50% of total houses71 (separate dwellings only) as the limit for 2020 and 80% for 2050.

Large scale systems are subject only to limits on the availability of high insolation sites with suitable access to the electricity grid. MMA has limited these large scale systems to 10 power stations of 25 MW in 2020 and 20 systems of 50 MW in 2050.

The capacity factor for photovoltaic systems depends on the geographical location (since clear sky increases closer to the equator), local geography, the PV panel’s location (i.e. optimum vertical angle and facing direction) and presence of sun tracking systems (which enable significant increase in capacity factor, but are usually only economic for large scale systems).

MMA has assumed an Australia wide average capacity factor for small roof top systems of 15%; this is reduced according to number of systems to a value of 12%, as capacity factors will decrease as less optimal sites are utilised. Centralised photovoltaic systems have been assigned a capacity factor of 20% based on the published data on the Solar Systems Victorian 154MW Heliostat Concentrator Photovoltaic (HCPV) system.

The cost of installed photovoltaic systems in the 1 to 5 kW range is currently in the vicinity of $10,500 /kW, operating costs of $20/MWh have been assigned based on a service check being required every five years at a cost of $200. Capital costs are expected to decline over time, as they have in the past due to improvements in efficiency and economies of scale in manufacturing. MMA accounts for this during the annualisation of capital cost figures. Short-term price increases may occur if the supply cannot keep up with any demand increases for solar panels.

The cost of large scale, centralised PV power stations is more difficult to estimate as significant variation between projects occurs as a result of different technologies and concentrator design. The Solar Systems Victorian 154MW solar power station discussed above will reportedly cost $420 million or $2,730/kW, using sunlight concentrating systems and high efficiency photovoltaic cells. Differing sunlight concentrating geometries of concentrating solar systems are also likely to give significantly different cost. It has been reported that using photovoltaic panels in conjunction with a Fresnel lens to concentrate sunlight may halve the cost of electricity generation relative to flat panel

71 Household and Family Projections, Australia, 2001 to 2026, ABS Cat. No. 3236.0, 2004.



systems. Amonix Incorporated72 has reported that concentrating photovoltaic systems will be able to be produced commercially at a cost of $3,000/kW at a production rate of 100MW per annum. Capital costs of large non-concentrating solar arrays are likely to be in the vicinity of $10,000/kW.

A conservative estimate of capital costs of $6,000/kW for large scale photovoltaic generation has been assumed with an annual reduction of 2.5%. If the costs reported above eventuate, the model will overestimate the costs of centralised PV systems.

Solar thermal power stations use the heat of the sun, normally concentrated with parabolic mirrors or a heliostat mirror system, to produce steam to drive a turbine. Alternatively, solar thermal heating may be utilised in existing fossil fuel power stations to preheat the boiler water, resulting in lower fuel use.

The available resource for solar thermal generation is similar to that of photovoltaic technology. We have imposed limits of 2 standalone power stations of 50MW in 2020, and 10 standalone power stations of 100 MW in 2050. The capacity factor for solar thermal plant is assumed to be 25%.

Solar thermal power stations are likely to have capital costs around $3,500/kW, reducing at 1%/year, and operating costs of about $10/MWh.

Geothermal

Geothermal energy involves using the heat from the earth to power electricity generation or directly for heating. Heat is extracted using water and the hot water is used to generate electricity (or directly for heating which avoids efficiency losses). The technology currently supplies around 11% of New Zealand electricity73.

Geothermal plants supply base load power and only emit zero or negligible amounts of CO2 during production of electricity. Kuth Energy has identified a very large area in the north west of the state with heat flows similar to the Cooper Basin, which is therefore a potentially large resource. Researchers are now applying 3D heat flow modelling and resource estimation to validate the potential for Tasmania.

Biomass

The bio-energy sector consists of electricity generating facilities that utilise some form of biomass as the primary fuel source. The sector may be subdivided into three sub-sectors:

Generators that burn biomass directly to raise steam to drive a steam turbine.

Generators that anaerobically decompose the biomass to produce methane that is used as fuel.

72 http://www.terradaily.com/news/solarcell-05f.html “Cost Of Photovoltaic Concentrators Falling Fast”, Golden CO

(SPX) Jul 19, 2005. 73 “Geothermal Potential in Tasmania”, Roger Lewis, Kuth Energy, Royal Society Tasmania Winter Lecture June 16, 2009.



Generators that employ slow pyrolysis to create a synthetic gas that is subsequently used as a fuel. In this case the primary output is biochar and the secondary output is electricity. This is a newly emerging technology and the biochar can be used as a direct replacement for coal or as a soil conditioner.

The biomass fuel may be waste from existing activities or crops grown specifically for the purpose of generating electricity. Clearly, specifically grown biomass will likely have a higher cost, and the conversion of land to bio-energy plantations raises significant social, ethical and environmental issues through competition for food crops.

Limits for bio-energy relate to the availability of waste materials and availability of land for bio-energy crops. The degree of dispersion is also a key parameter. The more dispersed the resource is the higher transport costs associated with delivering the material to the power station.74 Location of a generator near to a fuel source, such as sawmills, will result in reduced fuel costs.

The limits have been set as a percentage of the estimated available resource considering the geographical dispersion of the fuel and possible plantation sites. Thus the limits for sawmill and forestry waste are higher than the other sub-sectors due to the co-location of much of the material.

Sewage is utilised as a fuel for electricity generation by employing anaerobic digestion of the material to neutralise the waste product and generate methane to be used as fuel. Sewage is currently treated this way in a number of treatment works around Australia, and the current uptake of this technology is used as a baseline for the calculations.

The limits for sewage are set as a percentage of the available waste that could be utilised. The limits for both sub-sectors are set at 80% in 2020 and 90% in 2050. The high limits for these sectors are reflect the concentration of the waste materials and the fact that they currently need to be treated prior to disposal so low collection and transport costs are incurred.

74 NSW Bioenergy Handbook, Department of Energy, Utilities and Sustainability, 2004.



Table B-9: Bio-energy assumptions


Upgrade to Hydro

Currently, the bulk of Australia’s renewable generation is provided by hydroelectric systems, particularly those in Tasmania and those associated with the Snowy Mountains Scheme. It is unlikely that hydro systems of that scale will be built again due to environmental concerns and public opposition. There is, however, potential for significant levels of new hydro power at existing dams, weirs, and other water way obstructions. There is also potential to build pump storage facilities which act as a backup for both wind and hydro energy.

The limits for hydro are set in terms of total MW of capacity installed to 2020 and 2050. Realistic limits are difficult to determine as there are many options for new and upgraded hydro generation but some are likely to be prohibitively expensive. This depends heavily on the uptake of wind and other intermittent generation technologies the reliability of which can be improved through pumped storage. MMA has estimated the limit to be 100 MW for 2020 and 400 MW for 2050.

Tidal / Wave

Ocean energy is currently at a pre-commercialisation stage and utilises the energy in tides or waves to generate electricity. Numerous methods of harnessing this power have been demonstrated at various locations around the world, and BioPower currently hold

Parameter Agricultural residues

Forest & sawmill wastes

Sown Pasture

Crop Land

Sewage

Bio-energy resource size, ha 300,000 480,000

Bio-energy resource size, t/year 2,000,000 1,000,000

Bio-energy conversion efficiency, % 20 25 25 25

Energy Content of bio-energy residues, MWh/t 3 5 25 30 7.4

Maximum available units 39 32 24 46 3

Existing methane fraction utilized, % 60

Fraction treated anaerobically, % 36

BOD Load per capita, kg/person 22.5

Sewered population, % 82

Proportion of industrial load, % 45

Methane Emission rate, kg CH4/kg BOD 22



proposals for a 250 kW Wave powered pilot plant at King Island and a 250 kW tidal powered pilot plant at Flinders Island75, both in conjunction with Hydro Tasmania.

Wave energy works by generating electricity either on or off-shore and harnessing the required wave energy from submerged wave farms. The wave farms capture energy in off-shore ocean waves by pumping water through a circuit. The circuit can also allow for water to be pumped through a desalination process if required. Generating the electricity off-shore has the advantage of lower complexity as standard generation equipment is employed and therefore lower capital and maintenance costs are incurred. Unlike wind energy however, wave energy is suitable for base-load power generation if the regional wave characteristics meet appropriate criteria. This is because waves are more consistent and are predictable days in advance, and the energy density in waves is higher.

Tidal energy can work in either of two ways; using the current created by the rise and fall in sea level to generate power (similar to underwater wind turbines), or traditionally by use of a barrage to drive a turbine similar to traditional hydropower technology. Tidal energy is more predictable than wave energy since tidal capability can be predicted years in advance, but cannot meet peak demand due to the timing of the tides in 12 hour intervals. A 240 MW station has operated reliably in France since 1966, as well as a 20 MW station in Canada since 1984 and various stations in China since 1977 totalling 5 MW76. Current based systems are gaining popularity because of lower capital cost and lower impacts on the environment.

The capacity factor of barrage systems is also relatively low, reported at around 20-35%, but the predictability of this form of generation enables scheduling of tidal power years in advance if necessary. The power output for current based systems are proportionately affected by the square of the diameter of the blades, the density of the surrounding medium and the cube of the velocity of the water currents. Since seawater is 1000 times denser than air, more power can be output by a tidal turbine than a wind turbine, and small changes in current and blade diameter can have large impacts on power supply. A proposal77 by Tenax Energy Pty Ltd for the generation and supply of renewable offshore tidal energy to the entrance of Port Phillip Bay in Victoria, indicates that the tidal turbines can operate around 75% of hours over the year, and that yearly inspections of the turbines are required with 4 yearly refurbishment of each turbine. MMA translate this as an increase in capacity factor over the traditional barrage/dam based system and understand the range of capacity factors for current based systems are around 30-50%.

Wave technology and current based tidal technology is not yet mature enough for commercial application. The Carnegie Corporation expects to build a first commercial wave demonstration plant in Australia in 2010 (2-5 MW at $10,000/kW) and expand this to 50MW by 2013 ($6,000/kW)78. There is considerable interest in the technology, so the

75 http://www.theaustralian.news.com.au/business/story/0,28124,25600893-5018910,00.html 76 http://www.oceanenergycouncil.com/index.php/Tidal-Energy/Tidal-Energy.html 77 http://www.environment.gov.au/cgi-bin/epbc/epbc_ap.pl?name=current_referral_detail&proposal_id=4480 78 http://www.carnegiecorp.com.au/files/brokerreports/2009/090610_Patersons_The%20new%20wave%

20in%20clean%20energy.pdf



option has been included at a limited uptake level. The limits are 1 power station of 50 MW for 2020 and 5 power stations of 100 MW by 2050. It has been assumed that similar technology is available from 2015 and that capital costs decline by 2% per annum. Tidal technology is assumed to follow the current based model and an assumption of $2,000/kW for capital costs, $20/MWh for operating costs and a 40% capacity factor is made.

Nuclear

Whether or not Australia should build nuclear power plants is a matter of policy and public discussion. Given the long lead times involved in regulatory approvals, site selection, engineering design and construction, it is unlikely that a nuclear plant would be built in Australia prior to 2025, so this option is only available in the period from 2025 to 2050 in modelling. It is assumed that at the maximum only 500 MW would ever be built during this time.



Table B-10: Renewable emission abatement options – summary of assumptions Assumptions

Wind1 Wind2 Wind3 Solar PV Small scale wind

Solar PV Central

Solar Thermal

Farm residues

Forest and

sawmill wastes

Sown Pasture

Crop Land

Sewage Hydro Geo-thermal

Tidal Wave Nuclear

Limit type

No of

farms

No of

farms

No of

farms

% houses % houses No of

power

stations

No of

power

stations

% for

bio-

energy

% for

bio-

energy

% for

bio-

energy

% for

bio-

energy

% of CH4

used

MW No of

power

stations

No of

power

stations

No of

power

stations

No of

power

stations

Limit 2020 10 - - 50% 10% 10 2 30% 25% 25% 25% 80% 45 4 1 1 0

Limit 2030 10 10 - 60% 20% 24 15 30% 25% 25% 25% 80% 90 52 52 2 1

Limit 2050 10 10 30 80% 30% 50 50 40% 50% 100% 20% 90% 193 150 150 5 1

First year available 2012 2020 2030 2012 2012 2012 2012 2012 2015 2012 2012 2012 2012 2015 2020 2015 2028

New capacity 2020, MW 100 100 100 0.001 0.001 25 50 25 25 50 50 5 15 50 100 50 0

New capacity 2030, MW 100 100 100 0.001 0.001 35 70 25 25 50 50 5 15 50 100 50 500

New capacity 2050, MW 100 100 100 0.001 0.001 50 100 25 25 50 50 5 15 100 100 100 500

Capacity factor 42% 35% 27% 12.8% 42% 17.0% 21.3% 70% 70% 70% 70% 80% 25% 85% 40% 95% 85%

Capex 2010, $M /MW $2.32 $2.32 $2.32 $12.5 $4.62 $8 $5.5 $3 $2.77 $3 $3 $2.2 $2.2 $4 $2 $6 $3.7

Capex decline, real % 2.5% 2.5% 2.5% 2.5% 2.5% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 0.5% 0.5% 2.0% 2.0% 0.5%

Capex decline post 2020, real % 1.0% 1.0% 1.0% 1.5% 1.5% 1.0% 1.0% 1.0% 1.0%

Operating cost, $/MWh $0 $0 $0 $10 $5 $10 $10 $15 $15 $15 $15 $5 $3 $12 $20 $10 $23

Fuel cost, $/MWh $0 $0 $0 $0 $0 $0 $0 $45 $40 $45 $45 $0 $0 $3 $0 $0 $6

Ancillary service costs, $/MWh $10 $15 $20 $0 $0 $5 $5 $0 $0 $0 $0 $0 $0 $3 $0 $0 $5

Transmission costs, $/kW $100 $500 $500 $0 $0 $100 $100 $100 $100 $100 $100 $100 $100 $100 $100 $100 $100

Life 25 25 25 20 20 20 20 20 20 20 20 20 25 25 25 26 25




B.6 Co-generation

Cogeneration systems provide on-site generation of electricity and use waste heat for other purposes such as heating and water heating. They can operate in businesses which primarily produce electricity and provide heat to nearby facilities, or alternatively use waste heat from industrial processes to raise steam which is in turn used to generate electricity.

Cogeneration plants result in lower emissions primarily because the heat produced during electricity generation is used. This can make a cogeneration facility around 85% efficient compared to a typical electricity generation facility with efficiency of around 35%. Displacement of coal for gas during the equipment upgrade can further reduce emissions. There are also savings from avoided transmission costs, reductions in peak load demands and potentially, stabilisation of regional load profiles. Cogeneration is only suitable where the heat may be used reasonably close to the generation site. In some industrial regions in particular, this may be in neighbouring facilities.

Medium to large scale commercial and industrial facilities are ideally suited to cogeneration because they have significant heating loads for process heat, space heating or hot water as well as significant electricity demands.

Cogeneration plant installations were considered in the sub-sectors listed in Table B-11. The key input is the percentage of facilities in each sub-sector that should have cogeneration installed. The limits on this value for facilities likely to be on the gas network are set to 50% and 90% in 2020 and 2050 respectively. Because the Tasmanian gas grid is not extensive, however, a number of facilities have been reduced to 20% in 2020 and 40% in 2050.

For each installation, it is assumed that a single cogeneration plant of a representative size may be installed at each facility. The size of the cogeneration plant defines the quantity of electricity and heat that will be replaced at an assumed ratio of 3:5 for electricity to heat. The actual configuration required for various sites may vary from this. The analysis determines the emissions that were emitted prior to the cogeneration installation based on the emission intensity of the grid electricity replaced and the fuel consumed in the boiler that provided the heat loads replaced.

The installed cogeneration unit size for new houses is based on 180 kW systems serving clusters of around 16 houses. In order to model the effect a per house electricity consumption of 11 kW has been used.

Installation of a cogeneration plant causes fuel consumption to increase and grid supplied electricity to reduce by the output of the cogeneration unit. Heat demand is assumed to remain constant. Cogeneration is assumed to displace imports from the mainland rather than renewable energy and hence the emissions savings are calculated as the output of the unit multiplied by the average fossil fuel intensity in the business as usual case. As more renewable forms of generation enter the model the emission intensity may change as Tasmania realises the potential to become a net exporter rather than a net importer.



Costs include the capital and operating costs of the cogeneration plant, and the difference between the cost of gas consumed and the grid electricity consumed before and after cogeneration installation.

The number of facilities in Tasmania that would be suitable for a cogeneration installation has been estimated for each facility category based on ABS data, the Emporis79 website, the Australian Institute of Health and Welfare80, and estimates of the number of industrial plants of suitable size.

Costs and operational parameters of cogeneration facilities are based on MMA’s database of generation technologies.

Within most of these sectors, cogeneration plant would displace electricity imports in favour of gas used for cogeneration. The only exception is the industrial sector - where cogeneration plant would displace either coal or coke currently used for direct combustion purposes. MMA understand that some fuel switching has already occurred in larger hospitals which have already or are about to be switched from use of fuel oil and/or LPG to natural gas.

Capital expenditure is typically around $1.5M/MW assuming a 1% p.a. decline annually but is varied to allow for economies of scale using the a logistic functional form.

The main assumptions relating to the modelling of the installation of cogeneration plants are found in Table B-11.

79 http://www.emporis.com 80 Australian hospital statistics 2005-06, Australian Institute of Health and Welfare, AIHW cat. no. HSE 50, May 2007.



Table B-11: Cogeneration options

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A

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B

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C

Fuel displaced Electricity Coal Coal Coke

Fuel 1 (considered as a

substitute for coal and a

cogeneration opportunity) Gas

Gas

Gas

Gas

Gas

Gas

Gas

Bio

fuel

Gas

Gas

Gas

Gas

Gas

Gas

Gas

Gas

Gas

Gas

Gas

Bio

fuel

Gas

Fuel 2 (only considered as

a substitute for coal)

Wood pellets

Limit 2020 50% 100% 20% 50% 20% 20% 20% 20% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Limit 2030 90% 100% 30% 90% 30% 30% 30% 30% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Limit 2050 90% 100% 40% 90% 40% 40% 40% 40% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Number of Facilities 14 1 22 29 1 2,000 10 1 1 1 3 1 1 1 1 1 1 5 9 1 1

Typical Size, MW 0.50 5.00 0.50 1.00 1.00 0.01 3.00 3.00 1.00 1.00 0.67 2.00 2.00 0.67 1.00 15.00 2.00 0.60 100 100 100

Typical capacity factor, % 85% 85% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70% 70%

Fuel Use, PJ 0.04 0.37 0.03 0.06 0.06 0.00 0.18 0.22 0.06 0.06 0.04 0.12 0.12 0.04 0.06 0.92 0.12 0.04 6.13 7.36 14.89

Heat rate of fuel 1, GJ/

MWh

10 10 10 10 10 10 10 12 10 10 10 10 10 10 10 10 10 10 10 12 10

Heat rate of fuel 2, GJ/

MWh

10 10 10 10 10 10 10 12 10 10 10 10 10 10 10 10 10 10 10 12 10



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Ind

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A

Ind

ust

rial

B

Ind

ust

rial

C

Heat rate of existing fuel,

GJ/tonne

1.55

27 27 27

Boiler efficiency, % 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80%

Electricity requirements

above basic boiler, ratio

1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60 1.60

Operating cost with

existing fuel, $/ MWh $3 $3 $3 $3

Operating cost under fuel

1, $/ MWh $3 $3

Operating cost under fuel

2, $/ MWh $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3 $3

Life, years 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20

IDC factor 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 1.049 Source: MMA Analysis. * May not be on grid so maximum limits have been reduced.



B.7 Fuel Switching

In addition to cogeneration, fuel switching was also considered for those sites which currently use coal for direct combustion. Fuels switched include gas and biofuels.

With regard to biofuel for coal replacement, wood pellets were specifically considered as use of these require no capital upgrade costs even though the cost of the fuel itself is significantly higher than coal. This option has been considered as a result of significant increases in the use of wood pellets elsewhere. Use of wood pellets in Europe has increased substantially as a result of mandated increases in renewable energy use in the EU. MMA has assumed that the cost of switching to either wood waste or wood pellets is capped at $150 per tonne.

With regard to biofuel for replacement of liquid fuels in the direct combustion sector, MMA has assumed that costs are feasible although there is significant uncertainty about this option. MMA assume that significant amounts of biofuel will not become available until after 2020 when second generation biofuels are likely to become feasible and that gradual replacement of fossil fuel can occur between 2020 and 2050 up to 100% by 2050.

B.8 Fossil Fuels

New generation in Tasmania is likely to take the form of renewable generation, further co-generation, extended inter-connection capacity of Bass-link, or additional gas fired generation capability. Carbon capture is an uncertain new technology that is unlikely to enter the market before 2020, particularly in Tasmania where additional gas generation is likely to take the form of peaking generation. As the existing gas fired generation is relatively new, improved efficiency would likely have a fairly minor effect, though conversion to carbon capture and storage capability might be considered post 2020. Assumptions are detailed in Table B-12.

Table B-12: Fossil fuel abatement option assumptions

Option Unit Improve efficiency standards

Carbon Capture

Limit 2020 % 3% 50%

Limit 2050 % 100% 100%

CCS - efficiency of capture % 85%

CCS - efficiency of storage % 90%

Capital Cost $/kw $500 $1,500

Operational cost $/tonne $154

Capex Deflator % 1.50%

Capex deflator 2021 - 2050 % 0.00%

Earliest year/ phase in time Year(s) 10 2015 Source: MMA Analysis.



APPENDIX C TRANSPORT ABATEMENT OPTIONS

Abatement options in the transport sector generally focus on road emissions, which form the largest group of emissions. These abatement options include:

Diversion of vehicle kilometres from private vehicle travel to buses, cycling, walking, light rail.

Improved freight efficiency in commercial sector (i.e. transporting same amount in fewer kilometres).

Improved efficiency of road vehicles.

Substitution of existing road vehicle technologies to less emissions intensive road vehicle technologies.

Use of biofuels as a substitute for fossil fuels.

Diversion of air kilometres travelled by use of teleconferencing and biofuel.

The model emission abatement initiatives are described in further detail in Table C-1.

Table C-1: Emission abatement initiatives for the transport sector

TECHNOLOGICAL MEASURES

Private and Light Commercial Vehicles (separate categories modelled). % of new vehicles running on:

‐ LPG (estimated to reduce GHG emissions by 10%) ‐ Diesel ‐ Petrol-electric/hybrids (estimated to reduce GHG emissions by 5%

now and significantly more later) ‐ Fully electric vehicles (only estimated to reduce GHG emissions

when a significant proportion of produced electricity comes from renewable generation)

‐ Hydrogen-fed fuel cells (estimated to reduce GHG emissions by 87% but not expected to be viable before 2020)

‐ An 85% ethanol, 15% petrol mix.

Buses, Rigid and Articulated Trucks (separate categories modelled). % of new vehicles running on:

‐ LNG ‐ Diesel ‐ Hybrids (employs regenerative braking to generate electricity) ‐ Fully electric vehicles (only estimated to reduce GHG emissions

when a significant proportion of produced electricity comes from renewable generation)

‐ Hydrogen-fed fuel cells (estimated to reduce GHG emissions by 87% but not expected to be viable before 2020)

‐ An 85% ethanol, 15% petrol mix

Year B5 fuel standard implemented (i.e. 5% biodiesel and 95% diesel blend)

Year E10 fuel standard implemented for all cars newer than 1990 (i.e. 10% ethanol and 90% petrol blend)

Biofuels (existing vehicles)

% of diesel vehicles switched to biodiesel B100



TECHNOLOGICAL MEASURES

Year of mandatory efficiency standards on internal combustion engines to match world’s best practice (1.5% improvement per year)

Eco-Driving Options 1 No eco-driving 2 New drivers only 3 New drivers + every 20 years (5% of driver population) 4 New drivers + every 10 years (10% of driver population)

Improved fuel efficiency

Reduce vehicle speeds on highways from 110 kph to 100 kph to encourage better fuel efficiency in vehicles

DIVERSION MEASURES (i.e. Diversion of road and air transport to less emission intensive modes)

Improve Urban Transportation services (i.e. create 'liveable cities')

For each year 2020, 2030, 2050 select (yes/no): ‐ Improve bus frequency level (10%, 100%) ‐ Put road prioritization put in place ‐ Improve bicycle paths and walking tracks (5 levels)

Cycling and Walking Options 1 Do nothing 2 5 km radius of Hobart CBD 3 15 km radius of Hobart CBD 4

15 km radius of Hobart CBD + additional connectivity to shops, schools, public transport etc

5

15 km radius of Hobart CBD + extended network in local areas + Additional measures such as bike parking, awareness/training campaigns, linking of bikes to public transport, bike rentals and incentives for shoppers. ‐ Reduction of public parking space and raise parking fees ‐ Year 10% of Hobart bus network replaced with Light Rail ‐ Better urban planning

Improve Freight Logistics

For each year 2020, 2030, 2050 select (yes/no): ‐ Rationalise ports ‐ Allow heavier tonnages on trucks by approving quad axle

configuration ‐ Upgrade rail and return lost market share to rail by year x ‐ Improve load factors on freight vehicles using real time freight

management systems

AIR TRANSPORT MEASURES

Tele-conferencing Year business teleconferencing promoted to reduce air travel

Biofuels Mandate minimum levels of biofuel in jets of x%



C.1 Abatement options involving changes to vehicle technology

Changing vehicle technologies reduces emissions by either altering the fuel requirement to a form of fuel that has a reduced emission factor (e.g. switching from a standard internal combustion engine to either an LPG/LNG, E85, hydrogen fuel cell or electric /hybrid electric vehicle), or reduces emissions by dramatically affecting vehicle efficiency and reducing the amount of fuel required to travel the same distance (e.g. switching from a standard internal combustion engine to either a hybrid or diesel vehicle). Switching vehicle technology when replacing an existing vehicle or purchase a new vehicle usually involves an assessment of the cost involved in moving from the commonly accepted technology that may be in place. This assessment of cost will normally involve a comparison of up-front vehicle costs and fuel costs over a period of, say, 10 years. The abatement options model compares costs and emissions of new technology mixes of vehicles to a reference case technology mix and can either select the cheapest mix that maximises abatement or select the mix that meets an abatement target at a specified cost.

The technology mix answer provided by the model can change depending on which other abatement options are currently in place. For example, in a scenario with unlimited biofuel available the technology mix chosen is likely to include a lot of E85 vehicles as a cheaper alternative for greenhouse abatement to more advanced technology change. Similarly, mandated efficiency measures for internal combustion engines can reduce switching from internal combustion engines to alternative technologies.

A deficiency of the model is that it accommodates technology switching for new vehicles only and does not allow for retrofitting.

The sum of new vehicles being converted to the new technologies must be limited to 100%. Fuel cell and E85 vehicles are only allowed post 2020.

Assumptions for the technology switching measures can be viewed in Table C-2, Table C-3 and Table C-4.

C.1.1 Fuel switching

Costs include change in cost of alternative fuels, changes in purchase price and the cost of setting up alternative refuelling stations should that be necessary.

MMA has assumed that maintenance costs will be the same under all technologies. With regard to refuelling stations MMA has only considered fuel cell technology and have allowed $1,000 per vehicle in the first year this technology is available decreasing to $200 after 6 years of the technology being established. Currently, fuel cell technology is assumed to not be available before 2020.



Table C-2: Economic and technical assumptions for switching vehicle technologies – private and light commercial vehicles

2006 calibrated new vehicle

efficiencies (L/100 Km) 2050 calibrated vehicle efficiencies (L/100 Km) 2006 vehicle costs ($000s) 2025 vehicle costs ($000s) 2050 vehicle costs ($000s)

First Year Light Medium Heavy Light Medium Heavy Light Medium Heavy Light Medium Heavy Light Medium Heavy

ICE 2006 8.74 9.79 13.44 6.53 7.30 9.98 14 25 41 14 25 41 14 25 41

LPG 2006 11.62 13.06 17.86 8.26 9.22 12.67 17 25 41 15 25 41 14 25 41

Hybrid 2006 8.30 9.30 12.77 4.57 5.11 6.99 17 28 44 15 26 42 14 25 41

Plug-in hybrid 2006 4.55 5.10 7.00 0.71 0.79 1.08 29 48 64 19.6 34 50 19.6 34 50

Diesel 2006 6.05 6.48 8.85 5.18 5.56 7.57 17 26 42 15 25 41 15 25 41

E85 2020 12.29 13.73 18.82 8.26 9.22 12.67 20 36 59 17 30 50 14 25 41

H2 2020 35.23 39.46 54.05 22.37 25.06 34.27 80 143 234 40 71 117 20 36 59

Private Vehicles

Elec 2015 19.20 19.20 24 17 14

ICE 2006 7.07 7.89 10.81 5.30 5.92 8.09 14 25 41 14 25 41 14 25 41

LPG 2006 9.38 10.54 14.42 6.66 7.48 10.20 14 25 41 14 25 41 14 25 41

Hybrid 2006 6.72 7.49 10.27 3.71 4.14 5.66 17 28 44 15 26 42 14 25 41

Plug-in hybrid 2006 5.20 5.80 7.95 0.78 0.87 1.19 29 48 64 19.6 34 50 19.6 34 50

Diesel 2006 4.90 5.51 7.55 4.22 4.76 6.46 17 26 42 15 25 41 15 25 41

E85 2020 9.93 11.15 15.23 6.66 7.48 10.20 20 36 59 17 30 50 14 25 41

H2 2020 28.42 31.89 43.66 18.09 20.20 27.68 80 143 234 40 71 117 20 36 59

Light Commercial

Vehicles

Elec 2015 13.60 13.60 24 17 14

Source: CSIRO, MMA Analysis



Table C-3: Economic and technical assumptions for switching vehicle technologies- other

Calibrated vehicle

efficiency (L/100km) Vehicle Costs ($000s)

Earliest Year

Available 2006 2050 2006 2025 2050

Infra-structure

costs

ICE 2051 29.68 22.14 400 400 400 0

LNG 2006 39.44 27.96 519 400 400 0

Hybrid 2006 28.20 15.50 477 400 400 0

Diesel 2006 21.89 18.86 480 400 400 0

BioDiesel 2051 22.55 19.35 480 400 400 0

Buses

H2 2020 119.39 75.77 2,021 800 300 7,706

ICE 2051 59.94 44.77 300 300 300 0

LNG 2006 69.86 57.15 320 300 300 0

Hybrid 2006 56.94 40.29 370 300 300 0

Diesel 2006 44.28 38.05 320 300 300 0

BioDiesel 2051 45.59 39.20 320 300 300 0

Articulated Trucks

H2 2020 211.23 163.51 1,841 729 273 7,706

ICE 2051 32.14 24.03 61 61 61 0

LNG 2006 42.80 30.34 65 61 61 0

Hybrid 2006 30.54 16.82 75 61 61 0

Plug-in

hybrid 2006 19.6 5.86 160 87 87 0

Diesel 2006 23.70 20.42 65 61 61 0

BioDiesel 2051 24.44 20.99 65 61 61 0

Rigid Trucks

H2 2020 129.40 82.08 374 180 90 7,706

Source: CSIRO, MMA Analysis

Table C-4: Fuel cost assumptions (c/L)

2006 2020 2030 2050

Petrol 110.2 145.5 158.6 183.8

LPG 77.9 112.3 120.4 134.8

LNG 24.8 31.1 35.1 38.1

Diesel 113.4 144.8 159.3 187.5

BioDieselB20 118.8 143.9 157.2 179.8

BioFuelE85 98.6 112.5 116.3 122.9

H2 500.0 647.7 699.3 782.5

Electric 140.7 182.3 196.8 220.3 Source: IEA, MMA Analysis.



C.2 Abatement options involving increased use of biofuels

Specification of what may occur with regard to availability and cost of biofuels post 2020 is speculative. In a 2007 CSIRO report on Biofuels81, CSIRO state that the conversion of export fractions of wheat and coarse grains could theoretically have supplied upper limits of 11% to 22% of Australia’s current petrol usage. Similarly conversion of domestic waste oil, tallow exports and oilseed exports could have theoretically provided upper limits of 4% to 8% of Australia’s current diesel usage. The report also states that second generation technologies such as lignocellulosic feedstock or from new trees and crops for biodiesel can provide the potential to give Australia fuel security by enabling from 10% to 140% replacement of petrol with ethanol and up to 40% replacement of diesel with biodiesel. Indeed, if algal sequestration in the industrial processes sector delivers the promised yields, algae farming could deliver around 47% of the reference case transport sector82 diesel requirement by 2050, more if the option were extended to other large emitters.

The emissions intensity of biofuels is also variable depending on the feedstock and processing requirements of each fuel. If first generation feed-stocks were only considered (i.e. tallow, canola, etc), there are large differences to be found between each. For example, one can expect a 29% drop in emissions when using 100% biodiesel from canola or tallow as compared to ultra low sulphur diesel, but a 90% drop if the biodiesel is produced from waste oil, which has limited supply.

Table C-5: Limits on biofuel (first generation only)

Maximum 2020 (ML) Maximum 2050 (ML)

Ethanol limit 10 19

Bio-diesel B100 2 16 Source: MMA analysis

C.3 Efficiency abatement options

C.3.1 Mandated fuel efficiency on all new internal combustion vehicles

Japan has the most stringent fuel economy standards for passenger vehicles in the world and leads the world in reducing greenhouse gas emissions for new passenger cars with fuel economy ratings range from 5.88 litres per 100km in 2006 to 5.47 litres per 100km in 2010 from which first tier costs83 were determined. Further expected improvements lead to improved standards of 5 litres per 100km in 2015.

Europe is also a world leader in fuel economy but the expanded use of diesel engines in Europe effectively increases Europe’s passenger vehicle carbon emission intensity relative to Japan84. By comparison fuel economy standards in Australia for new passenger vehicles

81 Biofuels in Australia – Issues and Prospects, A report for the Rural Industries Research and Development Corporation,

CSIRO May 2007. 82 There is also diesel requirement in the Energy sector. 83 International Council on Clean Transportation (ICCT), July 2007. 84 http://www.lowcvp.org.uk/assets/reports/ICCT_GlobalStandards_2007.pdf



range from around 7.35 litres per 100km in 2006 to 6.92 litres per 100km in 201085, since Australian fuel economy standards operate on a voluntary rather than regulated basis. As higher efficiency vehicles are available internationally it would be possible to introduce more stringent, mandatory standards in Australia. The costs of creating these vehicles within Australia may consist of factory refurbishment to create a different style of car but could also be more administrative in nature with regard to the form of imported cars that Australia may allow. As more efficient cars penetrate the Australian market cost differentials are likely to narrow. Cost differentials are assumed to be $5,000 per new vehicle in the first year of the program reducing to $0 per new vehicle 5 years later. This cost will be offset by fuel savings.

Vehicle fuel technology improvements can be realised in a number of ways. These include, but are not limited to86:

Reducing vehicle weight, reducing drag and tyre rolling resistance. Weight reductions are achieved by keeping vehicles small and/or using lighter weight construction materials. Drag reductions have been achieved at a rate of approximately 10% per decade, with practical upper limits expected to be achieved between 2015 and 2020. Rolling resistance has been achieved at around 5% improvement per decade. The main improvements in these areas have historically been offset by increasing vehicle size, power and features.

Increasing engine efficiency. This can be done by higher compression ratios, improved intake and exhaust manifolds, improved cylinder head and valve port design, use of two or more intake valves and two exhaust valves, reduced internal friction, application of electronic injection and engine management systems.

Reducing power consumption by accessories. Efficiency improvements with regard to alternators, water and oil pumps and the introduction of power steering are examples of gains made in this area.

Limiting engine speeds (by changing transmission ratios and through the use of automatic gear changing). Some of the changes that have already been implemented in some cars include elimination of the torque converter, introduction of shift indicator lights and continuously variable transmission.

The key assumptions are displayed in Table C-6 and Table C-7.

85 ICCT, July 2007 (see ref 17). Note that these are significantly lower than the 8.87 L/100km assumed for the reference case

(small vehicle). This value comes from CSIRO and is a more typical value for a new car in the Australian market under Australian driving conditions. The ICCT efficiency values are based on CAFÉ test cycle data.

86 http://www.iea.org/textbase/nppdf/free/2005/fuel_efficient.pdf



Table C-6: Vehicle Efficiency Assumptions

Improvement on existing efficiency of new vehicles Comments

Upper limit 2020 2.5% 1.3%

Upper limit 2050 3.0% 1.3%

First year improvements begin 2010

De-rating of efficiency improvements 0.99

A de-rating factor of 0.99 means that a 1 year old car is 1% less efficient than a brand new car.

Best possible efficiency (l/100km) 3 It is assumed that efficiency standards can not be brought below this level.

Years to achieve an efficiency goal 3

It is assumed that it may take this long to put in the procedures to bring about the efficiency changes.


Table C-7: More Vehicle Efficiency Assumptions87

Year 1 Year 2 Year 3 Year 4 Year 5

Cost to implement efficiency improvement to world standard ($/car) $5,000 $4,000 $3,000 $2,000 $1,000

C.3.2 Eco driving

Based on the ATC Climate Change, Energy and Environment Working Group Eco Driving Proposal discussion paper, Eco-Driving principles include:

shifting up gears as soon as possible (1500rpm-2500rpm)

maintaining a constant speed

keeping revs as low as possible by driving in a high gear

decelerating smoothly

accelerating steadily

anticipating traffic flow to avoid stop-start driving

switching off the engine when stopping to limit idling

not pressing down the accelerator pedal while switching on the motor

driving up hills in the lowest possible gear with the accelerator fully depressed

avoid using air-conditioning or driving with windows down.

Other possible actions that can be included involve vehicle maintenance and consumer choice education. Eco driving improves the efficiency of all cars, not just new cars.

87 These values are assumed as no relevant data could be located.



MMA has assumed 3 possible levels of training to be provided on a large scale, with each lesson costing $300 per participant. The levels of training relate to the number of participants reached, and how quickly the driving population receive training. The benefit of training is of the order of 5% to 10% of fuel reductions per participant, based on the discussion paper previously described. MMA has assumed 7.5% reduction in fuel use per participant, and recognise that often training will need to be repeated at some point or the benefit of training will diminish. MMA has chosen the following training paradigms:

Train new drivers only

Train new drivers only + those reaching their 20 year license renewal

Train new drivers only + those reaching their 10 year license renewal.

C.3.3 Speed limit reductions on highways

The efficiency of most vehicles is optimal between 50 kph and 90 kph. Reduction of speed limits on highways can serve to optimise fuel efficiency and therefore reduce emissions.

MMA received advice from the Land Transport Safety Division in DIER that a 10 kph drop in the speed limit may equate to a 2.5 kph drop in actual average travel speed, yet may translate to significantly larger reductions in travel accidents of the order of 7%. MMA has used a fuel efficiency curve88 to estimate that this will also translate to an approximate 2% drop in fuel use for the portion of traffic on 110 kph highways and that costs of such a move may include additional travel time of around 2.3%, the value of which may be different for commercial and private vehicle drivers. The fuel efficiency curve for smaller vehicles (private, light commercial vehicles and rigid trucks) and the fuel efficiency curve for larger vehicles are shown in Figure C-1.

Figure C-1: Efficiency curve used to estimate fuel savings from reduced vehicle speeds

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140

KPH

L/1

00 k

m

PV/LCV/RT

AT

88 Managing Speed: Review of Current Practices for Setting and Enforcing Speed Limits -- Special Report 254

http://www.nap.edu/catalog/11387.html, Figure 2-5, p69.



Around 60% of all passenger vehicle travel is outside Hobart, as is 80% of all light commercial and rigid truck travel and 90% of articulated truck travel89. As it is not straightforward to estimate traffic on highways, MMA has assumed that highway travel makes up 20% of all travel for private vehicles, 40% of all travel for light commercial vehicles and rigid trucks and 80% of all travel for articulated trucks. These ratios are multiplied by the projected kilometres travelled for each type of vehicle and then the efficiency improvement to determine fuel and greenhouse savings.

Direct costs of this measure are assumed to be negligible. The indirect cost of additional time travel was estimated to be 0.59c/km, based on an hourly rate of $27.6890, and this is offset by fuel cost savings and a reduced accident benefit of 4.97c/km91. Differences in travel time costs for private and commercial vehicles were ignored.

The analysis conducted here is not a detailed cost/benefit analysis that considers change in average speed as opposed to change in maximum speed, average speeds of different vehicle types, or willingness to pay for additional travel time. MMA understands that DIER is commissioning such a study and this work may provide an opportunity to update the values used92.

C.3.4 Improved freight efficiency

Rationalise ports

Over the long-term, there is the potential for a shift in container traffic to Bell Bay as Tasmania’s principal container port. This will have potentially significant impacts on the land transport network, with higher volumes of freight travelling from the North West and Southern Region to Bell Bay. Change to VKT as outlined in Table C-8 and direct costs of $1M p.a. were obtained from DIER. In evaluation of this option, fuel savings are also considered.

Table C-8: Change to VKT after port specialisation

Port specialisation 2013 2018 2038 2050

Arterial truck reduction to VKT 8.8% 18.7% 19.0% 19.1%

Rail reduction to emissions 24.7% 25.0% 25.0% 25.0% Source: DIER, Tasmanian Government

Quad axle reconfiguration

Quad axle reconfiguration enables greater loads to be carried on articulated trucks, reducing VKT but possibly increasing road maintenance. Change to VKT as outlined in and direct costs of $0.3M p.a. were obtained from DIER. In evaluation of this option, fuel savings are also considered.

89 Estimated from 2007 MMA data for Tasmania and BTRE data for Hobart, Working Paper 71, “Estimating urban traffic

and congestion cost trends for Australian cities”. 90 Derived from ABS Average Weekly Earnings Feb 2009, Tasmanian statistic, and assuming a 40 hour working week. 91 Based on MMA analysis of Tasmanian accident data from ABS 1307.6, July 2008. 92 See “Potential Benefits and Costs of Speed Changes on Rural Roads”, Max Cameron, Monash University Accident

Research Centre



Table C-9: Savings in VKT expected with quad axle reconfiguration

Quad Axle Reconfiguration 2013 2018 2038 2050

Arterial Truck Reduction to VKT 0.6% 0.7% 1.1% 1.5% Source: DIER, Tasmanian Government.

Upgrade of rail

The road and rail network connecting Hobart, Launceston and Devonport-Burnie industrial areas and Tasmania’s ports consists of 542 kilometres of road and 432 kilometres of rail93. According to DoTaRS 2007 Tasmanian Corridor Strategy, 84% of Tasmania’s freight task is transported on the network and of this 88% is transported by road. The major factor inhibiting rail’s ability to increase freight market share between Hobart and the northern ports are the 48 hour turnaround time between Hobart and Burnie and the limited load capacity. This is because the rail system was originally designed in the late 1800s and low levels of investment has made the condition of the rail network infrastructure and rolling stock poor in comparison with the road network, which has had significant injections of investment over time. The road network runs in parallel with the rail network for much of the rail line, and thus there is little incentive to move to rail other than with regard to cost. Limits on haulage levels, speed limit restrictions and on train length due to tight curves, as well as inefficiencies at intermodal terminals all increase rail turnaround times in comparison with road.

There are currently no reliable estimates of the investment required to make rail freight competitive with road freight. The Australian and Tasmanian Governments have contributed $118M to address urgent infrastructure work and ongoing maintenance of the rail network, and that this expenditure has been outlaid primarily to avoid costs associated with a forced shift from rail to road assessed at approximately $24M per annum94. These costs include additional direct costs of more than $17M each year to Tasmanian businesses that rely on rail, as well as $1M each year for additional road maintenance, as well as accident costs and pollution costs (including greenhouse gas emissions and noise, air and water pollution).

Upon sale of the railway to the Tasmanian Government in 2007, a declaration application by the DIER Railway unit was made. According to this document, 99% of the freight carried by the rail network was allocated to cement, various containers, concentrates, coal, news-print, logs paper pulp, timber, gypsum-bulk and fertiliser in 2002/03. Freight volumes amounted to 3,205 kt, or 549 million tonne-kilometres. These volumes continued through to mid 2004 and then freight volumes began to drop as a result of locomotive unavailability. Assuming that significant investment can occur to return volumes to these levels and allow for future growth, the document indicates that the existing network is of sufficient capacity to accommodate the increase in freight activity as compared to current levels. MMA has taken values from this document and have assumed that the potential

93 2007 Tasmanian Corridor Strategy, DoTaRS, Commonwealth of Australia, August 2007. 94 Tasmanian Railway Network Declaration Application by DIER Rail Unit 1 May 2007.



increase to rail haulage is approximately 2.5 Mt per annum by 2010 and 3.2 Mt per annum by 2020. Uptakes to these levels are phased in over 7 years since it is expected that marketing, regaining customer confidence and changes to existing service contracts will take time. Extrapolation of this growth in market share to 2050 was taken as 4.3 Mt, assuming reference case growth projections in various markets. However, BTRE states that ‘substantial and sustained improvements in rail freight rates and rail service quality, relative to road, would be required to significantly increase rail’s share of inter-capital freight’95.

Given the spare capacity in the current network, the Tasmanian Government faces the choice of utilising it fully or abandoning the rail network at a cost of $24M per annum, or something in-between. Doing nothing (i.e. no further investment) is likely to lead to further declines in market share from rail to road and could eventually lead to significant externality costs in the form of greater road maintenance, accidents and higher pollution, potentially at a level that is much higher than $7M per annum as growth in the transport sector continues. The Transport Freight Logistics Council in their submission to Infrastructure Australia believes capital spending in excess of $127M is required to provide a basic infrastructure improvement to accommodate growth.

MMA has included the upgrade of the rail network as an abatement option. The cost of upgrade is assumed to be $13M per annum.

Rail can be a significantly cheaper means of transporting goods than road, with fuel costs expected to be around less than a third per tonne kilometre than possible on road vehicles (refer to Table C-10). In Tasmania, however, distances are short, and costs of using rail go up the more rail is linked to road transport to deliver goods from site to the railway station. Rail is also much slower than road in Tasmania due to the hilly terrain. Freight costs (excluding pick up and delivery) are less for longer transit distances than shorter transit distances. With only 432 km of rail in the entire Tasmanian rail network, line lengths are certainly at the lower end of the spectrum of Australian rail lines. Excluding pick up and delivery, road and rail freight costs on the Australian mainland for inter-capital freight are about the same for distances of 500km, and cost of rail is around 1.8 times that of road if pick up and delivery are considered. For distances of 3,000km, rail freight costs are only 60-75% of the cost of road freight depending on whether pick up and delivery is included in the cost estimate. Distances (on the Australian mainland) generally need to be above 1,000 km for rail freight to be cost competitive with road freight when one includes the cost of pick up and delivery. The results may change however, with higher estimates of fuel prices, particularly in a carbon constrained world.

95 BTRE Information Sheet 34, “Road and Rail Freight: competitors or complements?”. BTRE quote an empirical study

undertaken by Booz Allen Hamilton 2001, entitled “Interstate Rail Network Audit”, Report prepared for the Australian Rail Track Corporation, Adelaide.



Table C-10: Comparison of costs between road and rail

Road Rail

Cost of Infrastructure and Maintenance c/ntkm 1.00 0.90

Accidents c/ntkm 32.00 3.00

Fuel c/ntkm 77.00 21.00

Operating Costs c/ntkm 24.91 10.75

Total: c/ntkm 134.91 35.65

Rail Benefit: c/ntkm - 99.26

Source: BTRE, “Competitive Neutrality between Road and Rail”, 1999.

MMA has assumed zero cost differences between road and rail, as a result of the BTRE analysis explaining that road and rail have equivalent cost for 500km hauls when pickup and delivery is not considered. Since the model works on the basis of vehicle kilometres rather than tonne-kilometres, it is also assumed that there are 2.17 tonnes for each km shifted from articulated trucks to rail and that the rail emission intensity is 0.01 t CO2e/tonne moved, which is the rate that appears to match the data history. These assumptions imply low greenhouse benefit in shifting from road to rail.

Improvement of freight load factors

Improvement of freight load factors incorporates real time freight management practices encouraging freight on under-loaded vehicles to be combined with freight of other under-loaded vehicles so that fewer vehicles are on the road and that each vehicle takes loads that more closely match vehicle carrying capacities. This option reduces unnecessary trips on roads via employment of real time information technology systems.

In 2004, BTRE and the Australian Greenhouse Office commissioned a study done by a consortium led by CSIRO to examine the responses of urban freight patterns in Sydney. The study included a measure to improve load factors on freight vehicles and the study found that VKT improvements of the order of 20%, 27% and 18% were made for light commercial vehicles, rigid and articulated trucks respectively. MMA assumed that the cost of the real time system was $5M per annum.

C.4 Urban transport change

Most behaviour change options included here (excluding fuel price increases) target urban VKT in private vehicles (as opposed to all VKT), as it is not thought that the options provided within the model would have much effect outside metropolitan areas.

Behavioural change policies are diverse and encompass a range of localised efforts to improve the desirability of using alternative forms of transport.

Monetary policies to reduce private vehicle VKT are not effective unless there are attractive alternative transport options available. MMA has assumed that the effect of the option will be reduced by half unless measures to provide alternative transport



infrastructure are also taken. In addition, MMA has assumed a long-term effect will only be gained with the improved infrastructure. Elasticity estimates are shown in Table C-11.

Table C-11: Elasticity assumptions

Short-term private vehicles

Long-term private vehicles

Long-term light commercial

vehicles

Fuel Price Elasticity - 0.10 - 0.10 - 0.08

Parking Price Elasticity96 - 0.15

Air Fare Elasticity - 0.11

Urban Share of VKT 40%

Source: MMA analysis.

Urban transport measures involve diverting travellers from private vehicle travel to public transport, walking or cycling. The State Government is currently undertaking an urban passenger transport study for Greater Hobart. The study will identify opportunities for modal shift to transport modes other than the car in order to lower greenhouse gas emissions, promote physical activity, increase social interaction and accessibility.

The study identified and reviewed a list of travel demand measures that were applicable to the transport and land use characteristics of Greater Hobart. The list of measures that were reviewed included the following abatement options:

Improved land use planning (urban growth boundary/consolidation).

Car parking - pricing and reduction in spaces for commuters.

Improved bus frequency level (100%).

Bus prioritisation for key routes e.g. peak period bus or transit lanes, traffic signal pre-emption.

Improve/added bicycle and walking routes.

Light rail.

Each of these initiatives is described below with key assumptions described in Table C-13.

Improved land use planning - Introduction of specific land use planning strategies e.g. urban growth boundaries, active consolidation of inner urban areas to improve accessibility and maximise the use of existing and planned transport infrastructure and services.

Car parking – Managing availability, pricing and location of car parking, especially in inner urban activity centres is a key measure in managing car use.

96 Parking price elasticity is applied to the commuter share of urban vkt only.



Improved bus frequency level - More frequent bus services, particularly along key corridors with higher population catchments and densities is a key measure in increasing the patronage and attractiveness of public transport.

Bus prioritisation for key routes – Priority measures for buses along key bus corridors can be used as a complementary measure once bus frequency is established and patronage increased.

Improve/added bicycle and walking routes – Provision and improvements of cycling and walking routes are a key component in encouraging people to replace car trips with walking and cycling for short trips, especially within local areas.

Light rail - light rail, or bus rapid transit is a long-term passenger transport option. The future introduction of such measures is based on the achievement of long-term land use changes to significantly increase the number of people living around key transit corridors.

Cycling and walking

Hobart is already one of the best ‘walking’ cities in Australia, with the proportion of trips made by walking already at a relatively high 26% (see Table C-12) as compared to cities such as Melbourne with walking rates closer to 17% (see Table C-13). Walking trips are usually extremely short trips, which typically are less than 1 km in length. Short trips of less than 2km make up around 37% of all trips made in Hobart. Further uptake of walking can be limited by weather and perceptions of safety at different times of day, as well as mobility issues for the less mobile public. Potentially there is still room for more walkers though this may be limited by the barriers described.

Cycling trips can typically be longer, and in Hobart the average length of a cycling trip is around 7km. The uptake of cycling in Hobart is low (around 0.4%), even though trips of up to 5km make up more than half of all journeys and a trip of up to 5km should typically be easily handled by most bike riders of varying levels of fitness.

Table C-12: Hobart mode share by trip length Distance of trip (km)

Walking % Bike % Car % Public transport

%

Other % Total % % all trips in

km band

0 to 2 62.86 0.30 35.91 0.92 0.00 100.00 37.31

2 to 5 10.18 0.53 83.28 5.99 0.03 100.00 19.16

over 5 0.60 0.51 93.24 5.58 0.06 100.00 43.53

Total 25.67 0.44 69.94 3.92 0.03 100.00 100.00 Average trip length (km) 0.86 7.45 11.79 8.72 10.77

Source: Hobart Travel Survey – unpublished preliminary data made available by the Tasmanian Department of Infrastructure, Energy and Resources (DIER).



Table C-13: Diversion of private transport to public transport, walking and cycling in Hobart

Infrastructure Policy – Levels Measure 1 Measure 2 Measure 3 Measure 4 Measure 5 Measure 6 No Measures

Description Good planning (urban

growth boundary /

consolidation)

Car

parking -

pricing

and

reduction

in spaces

for

commuters

(13%)

Doubled

bus

frequency

(100%

more

services)

Bus priority Added bicycle

paths and

walking tracks

Light rail Only fuel price

increases

First year of implementation 2010 2012 2015 2017 2017 2039 2010

Delay to full take-up (years) 35 6 3 3 See below 3 1

Reduction in VKT - urban (additive) -2.5% -2% -5% -3% See below -2% N/A

Multiplier on bus VKT (multiplicative with

other measures)

1.0 1.0 1.8 0.84 1.0 0.9 1.0

Multiplier on bus VKT to get light rail VKT 0.1

Multiplier on short-term Fuel and Parking

Elasticities when infrastructure measure

applied

100% 100% 100% 60% 60% 60% 50%

Cost ($M p.a.) $1.1 $1.6 $3 $5 See below $75 $-

Savings in Travel Time under measure (Public

Transport users) – Additive

0% 0% 25% 16% 5% 5% 0%

Savings in Travel Time under measure (Other

vehicles) – Additive

0% 0% 5% 0% 0% 0% 5%




The primary reason for low uptake of cycling in Hobart is lack of suitable cycling infrastructure. The Hobart City Council used a funding grant provided by the Department of Infrastructure, Energy and Resources to develop plans for a regional arterial bicycle network97. The plans are on the internet for public review, in an attempt to address issues such as provision of efficient and cost effective transport for residents, healthy communities, environmental sustainability and tourism. The cycle paths suggested are designed with the aim of providing a cycling experience with greater separation from motor vehicle traffic and fewer hills and interruptions and include thought on end of trip facilities such as secure bicycle parking, public phones, water fountains and toilets.

Widespread use of paths is possible if measures to address culture change are taken up and appropriate infrastructure exists. Analysis of cities with large cycling uptake indicate that the key to achieving high levels of cycling is the provision of separate cycling facilities along heavily travelled roads and at intersections, combined with traffic calming of most residential neighborhoods98. Parking for bicycles and integration with public transport are further enhancements which can assist with increasing uptake of bicycles. These, combined with extensive promotion and well targeted training programs, deliver significant public support for cycling and provide the means of encouraging take-up of cycling among men, women and children of all ages in countries where cycling levels can make-up as much as 30% of trips. As well as providing incentives, car use can be discouraged by making driving both an expensive and inconvenient means of getting around cities, so this measure is complementary to the other urban transport measures that have been described. Extension of cycling and walking paths could also be considered in other towns in Tasmania.

MMA has assumed 5 levels of possible investment in cycling paths, each delivering different levels of uptake of cycling and walking. These are:

Do nothing (Plan 1)

Build recommended track within 5 km radius of Hobart CBD (Plan 2)

Build recommended track within 15 km radius of Hobart CBD (Plan 3)

Build recommended track within 15 km radius of Hobart CBD and additional connectivity to shops, schools, and public transport (Plan 4)

97 Hobart Regional Arterial Bicycle Network, Draft 2008, Department of Environment, Water, Heritage and the Arts and Hobart

City Council in co-operation with the councils of Clarence, Glenorchy, Kingborough and Brighton., and Cycling South. 98 Transport Reviews, Vol. 28, No. 4, 495–528, July 2008; “Making Cycling Irresistible: Lessons from The Netherlands, Denmark and

Germany”, JOHN PUCHER and RALPH BUEHLER, Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, New Jersey, USA.



Build recommended track within 15 km radius of Hobart CBD + extend network in local areas + Additional measures such as bike parking, awareness/training campaigns, linking of bikes to public transport, bike rentals and incentives for shoppers (Plan 5).

A logistic take-up of cycling and walking has been assumed once some investment in infrastructure is made, with a slow start to take-up as is evident from overseas experience in the US12. Indicative costs were provided by the Hobart City Council. Figure C-1 shows the effect of each investment strategy on VKT.

Figure C-1: Assumed effect on VKT of different cycle path infrastructure strategies

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Years

Eff

ect o

n V

KT Plan 2

Plan 3

Plan 4

Plan 5




Table C-14: Cycling and walking assumptions

Cycling / Walking initiatives

Maximum effect on Hobart VKT by

2050

Off road track km

On road track km

Capital Cost ('000s)

Maintenance Cost / yr

Total cost

$’000 / yr

Years to build infra-

structure

Do nothing 7% 0 0 0 0 0 0

5 km radius of Hobart CBD 8% 5 25 6,050 180 1,165 2

15 km radius of Hobart CBD 12% 25 125 30,250 900 5,823 5

15 km radius of Hobart CBD + additional connectivity to shops, schools, public transport. 17% 125 625 98,125 4,500 20,469 10

15 km radius of Hobart CBD + extended network in local areas + additional measures such as bike parking, awareness/training campaigns, linking of bikes to public transport, bike rentals and incentives for shoppers. 34% 125 625 30,000 12

Source: MMA Analysis. Cost data was derived from data provided by Hobart City Council.

C.5 Air Transport

Air travel nationally is the fastest growing mode of transport, even though it is one of the smallest transport sectors. In Tasmania, air travel is mainly to the mainland and generally emissions measurements only include the domestic air travel component.

Fuel costs dominate expenditure by air carriers. As fuel prices have rose, and as economic conditions have become tighter, the air travel industry has become cost conscious, leading to investment in efficiency improvements and steadily increasing load factors on flights, which have been partially offset by growth in customer demand. Some airlines are also considering biofuels, with research currently being undertaken on the practical use of biofuels in aircraft99. At least two feed-stocks are currently under consideration for this purpose, including algae and camelina oil. MMA assume that the emission intensity of jet biofuel to jet fuel shares the same relativity as for ordinary biofuel and petrol. Similarly costs of jet biofuels are assumed to have the same cost relativity as ordinary biofuel and petrol.

Another option is diversion of air travel for other less emission intensive substitutes. One such substitute is the use of teleconferencing facilities, particularly useful for business meetings where distances travelled are long and meeting times are short. Teleconferencing

99 Virgin airlines are trialling biofuel flights at present; http://www.wired.com/autopia/2008/02/virgin-atlantic/



can be done cheaply (e.g. using Skype), but video quality can sometimes be an issue as many levels of non verbal communication can be missed when people do not meet face to face. Communications businesses now offer online conferencing facilities with ‘in-person’ quality that can save businesses costs in terms of air-fares and travel expenses, time, energy and emissions100, and reduce the risk of missing non verbal cues. In addition to these benefits teleconferencing can increase social inclusion for those disadvantaged or in remote or non central locations. The Tasmanian Government has already recognised the value of teleconferencing and has begun implementation of teleconferencing facilities in a number of government buildings, leading the way for many Tasmanian businesses.

Results for British Telecom as reported in “Smarter Choices – Changing the way we travel”101, indicate that when teleconferencing occurs, most people feel that it reduces their travel. Reductions of between 10% and 30% of company travel are typically reported for organisations that promote teleconferencing, and typical proportions of air travel witnessed in many cities are somewhere near 50%. MMA assumes that potential air travel reductions of 50% x 20% = 10% may be possible with significant incentives to make teleconferencing more available to business.

The cost of teleconferencing facilities were also taken from this 2004 document and adjusted to 2009 dollars. These were based on commercial rates of British Telecom and assume that a typical meeting would go for 2 hours, replacing an 8 hour round trip with meeting (i.e. saving 6 hours per meeting). It is also assumed that avoided plane trips would be to cities on the mainland even though it is also possible that international travel or car travel between cities within Tasmania could instead be avoided. Thus it is assumed that videoconferencing facilities could be hired for $120/hour for use within Australia and that provision of videoconferencing infrastructure ranges between $12K and $100K, depending on the type of facilities provided. For the purpose of this study, we use $50K per facility.

Using BTRE data, it is estimated that around 24,000 trips per million litres currently occurs (based on Hobart airport movements102). It is assumed that each facility could accommodate 500 meetings per year and thus around 370 facilities would be required in 2010, growing to around 530 facilities in 2050. If the capital costs involved are considered and this is offset these against fuel savings and hours, there is a net benefit per tonne of around $2/t CO2e by 2050.

100 Study commissioned by Telstra and authored by Climate Risk Pty Ltd, “Towards a High-Band width, Low-Carbon Future:

Telecommunications-based Opportunities to Reduce Greenhouse Gas Emissions Version 1.0”. 101 http://www.dft.gov.uk/pgr/sustainable/smarterchoices/ctwwt/chapter11teleconferencing.pdf 102 Based on fin year 2009 annual passenger movements in Hobart airport, BITRE report, “AVIATION STATISTICS, Australian

Domestic Airline Activity 2008-2009, Domestic Annual 158”.



APPENDIX D INDUSTRIAL PROCESS ABATEMENT OPTIONS

Industrial process emissions refer to emissions arising from chemical reactions during production processes, and exclude emissions arising from direct combustion of fuels within those processes.

The industrial processes sector is unique in that the emissions form an essential part of creating a product. It is (usually103) not possible to manufacture these products without the chemical reactions inherent in the processes creating these greenhouse emissions. As an example, heat is used to separate calcium carbonate into lime and carbon dioxide during the manufacture of clinker for cement production.

Abatement options are described in the following sections for each of the 3 biggest industrial process emitters in Tasmania - the cement industry, the ferroalloy industry and the aluminium industry.

D.1 Cement production

Direct emissions from the cement industry arise from the manufacture of clinker, in which raw materials such as limestone and chalk containing calcium carbonate (CaCO3) undergo a calcination process at high temperatures to form lime (CaO) and carbon dioxide:

CaCO3 + heat CaO + CO2.

The raw feed generally also contains magnesium carbonate (MgCO3) that oxidises in the kiln to form magnesium oxide (MgO) and carbon dioxide. There are also small quantities of emissions associated with non-carbonate sources, but these are deemed to be insignificant and are ignored for emission accounting purposes. There are emissions associated with the calcination of cement kiln dust (CKD) that must also be accounted for, and it is assumed that the calcination factor of CKD is one.

The process emissions intensity assumed in the NGI Factors and Methods Workbook is calculated as 0.534 t CO2e/t cement. Recent yet preliminary work indicates that cement may actually sequester carbon dioxide104. If this is shown to be true, in the future this factor could drop.

The lime is then combined with silicon based materials such as sand and clay to form clinker. After cooling, the clinker is ground and then blended with gypsum and some limestone (as a minor constituent), to form cement. Gypsum slows down the hardening process in the

103 In a well designed process this is the case. For some years the aluminium industry emitted significant levels of greenhouse

gases as a result of anode effects which were caused by process disruptions. These have now virtually disappeared due to careful redesign of the process.

104 Recent studies have indicated that cement may actually sequester some CO2 over time, by absorbing small amounts resulting in a compound called calcite which can make the cement even stronger. http://www.sciencedaily.com/releases/2009/05/090518121000.htm



resulting cement mix which creates a stronger end product. Limestone and gypsum form a minor part of the product mix, usually less than 10% for Portland cement.

The Railton plant uses the fuel-efficient pre-heater kiln technology, considered to be the most advanced commercial technology for making cement clinker. It operates on dry feed which is inherently more fuel efficient than older kilns, which require a process of driving off water before clinker production can occur.

Emissions are proportional to the volume of clinker produced. Hence, abatement can be achieved either through changing the clinker chemistry or capturing the outlet flue gases of kilns. Assumptions around abatement option for the cement industry are shown in Table D-2.

Substitution of raw materials in clinker production

Substitute materials can be added to the kiln or cement, displacing lime and clinker respectively, thereby directly reducing calcination emissions. The most common raw material substitutes are fly ash, slag and gypsum, all of which are readily obtainable within Australia. The cost of fly ash and slag may be higher in Tasmania than on the mainland due to low availability and transportation costs. For each kilogram substituted, around 0.67 kg of lime is displaced, with a third replacing inert ingredients such as sand. The full emission abatement potential is specific to the exact composition of the pre-calcined substitutes, with the calcium oxide and magnesium oxide content varying between 3% to 43% and 2% to 8% respectively as shown in Table D-1. Current substitution rates are around 1.5% with the total replacement limited to world’s best practise estimates of 25%105.

Table D-1: Chemical composition of clinker and kiln feed substitutes

Material CaO content MgO content

Clinker 66% 1.5%

Fly ash 5% 1.5%

Slag 43% 7.8%

Gypsum 3% 2.6%

Extension of minor constituents

Cement production in Railton has traditionally been of the Portland variety, containing up to 95% clinker and 5% gypsum, and more recently, up to 5% of crushed limestone which is known as a minor constituent. The crushed limestone addition has been capped to 5% by building regulations, and Cement Australia has actively sought relaxation of these standards.

105 ACF (2005), Cememting the Future; ACF (2007), Update on Technology Pathway.



The consequences of relaxed building standards may include altered structural properties, different ways of mixing the cement with aggregate and other materials to form concrete (at the distribution point), and further regulation regarding the use of cement by purpose. Should these building standards be altered to allow extended use of limestone as a minor constituent, the impact on emissions abatement in the cement industry will be proportional to the amount of increased substitution.

Substitution of cementitious materials

Railton cement works currently only produces one product – Ordinary Portland cement, which is typically made up of clinker, limestone and gypsum. Ordinary Portland cement is the most traditional and common type of cement in use and currently dominates cement sales in Australia. The Railton plant is part of a national company which also produce blended cements – those which incorporate naturally cementitious materials with pozzolanic properties – and combine these with ordinary Portland cement. These blends have different structural properties to ordinary Portland cement and therefore may have different uses.

Substitute materials can theoretically make up as much as 40% of the total cement mix (assuming no market limitations from customers), and Cement Australia is currently targeting a mix of around 29% by 2012. Examples of cementitious materials which may substitute for clinker include fly ash, blast furnace slag, silica fume (which was but is now no longer available in Tasmania), and sewage sludge ash. Availability of these materials within Tasmania is limited. MMA understand that around 10 kt to 20 kt per annum of dry sewage sludge is available106 and up to 120 kt of blast furnace slag from TEMCO. Assuming 135 kt of substitutes this equates to around 11% of substitutes available from local sources. Since only around 20% of supply is for the Tasmanian market, a maximum limit of 20% x 40% = 8% has been set.

Since demand for cement in Australia exceeds supply (and is likely to continue to do so - imports make up 16% of cement use in Australia107), substitution of cementitious materials is unlikely to reduce production levels at Railton.

Carbon Capture

Abatement can occur through post-combustion carbon capture technologies. The high emission intensity of the calcination process means that the concentration of carbon dioxide in the off-gas from the kilns is typically twice that of power plants. This provides an attractive prospect of using chemical absorbents to capture the CO2. The carbon capture and storage options are limited to post-combustion capture as it is more likely to avoid process re-design

106 Estimate from the Tasmanian EPA. The Tasmanian EPA are setting up a controlled tracking system expected to be up and

running in 6-12 months. 107 Cement Industry Action Agenda 2006-2012, Cement Australia.



that would be necessary for technologies like oxyfuel pre-combustion capture108. It is assumed that this technology could potentially be available to the smaller cement kilns from 2015.

Investment costs are estimated to be of order $200 per tonne of CO2109, with annual operating costs comprised of increased labour and maintenance, increase in energy costs to capture and compress the CO2, and transport and storage costs. The latter cost was assumed to be of the range $60-$70 per tonne of CO2 depending on the locality of the storage. This value is much higher than the associated costs for power plants and was adopted to reflect the larger burden on the kilns of the necessary infrastructure. This constitutes the largest component of the cost. Assuming an 85% capture rate, specific abatement costs for CCS in the cement industry are approximately $65-$78 per tonne of CO2 in 2030. Due to being located on a highly porous limestone landscape (with no impermeable rock cap), this option could be more expensive than noted here – storage will have to either go deeper or further away from the site. One option is to pipe the CO2 to a depleted well offshore (as Yolla becomes depleted), which is a long-term option.

Another option is the possibility of capturing carbon dioxide in algal ponds, with the biomass created being able to be processed into useful products of biofuels and other carbon based materials. The process involves piping CO2 from flue gas from a heavy emitter through to an algae farm. Sunlight, nutrients and water are added to the growing algae which sequesters the piped CO2. Harvested algae is processed to make around 35% algae oil and 65% algae meal. The oil can be used to make biodiesel or jet fuel, or be used for plastics production. The meal can be used as feed for livestock industry, fertilizer, feed for electricity or plastics production and even ethanol production. Loy Yang Power in Victoria has invested $2.1M in a pilot display plant by MBD Energy to test the feasibility of this option. If successful, this could turn into a commercial scale project by 2013. The technology not only sequesters greenhouse gases but also provides valuable bi-products with lower land requirements per tonne of algae than many other energy crops. The number of hectares required to yield 100,000 litres of oil is approximately 0.24% of what would be required using an alternative crop such as canola. Capital costs and yields have been taken from the MBD Energy information brochure110.

Tec-Kiln

There has been some preliminary research from Washington State University indicating that cement may partially sequester CO2 by absorbing 5% or more of the created greenhouse gases by combining the carbon dioxide with lime to form calcite (calcium carbonate) and other compounds. This process can be viewed as a form of bio-mimicry – shellfish for example use carbon dioxide in the atmosphere to add further calcium carbonate to their shells.

108 IEA, Prospects for carbon capture and storage, 2006. 109 IPCC Working Group III Fourth Assessment Report . 110 http://www.mbdenergy.com/catalogue/c5/p93/cp4



Tec-Eco in Tasmania have developed a means of making hydraulic cements (either Portland Cement or their own patented Eco-Cements) which form hydroxide phases and carbonate in permeable substrates, thereby sequestering much more CO2. Manufacture is achieved in their patent pending ‘Tec-Kiln’ in which process emissions can be reduced up to 50%. Tec-Eco also promote a complementary ‘Greensols’ process that makes use of the chemical releases to make man-made carbonate which can be used as an aggregate in concretes111. Although this is a separate technology that has not been considered in this work, this process of manufacturing man made carbonate for use as an aggregate is in itself a form of carbon dioxide sequestration which could arguably be considered alongside other forms of sequestration such as geo and algal sequestration.

There are three other greenhouse abatement advantages of the Tec-kiln technology. Firstly, the kiln is completely enclosed and there is no need to supply additional energy for the purpose of emissions capture (e.g. for use in geo or algal or carbonate sequestration). Secondly, the kiln also combines calcining and grinding and so uses around a third less energy than a traditional kiln. Finally, the kiln can run directly from intermittent energy such as from wind (although this has operational implications regarding economic production of cement). Since electrical energy is more expensive and less efficient than coal for the purpose of heating, improved efficiencies will partially compensate.

D.2 Ferro-alloy production

Ferro-alloy production refers to the production of ferro-manganese and silica manganese by TEMCO in Bell Bay. Industrial process emissions arise from three sources during the smelting process: (i) use of coking coal, coke and black coal as a reductant, (ii) use of lime and dolomite as a flux, and (iii) use of carbon electrodes made of anthracite and coal tar pitch. For the financial year ending June 2008, the emission intensity from these sources came to around 1.7 tonnes CO2 equivalent per tonne of production on average. Reduction of industrial process emissions is difficult in this industry and is generally only possible by reducing production. TEMCO have achieved limited reductions by enabling waste recovery via sale of fines in place of ingots to some customers and improving the ratio of saleable to total product.

A suitable substitute for coal can be found in the form of biochar. This change can partially or completely replace coal in the process provided it is of a sufficiently suitable grade to suit the process. The substitution should not require capital cost upgrades although the cost of biochar itself can be prohibitive compared to the cost of coal/coke.

Another option that should be considered given the relatively large emission intensity of the ferroalloy process is geological or algal sequestration, as suggested for the cement industry.

111 Occasionally people use the words cement and concrete interchangeably. In this document, cement is used to form the glue

that binds aggregate in the making of concrete. Cement is not the concrete itself.



Table D-2: Assumptions for cement industry abatement options

Cement Process Replacement of raw

materials

Carbon capture - geo-sequestration

Carbon capture - algal sequestration

Tec-Kiln Blended cement

Extension of limestone

CCS - efficiency of capture % 85% 85%

CCS - efficiency of storage % 90% 100%

Capture, transport and storage costs

$/t CO2e

$60 $5

Capture, transport and storage cost deflator

-2% -2%

Capital cost - emissions reduction technology

$/t CO2e

$200 $214

Capital cost – production technology

$/t 0

$117 0

Production of biodiesel $/t CO2e 214

Production of algae meal t/t CO2e 0.32

Process emission reduction with Tec-kiln

50%

Capital cost - cost of grinder $M 1.32

Capex Deflator % 0.30% 0.30% 3.00%

Current share of blended cement % 0% 0%

Best practice share of blended materials

40%

5%

Current share of replacement raw materials

% 0%

Best practice share of raw materials

% 25%

Cost of Gypsum $/tonne 11.87 `` 11.87 11.87

Cost of Limestone $/tonne 15.29 15.29 15.29

Cost of sewage sludge $/tonne 10.00 10.00 10.00



Cement Process Replacement of raw

materials

Carbon capture - geo-sequestration

Carbon capture - algal sequestration

Tec-Kiln Blended cement

Extension of limestone

Cost of slag $/tonne 28.67 28.67 28.67

Calcium oxide share of sewage sludge

% 20%

Calcium oxide share of slag % 43%

Calcium oxide share of traditional clinker

% 66%

Magnesium oxide share of sewage sludge

% 3%

Magnesium oxide share of slag % 8%

Magnesium oxide share of traditional clinker

% 2%

Lime displaced for each kg replacement material

kg lime displaced

0.67

Existing blend of clinker % 91.2%

Existing blend of gypsum % 4.8%

Existing blend of limestone % 4.0%

Additional labor costs $/tonne 2.50

Electricity use - emissions reduction

kWh/t CO2e

180 6

Electricity use - production kWh/tonne clinker

106 106 106 806 106 106

Fuel use GJ/tonne clinker

3.60 3.60 3.60 - 3.60 3.60

First year available Year 2012 2015 2025 2015 2012 2012 Source: MMA analysis of data based on information supplied by Cement Australia, TecEco, ACF ‘Cementing the Future’ 2005 and 2007, IPCC Working group 3 third assessment report and IEA Prospects for carbon capture and storage 2006.



Table D-3: Assumptions for Ferroalloy industrial process abatement options

Ferroalloy process Use of biochar

pellets as reductant

Carbon capture -

algal-sequestration

Carbon capture - geo sequestration

CCS - efficiency of capture % 85% 85% CCS - efficiency of storage % 100% 90% Capture, transport and storage costs

$/t CO2e

$20 $60

Capture, transport and storage cost deflator

-2% -2%

Capital cost - emissions reduction technology

$/t CO2e

$214 $200

Capex deflator % 0.30% 0.30% Production of biodiesel t/t CO2e 0.179 Production of algae meal t/t CO2e 0.321 Revenue from oil $/t oil 150 Revenue from meal $/t meal 100 Electricity use - emissions reduction

kWh/t CO2e

6 180

Heat rate of biochar GJ/tonne 35112 t CO2e / t FerroM 0.121113

Emission Factor when

biochar is used as a reductant t CO2e / t SilicaM 0.198

Cost of biochar $/GJ 8.81114 Biochar per tonne of ferroalloy

t/t 43.4%

First year available Year 2012 2025 2015 Source: MMA analysis from data supplied by TEMCO, Best Energy, MBD energy and other sources.

D.3 Aluminium production

The Rio Tinto aluminium smelter in Bell Bay had an industrial process emissions factor of around 3.7 t CO2e per tonne of production in 1998, which has steadily dropped and stabilised to around 1.7 t CO2e per tonne of production in 2008. The main cause of this decline in emissions per tonne of production is a dedicated effort to reduce what is referred to as anode effects. Anode effects create per-fluorocarbons (PFCs) CF4 and C2F6 which respectively have global warming potentials 6,500 and 9,200 times that of carbon dioxide. Anode effects occur when the concentration of alumina in the pot drops below a critical level, preventing the process chemical reactions involving alumina from occurring. Instead, carbon from the anodes combine with the fluorine in the cryolite bath to form the PFCs. During these events, the voltage across the cells rapidly increases, reducing the overall efficiency of aluminium production. Partially as a result of this poor efficiency and

112 Sohi, S., Loez-Capel, E., Krull, E., Bol, R., 2009. Biochar's roles in soil and climate change: A review of research needs.

CSIRO Land and Water Science Report 05/09. Actual heat rate depends on biochar feedstock used - median value taken.

113 Based on MMA analysis of data provided by TEMCO. 114 Assumes $200/tonne. Note that biochar is m ore expensive than this at the moment. Biochar is a technology that

requires significant startup support to realise economies of scale and lower prices.



partially as a result of the need to reduce electricity costs in what is an energy intensive business, the Bell Bay smelter has actively sought means of reducing these anode effects by improving the control process. This has occurred via better computer systems, alumina point feeders and personnel training. Further improvement in this area might be achieved by improving the reliability of the power supply to the plant. The company has managed to reduce these emissions from 1.88 t CO2e per tonne of production in 1998 down to 0.05 t CO2e per tonne of production in 2008. For this study, it is assumed that the status quo will be maintained for their reference case.

Aluminium is produced by the Hall-Hèroult electrolysis of alumina in a series of carbon-lined steel pots. The alumina undergoes a chemical reaction with high purity carbon anodes, with the oxidation of these anodes releasing carbon dioxide. Secondary processes may also occur, with a fraction of the CO2 reducing to carbon monoxide, however, it is assumed in NGI reporting that CO2 constitutes all of the gas produced during the carbon oxidisation. Emissions associated with the on-site manufacture of the anodes are not categorised in aluminium production and so are ignored here. Emissions from alumina refining are also not considered.

Figure D-1: Industrial Process Emissions Factor at Rio Tinto Alcan in Bell Bay115

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

t C

O2e

/t p

rod

uct

i

PFC t CO2

Carbon t CO2c

Source: Data courtesy of Rio Tinto Alcan (Bell Bay), May 2009.

Replacement of carbon anodes with inert anodes

Elimination of the emissions associated with the oxidisation of the carbon anodes can only be achieved by changing the chemical process of aluminium production. Currently, there is a large impetus to design a commercially viable inert anode that could replace carbon. In doing so, both carbon and PFC emissions would be eliminated. Research has focussed on the use of ceramics, cermets and metals, with cermets appearing to be more favourable.

115 Based on data provided by Rio Tinto, May 2009.



If successful, inert anodes could be retrofitted into existing cells without altering the alumina feeding infrastructure. Capital costs are estimated to be $120 per tonne of aluminium and are inclusive of the retrofit and new anode manufacturing equipment116. The lifetime of the inert anodes is much greater than their carbon counterparts, with the latter needing weekly replacements. Thus, the benefits of the inert technology are a reduction in anode manufacture and the associated energy costs. It is estimated that, due to the decrease in production requirements, material costs of the two anode technologies are comparable.

Additional predicted benefits include a more stable cell technology including a reduction in the anode cathode distance, resulting in more efficient production. Opinion on the likely success and cost of inert anodes varies. Industry suggests that they could be employed as early as 2012. However, the more conservative date of availability quoted by the IPCC is 2020-2025. MMA allow the technology to penetrate the market from 2020, with a limit on its implementation prior to 2025.

Relative operating costs are assumed to increase despite the decrease in labour. This is a result of an increase in the energy intensity of the electrolysis process. In the current electrolysis process, the oxidation of the carbon anodes provides some of the energy necessary for the chemical reactions to proceed. In the absence of carbon, this energy needs to be supplemented by an increase in electricity consumption per unit of aluminium produced. Currently, electricity intensity is around 14,000-15,000 kWh per tonne of aluminium, and is estimated to increase by 20% with the inert anodes. Labour costs are assumed to decrease by 5%. These values are in the median of the range of estimates in the literature. Under these assumptions, the specific abatement cost for inert anodes is approximately $25 to $40 per tonne of CO2e.

The other alternative to reduce emissions associated with the carbon anodes is to replace the electrolysis process. So far, the only optimistic route is by carbothermic reactions. This technology is less emissions intensive than the Hall-Hèroult process, with the chemical reactions occurring in a small reactor. Alumina and carbon are mixed to form an alumina-aluminium carbide slag that is then thermodynamically reduced to form aluminium. This process requires the installation of new capacity and has much smaller economies of scale. The reduction in energy intensity results in a decrease in indirect greenhouse emissions, however, the process still releases carbon dioxide, albeit at a slightly reduced intensity than the Hall-Hèroult process. Unless one considers the new capacity to be fitted with a CCS system, the abatement is not economical in a high carbon price context. For this reason, carbothermic reactors were not considered in the modelling.

Aluminium Recycling

Aluminium is one of the few metals that is eternally recyclable and provides the lightweight properties so suitable for reducing transport emissions via lightweight vehicle

116 J. Keniry, The economics of inert anodes and wettable cathodes for aluminium reduction cells, JOM: 44, 2001.



body design. As a result demand for this metal is likely to continue for a long time, perhaps increasing as customers demand better energy and greenhouse efficiency from their vehicles. Rio Tinto Alcan has in the past attempted recycling of aluminium but discontinued this practice due to safety concerns as a result of waste aluminium being contaminated with liquids. Further barriers to uptake of recycling initiatives include costs associated with the collection and transport of waste aluminium to the recycling centre.

Recycling uses only 5% of the energy requirement of primary aluminium, and does not cause the process emissions that primary aluminium production will cause, and so replacing more primary aluminium with secondary recycled aluminium will reduce emissions considerably.

However, the high demand for this metal makes it likely that even if a recycling facility were installed in Tasmania that the manufacture of aluminium itself would not decline, and hence Tasmanian emissions would not decline. This means that introduction of a recycling facility could actually increase Tasmania’s emissions, even if it decreased Australian or global emissions.

Assumptions for Aluminium sector process abatement options are shown in Table D-4.

Table D-4: Aluminium process abatement options assumptions

Aluminium process Inert anodes Recycling

Capital cost $/tonne Al $120 Capital cost $M $21 Life 25 25 Electricity intensity kWh/tonne Al 15,315 15,315 Capacity of recycling station t/year 50,000 Anode oxidisation emission intensity t/t Al 1.69 Increase in electricity costs % -10% -95% Materials(assumes 80% waste aluminium dross and 20% can collection)

$/tonne Al $362

Recycled aluminium $/tonne Al $1,233 Labour savings % 5% Operational cost $/tonne Al $1,076 $10117 Capex deflator % 3.30% 1.00% First year available 2020 2015

Sources: Various, including:- Rio Tinto (baseline electricity intensity), http://agmetalminer.com/2009/02/27/cost-build-up-model-for-primary-aluminum-ingot-production/ (Operating cost of primary aluminium production), http://www.aluminum.org/AM/Template.cfm?Section=Recycling1&TEMPLATE=/CM/ContentDisplay.cfm&CONTENTID=27361 (Source of capital recycling costs - adjusted by exchange rate of 0.5798 $AUD/Euro), http://205.153.241.230/P2_Opportunity_Handbook/7_I_A_2.html (Source of materials for recycling costs).

117 Conservative MMA estimate. According to the joint service pollution prevention handbook published by the US Navy

(http://205.153.241.230/P2_Opportunity_Handbook/7_I_A_2.html), recycling is considered a cost negative activity once putting aluminium cans in landfill and income from aluminium sold is considered.



APPENDIX E AGRICULTURE ABATEMENT OPTIONS

E.1 Introduction

Numerous measures have been proposed to reduce green house emissions from agriculture. Many of these measures are unproven and/or still being investigated and hence the gains that might be achieved and the commercial viability of these measures are to a large extent, unknown.

Soil related emissions reductions are complicated by the complex interactions that take place to produce emissions. For example, N2O production may result from three separate microbially mediated processes that take place at varying soil moisture contents, temperatures, soil carbon levels, soil carbon state, nitrogen levels, soil structures, salinity, availability of other nutrients and Ph levels118. Further, uncertainties in emissions factors, activity data, lack of coverage of measurements, spatial and temporal aggregations, and lack of information on specific on-farm practices, lead to uncertainties in direct emissions and emissions factors that are not representative119.

Increased irrigation is also likely to lead an increase in de-nitrification through increasing the water content of soils.

The uncertainty around the potential to reduce N2O emissions is summarised in Rodriguez, et al120 as follows:

Like other agricultural systems, nitrous oxide emissions from the grains industry arise as a result of the soil processes of denitrification and nitrification. Mitigation options aim to reduce the rates of these processes and ensure that nitrogen is emitted as harmless N2 rather than N2O. Denitrification and nitrification are affected differently by many soil and climatic factors. Options that decrease opportunity for episodic denitrification events will presumably decrease N2O emissions. Decreasing N2O loss due to nitrification is a much greater challenge in contrast to denitrification. As a result, experimental evidence of the impact of mitigation options on N2O emissions is limited.

CO2 production from agricultural soils depends on including temperature, moisture and soil type, so much so that practices such as minimum till (which is widely accepted as increasing soil carbon levels) may actually decrease soil carbon in some conditions121.

118 Ugalde, D., Brungs, A., Kaebernick, M., Mcgregor, A., & Slatery, B. (2007, December). Implications of climate change for

tillage practice in Australia. Soil and Tillage Research , 318-330. 119 Dalal, R. C., Wang, W., Robertson, P. G., & Parton, W. J. (2003). Nitrous oxide emission from Australian agricultural

lands and mitigation options: a review. Australian Journal of Soil Research , 41, 165-195. 120 Rodriguez, D., Merv, P., Meyer, M., Galbally, I., Howden, M., Bennett, A., et al. (2003). Background study into greenhouse

gas emissions from the grains industry. Grains Research & Development Corporation. 121 Valzano, F., Murphy, B., & Koen, T. (2005). The Impact of Tillage on Changes in Soil Carbon Density with Special

Emphasis on Australian Conditions. Department of the Environment and Heritage, Australian Greenhouse Office. Canberra: Australian Government.



Another difficulty in modelling abatement options in agriculture is the complex relationships between different abatement strategies. For example, in work conducted into the dairy industry122, it is found that while combinations of various abatement strategies they investigated result in the largest savings, the large reductions often attributed to individual measures cannot, in practice, be achieved. They suggest that herd based strategies have the potential to reduce CO2e emissions by 10% to 20% and urinary nitrogen by 10% to 50%. Feed based strategies could reduce CO2e emissions by 10% to 20% and that soil based strategies could reduce CO2e emissions by 10% to 20%. Many of the abatement opportunities modelled here are based on this report and others are modelled directly using the NGI methods.

As this study is considering a long time frame, some technological measures that are not yet feasible or fully developed have been included as abatement options. These are dietary supplementation and vaccination to reduce enteric fermentation emissions. The full effects of many forms of dietary supplementation are not yet thoroughly understood and no vaccine against enteric fermentation has been developed.

The effectiveness of most agricultural emissions abatement initiatives depends on the farm to which they are applied. Factors which affect effectiveness include: the scale of the enterprise, soil types, climatic conditions, management practices and the location of the enterprise122. Uncertainties surrounding the viability of these various measures have been ignored in the modelling, but are discussed qualitatively in the following sections.

E.2 Abatement Options

The abatement options proposed to include in this model are:

Optimal fertiliser use and timing

Cropland stubble retention and/or minimal till

Selective livestock breeding

Vaccination to reduce enteric fermentation emissions

Increasing the number of lactations

Reducing herd replacement rate

Increasing per animal productivity

Other dietary supplements

Nitrification inhibitor.

Other options that could be considered involve major changes to the structure of agricultural enterprises. For example, moving to non-rumen meat production has the

122 Christie, K., Rawnsley, R., & Donaghy, D. (2008). Whole farm systems analysis of greenhouse gas emission abatement

strategies for dairy farms. Final report, University of Tasmania, Tasmanian Institute of Agricultural Research.



potential to significantly reduce emissions, but would require major changes to farm infrastructure and cultural changes with regard to people’s choice of meat. Another example is increasing lactation lengths, but this is only really feasible for TMS systems122, so is likely to be of limited application in Tasmania.

In the longer term, however, these may be worth consideration as cultural views or farming practices may change.

Optimal fertiliser use

Application of inorganic fertilisers to agricultural soils results in the elevation of production of nitrous oxide through microbial and chemical reactions that involve ammonium nitrate and nitrite. The majority of the use of these fertilisers relates to cereal grains and to a lesser extent intensive grazing systems. The highest emission factors for these systems occur when application rates and timing of application produce soil nitrate concentrations substantially in excess of plant demand123. However, the relative emissions of N2O and the greenhouse neutral gas N2 depend on many factors. It is therefore hard to estimate the emissions reductions that may be achieved through improvement of fertilizer use.

A large number of measures that may decrease N2O production are given in Dalal et al119. It is beyond the scope of this project to consider all the options separately. Firstly, the effects of these measures will be highly inter-dependent and secondly, the effects will also differ significantly through time and space. Emissions factors are based on the same source used in the NGI.

Due to the frequency of wetting and drying events, and since the degree of wetting and temperature can affect N2O production, climate change is likely to change N2O in the future. This has been ignored in this modelling.

It has been estimated that emissions could be reduced using optimal fertiliser application rates by 5%–30%. It is assumed that an average reduction of 15% would apply to all land managed for fertiliser use.

There are no physical or economic limits to the implementation of this mitigation method as it is dependent largely on educating farmers and graziers to manage crops in a certain way. Costs associated with this education have been ignored. As such, there are only positive net benefits to farmers, even in the absence of a carbon price, which indicates that there must be some non-market barrier to uptake of this measure. In the absence of such a barrier it is hard to estimate how uptake would be affected by changes in carbon prices and it has been assumed that fertiliser use is already effectively managed for 30% of the existing cropland and pasture to which it is applied and that uptake follows the relation

123 Department of Climate Change. (2007). Australian Methodology for the Estimation of Greenhouse Gas Emissions and Sinks

2006: Agriculture. Retrieved from http://www.climatechange.gov.au/inventory/methodology/pubs/methodology-agriculture2006.pdf



shown in Figure E-1. The limits on uptake have therefore been set to 50% of cropland for 2020 and 100% in 2050.

Figure E-1: Proportion of land where fertiliser use and timing is optimally managed


The implementation of this measure results in the reduction of fertiliser used on crop and pasture land which results in a cost saving to the farmer. However, the labour and fuel costs will depend on the measure chosen. More frequent applications of smaller amounts may increase labour and fuel costs, but improved timing of application may not. There may also be increases in labour costs resulting from monitoring and analysis. It has been assumed that these costs balance to a net labour cost change of zero. Fertiliser costs are estimated to be $33/ha124 and the reduction in fertiliser is assumed to be directly proportional to the resulting reduction in emissions.

Cropland stubble retention and minimum till

While the effects of stubble retention and minimum tillage are separate processes and are likely to have different effects on emissions, most experiments consider them together and hence it is hard to disaggregate the benefits accruing to each individually. Hence, they are considered together here.

Minimum till is generally considered to increase carbon sequestration. However, in low rainfall areas of Australia this may not be the case125. Estimates of the amount of carbon that may be sequestered also vary. For example, Rodriguez, et al126 estimate that soil carbon sequestration on grain farms lies between 0 and 5 tCO2e per annum and Umbers127

124 Queensland Department of Primary Industries and Fisheries. (2006). Central Queensland Sustainable Farm Systems

Phase 2 Interim Report, Chapter 8b. 125 Ugalde, D., Brungs, A., Kaebernick, M., Mcgregor, A., & Slatery, B. (2007, December). Implications of climate change for

tillage practice in Australia. Soil and Tillage Research , 318-330. 126 Rodriguez, D., Merv, P., Meyer, M., Galbally, I., Howden, M., Bennett, A., et al. (2003). Background study into greenhouse

gas emissions from the grains industry. Grains Research & Development Corporation. 127 Umbers, A. (2007). Carbon in Australian Cropping Soils. The Grains Council of Australia.



claims that other research suggests much lower levels of between 0.1 and 0.2 t C (or between 0.36 and 0.74 t CO2e) per annum.

It is also generally recognised that additions of organic carbon to soil stimulates de-nitrification. This is attributed to two main reasons; it serves as a carbon source and hydrogen donor for heterotrophs or denitrifying bacteria and it increases anaerobiosity by depleting oxygen128. However, in their experiments the latter authors find that addition of straw reduced the production of N2O (while increasing the production of N2, which could imply that tilling stubble into the ground may actually reduce N2O emissions) and note that previous research had produced both supporting and conflicting results. There research was conducted in controlled laboratory conditions and did not take into account the initial disturbance to the soil. Other research has found that tillage increased production of N2O, but mainly in the short period following the tillage129 which may explain these results.

Cropland stubble retention and minimum till may reduce CO2 through requiring less energy through the tilling process. Aggressive tilling practices make up to seven passes through the soil, whereas zero tillage practices only make one pass. Minimum tillage practices also improve soil structure and hence make it easier to work, further reducing the energy required to sow crops125.

Another issue to consider is the long-term productivity of different soil management practices. A study in Mexico130 found that after 14 years of continuous practice, zero till practices with stubble retention resulted in both higher quality soil and higher yields for both maize and wheat. Kirby131 reports that retaining stubble improved the soil quality, aided in the uptake of nitrogen fertiliser and also improved yields.

Emissions reductions resulting from stubble retention and minimal till are based on values presented by the Department of Climate Change for irrigated maze in Griffith, NSW. These estimates are based on experiments where the stubble was burned and retained and fertilizer application was 300kg N/ha. The observed emissions reduction is

. The emissions factor for cropping calculated for all of

Australia on the basis that stubble retention is applied to approximately 50% of summer row crops nationally. The areas under severe stubble management practices in Tasmania are presented in Table E-1132. The emissions factors were based on these proportions for the reference case.

128 Avalakki, U. K., Strong, W. M., & Saffigna, P. G. (1995). Measurements of Gaseous Emissions from Denitrification of

Applied Nitrogen-15. II. Effects of Temperature and Added straw. Australian Journal of Soil Research , 33 (1), 89-99. 129 Baggs, E. M., Rees, R. M., Smith, K. A., & Vinten, A. J. (2000). Nitrous oxide emission from soils after incorporating crop

residues. Soil Use and Management , 16, 82-87. 130 Fuentes, M., Govaerts, B., De Leon, F., Hidalgo, C., Dendooven, L., Sayre, K. D., et al. (2009). Fourteen years of applying

zero and conventional tillage, crop rotation and residue management systems and its effect on physical and chemical soil quality. European Journal of Agronomy , 30 (3), 228-237.

131 Kirby, C. (2006, July). Stubble trial unearths big benefits. Farming Ahead , pp. 46-48. 132 While the column headed “Tas” should be the total, the empty cells contained data that was “not available for

publication but included in totals where applicable, unless otherwise indicated”. The column “Total” presents the row sums of the columns “North”, “North West” and “South” and the column “%” presents those totals as a percentage of the overall total.



Table E-1: Crop residue management practices in Tasmania, ‘000 ha

Region Tas North North

West

South Total %

Stubble was left intact (no cultivation) 10.7 7.2 0.9 2.5 10.6 21%

Most stubble or trash removed by baling or heavy grazing 12.4 4.3 16.7 33%

Stubble or trash removed by a hot burn (early season) 0.3 0.7 1.0 2%

Stubble or trash removed by a cool burn (late season) 1.0 0.4 1.4 3%

Stubble or trash was ploughed into the soil 10.4 6.4 16.8 33%

Stubble or trash was mulched 1.7 2.3 4.0 8%

Total 59.5 33.0 13.7 12.8 50.5 100%

Source: Australian Bureau of Statistics, 2009.

Potential improvements in productivity are given by Kirby131 which gives an average improvement of and reductions in energy consumption.

Selective livestock breeding

Results of a literature review122 report that several studies suggested that selective breeding could improve feed conversion efficiency in the range of 10 to 20%. Selective breeding would take a long time to implement and hence is modelled to have the potential to reduce per animal enteric fermentation emissions by 0.5% annual improvement. This would result in an improvement of 22% over a 40 year timeframe.

It is estimated that a selective breeding program would cost $250,000 per annum to run.

Vaccination to reduce enteric fermentation emissions

Considerable work is being undertaken in order to develop a vaccination or treatment that would reduce methane resultant from enteric fermentation in ruminant livestock. The vaccination is assumed to be conducted 3.5 times per year, and delivery would be impractical to some wide ranging herds.

Costs are not yet publicly available, but have been estimated as $20 per cow per, and $6.70 per sheep, declining at 5% per year (2008 dollars). Delivery costs have been set at zero for the dairy herd, $2 per sheep, and $5 per non-dairy cow. It is further assumed that one vaccination per year would have zero delivery cost, as it would be timed to coincide with other vaccinations.

A reliable vaccine with proven results is unlikely to be available prior to 2015, and vaccination would involve changing management practice.

Test results indicate that reductions in emissions may be in the vicinity of 20%.

The timing of uptake is estimated by calculating the cost and benefits of uptake in each year for each animal type (sheep, dairy cattle and beef cattle). Once the vaccine becomes cost effective it is assumed that uptake would occur over the 10 following years. It is



assumed that vaccination is practical for 100% of dairy cattle, 75% of beef cattle and 50% of sheep.

Under carbon price assumptions, vaccination becomes cost effective for:

Dairy cattle in 2034 and is fully taken up by 2043.

Beef cattle in 2046 and hence would not be taken up fully until 2055.

Vaccination never becomes cost effective for sheep.

Reducing herd replacement rate (dairy)

Increasing the milking lifetime of dairy cattle would decrease the number of cattle required for producing replacement stock hence increasing per animal productivity when all animals in the herd are considered. As milking cows are replaced by heifers this option is modelled by adjusting the stock structure, so that there are more milking cows and fewer heifers. While this increases overall emissions, the production of milk increases and hence the emissions per unit of milk solid decreases. It is assumed in the analysis that total milk production remains at the levels assumed in the reference case.

It is assumed in this analysis that the productivity of a cow remains constant through its extended lifetime. This is unlikely to be true in practice and will overstate the emissions reductions achieved. It is also important to note that this option interacts with the option of increasing per animal productivity, since older cows tend to produce less milk than younger cows, and hence per animal productivity when only milking cows are considered will decline.

Increasing per animal productivity (dairy)

The enteric fermentation emissions from a dairy cow increase with the cow’s milk production. However, the increase in milk production dilutes the emissions so more milk solids can be produced per unit of emissions.

Total milk production has been assumed remains constant and that the number of dairy cows and heifers is correspondingly reduced in size. As per head milk production is an input into the NGI calculations, the emissions factors are recalculated using the increased per animal production levels. The average annual milk production over the period 2005 to 2007 was estimated in the NGI to be 4586L in 2007. In the NGI calculations, Dry Matter Intake (DMI) is a function of milk production and this level of production implies a DMI of 15.02kg per head per day.133 Increasing annual milk production to 6000L implies a dry matter intake of 16.96kg per head per day using the same calculations. It is assumed that this intake in dry matter can be achieved from existing pastures.

133 This level of milk production (and the trend apparent in the NGI data) seems inconsistent with an estimate given in

(DairyTas, 2009) which states that Average production per cow is expected to increase from an average of 5513 litres to 5,756 litres. Presumably this from the 2008/09 to 2009/10 financial years.



MMA assume that an annual increase in productivity of 2.5% per annum is achievable up to a maximum production level of 6000L per head per day.

Nitrification inhibitor

Nitrification inhibitors (NIs) work by limiting the microbial processes within the soil that result in the production of N2 and N2O. By converting the nitrogen in the soil into more stable forms, they will also reduce nitrogen leaching and runoff and, potentially, atmospheric deposition. As well affecting the nitrogen in the contained in the fertiliser itself, they will also affect nitrogen already present in the soil further reducing emissions. This latter effect has been ignored in this modelling as it is dependent on unknown existing nitrogen levels.

The extent of the reduction depends on the rate of fertiliser application, environmental conditions and farm management practices at a given location and will vary significantly on the soil structure, moisture content and temperature which will vary throughout a year. Christie et al122 conclude from their literature review that nitrification inhibitors could potentially reduce emissions of N2O by up to 40-50% during times of the year when emissions are high. Experimentation has reported average N2O reductions of approximately 50% over three year trials of the nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) on various crops with no degradation in plant growth134. The experiments also found 23% lower levels NO3-, a major source of N in leaching and runoff, in treated plots and suggested that this NI has the potential to positively affect carbon dioxide fluxes and improve the properties of the soil as a methane sink. These effects on CO2 and methane have been ignored in the current work. By stabilising the nitrogen in the soil, other benefits (excluding reduced N2O emissions) are mentioned in Zerulla, et al135:

A significant reduction in the risk of NO3- leaching losses from N fertilisers, compared to conventional N fertilisers.

Smaller N losses and temporary NH4+ nutrition of crops, often leading to yield increases.

Better N utilisation of plants.

A reduction in the work-load of growers due to more flexible timing of fertiliser application, and the possibility of combining or saving application rounds.

By reducing nitrogen lost from the soil, they may also reduce the amount of fertiliser required. In this work it is assumed that an overall reduction of a 10% reduction in fertiliser requirements136. These reductions are in addition to those that could be achieved

134 Weiske, A., Benckiser, G., Herbert, T., & Ottow, J. C. (2001). Influence of the nitrification inhibitor 3,4-dimethylpyrazole

phosphate (DMPP) in comparison to dicyandiamide (DCD) on nitrous oxide emissions, cabon dioxide fluxes and methane oxidation during 3 years of repeated application in field experiments. Biology and Fertility of Soils , 34, 109-117.

135 Zerulla, W., Thomas, B., Dressel, J., Erhardt, K., Horchler von Locquenghien, K., Pasda, G., et al. (2001). 3,4-Dimethylpyrazole phospate (DMPP) - a new nitrification inhibitor for agriculture and horticulture. An introduction. Biology and Fertility of Soils , 34, 79-84.

136 No literature on actual use reduction potentials could be found, but advice from experts suggested that 10% is probably a conservative estimate.



with optimal fertiliser use. As the results cited above are from a single set of experiments, it is assumed that a reduction in leaching and runoff of 15% is possible. As noted in the points above, it is likely that the use of an NI will give farmers more flexibility and potentially reduce application frequency, reducing application costs. Further savings in both application and transport costs would accrue from the use of less fertiliser. These savings have been ignored here. Good information on the level of increased productivity could not be found and hence improvements in productivity have also been ignored here.

Nitrogen fertiliser costs are affected by the market prices of their constituent chemicals. A price of $950 per tonne has been assumed. The inhibitor is assumed to cost an additional $170 per tonne.

Dietary supplements

Supplementing an animal’s diet with fats and oils, monensin, and condensed tannins can reduce enteric fermentation emissions.

Research into the effect of dietary supplementation of fats and oils has shown variable potentials to reduce enteric fermentation emissions and some results indicate that it may increase milk production in dairy cattle. However, increased production may come at the cost of milk quality. The enteric fermentation emissions reduction potential is between 10 and 20%, but the full lifecycle emissions/environmental costs of fats and oils need to be considered when adopting this as an emissions reduction strategy.

Dietary supplementation of monensin may decrease enteric fermentation emissions as well as providing other benefits, especially in feedlots. The effect of monensin supplementation is dose dependent and may be time dependent, due to the protozoa population adapting over time. There might also be public perception issues with the use of antimicrobial chemical in animal production. It is estimated that these may have the potential to reduce enteric fermentation emissions by around 10%. However, as this measure is most effective in feedlot situations, its applicability to Tasmania may be limited.

Dietary supplementation of condensed tannins may reduce enteric fermentation emissions. Condensed tannins may also reduce N2O emissions by protecting protein in the rumen from degradation. Christie et al122 note that the inclusion of condensed tannins into the diet of dairy cows could potentially reduce urinary N emissions by up to 59%, with a corresponding increase in dung N137, 138.

Development of pasture species which reduce enteric fermentation in livestock has not been included as information on the cost, nor information on the overall benefits of doing so, could be found. Christie et al122 note that most of existing species that contain higher

137 It is interesting to note that the emissions factor for dung N voided at pasture is 25% larger than that for urinary N

voided at pasture in the NGI calculations (and N from both sources is treated identically for other N2O related emissions).

138 Christie, Rawnsley, & Donaghy ( 2008) only cite research done on cattle, but the ways in which condensed tannins affect nitrogen excretion are also likely to affect sheep.



levels of condensed tannin are tropical shrub legumes and their herbage quality is lower. Hence, they may have limited applicability in Tasmania and reduce productivity. Plant breeders in the USA are using genomic techniques to improve the condensed tannin concentrations in species which traditionally used to contain tannins, but have over time reduced these to minimal concentrations (e.g. lucerne and sorghum). Once these condensed tannin rich species are available to farmers, this will provide an abatement option that may be readily adopted by farmers122.

Feeding concentrates has not been included because it is very difficult to measure what the full lifecycle change in emissions might be. For example, the production and transport of grains will give rise to both agricultural and transport related emissions and it is unclear what the overall reduction in emissions might be139.

The combined effect of on multiple supplements is difficult to determine. This is complicated further if initiatives such as vaccination or selective breeding are also undertaken. As noted above, it is also difficult to estimate full lifecycle environmental and emissions impacts; particularly for fats and oils. The cost effectiveness of these supplements will also depend on the animal type (sheep, dairy and beef cattle) due to the farming practices and relative returns to each type.

For these reasons, an abatement option that includes all supplemental feeds has been included and assumes a relatively low abatement potential of 5% which may be fully exploited by 2050.

139 This is further complicated by the fact that intensive farming systems give rise to greater emissions in total, but reduced

emissions per unit of output.



APPENDIX F LAND USE CHANGE AND FORESTRY OPTIONS

This appendix presents an analysis of how varying rotation lengths may affect carbon sequestration within a forest itself, in wood products produced from the forest and in fossil fuel emissions avoided from using the biomass from the forest as a fuel. An option reflecting this analysis was not included as an emissions reduction measure as MMA were unable to obtain detailed enough information on the existing public forest estate in Tasmania to determine how varying the rotation length of public plantation forests would affect emissions through the study period.

Effects of rotation length on carbon sequestration

The way forestry is conducted has changed significantly over the course of the last century. Most plantation timber is now used for wood chips and a growing proportion of the wood products made do not require large logs so there is less requirement for large trees. There is also much more emphasis on the profitability of forestry, leading to investments with shorter duration becoming more attractive. There has also been significant conversion of agricultural land to plantation forestry due to favourable tax incentives under management investment schemes.

This section presents an analysis of the effects of rotation length on carbon sequestration. This analysis is applicable to both plantation and native forests, and is extended on in the following section. The optimal rotation length will be determined by interest rates, inflation, expectations about the future value of wood products, the type of trees chosen and climatic conditions.

In the analysis which follows a representative forest growth profile was used, based on a Tasmanian Blue Gum forest on a 100 year carbon regime growing at a peak marginal annual increment (MAI) of 10 cubic meters per hectare per annum. The growth profile for this forest was calculated using the Farm Forestry Toolbox (FFT) from Private Forests Tasmania140. Figure F-1 shows the total weights, mean annual increments (MAI) and current annual increments (CAI) for a hectare of this forest. For comparison, Figure F-2 presents analogous results for the same are presented for a Tasmanian Blue Gum forest on a 15 year carbon regime.

Assuming that the land is going to remain under forestry indefinitely, the maximal rate of carbon sequestration within the forest occurs when the MAI is maximised. Under the 100 year rotation this occurs in year 41 when the MAI is equal to 4.9 tonnes per hectare per annum.

Total sequestration within the forest is maximised when the total weight is maximised. This occurs some time after the forest is 100 years old. Thus, if the total weight continues

140 The results available from the FFT only extend to the end of the rotation cycle.



to increase until some equilibrium is reached and then levels off, maximising total carbon sequestered within the forest implies changing harvesting regimes.

Figure F-1: Total wood weight, marginal average increment and current average increment for Tasmanian Blue Gum forest on a 100 year regime

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Ton

nes

Total weight / 100 MAI CAI

Figure F-2: Total wood weight, marginal average increment and current average increment for Tasmanian Blue Gum forest on a 15 year regime

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age (years)

Ton

nes

Total weight / 15 MAI CAI

Typical rotation lengths for plantation forests are shown in Table F-1 For Eucalypt plantations, this is 15 to 35 years. As can be from comparison with Figure F-1 and Figure F-2, this is well below the point at which the MAI is maximised under either a 100 year or 15 year regime.

The above discussion highlights a contentious issue in forest management. Under the current emissions accounting rules when an area of forest is harvested, the carbon



contained in the wood removed is assumed to return to the atmosphere as carbon dioxide immediately. Under this assumption the only way to sequester carbon in the longer run is to increase the average carbon density of forests, expand the area of forest, or burn forest products to generate energy, effectively sequestering carbon in the form of fossil fuels.

Table F-1: Average rotation lengths for various forests

Forest type Rotation length

Native forest

Eucalypt (extensively managed) 90

Eucalypt (thinned regrowth) 65

Blackwood (swamp forest) 70

Myrtle (STMU) 200

Plantation

Eucalypt 15-35

Blackwood 40

Source: Tasforests Vol. 11 (http://www.forestrytas.com.au/assets/0000/0169/tasforests11_part_2.pdf)

Clearly some of the carbon contained in a harvested tree does not immediately convert to carbon dioxide and return to the atmosphere when the tree is harvested and is retained in wood products. If changes in soil carbon and forest debris are ignored, in the extreme hypothetical case that none of the carbon contained in the tree returns to the atmosphere as carbon dioxide, then maximum long run sequestration is achieved by harvesting at the time that the MAI is maximised. This is also the case if any of the harvest is used for renewable energy generation. These issues are discussed further below.

To simplify the following discussion and make the analysis concrete MMA assume: there is no way to completely stop the decay of wood products, the demand for energy will continue to remain high enough that it cannot be met through fully exploiting all the biomass resource and, finally, that renewable energy generation displaces fossil fuel generation. Under these assumptions the maximum long run sequestration of carbon from a forest is achieved by harvesting the forest at the time of peak MAI and using the entire forest mass for energy generation. This is because harvesting the forest at the time of peak MAI implies that the forest (as a whole) is sequestering carbon at the maximum possible rate and hence generating fuel at the highest possible rate. This fuel will displace fossil fuel generation and effectively sequester carbon in the form of fossil fuels. Further, if any of the forest harvest is used for energy generation, then the maximal long run rate of sequestration is also achieved by harvesting the forest at the peak MAI.

These results arise because the total displacement of fossil fuels accumulates. After n years, the total carbon sequestered in the form of fossil fuels is t=nxd, where d is the annual rate of displacement. This will eventually become larger than the total mass of the forest and any products derived from it which are in existence at a given point in time (call this total carbon mass T).



The total carbon sequestered in one hectare of forest and the products derived from it (excluding sequestration in the form of fossil fuels) is shown for various rotation lengths in Figure F-3. Figure F-4 shows the profile of the fraction of timber remaining a given number of years after a forest has been harvested141. Under these decay rates, maximum total sequestration is achieved through some rotation length of greater than 100 years.

Figure F-3: Total carbon dioxide sequestered in a hectare of forest and products produced from that forest for various rotation lengths

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100

Years

t CO

2e

Total sequestered in the forest Total sequestered in Product produced from the forest Total


Figure F-4: Assumed proportion of wood remaining by years after harvest

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90 100

Years

Frac

tion


141 This profile is hypothetical and is used to give rise to a longer lifetime than occurs in reality. This profile was chosen

because even with this longer lifetime, rotation lengths need to increase to maximise total sequestration. As discussed later on, determining the appropriate management practice is a trade off, and must take account of balancing carbon sequestration and profitability.



Figure F-5 shows total carbon sequestration in a hectare of forest and products derived from it under various decay scenarios. The assumed scenarios have the same form as that shown in Figure F-3, but are ‘stretched’ such that the time until complete decay of all derived products is 50, 100, 150 and 200 years. The rotation lengths that achieve maximum sequestration are greater than 100 years for the former two scenarios, and are 77 and 59 years respectively for the latter two scenarios.

Figure F-5: Total carbon dioxide sequestered in a hectare of forest

0

200

400

600

800

1000

1200

1400

1600

1800

0 10 20 30 40 50 60 70 80 90 100

Years

t CO

2e

50 years 100 years 150 years 200 years


This analysis demonstrates that if a forest is to be managed for carbon sequestration, optimal rotation rate is bounded below by the time of peak MAI, regardless of the end use of the biomass of the forest. In the long run, this lower bound will be met if some of the wood waste from the forest is used for energy generation. If the current rotation lengths for various forest types are significantly shorter than those which achieve the maximal MAIs, then significant sequestration could be achieved in the short to medium term by extending them.

Even if all the carbon contained in a harvested tree eventually returns to the atmosphere, sequestering carbon in wood products and forests may have an important role in reducing carbon dioxide levels while other abatement options are developed. It may be desirable to increase the rotation length in order to quickly sequester carbon. An optimal rotation length, that balances long-term and short to medium term sequestration targets, could be calculated once a temporal sequestration profile is defined. Achieving a given sequestration target may be controlled through indirect interventions such as changing the legislated production of sawn logs, encouraging the use of wood as a building material or increasing the rate of re-cycling of wood and paper products.



Reforestation of agricultural land

In the case of the reforestation of agricultural land (whether for forestry or as environmental plantings), the rotation lengths used in the management of new forests has a dramatic effect on the carbon sequestered through from now until 2050. Figure F-6 shows the carbon total carbon sequestered in forests planted on agricultural land between 2010 and 2050 under the current carbon accounting framework (i.e. that all carbon dioxide sequestered in a forest is released at the time of harvest). The average annual sequestration is shown in Table F-2. Under a 41 year rotation, this around 9 times as large as under a 15 year rotation. This is clearly an artificial result as a large loss of carbon would be observed under a 41 year rotation in around 2056, which is not captured in this calculation. The long-term carbon sequestration is, however, subject to the same analysis as presented above for existing forests. Through the period from now to 2050, the sequestration achieved under environmental plantings will be similar to that achieved under rotation lengths of 38 years or more. The analysis used in the reforestation of agricultural land assumes some rotation length of greater than 38 years.

Figure F-6: Carbon dioxide sequestered on agricultural land converted to forestry in Tasmania for various rotation lengths

-6000

-5000

-4000

-3000

-2000

-1000

0

1000

2000

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

2049

t CO

2e

15 year 25 year 41 year


Table F-2: Average annual sequestration from the reforestation of agricultural land over the period 2009 to 2050

Rotation length (years) Average annual sequestration (‘000 t CO2)

15 60

25 167

41 511 Source: MMA analysis. Assumes a Tasmanian Blue Gum forest on a 100 year regime growing at a peak MAI of 10 cubic meters per hectare per annum.



APPENDIX G WASTE ABATEMENT OPTIONS

The mitigation methods applicable to the waste sector that have been considered are listed in Table G-1.

Table G-1: Description of waste sector abatement options

Sub Sector Wedge Question to model user describing extent to which a given measure should be undertaken

Landfill gas flaring % of methane captured for flaring

Electricity Generation % of methane recovered for electricity generation

Diversion of degradable material from the waste stream

% of degradable waste diverted into composting, anaerobic digestion, or biochar

Solid Waste

Diversion of recyclable material from the waste stream

% of high embodied energy material to be recycled

Capturing and flaring methane from municipal waste water treatment

% of methane to be captured from Municipal waste water

Wastewater Capturing and flaring methane from industrial waste water treatment

% of methane to be captured from Industrial waste water

G.1 Main Assumptions

The main physical assumptions for each of the waste sector mitigation options are detailed in Table G-2. Each waste mitigation option has been assigned limits to the absolute quantity of emission reduction that may be implemented. Individual limits are assigned for the periods to 2020, 2030 and 2050.



Table G-2: Waste Sector – physical and cost assumptions

Land fill gas flaring

Landfill gas recovery for

energy generation

UR3R facilities

CH4 recovery for

energy generation from MSW

Ch4 recovery for energy generation from IWW

Municipal waste water

capturing and flaring

CH4

Industrial waste water

capturing and flaring

CH4

Limit Type % Total

Emission % Total

Emission Number % CH4

Captured % CH4

Captured % CH4

Captured % CH4

Captured

Limit 2020 35% 35% 1 25% 25% 25% 25% Limit 2030 40% 40% 2 35% 35% 35% 35% Limit 2050 45% 45% 3 40% 40% 40% 40% Existing Percentage 0% 30% 0 0% 0% 0% 0% Assumed years of phase in 9 9 9 9 9 9 First year available 2010 2010 2015 2010 2010 2010 2010 Fraction of Waste Water Emissions 60% 40%


G.2 Landfill gas flaring

Methane is generated in landfill by anaerobic decomposition of organic material, which occurs slowly and varies each year depending on the stock of organic materials present which are deposited over many preceding years. It is becoming increasingly common for methane to be captured and flared to control odour and gas emissions. Flaring of landfill gas converts methane to carbon dioxide and water vapour, lessening CO2e emissions. Carbon dioxide produced from the flaring of methane is considered carbon neutral as it has been derived from biomass sources. It has been estimated142 that 28.5 kt of methane were flared in Australia in 2005 and 3.4 kt in Tasmania in 2005. In 2006 the Tasmanian estimate is reduced to zero (see Table G-3).

Table G-3: Reported methane generated and captured 2006

Flared (kt) Recovered for power

generation (kt)

Not specified (kt)

Total captured (kt)

Total Methane

Generated (kt)

Australia 28.5 146.9 47.0 222.0 856.6

Tasmania 0.0 4.2 0.0 4.2 19.0

The limits for landfill gas flaring are set to be relative to the total methane generated from landfill sites. These limits are set to 35% by 2020, 40% by 2030 and 45% by 2050. An average methane capture rate of 85% has been reported50. MMA assumes that with rapid improvement in methane capturing technology, the methane capture efficiency can be improved to over 90% for new landfill and that this is feasible by 2050. Costs of mitigation for flaring landfill gas have been estimated to be $50/t CO2e.

142 Review of Methane Recovery and Flaring from Landfills, Austrialian Greenhouse Office, 22nd October 2007.



G.3 Energy generation from recovered methane

Captured methane can be used to generate renewable electricity. This will displace fossil fuel based electricity and is carbon neutral. The AGO estimated that 149.9 kt of methane were recovered for power generation in Australia and 4.2 kt in Tasmania. MMA has found data indicating that 3 landfill sites have power generation installed with a combined capacity of 3.6MW143,144.

The limits for landfill power generation are also set to 35% by 2020, 40% by 2030 and 45% by 2050 (relative to the total methane emissions from landfill sites). The energy content of landfill methane is 37.7145 MJ/m3 and its density is 0.717146 kg/m3. Hence the energy content of one tonne of methane is approximately 52.58 GJ. Assuming a heat efficiency of 38.5%, 5.6 MWh of renewable electricity can be generated per tonne of landfill gas.

G.4 Diversion of degradable components in municipal solid waste

If the degradable components in solid waste such as paper, cardboard, garden organics, wood and timber that contain degradable organic carbon can be separated and diverted to other uses, considerable anaerobic decomposition can be prevented in the landfill and therefore reduce the potential for methane generation. Using technologies that are available to divert degradable components will enable recovery of landfill and other resources for other uses. Some of these technologies include: composting to prevent anaerobic decomposition (households in Tasmania had the highest level of participation in household composting at 61%147), bio-char production to form a stable carbon, process-engineered fuel to be used to replace fossil fuels in coal fired power stations, as well as diversion of waste for use in cement kilns and standalone power stations. Very little diversion of this material has occurred in Tasmania due to the low cost of disposal in the landfill. Diversion of recyclable degradable organic material such as paper, cardboard and wood from the MSW stream will also reduce the potential for methane generation by minimising the production of waste that ends in landfill.

Recycling of non-organic materials such as glass, metals and plastic can also reduce the amount of energy used to manufacture commodities. The effect of recycling therefore is to reduce emissions associated with energy use. For example, recycled aluminium uses only 5% of the energy requirement of primary aluminium and also produces zero process emissions (aluminium recycling is discussed further in Appendix D).

MMA has found conflicting data on the level of recycling in Tasmania. Recycling and reuse has grown extensively in Australian households, from 91% in 1996 to 99% in 2006 and from 90%to 99% in Tasmania148 according to the ABS. The Hyder consulting study

143 http://www.agl.com.au/ABOUT/ENERGYSOURCES/Pages/LandGasGeneration.aspx 144 http://www.lms.com.au/default.cfm?page=news 145 Fuel and Electricity Survey 2008, Australian Bureau of Agricultureal and Resource Economics.

http://www.abareconomics.com/interactive/fuelsurveys/pdf/FES08_Fuelcodes.pdf 146 http://en.wikipedia.org/wiki/Density 147 ABS- Environmental issues: People’s views and practices (Mar 2006). 148 ABS- Environmental issues: People’s views and practices (Mar 2006).



found that Australia recycled about 49% of total generated waste in 2006-2007149 and that Tasmania only recycled about 12%150. When these conflicting sources are examined more closely, it is apparent that the ABS study is indicative of participation in recycling, and does not attempt to measure the amount of material going to recycling that could be recycled.

According to an ABS151 survey, three main factors influence recycling in Australian households. These include availability/accessibility to services and facilities, the quantity or volume of recyclable material generated by a household, and interest. Tasmania also faces other challenges in recycling: the lack of recycling facilities for recovered materials as well as distance to recycling markets on the mainland. Since paper accounts for more than 90% of recycled material and other potential recyclable materials generally do not contain an organic methane producing component, MMA does not consider this measure further.

To divert waste from landfill, new generation waste treatment technologies were considered. This is effectively the Reduction, Recovery and Recycling (UR-3R) Process similar to the UR-3R processing plant in Eastern Creek. The UR-3R plant in Eastern Creek is operated by Global Renewable Energy. The UR-3R facilities are designed to recover recyclable materials that are not picked up in kerbside recycling collection schemes. These materials enter the garbage stream and reduce the amount of waste to landfill by implementing four unit steps outlined below:

Waste Stream Separation: In this process the incoming mixed waste was separated into homogenous streams of paper, glass, plastics, metals, organics, and others using a combination of manual and automated sorting to maximize the recovery of valued materials going to landfill.

ISKA® Percolation: The ISKA percolation process breaks down and mobilises volatile organics present in the screened mixed solid waste. This is done in a percolator vessel using a washing action with warm acid water. The waste is periodically turned whilst water is sprinkled over its surface. The water permeated through the process is collected and reprocessed before treated through an anaerobic digester to generate biogas.

Composting and refining: Discharged solid material from the percolator is converted into Organic Growth Materials (OGM) in an enclosed building.

Energy Recovery: Biogas from the ISKA Percolation process is used to produce electricity to power the operation of the facility and the excess will be available for sale. It is assumed that 85% of the produced electricity is used to run the process, and also that around 100 kWh of electricity is generated for each tonne of waste processed. Emissions from stabilised products (i.e. OGM), residual emissions and process emissions are estimated to be 0.29 tCO2e per tonne of MSW processed in the UR-3R facility.

149 Waste and Recycling in Australia, Hyder consulting (Nov 2008) . 150 Tasmanian Waste and Resource Management Strategy (final Review Draft, 2008) . 151 ABS- Environmental issues: People’s views and practices (Mar 2006).



The base volume for a UR-3R facility is set to 175,000 tonnes of MSW per annum. MMA have also placed a limit of three such facilities by 2050 with an earliest implementation year of 2015.

The replacement of landfill disposal of garbage with the processing at UR-3R facility for Australia (weighted average) is estimated to cost $36 per tonne for use of the UR-3R facility plus costs of additional weekly garbage collection less the benefit of revenue from recovered recyclable materials and OGM sold. Weekly garbage collection is assumed to take the form of a 240 L MGB fortnightly pickup of fully co-mingled kerbside recyclables.

G.5 Capturing and flaring methane from municipal waste water treatment

Methane is generated in municipal waste water treatment by anaerobic decomposition of organic material. The generation of methane occurs in anaerobic ponds and these may be covered for the capture and flaring of the methane generated.

The limits for flaring at waste water treatment plant are set to 50% by 2020, 70% by 2030 and 80% by 2050. Overall costs of mitigation are estimated to be $15/t CO2e.

G.6 Limitations

Insufficient data is available for the waste sector in Tasmania. Previous data collection included problems of missing data, and was also subject to comparability and summation issues because different landfill owners used different calculation methodologies. In recent years data has not been publicly available. In order to better understand the effect and impact of waste abatement initiatives, better data is required.

G.7 Interplay with other sectors

Methane captured in landfill and used to generate electricity is emissions neutral. The electricity generated would displace fossil fuel generation and offset emissions. Increasing or decreasing activity waste collection, recycling or degradable components will affect the transportation sector.

Documents

APPENDIX A REFERENCE CASE EMISSIONS: METHOD AND …