Hanson Landfill Services / City of Whittlesea - · PDF fileHanson Landfill Services / City of Whittlesea Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill Final

Hanson Landfill Services / City of Whittlesea

Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill

Final report

—Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill Version 4

Hyder Consulting Pty Ltd-ABN 76 104 485 289 http://aus.hybis.info/projects0/vc/awarded/aa002567/f_reports/f0011-aa002567-aar-04 wollert ghg lca report.docx

Hyder Consulting Pty Ltd

ABN 76 104 485 289

Level 16, 31 Queen Street Melbourne VIC 3000 Australia

Tel: +61 3 8623 4000

Fax: +61 3 8623 4111

www.hyderconsulting.com

Hanson Landfill Services / City of Whittlesea

Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill

Final report

Author Joe Pickin

Modellers Kyle O’Farrell, Joe Pickin

Internal review Jessica North

External peer review Hannes Partl

Approver John Nolan

Date 16 August 2010

Version 4

This report has been prepared for Hanson Landfill Services and the City of Whittlesea in accordance with the terms and

conditions of appointment for Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill dated 22 May

2009. Hyder Consulting Pty Ltd (ABN 76 104 485 289) cannot accept any responsibility for any use of or reliance on

the contents of this report by any third party.

Front cover picture: Wollert Renewable Energy Facility


Hyder Consulting Pty Ltd-ABN 76 104 485 289 Page i http://aus.hybis.info/projects0/vc/awarded/aa002567/f_reports/f0011-aa002567-aar-04 wollert ghg lca report.docx

CONTENTS

Summary......................................................................................................... 1

1 Introduction ........................................................................................... 5

1.1 About life cycle assessment ................................................................. 5

1.2 Notes on the study method ................................................................... 6

1.3 Notes on uncertainty and interpretation ................................................ 6

1.4 Limitations of the study ......................................................................... 7

1.5 Structure of this report .......................................................................... 8

2 Goal, scope and functional unit ............................................................ 9

2.1 Goal of the study .................................................................................. 9

2.2 Functional unit ...................................................................................... 9

2.3 Scope of the study ................................................................................ 9

3 Greenhouse gas factors and emissions at Wollert landfill ................. 20

3.1 Introduction ......................................................................................... 20

3.2 Methane generation potential (Lo) ...................................................... 22

3.3 Methane oxidation factor (OF) ............................................................ 26

3.4 The decay constant (k) ....................................................................... 27

3.5 Methane recovery rate (R) .................................................................. 28

3.6 Other emission factors at Wollert landfill............................................. 35

4 Inventory of non-landfill factors and emissions .................................. 37

4.1 Processing mass flows ....................................................................... 37

4.2 Transport ............................................................................................ 38

4.3 Facility construction ............................................................................ 38

4.4 Energy use and energy yields in waste processing ............................ 38

4.5 Offsets for recovered recyclables ....................................................... 40

4.6 Organics degradation and storage ..................................................... 40

5 Results ................................................................................................ 44

5.1 Results over the standard assessment timeframe .............................. 44

5.2 Uncertainty and sensitivity analyses ................................................... 45

5.3 Results when other assessment timeframes are applied ................... 48

6 Discussion and conclusions ............................................................... 49

7 References ......................................................................................... 51


Hyder Consulting Pty Ltd-ABN 76 104 485 289 Page ii http://aus.hybis.info/projects0/vc/awarded/aa002567/f_reports/f0011-aa002567-aar-04 wollert ghg lca report.docx

Appendices

Appendix A – Peer review and responses

Appendix B – An improved method of estimating warming effects of waste management

scenarios over limited timeframes

Appendix C – Uncertainty estimates: description of applied distribution types

Appendix D – Summary of the parameter values and uncertainty distributions used in the model

Appendix E – Review of three significant international models of greenhouse gas emissions

from waste

Appendix F – Further detail on the analysis results

Explanation of acronyms used

AS/NZS ISO Australian, New Zealand and International Standard

C&I commercial and industrial waste

CH4 methane

CO2 carbon dioxide

CO2-e carbon dioxide equivalent

DCC Department of Climate Change

DOC degradable organic carbon

DOCf fraction of degradable organic carbon that actually degrades

EPA Environment Protection Authority Victoria

F fraction of methane in landfill gas

GWP global warming potential

HLS Hanson Landfill Services

IPCC Intergovernmental Panel on Climate Change

k degradation constant representing the rate at which a material degrades

LCA life cycle assessment

LMS Landfill Management Services (landfill gas contractor at Wollert landfill)

L0 methane generation potential of a material

MBT mechanical biological treatment

MCF methane correction factor

NATA National Association of Testing Authorities

NGERS National Greenhouse and Energy Reporting Scheme

N2O nitrous oxide

n/a not applicable

OF fraction of methane that is oxidised in a landfill cap

PAS 2050 Publicly Available Specification 2050 (a British standard for assessing greenhouse gas

emissions from products or services)

R recovery rate of methane in a landfill

t tonne


Hyder Consulting Pty Ltd-ABN 76 104 485 289 1 http://aus.hybis.info/projects0/vc/awarded/aa002567/f_reports/f0011-aa002567-aar-04 wollert ghg lca report.docx

Summary

Hanson Landfill Services (HLS) and the City of Whittlesea commissioned Hyder to undertake a

robust, credible and holistic assessment of the greenhouse performance of the HLS Wollert

landfill compared with four alternative waste management scenarios. The agreed method was

life cycle assessment. As the study is restricted to greenhouse gas implications, it represents

only a partial environmental assessment of the scenarios.

The wastes to be assessed were 144,000 tonnes of household garbage and 96,000 tonnes of

commercial and industrial garbage sent to Wollert landfill in 2008, and also source-separated

organic waste generated in 2008 from households that used Wollert landfill for their garbage

disposal. The organics waste comprised 33,000 tonnes of material in the base case.

The scenarios compared are outlined in Table S1. The scenarios were considered as

‘alternative worlds’ in which the 2008 waste streams enter fully-developed management

arrangements without any phase-in period. In two of the scenarios, garbage was processed

through mechanical biological treatment (MBT), which involves separation of waste into

fractions and treatment of the organic fraction by aerobic composting (in Scenario 1) or

anaerobic digestion (in Scenario 2).

Table S1 Description of the scenarios modelled

Scenario no.

& name

Waste type

Base case Scenario 1 Scenario 2 Scenario 3a Scenario 3b

Landfill Aerobic

MBT

Anaerobic

MBT

Food separation

with garbage collection

fortnightly weekly

Household organics – garden waste (all

councils) + food waste (Nillumbik only)*

Enclosed composting

Enclosed composting Household organics – food waste (other

councils) **

Landfill

(Wollert)

Aerobic MBT Anaerobic

MBT Household garbage – non-food

Landfill (Wollert) Commercial and industrial garbage

Residuals from waste processing operations Landfill (Wollert)

Notes * Source-separated

** Source-separated for Scenario 3 only

As shown above, the residuals from waste processing facilities are all disposed to landfill, and

hence this activity is considered in the assessment.

The most important greenhouse gas issue associated with landfill is the generation of methane

from the anaerobic degradation of organic materials. Methane emissions were assessed using

the Intergovernmental Panel on Climate Change’s first-order decay model and the

corresponding instantaneous emissions model. The model parameters were populated using

data from Wollert landfill and values from reviewed literature. Of the methane potential of the

2008 waste deposited at the landfill, an estimated 56% derived from food waste and 22% from

paper and cardboard.



Like most large modern landfill operations, methane generated at Wollert landfill is collected and

burned to produce electricity for the grid. The recovered proportion of methane generated from

the 2008 waste deposits is estimated at 60% to 88%, based upon a range of sources including

direct measurement. The model credited carbon in the waste that is not emitted as equivalent to

carbon dioxide (CO2) removal from the atmosphere. It also provided credits for recovered

metals and electricity generated using recovered methane.

There is significant uncertainty about the net energy yield from anaerobic MBT. For this

parameter Hyder has relied on overseas data as these technologies have not been applied and

operated in Australia for sufficient time to produce a representative range of local data.

Other emission sources in the various scenarios included: transport; facility construction; energy

use and energy yields in waste processing; offsets for recovered recyclables; and organics

degradation and storage.

The results are presented in Figure S1 using mid-range estimated values and a standard 100

year assessment timeframe. This assessment method assigns a relative warming value to

methane on the basis that warming effects over the next century are equally important and

those thereafter are not important. The base case and Scenarios 2, 3a and 3b produced similar

outcomes, resulting in net savings of between 66,000 and 72,000 tonnes of carbon dioxide

equivalent (CO2-e). Aerobic MBT (Scenario 1) had lower net savings estimated at 33,000

tonnes CO2-e. The savings (avoided emissions) mostly arose from carbon storage (in the landfill

and compost) and product offsets (metals, plastics and especially electricity). The avoided

emissions outweighed the actual emissions, which were mostly associated with landfill methane

and process inputs.

The base case and Scenario 2 were shown to outperform Scenarios 1, 3a and 3b when the

values for two critical parameters (the methane recovery rate at the landfill and net energy yield

from anaerobic MBT) were fixed to the mid-point values. When the methane recovery rate was

put at the high end of its range, the base case (Wollert landfill) was the best performing scenario

(see Figure S2). At the low end of the methane recovery rate, the base case outperformed only

Scenario 1 (aerobic MBT). Similarly, over the range of potential values for net energy yield from

anaerobic MBT, Scenario 2 ranged from the best to the second worst option.

When the warming effects of methane and CO2 are assessed over different timeframes the

results change significantly (see Figure S3). When only warming over the next 20 years is

considered, the base case (Wollert landfill) performs worst and the other scenarios are relatively

equal. When warming effects over the next 500 years are included, the base case is best and

Scenarios 1 and 2 (MBTs) are worst.

While there was no clear ‘winner’ amongst the scenarios and technologies assessed, all of them

– including the current base case involving disposal at Wollert landfill – resulted in net savings

of greenhouse gases. The key elements that determined the extent of the savings from each

scenario were landfill methane emissions, carbon storage and recovery of energy.



The following conclusions can be drawn:

� Best practice landfill with good performance management is a potentially sound option

from a greenhouse gas management perspective.

� Anaerobic MBT appears to be a better greenhouse option than aerobic MBT. This

suggests that from a greenhouse gas perspective it is better to focus on maximising

energy recovery from biological material rather than to generate stabilised organic

products. The low-range estimate of net electricity output from Wollert landfill (around 150

kWh/t) greatly exceeded the high-range estimate for anaerobic MBT (68 kWh/t).

� Diversion of household food organics to the compost stream has a similar performance

(but usually slightly worse) to disposal at Wollert landfill, from a greenhouse gas

perspective.

� When food is diverted it makes little difference from a greenhouse perspective whether

garbage is collected weekly or fortnightly.

Figure S1 Results using mid-range parameter values assessed over a 100 year instantaneous emission basis

-150

-100

-50

0

50

100

Wollert landfill

1 - Aerobic MBT

2 -Anaerobic

MBT

3a - Food separation, fortnightly garbage

3b - Food separation,

weekly garbage

Gre

enhouse g

as e

mis

sio

ns (

kt C

O2-e

)

Methane from landfill

Emissions from other organic degradation processes

Transport

Process inputs

Product offsets (electricity, recyclables & compost)

Carbon storage

Net emissions



Figure S2 Results with high (88%), mid (74%) and low (60%) values for methane recovery from landfill, and high (68 kWh/t) and low (0 kWh/t) net electricity yield from anaerobic MBT. The charts use mid-range values for other parameters and a 100 year instantaneous emission assessment. Only Scenario 2 is affected by changes to the anaerobic MBT energy yield so the results for the other scenarios are not shown.

Figure S3 Results using different approaches in relation to time (mid-range parameter values)

-120

-100

-80

-60

-40

-20

0

High methane recovery

from landfill

Mid-range methane recovery

from landfill

Low methane recovery

from landfill

High net energy

yield from anaerobic

MBT

Low net energy

yield from anaerobic

MBT

Gre

enhouse g

as e

mis

sio

ns (kt C

O2-e

) Wollert landfill

1 - Aerobic MBT

2 - Anaerobic MBT


3b - Food separation, weekly garbage

-125

-100

-75

-50

-25

0

Time sensitive, 20 years

Instantaneous emissions, 100

years

Instantaneous emissions, 500

years

Gre

enhouse gas e

mis

sio

ns (kt C

O2-e

)

Wollert landfill

1 - Aerobic MBT

2 - Anaerobic MBT


3b - Food separation, weekly garbage

High methane recovery

from landfill

Mid-range methane recovery

from landfill

Low methane recovery

from landfill

High electricity yield from anaerobic

MBT

Low electricity yield from anaerobic

MBT



1 Introduction

Hanson Landfill Services (HLS) and the City of Whittlesea commissioned Hyder to undertake a

robust, credible and holistic assessment of the greenhouse performance1 of the HLS Wollert

landfill compared with four alternative waste management scenarios. The agreed method was

life cycle assessment (LCA), carried out in accordance with the ISO AS/NZS ISO 14040 series

of standards.

For HLS the primary use of this work is to inform discussions with council clients. For the City of

Whittlesea it is to support their waste management planning.

Over the past few years the greenhouse performance of waste management technologies has

developed into a key policy concern at local, state and federal government levels. Waste policy

decisions have increasingly relied upon life cycle based greenhouse gas assessments. The

methods and assumptions applied in these analyses have not always been consistent. There is

uncertainty about methane recovery rates at landfills, about the fate of carbon that does not

rapidly degrade in landfills and about the energy yields of some waste processing technologies.

The reporting of results has not always made clear the sensitivity of the results to these

uncertainties. Robust uncertainty and sensitivity analysis are important components of this LCA.

From a greenhouse gas perspective, developments in scientific understanding have moved

towards a lower assumed greenhouse impact of landfills since the first Intergovernmental Panel

on Climate Change (IPCC) models and the seminal paper of Bingemar and Crutzen (1987). The

proportion of non-degraded carbon assumed by the IPCC has increased from 23% to 50%.

Recent studies suggest that methane oxidation rates may generally be higher than the IPCC’s

0% default or the Department of Climate Change (DCC) value of 10%. High rates of methane

recovery have been reported in modern landfills. This study attempts to ensure that all of these

issues are assessed properly by means of a thorough review of the academic literature.

1.1 About life cycle assessment

Awareness of the environmental impacts of products manufacture and consumption has

increased interest in methods to better comprehend and compare these impacts. One of the

methods developed for this purpose is life cycle assessment (LCA).

LCA is a technique for assessing the environmental aspects and potential impacts associated

with a product or service by:

� compiling an inventory of relevant inputs and outputs of the product or service system

� evaluating the potential environmental impacts associated with those inputs and outputs

� interpreting the results of the inventory analysis and impact assessment phases in

relation to the objectives of the study.

LCA models the environmental impacts from each stage of a lifecycle across raw materials

acquisition, manufacture, use and end-of-life. It incorporates a number of steps as outlined in

Figure 1.

1 We use the term ‘greenhouse performance’ to refer to measures of the net climate change impact of greenhouse gas

fluxes associated with that waste management scenario.



Figure 1 Life cycle assessment stages (Singh et al 2006)

1.2 Notes on the study method

This study is a comparative LCA of waste management scenarios that has been undertaken in

accordance with the LCA standard set out in AS/NZS ISO 14040. As the study is concerned

only with the climate change impact of the modelled scenarios, the impact assessment and

interpretation stages are relatively simplified. The study is largely, but not fully, compliant with

the British standard PAS 2050 for assessing the greenhouse gas emissions of goods and

services (BSI 2008)2.

As this is a public study, it has undergone critical review by an external peer reviewer in

accordance with AS/NZS ISO 14040. The external peer review and Hyder’s responses are

given in Appendix A.

1.3 Notes on uncertainty and interpretation

A challenge in undertaking a study of this type is presenting the results clearly while still

reflecting their uncertainties and complexities. These are often highly significant but are easily

masked if attention is focused on a summary chart in which the tallest or shortest bar signifies

the ‘winner’. This is especially problematic if the size of the bar is very sensitive to factors about

which there are high levels of uncertainty.

Two major types of uncertainty are important in this study.

2 Our study diverges from PAS 2050 in several ways: as a comparative LCA, it excludes emissions from processes that

are the same across the modelled life cycles; it includes emissions relating to the production of capital goods; it assesses methane emissions in ways that are more sophisticated than the standard.



The first is the uncertainty in emission fluxes. There is uncertainty about: how much methane is

emitted from Wollert landfill; how much energy is generated during anaerobic processing of

organic waste; and what is the greenhouse benefit of the energy so produced. We attempt to

deal with these issues by applying range values and assessing the results probabilistically (see

Section 2.3.6).

A second area of uncertainty rests in how the warming effects of different greenhouse gases are

compared. Different greenhouse gases are resident in the atmosphere over different

timeframes. In particular, a pulse of methane generated during the anaerobic decay of organic

wastes would be entirely oxidised after about 12 years whereas some of the carbon dioxide

(CO2) emissions from transporting these materials may be resident in the atmosphere centuries

later3. This creates a problem for comparing the warming effects of the two gases: they could be

compared over a short period in which case the long-term effects of CO2 are overlooked; or they

could be compared over a long period in which case we are giving the same importance to time

periods in hundreds of years as we are for time periods in a few decades.

The warming impacts of gases are expressed technically in terms of global warming potential

(GWP) relative to an equal mass of CO2 emitted at the same time in units of carbon dioxide

equivalent units (CO2-e). The Intergovernmental Panel on Climate Change (IPCC) publishes

GWPs for different gases over 20, 100 and 500 year assessment periods to help with policy

analysis (Shackley and Wynne 1997). Because its atmospheric lifespan is much shorter than

CO2, the GWP of methane4 varies strongly with the assessment period: the GWP20 is 72,

GWP100 is 255 and GWP500 is only eight (Forster et al 2007).

Over the past 15 years the 100 year assessment timeframe has gradually become the standard.

The implication of using the GWP100 is that we care equally about warming effects in each year

for 100 years but care nothing about warming effects thereafter. And as noted by Reilly et al

(2003 p.v), to the extent we are concerned about slowing climate change over the next 50 years

“the control of methane … has an importance that is obscured when 100 year GWPs are used”.

A number of leading climate change researchers have argued that methane management

should be considered separately and more urgently than the standard 100 year assessment

period implies (e.g. Hansen and Sato 2007).

While emphasising results over the standard 100 year assessment timeframe, in this study we

elect to include results in which warming effects are compared over other assessment periods

(i.e. 20 and 500 years). We believe this provides for a deeper understanding of the findings

even if it complicates them. The method for the assessment is discussed in Section 2.3.5.

1.4 Limitations of the study

The results of the assessment should not be interpreted as endorsing any of the waste

management scenarios or technologies. The study is restricted to greenhouse gas implications

only and so represents only a partial environmental assessment of the scenarios. The results of

the study are specific to the HLS landfill but, since many other landfills have similar design

standards, similar levels of performance could be expected.

3 The atmospheric lifespan of CO2 in the atmosphere is variable and complex. The IPCC no longer provides an average

or range estimate. Much of the gas is removed into the oceans within a few years but some of this is subsequently released. Some CO2 is likely to be present in the atmosphere thousands of years after emission (Inman 2008). 4 The GWP of nitrous oxide, the other non-carbon dioxide gas that features in this study, varies less with assessment

timeframe. 5 Currently the IPCC and DCC are using a GWP100 value for methane of 21, but we understand that the value 25 has

been agreed and will be used in the next IPCC assessment report (Forster et al 2007).



The modelling of the aerobic and anaerobic MBT scenarios (Scenarios 1 and 2) assumed that

the residual product was either landfilled or applied to land. Internationally it can be the practice

to use a proportion of the input as a Refuse Derived Fuel (RDF) or as feedstock into Energy

from Waste (EfW) plant as part of a fully integrated plant. Although not modelled in this study,

this could increase the energy generation and potentially the net energy generation after taking

increased energy consumption into account. Such fully integrated solutions might be

appropriate to Australian conditions, in some cases.

1.5 Structure of this report

Chapter 2 delineates the LCA by describing its goal, functional unit, and scope. Chapters 3 and

4 contain the life cycle inventory – that is, the greenhouse gas costs and benefits associated

with individual elements of the assessed waste management systems. The life cycle inventory

for Wollert landfill is set out in chapter 3, and the inventory for all other system elements is given

in chapter 4. At various points in chapters 2 to 4, key model aspects are stated in highlighted

boxes headed ‘modelling decision’. The results are set out in chapter 5 (impact assessment).

Chapter 6 (interpretation) discusses the project findings and draws conclusions.



2 Goal, scope and functional unit

This chapter describes the framework of the greenhouse gas LCA. Firstly, we briefly describe

the goal of the assessment. Secondly, we set out the ‘functional unit’ – that is, the specific

quantities and composition of materials examined. Thirdly, we describe the scope in several

subsections covering waste streams, scenarios, elements of the material life cycle, greenhouse

gases, assessment timeframes and uncertainty.

2.1 Goal of the study

The goal of the study is to undertake a robust, credible and holistic assessment of the

greenhouse performance of the HLS Wollert landfill compared with four alternative waste

management scenarios. The assessment needs to consider the net greenhouse impacts of the

disposal of residual and organic waste. It needs to consider the complete life-cycle of relevant

materials where differences in end-of-life fates have an impact on the relative greenhouse gas

emissions of the modelled options.

2.2 Functional unit

A functional unit common to all scenarios is required for the assessment. The functional unit for

this study is:

The disposal or processing of household and commercial and industrial (C&I) garbage

sent to Wollert landfill in 2008, and of source-separated organic waste generated in

2008 by households that used Wollert landfill for their garbage disposal.

The function of the modelled system is to provide a disposal route for organics and residual

waste disposed from residential and C&I sources. More detail on the waste streams is given in

Section 2.3.1.

2.3 Scope of the study

2.3.1 Waste streams

The waste streams to be assessed are:

� household garbage sent to Wollert landfill from the cities of Banyule, Darebin, Hume,

Moreland, Nillumbik and Whittlesea

� C&I garbage sent to Wollert landfill

� separated household organic waste generated within the cities of Banyule, Darebin,

Hume, Moreland, Nillumbik and Whittlesea (i.e. councils that currently send garbage to Wollert

landfill).

The first two items above represent the bulk of the greenhouse gas potential of materials

received at the Wollert landfill. In the study year, separated household organic waste was

collected as a standard service at Banyule, Moreland and Nillumbik and as a user-pays service

at the other councils. Nillumbik allowed food waste to be included in its organic waste services,

while the remaining councils accepted garden waste only6.

6 Hume also allows residents to deposit fruit and vegetable peelings in their organics bin. This is not included in the study

considerations for 2008.



Materials received at Wollert landfill, other than those listed in the dot points, above are

excluded from this study. The reasons for the exclusions are outlined in Table 1. The exclusions

do not affect the usefulness of the results in comparing waste management scenarios for the

major greenhouse-intensive waste streams.

Table 1 Materials received at Wollert landfill to be excluded from the analysis

Material Rationale for exclusion

Wastes from council transfer stations

� Suitability for processing through the landfill alternatives

proposed to be modelled is uncertain

� Composition data is unavailable

� Represents only 9% of incoming waste in the study year

Wastes from council operations

(including street sweepings and litter

bins)

� Suitability for processing through the landfill alternatives to

be modelled is doubtful

� Not a key greenhouse gas issue due to relatively low

quantities and organic content (based on EcoRecycle

Victoria 2005b landfill audit).

� Represents only 4% of incoming waste in the study year

Construction and demolition wastes � Unsuitable for processing through the landfill alternatives to

be modelled

Low level contaminated soil � Unsuitable for processing through the landfill alternatives to

be modelled

� Not a key greenhouse gas issue due to relatively low

quantities and organic content. Asbestos

The base case quantities of the relevant waste streams are given in Table 2 and estimated

compositions are set out in Table 3.

Table 2 Base case waste quantities

Data sources:

1 HLS weighbridge data

2 Sustainability Victoria local government survey data

Material 2008 tonnes Data source

Household garbage sent to Wollert landfill 144,402 1

Commercial and industrial (C&I) garbage sent to Wollert

landfill

96,352 1

Separated household organic waste generated within

councils that currently send garbage to Wollert landfill

33,280 2



Table 3 Base case waste stream composition

Data sources:

1 EnviroCom Australia (2006a, b, c, d); Wastemin (2006a, b)

2 EcoRecycle Victoria (2005a, b)7

Material Household garbage C&I Household organics

Data sources 1 2 1

Food waste 44% 29% 5% 8

Paper and cardboard 9% 16% 0%

Garden waste 8% 5% 92%

Disposable nappies 7% 0% 0%

Plastics 1-3 1% 13% 0%

Plastics 4-7 2%

Glass containers

5%

2%

0% Steel cans 2%

Aluminium 1%

Timber

25%

11% 1%

Textiles 7%

2% Rubber and leather 0%

Other 14%

2.3.2 Scenarios

Five scenarios are modelled and compared as outlined in Table 4. The scenarios were

considered as ‘alternative worlds’ in which the 2008 waste streams enter fully-developed

management arrangements without any phase-in period.

Table 4 Description of the scenarios modelled

Scenario no.

& name

Waste type

Base case Scenario 1 Scenario 2 Scenario 3a Scenario 3b

Landfill Aerobic

MBT

Anaerobic

MBT

Food separation

with garbage collection

fortnightly weekly

Household organics – garden waste (all

councils) + food waste (Nillumbik only)*

Enclosed composting

Enclosed composting Household organics – food waste (other

councils) **

Landfill

(Wollert)

Aerobic MBT Anaerobic

MBT Household garbage – non-food

Landfill (Wollert) C&I garbage

Residuals from waste processing operations Landfill (Wollert)

Notes * Source-separated

** Source-separated for Scenario 3 only

7 C&I composition figures released in Nov. 2009 differ from those presented (paper and cardboard is up; food is down).

Even if the new data is more representative, however, we believe the impact on the study findings would be minor. 8 This relatively high proportion is because Nillumbik provides for food waste to be placed in the organics waste bin.



While not modelled within this study – but outlined here for illustrative purposes – the scenario

that would result in a zero net greenhouse-gas impact would involve:

� the aerobic decomposition of all organics (with no carbon sequestration or fertiliser offset)

� no recovery of metals or plastics

� no energy recovery, and thus no electricity off-set for electricity generated elsewhere

(primarily by coal-fired power stations)

� no transport, construction or operational related greenhouse-gas emissions.

In all five scenarios, source-separated organic waste is sent for composting. In the base case,

all other materials except Nillumbik’s food waste are sent to Wollert landfill. In Scenarios 1 and

2, other materials are sent for mechanical biological treatment (MBT) – aerobic and anaerobic

respectively. In these technologies, mixed waste is pre-sorted to recover some recyclables, the

remaining organic-rich fraction is processed to a compost-like material9 that may be used for

land rehabilitation, and residuals are sent to landfill. In Scenarios 3a and 3b, councils have

established a system for diverting food waste to the source-separated organics waste stream for

composting with the garden waste. In Scenario 3a the remaining garbage is collected fortnightly.

In Scenario 3b it is collected weekly.

It is assumed that all composting occurs in an enclosed facility. This is consistent with recent

signals from Victoria’s Environment Protection Authority that open windrow composting is no

longer acceptable in the Melbourne area due to odour risks. Composting and MBT facilities are

assumed to be co-located to the Wollert landfill site. The transport requirements for the base

case and Scenarios 1 and 2 are therefore identical. There are greater transport requirements for

the food separation scenarios since the collection system must be expanded (at present not all

residents at the relevant councils have access to an organics bin) and, for Scenario 3b, there is

an increase in collection frequency.

The waste quantities and flows for Scenarios 1 and 2 are similar to the base case (discussed in

the previous section), but they are different for Scenarios 3a and 3b. In these scenarios, the

organic waste collection systems at Darebin, Hume and Whittlesea are assumed to expand to a

regular service covering all residents at all councils. We assume that a typical 200 kg of garden

waste is collected per household per year at these three councils. This quantity includes a

proportion of ‘new’ waste that is currently not part of the formal waste stream – that is, it is not

included in either the current organic or garbage service. The waste composition also changes

for Scenarios 3a and 3b because food waste is diverted from the household garbage to the

organics bin. Based on an international literature review (including Bridgewater and Parfitt 2009)

we assume that 60% of food is diverted in Scenario 3a (fortnightly garbage collection) and 50%

is diverted in Scenario 3b (weekly garbage collection). Scenarios 3a and 3b differ firstly because

of the varied transport effort required and secondly due to the different proportions of food

waste diverted. In all scenarios, residual wastes are deposited in the Wollert landfill.

9 The substantial compost differences between the outputs of aerobic and anaerobic MBTs are not considered important

for this study.



Figure 2 Quantities of domestic waste in the various scenarios

2.3.3 Elements of the material life cycle

Flowcharts of the life cycle system boundaries are provided for the each scenario in Figures 3 to

6 respectively. The boundaries encompass the waste management aspects of collection,

transport, sorting, processing, disposal and organic decay.

Some products generated from waste management are assumed to offset the use of an

equivalent quantity of conventional product. Recycled metal and plastic is assumed to offset the

use of virgin product and production of electricity from biogenic materials is assumed to offset

the use of conventional electricity. The study includes ‘credits’ where this results in greenhouse

benefits. Credits are also attained due to the storage of biogenic carbon that is prevented from

decaying back to carbon dioxide.

The greenhouse impacts of human labour associated with the scenarios are not considered.

0

50

100

150

200

Base case

and options

1 & 2

Option 3a Option 3bQ

uantity

of

wa

ste

(kt)

Organics

Garbage



Figure 3 Base case life cycle boundary



Figure 4 Scenario 1 life cycle boundary



Figure 5 Scenario 2 life cycle boundary



Figure 6 Scenarios 3a and 3b life cycle boundaries



2.3.4 Greenhouse gases

The study considers the main greenhouse gases CO2, methane (CH4) and nitrous oxide (N2O).

The global warming potential (GWP) of these gases is expressed in CO2-e over the relevant

assessment timeframe (see below).

CO2 emissions can originate from fossil or biogenic10

sources. In the IPCC (2006) method for

reporting emissions from waste, biogenic carbon is included only if it is emitted as methane. The

fate of other carbon is not counted in the greenhouse gas balance on the assumption that CO2

emissions are balanced by the CO2 removed from the atmosphere during the growth of this

organic material11

. Within this study, this assumption is considered inappropriate in that it does

not take into account storage of carbon during waste management processes. We therefore

ignore biogenic CO2 from organic degradation processes except for this stored portion.

A key focus of the study is emissions from organic degradation processes in landfills. These are

primarily methane but we also need to consider whether nitrous oxide emissions should be

included. Measurements at a Finnish landfill found that nitrous oxide represented about 3% of

the GWP of the landfill’s emissions12

(Rinne et al 2005). Barton and Atwater (2002 p.144)

measured nitrous oxide discharges as “small” but noted that “the likely pathway of N2O

emissions” would be from landfill leachate, which was not included. Lou and Nair (2009)

suggest that nitrous oxide emissions are promoted when leachate is recirculated as it is in the

Wollert landfill. The IPCC (2006 p.3.6) maintains that nitrous oxide emissions are “not

significant”, and the European Life Cycle Database provides the extremely low factor of 2.7g per

tonne of municipal waste13

.

Modelling decision: Because they are relatively small, nitrous oxide emissions from landfills will

be ignored in this assessment.

2.3.5 Consideration of time

Time is relevant to the study in two main ways:

1 Organic degradation processes can occur over long time-frames. It can take decades

before organic materials in landfills or in deposited compost can be considered stable.

2 The warming effect of different greenhouse gases occurs over different timeframes (see

the discussion in Section 1.3).

It is common for greenhouse gas assessments to ignore the time frame over which emissions

occur. This is appropriate for liability-type calculations of the net greenhouse gas impact of a

course of action. For example, the DCC (2009a) sets a value of 1.0 t CO2-e per tonne for the

total emissions from landfilling municipal waste over the whole decay cycle (assuming no

methane recovery). This is equivalent to assuming that an instantaneous pulse of methane

occurs when waste is deposited. These instantaneous models usually assess the GWP of

emissions over 100 years only, therefore simultaneously including and ignoring time effects. If

emission time frames are to be excluded it would arguably be more appropriate to apply a

warming assessment period encompassing more of the atmospheric residency time of the CO2

e.g. 500 years.

10 Arising from the degradation of vegetation and animal matter

11 More accurately, it is assumed that any difference in the CO2 flux to the biosphere is captured by estimates within the

land use change and forestry section of national greenhouse gas inventories. 12

Measured over a 100 year assessment period. 13

Accessed 24 September 2009.



An alternative approach would be to take account of both time issues introduced above by

including the warming effects up until some defined date in the future. This approach is

supported by the PAS 2050 standard which states that a “factor shall be applied” to life cycle

emissions “to reflect the weighted average time these emissions are present in the atmosphere”

BSI (2008, p.15).

As discussed in Section 1.2, we elect to assess emissions over multiple timeframes rather than

only the standard 100 year assessment period. An estimate of warming potential to a defined

date can be obtained by projecting emissions year by year and applying the warming effect of

each annual pulse prior to the set date. The warming effects need to be expressed as a multiple

of the warming effect of a pulse of identical mass of carbon dioxide emitted in the first year.

While not a standard approach, we believe this is more logical. An estimate can be made by

applying annual GWP values as described in Appendix B14

.

Modelling decision: The results will be reported and discussed using three different measures.

� A time sensitive model calculated using the first-order decay model and taking into

account global warming due to the scenarios over the standard 100 year assessment

timeframe (i.e. from 2008 up until 2107), calculated using the approximation method

described in Appendix B.

� An instantaneous emissions model using the standard IPCC/DCC modelling approach

and 100 year GWPs.

� Supporting assessments that compare short-term (20 year) and long-term (500 year)

warming effects. The short-term assessment is time sensitive, applying the first-order

decay model to take into account warming effects from 2008 to 2027 using the

approximation method described in Appendix B. The long-term assessment applies an

instantaneous emissions model using 500 year GWPs.

2.3.6 Parameter uncertainty

In Section 1.2 it was argued that presenting the results of a study of this type as single values is

potentially misleading. In addition to the complexities of comparing greenhouse gases, many of

the values to be applied in the study have a significant uncertainty range, and the results are

highly sensitive to the values of some parameters. This study therefore takes a probabilistic

approach. Wherever possible, the range of possible values is described with reference to the

likelihood of certain values occurring. The model then uses ‘Monte Carlo’ analysis to assess

overall probabilities for the LCA results.

A number of standard distribution types were used to approximate the uncertainty of all key

variables. The distributions applied are summarised in Appendix C. Parameter values and

uncertainty distributions are set out in Appendix D.

14 We correct GWP values for methane by subtracting one unit. This is because the calculated GWPs provide for the

warming effect of the CO2 to which methane is oxidised in the atmosphere. However, this CO2 is biogenic and so, further to the discussion in section 2.3.4, should not be counted.



3 Greenhouse gas factors and emissions at Wollert landfill

This is the first of two chapters in which we identify parameters to be used for modelling

emissions and decide on the values to be applied. This chapter focuses on emissions from

landfilling. The next chapter addresses all other aspects of the model. The emission

parameters, values, and uncertainty ranges and distributions are summarised in Appendix D.

3.1 Introduction

The net greenhouse impacts of landfills are envisaged to comprise four elements as tabulated

below.

Table 5 Types of greenhouse gas cost and benefit from landfilling

Type of emission factor Cost or benefit? Description / source

Methane emissions (CH4 em) Cost Methane emissions from the anaerobic degradation of organic materials

Carbon storage (CS) Benefit Carbon stored in the landfill

Product offsets (PO) Benefit Energy from landfill gas and metals recovered from the landfill that offset society’s need to source metals and energy from elsewhere

Construction & operational emissions (C&O)

Cost Fossil fuel and electricity use associated with operating the landfill (mostly CO2)

15

The net greenhouse gas emissions from the landfill can be calculated from the following:

Net emissions = CH4 em – CS – PO + C&O

The most important greenhouse gas issue associated with landfill is the emission of methane

from the anaerobic degradation of organic materials. This is the topic of sections 3.2 to 3.5. The

other factors are discussed in section 3.6.

In the remainder of this section we describe the method used for estimating methane emissions,

and how we obtained data for the each of the four types of greenhouse gas costs and benefits.

3.1.1 Estimating methane emissions

Models of methane emissions from landfill are not very reliable. In one study, a variety of

models produced results for the same site that varied by a factor of seven (Scharff and Jacobs

2006). The first-order decay method is commonly used – it is recommended by the IPCC for

countries preparing greenhouse gas emission inventories and is mandated for use by landfills in

reporting their greenhouse gas emissions under the National Greenhouse and Energy

Reporting Scheme (NGERS). More sophisticated models are under development (Bogner pers.

comm. 2009, Thompson et al 2009) but the IPCC first-order decay model is currently the most

accepted model. Its parameters can also be used to derive both the time sensitive and

instantaneous emissions estimates required for this study (see Section 2.3.4).

Modelling decision: Methane emissions will be estimated using the IPCC/DCC first-order decay

model (time sensitive assessment) and the corresponding instantaneous emissions model.

15 Recall that biogenic CO2 is excluded from the study – see section 2.3.4).



The basic equation applied in the IPCC/DCC models is that methane emissions per tonne of

waste are given by16

:

CH4 em = Lo x (1 – R) x (1 - OF)

where Lo = DOC x DOCf x MCF x F x 16/12.

These symbols are explained in Table 6. For the time sensitive model the rate of decay,

represented by the decay constant k, also needs to be taken into account17

.

Table 6 Explanation of parameters for estimating net greenhouse gas emissions from landfill

Parameter Symbol Explanation

Methane generation potential Lo The quantity of methane emitted per unit waste deposited

Degradable organic carbon DOC The fraction of a material that is made up of carbon that could theoretically be released through degradation processes

Dissimilable fraction DOCf The fraction of the DOC that actually degrades in landfill

Methane fraction F The fraction of methane in landfill gas

Methane correction factor MCF A correction factor to represent the extent to which decay processes are aerobic, rather than anaerobic

Decay constant k A constant that represents the rate at which a material decays given the specific circumstances of the Wollert landfill

Oxidised fraction OF The fraction of the generated methane that is oxidised in the upper layers of the landfill or landfill cap

Methane recovery rate R The fraction of the generated methane that is recovered and oxidised in the gas collection system

3.1.2 Assigning values to model parameters

Values for these parameters were needed for the Wollert landfill. In estimating them we made

reference to three types of information:

1 scientific and other academic literature

2 previous models of emissions from landfills

3 data for Wollert landfill provided by HLS.

We discuss these briefly prior to describing how parameter values were selected.

Scientific and other academic literature

We reviewed the scientific and other academic literature in relation to the degradation of

materials in landfill. An initial search using the Google search engine uncovered leads that were

followed up using an academic database and the Melbourne University library catalogue. Over

50 papers were reviewed.

16 The equation is amended slightly here to include R as a proportion rather than an absolute value.

17 The decay constant for each material type is needed only for calculations in which the time of the emissions is

relevant, and so is omitted from the equation above. See Section 2.3.5 for a discussion on assessment timeframes, and see IPCC (2006) for model equations when the time of the emissions is relevant, including k factors.



Existing models of emissions from landfills

We reviewed several major models of emissions from landfills, comprising four from overseas

and eight from Australia. These are listed in Table 7. Most are LCAs of waste management

options with a similar scope to this study, though some assessed other environmental impacts

as well as greenhouse gas emissions. We also include the greenhouse gas inventory guidance

provided by the IPCC. Each of the studies presents a method for estimating or comparing

greenhouse gas emissions from waste. The international studies, in particular, result from a

significant amount of work including detailed literature review – the first three of these listed in

the table are particularly significant to this report and are reviewed in Appendix E.

Table 7 Reviewed models of greenhouse gas emissions from waste

Study Application Model type

International studies

IPCC (2006) Global Inventory

Smith et al (2001) Europe Greenhouse gas LCA

US EPA (2006) US Greenhouse gas LCA

Hogg et al (2008) London Greenhouse gas LCA

Australian studies

Centre for Design (2007) Melbourne LCA

Nolan-ITU (2004) Australia LCA

Centre for Design and Nolan-ITU (2003) Victoria LCA

Warnken Ise (2007) Australia Greenhouse gas LCA

Centre for Design (2001) Victoria LCA

Hyder Consulting (2008) Melbourne Greenhouse gas LCA

Data specific to Wollert landfill

HLS provided a range of information to allow the development of the model, including:

� historical data on waste deposited at the site since its inception

� historical data on methane recovery data from LMS, the site’s landfill gas service provider

� the report of an emissions survey undertaken by GHD (2009)

� site data on diesel consumption, power use and steel recovery.

The methane recovery and emissions data provided the basis for estimating the proportion of

methane recovered at the site. The historical waste and methane recovery data allowed a

‘reality check’ of the theoretical values obtained from literature – especially the degradation

rates to be applied in the time sensitive assessments.

3.2 Methane generation potential (Lo)

The quantity of methane that can be generated as material decays anaerobically can be

scientifically conceived as the methane generation potential (Lo). This value varies with material

type and depends on temperature and other factors (Bingemar and Crutzen 1987, Jensen and

Pipatti 2002). Most of the experimental and scientific literature discusses Lo as an aggregated

measure but for modelling using the IPCC first-order decay model, Lo needs to be

disaggregated into component factors as listed in Table 6, namely DOC, DOCf, F and MCF. We

consider these in the following sub-sections.



3.2.1 Degradable organic carbon and the fraction that degrades

DOC and DOCf have been estimated for various waste types including through laboratory

experiments (e.g. Barlaz et al 1997) and chemical examinations of landfill residues (e.g.

Gardner et al 2002). Three of the existing international models that we examined developed lists

of DOC and DOCf values based on reviews of the scientific literature. These results are listed in

Table 8. Both Smith et al (2001) and US EPA (2006) apply disaggregated DOCf values based

on experimental data taken from the scientific literature. For LCA purposes, this approach would

appear superior to the IPCC (2006) method of averaging DOCf across the various waste types.

But the IPCC values are more widely used and are required for reporting under the NGERS.

The previous Australian models reviewed mostly took values from international studies. The

Centre for Design and Nolan-ITU (2003) Centre for Design (2007) used values from Smith et al

(2001). Warnken Ise (2007) and Hyder Consulting (2007b) used values from IPCC (2006).

Hyder Consulting (2008) used values from US EPA (2006). The Centre for Design (2001) and

Nolan-ITU (2004) constructed three broad scenarios with different degrees of degradation, from

zero to 100%. The reasons for the variability across the studies are thought to include the extent

of knowledge of the data, the need for consistency with selected waste categories, and the

perceived need to be consistent with Department of Climate Change approaches.

Since methane generation is proportional to the product of DOC and DOCf, and it can be readily

seen from Table 8 that different materials may generate widely different quantities of methane

per tonne. The US EPA (2006) data, for example, suggests that a tonne of office paper would

generate five to ten times as much methane in landfill as a tonne of garden organics.

The categories of organic waste to landfill to be used for this study are those listed for the IPCC

(2006) in the above table, plus residues from MBTs. The IPCC DOC values are required for

NGERS reporting and are believed to be robust.

In relation to DOCf values, both the Smith et al (2001) and US EPA (2006) values could

potentially be used for most materials. But no specific DOCf values are available in either of

these studies for five material categories:

� Timber – a literature review by Barlaz (2004, pp.34, 35) cites two studies that found

methane generation from timber to be similar to that of paper and cardboard or average

municipal waste, suggesting DOCf values of perhaps 30-40%. On the other hand an

Australian study found that only 2.5% and 4.1% of timber samples had decayed after 19

and 29 years respectively (Gardner et al 2002).

� Nappies - no data were identified to provide a DOCf value but a degree of degradation is

expected since much of the weight would be urine and faeces, and a proportion of

nappies contain plant based absorbent material and other components.

� Rubber and leather are resistant to decay and unlikely to have high DOCf values.

� Residues from MBTs have been shown to produce methane in landfills but at rates much

reduced from unprocessed wastes. Experimental results found reductions in methane

potential of 82-91% after 15 weeks of composting (De Gioannis et al 2009).

� A proportion of the waste is uncategorised. Typically this material contains some DOC.



Table 8 DOC and DOCf values for waste types as specified in reviewed models

Study Waste material DOC DOCf

IPCC (2006) Paper Food Garden Timber Nappies Textiles Rubber & leather

40% 15% 20% 43% 24% 24% 39%

50%

Smith et al (2001) Paper Food Garden Textiles

33% 15% 24% 19.5%

35% 75% 50% 30%

US EPA (2006) Cardboard Newsprint Office paper Coated paper Food Garden

44% 47% 38% 32% 15% 13-19%

45% 15% 88% 25% 84% 23-32%

Hogg et al (2008) Proprietary model unavailable for review. Waste materials are characterised by chemical constituents e.g. lignin, cellulose. Decay of these constituents is then modelled.

Modelling decision: The DOC and DOCf values to be applied will be as tabulated below. We will

apply the uncertainty values for DOC provided by the IPCC (2006 p.3.27) i.e. ± 20%.

Table 9 DOC and DOCf models applied in the LCA

Waste material DOC Source DOCf Source

Paper 40%

IPCC (2006)

35-45%

Smith et al (2001), US EPA (2006) Food 15% 75-84%

Garden 20% 25-50%

Timber 43%

5%-35% Hyder estimate based on Gardner et al (2002) and citations in Barlaz (2004)

Nappies 24% 40-60% Hyder estimate

Textiles 24% 30% Smith et al (2001)

Rubber & leather 39% 15% Hyder estimate

Residues from MBTs 15-25% 5-10% Both DOC and DOCf values are Hyder estimates based on De Gioannis et al (2009)

18

Non-categorised materials

10% 5-10% Both DOC and DOCf values are Hyder estimates

Using mid-range values, the weighted average DOCf is 0.49 in the base case, which is very

close to the default IPCC / DCC average value of 0.5.

18 For municipal waste, these figures would give rise to an 86% reduction in L0, based on a weighted average value for

DOC x DOCf of 7%.



3.2.2 Methane fraction

The proportion of methane in landfill gas varies from 40-60% by volume (and molar

concentration) but is more normally in the higher part of that range (Micales and Skog 1996,

Schuetz et al 2003, Barlaz 2004, Spokas et al 2005, Bramryd et al 2007, Thompson et al 2009).

Data on the composition of landfill gas collected at Wollert landfill was provided by LMS and is

consistent with this range. The measured proportion varies across time, reflecting the stage of

the decay of the waste material. The measured proportion does not necessarily represent that

generated because air can be sucked into the landfill by the gas collection system and because

some CO2 dissolves in the landfill leachate. Models generally put the proportion of methane in

generated landfill gas at 50% (Smith et al 2001, Weitz et al 2002, Berger et al 2005, IPCC 2006,

Themelis and Ulloa 2006, US EPA 2006, Park et al 2009).

Modelling decision: The value of F will be put at 50%. We will apply the uncertainty value

provided by the IPCC (2006 p.3.27) i.e. ± 5%.

3.2.3 Methane correction factor

MCF is a factor in the IPCC model that corrects for the proportion of the organic degradation

that occurs in an aerobic environment in which carbon from degradation processes is emitted as

CO2 and no methane is generated. Based on its literature reviews, the IPCC (2006) provides

default values related to the size and degree of management of landfill sites. For sites such as

Wollert where the waste mass is deep, compacted and covered daily, the default MCF value is

1 i.e. there is no correction and all degradation is modelled to occur anaerobically. Australia

assumes a MCF of one for its national inventory.

Some degradation must occur immediately after deposition before anaerobic conditions have

developed. This is supported by a landfill gas survey undertaken at the site that found relatively

high gas fluxes from the new waste and a high ratio of CO2 to methane in the gas (GHD 2009).

In a background paper undertaken for the IPCC, Jensen and Pipatti (2002) point out that “[e]ven

a managed site will have brink zones, top layers etc., where aerobic conditions occur” and

question “if perhaps 0.95 as MCF would be a more realistic figure.” Based on this assessment

and the experimental evidence, we use this value within the LCA.

Modelling decision: The value of MCF will be put at 0.95 ± 5%.

3.2.4 Overall methane potential

Based on the values given above and the waste types and proportions accepted at the Wollert

landfill, the net methane potential can be estimated by material. This is illustrated in the

following pie chart using mid-range parameter values. Food waste represents more than half of

the methane potential and paper almost a quarter.



Figure 7 Methane potential of input wastes, base case

3.3 Methane oxidation factor (OF)

As landfill gas passes through the landfill cover or cap, methanotrophic bacteria oxidise some of

the methane. The extent of oxidation varies with the type and thickness of cover material,

moisture levels, temperature and the gas flux rate (Streese and Stegman 2003, Schuetz et al

2003, Gómez et al 2009). The IPCC (2006) guidance sets a default value of zero for national

greenhouse gas inventories but indicates that a value of 10% may be appropriate where

landfills are well managed. The Australian national inventory uses the 10% value and requires

this in NGERS reporting. All but one of the reviewed LCAs put OF to 10% (Centre for Design

2001 assumed a value of 50%).

Methane oxidation rates can be estimated in laboratory experiments and also in situ through

carbon isotopes measurements in gases below and above the cap. This work shows that

oxidation greater than 10% are readily possible. A literature review by Jensen and Pipatti (2002)

concludes that up to 30% could be expected, while the US EPA (2006) suggests up to 40%. A

more recent literature review of 42 studies (Chanton et al 2009) found a mean OF value of 36%

and only four reporting values of 10% or less. In clayey soil covers the average OF was 18%.

The field studies, on average, had a lower OF than the laboratory studies, probably because

“cracks and fissures … in the field allow some CH4 to bypass oxidation” (Chanton et al 2009

p.658).

Modelling decision: The value of OF will be put at 10% to 30%.

Food 56%

Garden 5%

Paper 22%

Wood 6%

Textiles 4%

Nappies 6%

Rubber & Leather 1% Non-

categorised 2%



3.4 The decay constant (k)

An estimate of the rate at which waste decays is needed for the time sensitive model used for

assessing the greenhouse impacts of landfills up until a particular date. Different waste types

decay at different rates e.g. food waste decays more quickly than timber. Decay rates are

sensitive to moisture and temperature.

The Department of Climate Change (DC 2009b p.266) produces a list of default decay

constants (k values) for each material type linked to the climate. These are given in the middle

three columns of Table 10. Anecdotal evidence suggests that the k values assigned to Victoria

may be too low, especially at a site such as Wollert where leachate is recirculated into the waste

body so that moisture levels are elevated.

To calibrate the k values to the Wollert landfill, Hyder used a first-order decay model to assess

methane generation rates between September 2007 and August 2009. The model was

populated using: data on waste received at the landfill; mid-range values for the parameter

values specified above; and estimated methane generation based on recorded collections and a

notional collection efficiency of 80% (see discussion in section 3.5). In this way we could ‘solve’

the model find a best fit set of k values that explain the methane generation patterns. While

there are many uncertainties in this estimate19

it represents an improvement on the DCC default

values, which are clearly too low for this site. The resultant values are listed in the right-hand

column of Table 10.

Table 10 Decay constants (k values) by material type as specified by DCC (2009)

Waste type Qld, NT NSW Victoria & other

jurisdictions

Estimate for

Wollert landfill

Food 0.4 0.185 0.06 0.32 Paper & cardboard 0.07 0.06 0.04 0.06 Garden 0.17 0.1 0.05 0.14 Wood 0.035 0.03 0.02 0.03 Textiles 0.07 0.06 0.04 0.06 Nappies 0.07 0.06 0.06 0.07 Rubber & leather 0.07 0.06 0.04 0.06 Residues from MBTs - - - 0.11

20

Non-categorised - - - 0.06

Modelling decision: The k values to be applied in the model will be as listed in the right-hand

column of Table 10.

19 For example, waste composition data is lacking for some materials received at the landfill. In addition, the approach

assumes all error in the model defaults is in the k values, which may be incorrect. 20

Calculated from the weighted average of other materials.



3.5 Methane recovery rate (R)

Like most large modern landfill operations, at Wollert landfill some proportion R of the generated

methane is collected and burned, mostly to produce electricity for the grid. In estimating a value

for R, we need to take into account gas that is generated before and after the collection

equipment is operating, the proportion of gas that is collected while the turbines or flare are not

operating, and any methane that passes through the turbines or flare without being oxidised.

The value of R at Wollert landfill is critical to the results of this LCA.

The section opens with a review of R in the literature. It then moves on to consider methane

recovery at the Wollert landfill, firstly deriving three estimates for the collection efficiency during

some defined time period. It then considers ways in which the collection efficiency needs to be

adjusted to derive a value for R. These are: the extent to which the estimated collection

efficiency is representative; methane that is emitted despite being collected; methane that is

emitted before gas collection starts; and methane that is emitted after methane collection stops.

After this, in section 3.5.7, we draw the results together to generate an estimate of R for Wollert

landfill

3.5.1 R in the literature and reviewed models

Emissions from landfill are difficult and expensive to measure, vary with atmospheric pressure

and rainfall, change over time, and are highly dependent on management factors such as

whether edge, crack and piping leaks are monitored and remediated (Bogner et al 2007).

Because of these factors, R is difficult to estimate across sites, jurisdictions and time, and is the

subject of much debate.

Table 11 lists values for methane recovery as measured in scientific studies and estimated in

literature reviews and the reviewed models.

There is enormous variety in these estimates and measurements. Each of the reviewed

international LCAs discusses the paucity of data to support their assumed value and also the

sensitivity of the analysis results to this parameter. The rates given in the reviewed Australian

studies vary less – from 50% to 75%21

. Since they are examining different jurisdictions, some

differences are to be expected.

Most of the values given in Table 11, it seems, refer to collection efficiency rather than whole-of-

life estimates i.e. they do not take into account methane losses before the installation of the

landfill gas recovery system and after it is decommissioned. They may also exclude methane

that is collected but not burned.

21 The studies’ estimates of net methane recovery varied more widely because they assumed differing proportions of the

landfilled waste stream was sent to landfills fitted with methane recovery systems. This value varied from 55% (Centre for Design 2001) to 80% (Centre of Design and Nolan-ITU 2003, and Nolan-ITU 2004b) to 100% (the other studies).



Table 11 Methane recovery rates in the reviewed literature and models

Study R Application Derived through

Spokas et al (2005) 35% (operating cell) 65% (temp. cover) 85% (clay cap) 90% (geomembrane cap)

France

Estimate based on measurements at three landfills

Themelis and Ulloa (2006) 36% 25 sites in California

Estimate based on theoretical generation and measured capture

Scharff and Jacobs (2006) 20% Site in The Netherlands

Measurement

Lou and Nair (2009) 50-100% 24-60% 40-60% 25-50%

Uncertain

Pipatti & Wihersaari (1998) Oonk and Boom (1995) Hummer and Lechner (1999) Bogner and Spokas (1995)

IPCC (2006) 20% Global default value

Literature review

Smith et al (2001) 49%22

European average

US EPA (2006) 75% US average

Hogg et al (2008) 60% London average

Thompson et al (2009) 60-90% Canada average

Centre for Design (2007) 60% Melbourne average Hyder Consulting (2008) 50-70%

Centre for Design (2001) 55%

Victorian average Centre for Design and Nolan-ITU (2003)

55%

Nolan-ITU (2004) 55% Australian average

Warnken Ise (2007) 60-75%

Hyder Consulting (2007b) 50% (conventional) 70% (bioreactor)

Sydney average

3.5.2 Methane collection at Wollert landfill

We used three methods to estimate the proportion of the methane generated from the wastes

under assessment that will be collected. Two involved comparing a measured quantity of

methane collected with an estimated quantity of methane generated. The third referred to the

published literature. The methods were:

� comparing methane collected with a high estimate of methane generated derived using a

first-order decay model incorporating very high k values – this should generate a low

estimate of the methane collection rate

� using a survey of methane emissions from Wollert landfill undertaken by GHD (2009) to

estimate the uncollected portion of the generated methane

� reference to Spokas et al (2005).

A discussion on each of these follows.

22 The operational recovery rate is averaged to 54%, with 10% losses before recovery infrastructure is installed and after

it is taken out.



Comparison based on a high estimate of methane generation

A low range estimate of the proportion of methane collected can be obtained by comparing

records of methane collection over a fixed period with a ‘worst-case’ modelled estimate of

methane generation during the same period. The period from September 2007 to August 2009

was selected for this comparison. This period starts immediately after the gas collection system

was fully operational at the site, and runs for a full two years.

We ran the first-order decay model using waste composition and quantity data for Wollert since

the landfill opened. Assuming mid-range values for DOCf, F and MCF, we applied very high k

values (i.e. high degradation rates) commensurate with the hot wet climates of Queensland and

the Northern Territory. This should produce a high estimate of methane generation since actual

generation rates in cooler and dryer Victoria are unlikely to be this high. The model projected

generation of about 16.9 kt of methane during the selected period.

LMS data showed that about 11.8 kt 23

of methane was collected during this same period. The

ratio of methane collected to methane generated represents an estimate of the collection

efficiency. The low-range estimate using the initial model is 70%.

These figures cover a snapshot in time. To convert them to an estimate for R we need to take

into account emissions before and after the gas collection system is installed and also methane

that passes unoxidised through the collection system. In addition, we need to consider the

extent to which this snapshot is representative of how the collection system performs throughout

is operating life or, at least, while material deposited during 2008 is generating methane. This

assessment is undertaken in subsections 3.5.3 to 3.5.7.

Comparison based on a survey of methane emissions

GHD surveyed methane emissions from the Wollert landfill in November 2008 using the flux

chamber method. By comparing the estimated emissions with recorded methane collection

during this same period, an estimate of the proportion of methane collected can be derived. We

reviewed the survey report (GHD 2009) and communicated with its author about the method

and results.

The GHD methane emission survey involved:

� a walkover survey of the landfill using a low level combustibles gas detector to identify

‘hotspots’ i.e. where gas is leaking through a defect in the capping layer, the results of

which were used to assist in selecting representative locations for flux chamber tests

� measurement of methane and CO2 flux from the surface of the landfill capping layer at 33

selected locations using a closed and sealed 18L flux chamber

� measurement of the gas composition profile within the capping / cover layer at three

locations using a gas sampling probe and a landfill gas analyser

� analysis including assumptions about the representativeness of particular measurements

and comparison with the quantities of gas collected for energy recovery at the same time.

23 Assuming a density of 0.667kg/m

3 based on an average temperature of 19

oC and density at 0

oC of 0.717 kg/m

3.



During the survey atmospheric pressure was low and falling. Around 13mm of rainfall fell three

days before it started and 0.4mm on the last day. These conditions should be conducive to

higher than average emissions. Cells 1 and 2 were filled and capped with a gas system

installed. Cell 3 was filled and was connected to the gas system but had only intermediate

cover. Filling was occurring in Cells 4 and 5. Due to the cell development approach there will

usually be some waste at Wollert that has only intermediate cover, and sometimes this will not

be connected to the gas collection system.

GHD (2009) estimated the methane emissions at Wollert during the study period amounted to

approximately 120 m3/hr

24. Emission fluxes were very low methane over most areas of the site

where capping was complete. Methane fluxes were also low over the freshly deposited wastes,

which were emitting large amounts of carbon dioxide. There were higher emission rates from

waste that was not fully capped and from ‘hot spots’ at the edges of the completed cells. GHD

(2009 p.21) estimated that almost 80% of the total methane was emitted from 6% of the site

area.

There is no standard or widely accepted best method for measuring emissions from landfills.

For the NGERS, the DCC (2009b p.252) does not provide a method for directly reporting landfill

emissions, stating that “[f]urther development work and consultation is required to establish the

appropriateness of various monitoring techniques before their inclusion”. The flux chamber

method is relatively cheap and, according to the UK Environment Agency (2004 p.9), it is “an

effective technique for measuring the normal surface emission over a landfill cap”. It is widely

used in scientific studies (e.g. Schuetz et al 2003, Savanne et al 2005, Spokas et al 2006),

although often in combination with other techniques.

The accuracy of the flux chamber method is questionable. Spokas et al (2006 p.521) found

“good agreement” between the results of the chamber and other measurements. But in a site

trial of seven methods for measuring methane flux, Trégourès et al (1999) found only that it

produced results in the same order of magnitude as five of the other methods. It put the level of

uncertainty at 30%. The UK Environment Agency (2004 p.33), on the other hand, estimates the

uncertainty to be “as high as one order of magnitude”.

There are two obvious sources of uncertainty in the method: uncertainty about measurements

and uncertainty about their spatial representativeness.

In relation to the first of these, Standards Australia (2009 p.7)25

states that the measurement

accuracy “ranges from 50% to 124%”26

. Accreditation through a reputable accredited agency

helps to verify the accuracy of measurements to third parties. The National Association of

Testing Authorities (NATA), Australia’s national laboratory accreditation authority, offers

accreditation that testing, measurement, inspection and calibration are carried out to a high

standard. GHD (2009) does not refer to NATA accreditation.

24 Review of the report revealed an apparent addition error – we believe that a total flow 129 m

3/hr is more consistent

with the reported data and assumptions. 25

This is a recently finalised standard for general area source sampling using the flux chamber technique. It was finalised after the Hanson study was complete. It is not clear whether GHD’s method was consistent with this standard. 26

This is for a flux chamber design approved by the US EPA, which may differ from that used by GHD (2009).



The second source of uncertainty – the spatial representativeness of measurements – is harder

to quantify. In the absence of a standardised and statistically verifiable approach on how to

carry out these surveys, the assumptions about representativeness rest wholly on the

judgement of the analyst team. The only standardised way to carry out such a survey known to

Hyder (UK Environment Agency 2004)27

was not used, and the number of tests was

substantially lower than that standard recommends. GHD’s allocation of emission readings to

representative areas was not transparent, and due to the very high spatial variability small

changes in the assumptions could strongly affect the estimated results. In more than one high-

emission areas of the site the emission value assigned was not a direct measurement but was a

judgement based on extrapolations or averages of other measurements.

On being queried by Hyder on its provision of a single ‘best estimate’ value for emissions from

the site, GHD revised their original 120 m3/hr estimate to a range value of 68 to 358 m

3/hr. Most

of this variability was apparently attributed to uncertainty about spatial representativeness rather

than instrumentation. LMS data show that an average of 1,105 m3/hr of methane was collected

during November 2008, while the emission survey was taking place28

. A range estimate of the

collection efficiency can be calculated using these figures, assuming that 30% of non-collected

methane is oxidised at the low end of the range and that only 10% is oxidised at the high end of

the range. This calculation yields a collection efficiency of between 68% and 94%.

As with the previous subsection, this collection efficiency estimate is for a particular time period.

For conversion to R we need to take into account: the degree to which this collection efficiency

is representative of the collection system performance while 2008 waste is degrading; methane

that passes unoxidised through the collection system; and emissions before and after the gas

collection system is installed. This is undertaken in subsections 3.5.3 to 3.5.7.

Reference to Spokas et al (2005)

R values as set out in the literature were summarised in Table 11. Of the studies that reported

values based on direct measurements, the most sophisticated in terms of allowing an estimate

for Wollert to be generated is Spokas et al (2005). This study provides emission estimates

based on the condition of the cell i.e. whether waste is open, capped or under temporary cover.

These estimates have reportedly been adopted as defaults by French regulators.

A collection efficiency estimate can be obtained using these values in a first-order decay model.

We assume that the cells containing 2008 waste are open for one year, under temporary cover

for another year, then sealed with a clay cap. If the collection equipment ran until 2035 then the

collection efficiency would be 70%.

3.5.3 How representative are the collection efficiency estimates?

Three insights were obtained into the methane collection efficiency during limited time periods.

But we need to estimate the average collection efficiency over the whole period during which

the system is collected methane generated from the 2008 wastes we are assessing.

27 This standard is linked to a UK regulatory requirement. It sets out a sophisticated method for defining representative

zones and undertaking measurements within those zones. It was apparently determined to be too onerous for the Wollert study. 28

The November value is almost identical to the average for May 2008 to April 2009, and so is not anomalous.



LMS has an incentive to collect as much methane as is economically recoverable. GHD (2009)

showed that most emissions occurred through fissures and cracks, which are readily identifiable

and can be remediated. While there is currently no formal mechanism to ensure that this occurs

(e.g. licence compliance or audit system) the proposed carbon pollution reduction scheme, if

working properly, should provide HLS with some incentive to minimise methane losses. As a

proportion of the total deposited waste, the uncapped portion will decrease over time, tending to

increase the proportion of total emissions captured. On the other hand, there is some anecdotal

evidence of methane collection efficiencies declining with methane generation (Partl 2009a

pers. comm.). This is probably due to equipment performance declining with age. Again,

however, climate change policy is likely to work against this trend.

Overall, it seems reasonable and probably conservative to assume that collection efficiencies

will not decline from estimated levels while the methane collection equipment is operational.

3.5.4 Methane that is collected but not oxidised

A small proportion of methane is emitted despite being collected. This comprises methane

collected during burning equipment downtime and methane that passes unoxidised through the

burning equipment.

LMS flares methane when the electricity engines are not operating but is unable to do this when

there is a grid shutdown. Site records indicate that this occurs during about 1% of total operating

time. It is assumed that this corresponds with emission of 1% of the collected methane. This

proportion is likely to decline over time due to climate change policy but, to be conservative, we

will assume it is maintained.

The DCC (2007 p.25) provides a default figure of 0.5% for the proportion of the methane that

passes unoxidised through engines and flares and is emitted to the atmosphere.

Modelling decision: The assumed proportion of collected methane that is not thermally oxidised

will be 1.5%.

3.5.5 Emissions before gas collection starts

Some methane is emitted before the gas collection system is installed. This is likely to represent

a small fraction of the total emissions at Wollert landfill because the collection system is

installed rapidly i.e. prior to capping. The GHD emission survey measured emissions over the

waste body and found that degradation was mainly aerobic, and not emitting a significant

quantity of methane. This is consistent with the discussion in section 3.2.3, in which we set the

value of MCF at 0.95. The emission survey measured methane emissions from an area in which

gas was not yet being collected, so this small proportion is included in the survey results.

3.5.6 Emissions after gas collection ceases

Methane may still be produced for decades or even centuries after energy recovery has stopped

being viable (Micales and Scog 1996, Lou and Nair 2009). Dever and Swarbrick (2008) estimate

that 20% of the gas generation may occur between 30 and 100 years post-closure, when gas

recovery is unlikely to be occurring. Smith et al (2001) put the post-gas collection proportion at

about 10% of generated methane. Some commentators argue that the proportion emitted after

collection could be very high but data to demonstrate this is lacking. This argument rests partly

on the notion that organic degradation is delayed by lack of moisture, a situation less likely to

arise at Wollert where leachate is recirculated over the waste body. Others maintain that

methane generated long after landfill closure would be all oxidised as it passed at a low flux rate



through the cap. This assumes that emissions mostly occur through the cap rather than fissures

in the cap, which would depend on the degree of care of the site decades after it closed.

Hyder understands that LMS has rights to the gas until 2035 and is required to leave its pumps,

flares and other equipment in place. This would allow gas flaring after energy recovery ceases

and LMS has left the site. This might oxidise a further 5% of the site emissions. Climate change

policy is likely to encourage this to occur.

Modelling decision: Based on the likelihood of an extended period of gas recovery it is assumed

that the proportion of methane generated after gas collection stops is 5% to 10%. For the time-

sensitive models energy recovery is assumed to be undertaken until 2035 with subsequent

flaring for 10 years in the high range estimates.

3.5.7 The value of R to be applied in this study

We are now in a position to develop a range estimate for R taking into account the discussions

in the previous subsections. Recall that in section 3.5.2 we obtained three estimates of

collection efficiency at the Wollert landfill. These estimates need to be adjusted to take into

account the findings set out in sections 3.5.4 to 3.5.6. The adjustments are undertaken in Table

12, culminating in an estimate for R. For the comparison between methane collection and high

estimate methane generation we discount the ‘worst-case’ value due to the very low probability

of its occurrence.

Table 12 Deriving estimates for R at Wollert landfill

Estimate basis Estimated

collection

efficiency

Assumed

emissions

before gas

collection

starts

Assumed emissions after gas

collection stops

Collected

but not

oxidised

Estimate

of R

Value Explanation

Comparison of methane collection and high estimate of methane generation

70% 0% 7.5% Mid-range value is consistent with estimate method

1.5%

64%

Comparison based on GHD (2009) emissions survey

68% to 94%

Included in the estimate

5% to 10% Range estimates used

60% to 88%

Spokas et al (2005) 70% 0% 62% to 66%

Based on the three information sources and the analysis contained above, a range estimate for

R is derived.

Modelling decision: In the instantaneous emissions model, the value of R is estimated to lie

between 60% and 88%. In the time sensitive models the proportion of methane recovered is

higher because a shorter timeframe is measured29

. Throughout, it is assumed that recovery

rates are the same for all scenarios, notwithstanding variation in the quantity of methane

generated.

29 These values are not calculated as proportions. Emissions are estimated directly using a first-order decay model.



3.6 Other emission factors at Wollert landfill

3.6.1 Carbon storage

Some proportion of the degradable organic carbon (DOC) put into landfills does not readily

degrade. After some decades, much of the content of the landfill ends up a lignin-rich humus-

like material that resists further anaerobic decay (Barlaz 1998, 2006). These conditions,

according to Bramryd et al (2007), are the anthropogenic counterpart to natural peat lands and

lake sediments. The IPCC (2006) inventory method assumes that 50% of the DOC in the landfill

does not degrade, but does not consider this as an offset against emissions. This is an artefact

of the inventory method, in which net fluxes of biogenic carbon are accounted for in a different

inventory sector. Some LCA analysts have also ignored carbon storage in landfills (Warnken

ISE 2007, Centre for Design 2007), but there seems to be no clear rationale for this other than

consistency with the IPCC. If carbon is not emitted it is stored and should be counted as such.

Modelling decision: The model will credit carbon that is not emitted as equivalent to CO2

removal from the atmosphere.

3.6.2 Product offsets

The landfill produces a greenhouse gas offset benefit by generating electricity and recovering

metals.

Electricity offset

As well as destroying methane, landfill gas collection for energy recovery has the benefit of

generating renewable electricity that can be considered to offset the use of conventional fuels.

Based on LMS site data, we assume that 98% of captured methane at Wollert is used for

energy recovery – the remainder is flared (1%) during engine down-time or vented during power

outages (1%). We assume that this holds throughout the operation30

. LMS data indicates that

the average conversion efficiency of their Wollert generators from June to November 2008 was

32.4%. The generated energy is sold directly into the grid and is assumed to offset energy

having the average greenhouse gas emission characteristics of Victorian electricity over the full

fuel cycle. This value is given in DCC (2009b p.59) as 1.34 t CO2-e/MWh31

. Generating

electricity at Wollert for use in Melbourne also saves transmission losses from the Latrobe

Valley which we estimate at 2.5% based on the average for the Victorian grid (Brown et al

2006). The energy content of methane is 55.5 GJ/t (based on ISO 6976:1995) and there are 3.6

GJ per MWh. The electricity offset at Wollert can therefore be estimated as equal to 55.5 / 3.6 x

98% x 32.4% x 1.34 x 102.5%, or 6.7 t CO2-e per tonne of methane collected.

30 Following is the assumed proportion of recovered gas used for energy recovery in the reviewed Australian studies.

Most assume that some waste goes to sites equipped with flares only.

% CH4 used for energy Applies to

Centre for Design (2007) 100% Melbourne average

Hyder Consulting (2008) 95%

Centre for Design (2001) 100% Victorian average

Centre for Design and Nolan-ITU (2003) 75%

Nolan-ITU (2004b) 75% Australian average

Hyder Consulting (2007b) 100% Sydney average

31

For simplicity we ignore the changes in this value that will occur over time assuming greenhouse policy is successful.



Metals recovery

HLS recovers ferrous metals from waste that is deposited at the tip face using a large vehicle-

mounted magnet. This represents a greenhouse gas benefit because the use of these materials

can be assumed to offset the processing of ferrous metals from virgin sources. An estimate of

the benefit is available from ACOR (2008).

The recovered quantity is recorded by HLS and can be partitioned between the different

incoming waste streams based on their estimated composition and quantity. On this basis we

estimate that 477 tonnes, or 0.2% of the incoming waste stream, was recovered from the

materials forming the functional unit of the LCA.

3.6.3 Construction and operation emissions

There is a greenhouse gas cost in diesel and power use in developing landfill cells and

operating the site. Per tonne of waste, HLS data shows consumption of diesel is about 1.40 L

and power use is 47 Wh. In addition, we include a greenhouse gas cost associated with the

plastic liner at the rate of 1.2 kg CO2e per tonne of incoming waste, based on Manfredi et al

(2009). This comes to a total of about 5.86 kg CO2-e per tonne.



4 Inventory of non-landfill factors and emissions

Greenhouse gas costs and benefits were calculated for all the non-landfill elements of the LCA,

including from: transport; non-landfill facility construction; energy use and energy yields in waste

processing; offsets for recovered recyclables; and organics degradation and storage.

These topics are discussed in successive sections below, following an opening section that

illustrates the mass flow of materials that is assumed during the processing operation. The

emission parameters, data, and uncertainty ranges and distributions are given in Appendix D.

The data sources for calculating the emission factors were mainly previous LCA models and, for

the organics degradation section, the scientific literature.

4.1 Processing mass flows

Each scenario envisages some waste being treated in either anaerobic or aerobic MBTs and/or

enclosed composting facilities. The fate of the average tonne of material entering these facilities

can be put into one of four categories: conversion into organic product; recovered metals and

plastics from processing of mixed residual wastes; contamination and rejected material that is

taken to landfill; and mass losses of carbon dioxide and water in the composting process. The

LCA modelling assumes mass flows through the treatment processes as charted below32

. Note

that the material entering the MBT technologies is residual garbage whereas the material

entering the enclosed composting facility is source-separated organic materials.

Figure 8 Proportional mass flows of material through the three treatment technologies

32 The assumption of 10% contamination of separated garden and food waste is consistent with the experience of the

Shire of Nillumbik (Pittle 2009 pers. comm.). We assume no difference in contamination rates between scenarios 3a and 3b based on the grounds that the Shire has no evidence of higher levels of contamination in their garden and food waste bin on weeks when garbage is not collected.

0% 20% 40% 60% 80% 100%

Enclosed composting (garden waste only)

Enclosed composting (garden and food

waste)

Aerobic MBT (residual waste)

Anaerobic MBT (residual waste)

Rejects and contamination Recovered metals and plastics



4.2 Transport

Transport emissions are considered in two parts: transport of materials to the waste facility and

transport of product to market.

Hyder used its Waste Transport Logistics model to estimate the truck hours required to collect,

transfer and deposit waste materials in each scenario. The number of serviced properties was

as reported to Sustainability Victoria in its annual local government survey. Collection from 175

households per hour was assumed. The task of transporting commercial and industrial waste

materials to the waste facility is identical for each scenario because all the waste treatment

technologies modelled are assumed to be located at the Wollert landfill site. The truck hours

under each scenario are set out in Table 13. The LCA model translates these values into

greenhouse gas emissions.

Table 13 Estimated truck hours required for material deliveries under each scenario

Base case and

Scenarios 1 & 2

Scenario 3a Scenario 3b

Garbage service 103,980 58,199 98,212

Organics service 27,197 83,479 83,479

Total 131,177 141,678 181,691

The model estimates the emissions from transporting recovered products on a tonne-kilometre

basis. Metals and plastics are assumed to be transported to the nearest major processing

operator. Compost is assumed to be transported 20km on average. Emissions associated with

transport of residual wastes are ignored since the waste facilities in each scenario are assumed

to be located at the Wollert site.

4.3 Facility construction

We estimated the emissions associated with construction of the non-landfill waste facilities

assuming a standard concrete construction with a metal roof and depreciation over a 30 year

lifespan, accepting in total 30 times the waste provided for in this study. The calculated

emissions are very low; in the order of 3kg CO2-e based on a 100 year assessment period.

4.4 Energy use and energy yields in waste processing

Energy used in and derived from processing waste in enclosed composting facilities and MBTs

has greenhouse gas implications. Composting and aerobic MBTs use on-site fuels and

electricity. Anaerobic MBTs generate methane from the organic material for use as an energy

source as a landfill with a gas collection system also does. This has an offset benefit if power

generation is greater than power consumption.

The assumed values tabulated below are derived from a literature review, previous

assessments, consultation with experts and Hyder knowledge.



In relation to anaerobic MBT, factors that influence net power generation include:

� whether the process is wet or dry

� retention time

� whether the temperature range is thermophilic or mesophilic

� whether one or two tanks are employed

� the extent of the recyclables pre-sort.

Information on the performance of anaerobic MBTs in relation to methane generation, methane

collection, electricity generation and power consumption across the wide range of proprietary

technologies is not readily available. Where this information is available to Hyder, confidentiality

constraints prevent its attribution to a particular technology or source.

The literature puts the volume of biogas generation from anaerobic digestion of mixed municipal

waste ranges at 85 to 130 m3/t. Conversion to electricity is dependent upon the energy content

which is typically 18 to 22 MJ/m3 and the efficiency of the gas turbine (30% to 38%). Energy

consumption ranges from 50 to 150 kWh/t.

Hyder’s research indicates that net generation of 25 kWh/t is a conservative estimate from

operating anaerobic MBT facilities in Europe. With performance design optimisation based upon

improving net energy performance higher figures are possible and have been achieved. A group

of international consultants to the Victorian Advanced Resource Recovery Initiative suggests

typical value of 68 kWh/t may be appropriate.

Because of the variability discussed above, and as these technologies have not yet been

applied and operated in Australia for sufficient time to produce representative local data, the net

energy yield from anaerobic MBTs is presented as a range for sensitivity analysis. Our typical

value, as applied to the initial results, is 25 kWh/t. The high range is 68 kWh/t, the figure

introduced above. We understand that some plants have not been net exporters and therefore

assume a low range value net value of 0 kWh/t.

Table 14 Assumed energy use and yield for non-landfill waste technologies

Waste technology Scenarios Diesel use (L/t) Net electricity use (kWh/t)

Enclosed composting All 1 50

Aerobic MBT 1 1.5 75

Anaerobic MBT 2 1.5 0 to -68, typical value -25

These figures were used to generate net energy emission factors for each technology.

Modelling decision: The applicable energy emission factors for the different technologies will be

as follows – enclosed composting: 68 kg CO2-e/t waste; aerobic MBT: 103 kg CO2-e/t;

anaerobic MBT 0 to -85 kg CO2-e/t, with a typical value of -28 kg CO2-e/t 33

.

33 Gases other than carbon dioxide form only a minor part of these net emissions, so the variable global warming

potentials over time are ignored.



4.5 Offsets for recovered recyclables

MBT technologies are equipped with front-end sorting technologies that remove contaminants

and recover materials for recycling or energy production. The materials recovered vary. For this

study, we assume that the MBT technologies in Scenarios 1 and 2 remove 75% of the incoming

metals and 50% of the incoming plastics for recycling34

. This produces a greenhouse gas

benefit by offsetting the need to produce these materials from virgin sources. The assumed

offsets are tabulated below.

Table 15 Greenhouse gas offsets from recovered recyclables

Material Greenhouse gas offset (kg CO2-e / kg)

PET plastic HDPE plastic PVC plastic Other plastics Steel Aluminium

2.6 2.2 2.4 2.6 1.5

19.1

4.6 Organics degradation and storage

In this section we discuss the greenhouse impacts of non-landfill organics degradation

processes and the use of the resultant products. The discussion is relevant to both the enclosed

composting that occurs in all scenarios and the MBT mixed waste processing in Scenarios 1

and 2 (we refer to the outputs of MBT processing as ‘stabilised organics’ rather than compost).

We consider the greenhouse impacts in three subsections:

1 organics processing

2 carbon storage

3 organic product offsets.

4.6.1 Organics processing

Degradation of organic materials in composting operations and MBTs generates greenhouse

gases. Emissions of CO2 from processing are biogenic and so are not counted as contributing

to global warming (see discussion in section 2.3.4). The IPCC (2006 p.4.6) inventory method

provides figures for emissions of nitrous oxide and methane from aerobic organic processing.

The very wide range of values given (19 to 354 kg CO2-e per tonne waste treated) is indicative

of high levels of uncertainty.

We reviewed the literature and previous modelling on this issue. It is widely established that

methane and nitrous oxide can be emitted from compost operations, especially when they are

poorly managed – for example if anaerobic conditions develop due to insufficient aeration

(Bogner et al 2007). When well managed, as are most Australian composting operations,

emissions are apparently very low (Hellebrand 1997, He et al 2000). Amlinger et al (2008)

measured total emissions of less than 0.4 kg CO2-e per tonne of input. A substantial literature

review by the US EPA (2006) concluded that effectively no emissions arise from US facilities.

Most LCAs identified for this study similarly concluded that these emissions are negligible or

non-existent, and can be ignored (Smith et al 2001, ROU 2001, ROU 2003, Hyder Consulting

2007). The Centre for Design (2007), followed by Hyder Consulting (2008), assumed a small

34 There are subsequent processing losses of up to 25% for plastics and 8% for metals.



emission of 7.6 kg CO2-e per tonne of waste treated but provided no source for this estimate.

The DCC (2009b) default values for NGERS reporting are mid-range figures from the IPCC

(2006) range and have not been adjusted for typical Australian composting operations.

The reviewed literature generally refers to open windrow composting. But organics processing

in enclosed composting facilities and MBTs are generally vented through a biofilter to control

odour. The extent to which biofilters remove methane emissions needs to be considered.

Where emissions are methane-rich (e.g. landfill emissions) biofiltration can reportedly lead to

oxidisation of all or almost all of the influent methane (Dever and Swarbrick 2008, Park et al

2009). But MBTs have much lower concentrations and in these circumstances methane removal

is less certain (Park et al 2009, Nikiema et al 2009). Measurements at a German aerobic MBT

found that “biofilter systems … reduce CH4 concentration in exhaust air by a maximum of 10-

20%” and “N2O concentrations in the effluent were higher than those in the influent”, probably

due to oxidation of ammonia (Amlinger et al 2008 p.54). Emissions of 130g/t of municipal waste

have been measured at MBTs that do not thermally oxidise waste air (Partl 2009b pers. comm.).

On the other hand, Streese and Stegmann (2003) measured substantial oxidation of methane

from old landfills that was vented through biofilters in low concentrations.

Modelling decision: The lowest range of emissions provided by the IPCC (2006) will be applied

to emissions from enclosed composting of garden waste i.e. emissions of 30g methane and 60g

nitrous oxide per tonne of material treated with an uncertainty range between zero and double

this amount. For MBTs processing food waste, emissions of 30g methane and 130g nitrous

oxide per tonne will be assumed, with the same uncertainty range. This is considered consistent

with a high standard of MBT operation.

4.6.2 Carbon storage

Carbon is stored in compost and stabilised organics but slowly released as it degrades in the

soil. Estimating the effect of this storage requires information on the quantity of carbon initially in

the material and the pattern of its degradation when applied to land. These factors will vary

depending on inputs, processes and applications: we need to estimate typical values.

The quantity of carbon in stabilised organics depends on the carbon in the inputs, the type of

process and the length to time it occurs. We can readily estimate the carbon in the inputs from

material composition (see section 2.2) and degradable organic carbon (DOC) values (section

3.2.1). We estimate carbon losses of 40% during organics processing, consistent with Centre

for Design (2008)35

.

35 The literature contains some wide discrepancies on this average value. Smith et al (2001) and Eklind et al (2007) both

estimate that more than 60% of carbon is emitted during composting. However, a literature review on emission rates by ROU (2001) concluded that average emissions were around 189 kg CO2 per tonne, a figure broadly consistent with another literature review on emissions by Amlinger et al (2008). Based on our estimated DOC values this figure implies carbon losses of only about 26% during the processing operation. Our selected figure is near the centre of this wide range.



The assumed 60% of DOC in the stabilised organics is exposed to aerobic conditions suitable to

gradual degradation, but the chemical pathways and timescales by which this occurs are

complex and variable. Among other things, they depend on the characteristics of the receiving

soil, rainfall, temperature and soil management (ROU 2003, Smith et al 2001). Several of the

reviewed LCA models applied values for the net storage of carbon in compost after 100 years.

All produced results of similar magnitude (within the range of 6-13%36

), but several referred to

high levels of uncertainty and variability. Smith et al (2001) is relied on for this study since it

applies a relatively simple method for estimating carbon storage over time. The method applies

an exponential decay function with a half-life of 28 years (equivalent to a k value of 0.025). The

carbon storage effect at the end of each assessment period can then be calculated. For the

instantaneous emissions modelling we assume zero carbon storage.

Consistent with the discussion on taking into account time factors in the modelling (see section

2.3.5), we also include a credit for the carbon that is stored for some of the years in the

assessment period but is emitted before the end of that period. As this is not done in the studies

discussed above, the net figures for carbon storage used in this study are higher.

There may also be an increase in carbon storage due to enhanced growth of plants. We know

little about this type of storage and so ignore it in the model. This will represent a small bias in

the results against composting and MBTs.

Modelling decision: The net storage of carbon in compost in t CO2-e per t organic materials

composted is as follows – 20 years: 0.34; 100 years: 0.16; instantaneous emissions model: 0.

4.6.3 Organic product offsets

The use of stabilised organics and compost in agriculture or horticulture may offset the need to

use fertiliser and other agricultural additives, the manufacture of which is greenhouse-intensive.

By increasing productivity it may reduce the emissions from farming to grow the produce

elsewhere. In some cases compost may offset the use of soil conditioning products derived from

peat or peat moss. The treatment of this issue varies across the reviewed studies as shown in

Table 16.

Table 16 Greenhouse gas offsets from the application of waste-derived compost and stabilised

organics as specified in reviewed LCAs

LCA model Offset

parameters

Parameter values

(kg CO2-e/t product)

Applied to

Smith et al (2001) Fertiliser 35.5 Agricultural compost

ROU (2003) Fertiliser & other additives

29.4 to 51 Agricultural compost

Centre for Design (2007) Fertiliser Farming

51 All product generated

ROU (2001) Centre for Design and Nolan-ITU (2003) US EPA (2006)

Not quantified or difficult to interpret

36 The values are US EPA (2006): 6%; Recycled Organics Unit (2003): 7%; Smith et al (2001): 8%, Centre for Design

(2007), Hyder Consulting (2008): 10%; Gibson et al (2002): 10-13%.



Advice obtained for this study indicates that virtually no compost that is generated from

Melbourne’s waste materials is used in settings where it would offset the use of fertiliser. Its use

is rather for soil conditioning and mulching. However, compost that is derived from food waste

has higher nutrient value and could substitute for fertiliser. We assume the ‘new’ material (i.e.

additional to the base case quantity of material) is used such that it offsets the use of artificial

fertiliser at the rate of 80 kg per tonne. Due to the greenhouse gas cost of manufacturing

artificial fertiliser, we estimate that this represents an offset of 84 kg CO2-e per tonne of compost

so used.

Peat is not used for soil conditioning in Melbourne, and the assumption that biogenic CO2 does

not have a global warming impact (see section 2.3.4) means there is no offset value associated

with any offset of sphagnum moss. The lack of offset benefits for compost is extrapolated to an

assumption of no offset benefits for lower grade stabilised organic materials from MBT

operations.

Modelling decision: For the households that supply garbage to Wollert landfill, no greenhouse

gas offset will be assumed in relation to the use of compost derived from garden waste or

stabilised organics from MBTs. For scenario 3a and 3b, we will assume that 44% and 39% of

compost respectively substitutes for artificial fertiliser, offsetting 84 kg CO2-e per t compost.



5 Results

The results of the study are expressed using several measures. Firstly we present the results

using mid-range values and a standard 100 year assessment timeframe. Second, we examine

the sensitivity and uncertainty associated with the results over this same timeframe. Thirdly, we

present the results using 20 year (time sensitive) and 500 year (instantaneous emissions)

approaches.

The results below are mostly expressed graphically. Tabulations of key results are given in

Appendix F.

In all scenarios and all assessment approaches, the models found net negative emissions. That

is, all the waste management scenarios result in the net removal of carbon dioxide equivalent

from the atmosphere. In this analysis of the results, therefore, we refer to savings rather than

emissions.

The scenarios are described in Table 4 on page 11.

None of the charts given below should be used in isolation to represent the full findings of the

study37

.

5.1 Results over the standard assessment timeframe

We undertook an initial comparison between the results of the two types of assessment over the

standard 100 year timeframe i.e. the time sensitive and instantaneous emissions models. Recall

that the instantaneous model assumes that emissions from Wollert landfill occur in a pulse in

the year of deposition, whereas the time sensitive model takes into account the reality of

gradual emission over several decades. Similarly, the instantaneous emissions model counts

carbon storage in compost at the end of the 100 year period whereas the time sensitive model

takes into account the extent of storage across the century. The initial comparison found larger

CO2-e savings in the time sensitive assessment for all scenarios, mainly associated with the

delayed emissions from landfill. However, the overall relativities between the scenarios are

similar (see Appendix F). We therefore elect to analyse results for the 100 year assessment

period using the instantaneous emissions model only.

The results of the 100 year instantaneous emissions model using mid-range parameter values

are shown in Figure 9. An explanation of the emission categories presented in the chart is given

in the table below the figure. The base case and Scenarios 1, 3a and 3b produce similar

outcomes, each resulting in net savings of between 66 and 72kt CO2-e from the treatment of the

2008 waste streams under assessment. Aerobic MBT has lower net savings estimated at 33kt

CO2-e 38

. The savings mostly arise from carbon storage (in the landfill and compost) and

product offsets (metals, plastics and especially electricity). The savings outweigh the emissions,

which are mostly methane from landfill and process inputs. Emissions from transport and non-

landfill degradation processes are small. There is negligible difference between Scenarios 3a

and 3b, indicating that when food waste is separated it makes little difference from a carbon

perspective whether the residual garbage is collected weekly or fortnightly.

37 A sound understanding of the results requires consideration of the issues raised in sections 1.3 and 2.3.5, namely:

emission flux uncertainty, the assessment timeframe for comparing different greenhouse gases, and time sensitivity. 38

MBT technologies were assessed as if they always operate correctly. This is not realistic since, as with all mechanical processes, there is scheduled and unscheduled downtime (breakdowns). However, downtime is not critical for this comparative assessment. During downtime waste may be diverted to landfill. Assuming landfill performance is similar across the scenarios, this would slightly reduce any performance gap between the MBT and landfill scenarios.



Figure 9 LCA results using mid-range parameter values assessed using a 100 year

instantaneous emission basis

Table 17 Explanation of the emission categories presented in Figure 9

Emission category Includes emissions discussed in ...

Methane from landfill Sections 3.2 to 3.5

Emissions from other organic degradation processes Section 4.6.1

Transport Section 4.2

Process inputs Sections 3.6.3, 4.3, 4.4 (energy consumption)

Product offsets (electricity and recyclables) Sections 3.6.2, 4.4 (energy yield), 4.5, 4.6.3

Carbon storage Sections 3.6.1, 4.6.2

5.2 Uncertainty and sensitivity analyses

The discussion in chapters three and four illustrated that there is a high degree of uncertainty

associated with many of the model parameters. This is not reflected in the results shown in

Figure 9.

We use two techniques to assess the results taking uncertainty into account:

1 Uncertainty analysis – this involves running the model many times with the values for

each parameter chosen at random from the available range based on the pre-set

probability distribution (as specified in Appendix D). This method is referred to as ‘Monte

Carlo’ analysis.

2 Sensitivity analysis – this involves choosing different values from the range of those

possible for particular parameters. The parameters selected are those expected to

strongly affect the results when different values are selected.

For this study, sensitivity analysis was undertaken on two critical factors: the value rate of

methane recovery from landfill (R); and the net energy yield of anaerobic MBTs. Uncertainty

analysis was undertaken in relation to all other model parameters by applying the uncertainty

ranges listed in Appendix D. We report on the uncertainty analysis first.

-150

-100

-50

0

50

100

Wollert landfill

1 - Aerobic MBT

2 -Anaerobic

MBT



weekly garbage

Gre

en

ho

use

ga

s e

mis

sio

ns (

kt C

O2-e

)

Methane from landfill

Emissions from other organic degradation processes

Transport

Process inputs

Product offsets (electricity, recyclables & compost)

Carbon storage

Net emissions



5.2.1 Uncertainty analysis

Monte Carlo analyses were run on the LCA data shown in Figure 9. The uncertainty analysis

focused on comparing the base case (involving disposal of waste at Wollert landfill) with each of

the other scenarios. The two factors to be assessed via sensitivity analysis were fixed at their

mid-range value. All other parameters were allowed to vary across their range using the

assigned probability values. The results are summarised in Table 18, in the form of the

assessed probability that the greenhouse performance of the base case (landfill) exceeds that

of each scenario.

Table 18 Uncertainty analysis (100 year instantaneous emissions; Wollert methane recovery

rate (R) set at 74%; net electricity yield from anaerobic MBT set at 25 kWh/t)

Probability that greenhouse performance of the base case (Wollert landfill) is better than scenario

1 - Aerobic MBT 98%

2 - Anaerobic MBT 57%

3a - Food separation, fortnightly garbage collection 100%

3b - Food separation, weekly garbage collection 100%

The table shows that when our mid-range values for R and MBT energy yield are applied, then

the performance of the base case (Wollert landfill) certainly exceeds that of the food separation

scenarios (3a and 3b) and almost certainly exceeds the performance of the aerobic MBT. The

probability of the base case outperforming anaerobic MBT is slightly better than even.

5.2.2 Sensitivity analyses

The uncertainty analysis was undertaken with two critical factors held constant in the middle of

their range. These were the methane recovery rate from Wollert landfill (R) and the net energy

yield of anaerobic MBTs. We undertook sensitivity analysis to investigate the impact of high and

low values for these critical parameters. These were carried out separately with all other

parameters held constant in the middle of their range.

Sensitivity of the results to the rate of methane recovery from Wollert landfill (R)

Recall that we set the uncertainty range for R at 60% to 88% (see section 3.5.7). The results

when R is high (88%), mid-range (74%) and low (60%) are illustrated in Figure 10. At the high

end of the range for R, Wollert landfill is the best performing scenario. At the low end it

outperforms only aerobic MBT. In addition to having a marked impact on the base case, the

value for R strongly affects the greenhouse performance of Scenarios 3a and 3b. This is

because in these scenarios large quantities of organic waste remain in the residual bin that is

sent to landfill.



Figure 10 Results with high (88%), mid (74%) and low (60%) range methane recovery rates

(using mid-range values for all other parameters, and with assessment using a 100

year instantaneous emissions basis)

Sensitivity of the results to the net energy yield of anaerobic MBTs

The uncertainty range for the net energy yield from anaerobic MBT extended from 0 to 68 kWh

per tonne of throughput (see section 4.4). The results when this value is high (68), typical (25)

and low (zero) are illustrated in Figure 11. Only Scenario 2 is affected by this sensitivity analysis

so the results for the other scenarios are shown only once in the figure. At the high end of the

range, anaerobic MBT is the best scenario but at the low end it performs worse than the base

case and Scenarios 3a and 3b.

Figure 11 LCA results with high, medium and low range values for the net energy yield of

anaerobic MBTs (using mid-range values for all other parameters, and with

assessment using a 100 year instantaneous emissions basis)

-120

-100

-80

-60

-40

-20

0

High Mid-range Low

Gre

en

ho

use

ga

s e

mis

sio

ns

(kt

CO

2-e

)

Wollert landfill

1 - Aerobic MBT

2 - Anaerobic MBT

3a - Food separation,

fortnightly garbage


weekly garbage

-100

-80

-60

-40

-20

0

Low Mid-range High

Gre

en

ho

use

ga

s e

mis

sio

ns

(kt

CO

2-e

)

Wollert landfill

1 - Aerobic MBT

2 - Anaerobic MBT


fortnightly garbage


weekly garbage



5.3 Results when other assessment timeframes are applied

Two additional model runs were produced, both using mid-range values but with different

approaches in relation to time. These were:

� time sensitive model that takes into account emissions and compares warming effects

over a 20 year period

� instantaneous emissions model that takes into account all emissions and compares

warming effects over a 500 year period.

These results are presented in Figure 12. When only warming effects until 2027 are considered

important, then the Wollert landfill scenario performs worst and each of the other scenarios are

relatively equal. At the other extreme, when warming effects in each of the next 500 years are

considered equally important, then Wollert landfill is the best performing scenario and the MBT

technologies are worst. The differences are mainly due to the reduced warming effect of

methane compared with CO2 as the assessment timeframe increases. In addition, in the 20 year

assessment timeframe much of the carbon in compost has not yet degraded and so is counted

as stored.

Figure 12 LCA results using different approaches in relation to time (mid-range parameter

values)

-125

-100

-75

-50

-25

0

Time sensitive, 20

years

Instantaneous

emissions, 100

years

Instantaneous

emissions, 500

years

Gre

en

ho

use

ga

s e

mis

sio

ns

(kt

CO

2-e

)

Wollert landfill

1 - Aerobic MBT

2 - Anaerobic MBT


fortnightly garbage


weekly garbage



6 Discussion and conclusions

This study examined several waste management scenarios from a life cycle greenhouse gas

perspective. Greenhouse gas implications are an increasingly important criterion for choosing

between public policy options.

While there was no clear ‘winner’ amongst the scenarios and technologies assessed, all of them

– including the current base case involving disposal at Wollert landfill – resulted in net savings in

greenhouse gas emissions. The key elements that determined the extent of the savings from

each scenario were landfill methane emissions, carbon storage and recovery of energy.

Wollert landfill’s greenhouse performance depends critically on its methane recovery rate. We

were unable to verify the effectiveness of its methane management with a high level of certainty

but, across the range of assumed recovery rate values, the greenhouse gas benefits of energy

recovery and carbon storage exceeded the costs of methane emissions (using the standard

assessment timeframe and mid-range values for all other parameters).

Landfill gas recovery is maximised when gas collection equipment is installed early, the gas well

network is dense, blocked wells and pipes are rapidly repaired, landfill cap construction at the

cell edges is sound, the cap integrity is closely monitored and problems are quickly rectified.

Greenhouse policy is likely to encourage actions of this type. HLS notes that it has recently

purchased a meter for measuring methane concentrations to more quickly identify fugitive

emissions, and that LMS has committed to drill 20 new vertical wells and to install horizontal

wells under a new geo-membrane apron to be constructed along cell edges.

Uncertainties and complexities in the modelling mean that comparing scenarios results is not

straightforward. Like most other LCAs of waste management options, this study is suited to

providing input to decisions rather than demonstrating technological superiority. Nevertheless

some conclusions can be drawn:

� Best practice landfill with good performance management is a potentially sound option

from a greenhouse gas management perspective. It will be important for the landfill

industry to demonstrate that its methane management is of a high standard. At the low

end of the assumed methane recovery range the landfill base case performed worse than

most other scenarios.

� Anaerobic MBT appears to be a better greenhouse option than aerobic MBT. This

suggests that from a greenhouse gas perspective it is better to focus on maximising

energy recovery from biological material rather than to generate stabilised organic

products. The low-range estimate of the net electricity output of Wollert landfill (around

150 kWh/t) greatly exceeded the high-range estimate for anaerobic MBT (68 kWh/t).

� Diversion of food organics to the compost stream has a similar performance (but usually

slightly worse) to disposal at Wollert landfill, from a greenhouse gas perspective. In the

modelling, enclosed composting out-performed Wollert landfill only where the methane

recovery rate was at the low end of the estimated range and where warming impacts

were considered over shorter timeframes.

� When food is diverted it makes little difference from a greenhouse perspective whether

garbage is collected weekly or fortnightly.



The standard 100 year assessment timeframe assumes that warming effects are equally

important in each year for the next century and not important thereafter. When this assumption

is varied the results change significantly. When emphasis is given to warming effects in the near

future, landfill performance worsens. When emphasis is given to greenhouse gas pollution over

centuries, landfill performance is better than that of MBT facilities or enclosed composting with

food separation.

Finally, it is worth considering why some of this study’s findings are more favourable to landfill

than those of many other Australian studies, including some reviewed in this report. There is no

single answer to this question but, importantly, our estimated rate of recovery of methane from

landfill is site specific and higher than historical data. We apply a relatively low benefit from the

use of compost and stabilised organics. Unlike some studies, we count carbon remaining in

landfill as a benefit.



7 References

ACOR (Australian Council of Recyclers 2008) Australian Recycling Values – A Net Benefits

Assessment. Prepared by Hyder Consulting, Sydney, January.

Amlinger F, Peyr S and Cuhls C (2008) Green house gas emissions from composting and

mechanical biological treatment. Waste Management and Research, Issue 26, pp. 47-60

AS/NZS ISO 14040:1998 Environmental management – Life cycle assessment – Principles and

framework. Standards Australia and Standards New, Homebush.

AS/NSZ ISO 14041:2001 Environmental management – Life cycle assessment – Goal and

scope definition and inventory analysis. Standards Australia, Homebush.

AS/NSZ ISO 14042: 2001 Environmental management – Life cycle assessment – Life cycle

impact assessment. Standards Australia, Homebush.

AS/NSZ ISO 14043:2001 Environmental management – Life cycle assessment – life cycle

interpretation. Standards Australia, Homebush.

Barlaz MA, Eleazer WE, Odle WS, Qian X and Wang Y-S (1997) Biodegradative Analysis of

Municipal Solid Waste in Laboratory-Scale Landfills. National Risk Management Research

Laboratory, September, pp. 1-6

Barlaz MA (1998) Carbon storage during biodegradation of municipal solid waste components in

laboratory-scale landfills. Global Biogeochemical Cycles, Volume 12, pp. 373-380

Barlaz MA (2004) Critical Review of Forest products Decomposition in Municipal Solid Waste

Landfills. National Council for Air and Stream Improvement, Bulletin 872

Barlaz MA (2006) Forest products decomposition in municipal solid waste landfills. Waste

Management, Issue 26, pp. 321-333

Barton PK and Atwater JW (2002) Nitrous Oxide Emissions and the Anthropogenic Nitrogen in

Wastewater and Solid Waste. Journal of Environmental Engineering, February, pp. 137-150

Berger J, Fornés LV, Ott C, Jager J, Wawra B and Zanke U (2005) Methane oxidation in a

landfill cover with capillary barrier. Waste Management, Issue 25, pp. 369-373

Bingemer HG and Crutzen PJ (1987) The production of methane from solid wastes. Journal of

Geophysical Research, Volume 92, pp. 2181-2187

Bogner J, Pipatti R, Hashimoto S, Diaz C, Mareckova K, Diaz L, Kjeldesn P, Monni S, Faaji A,

Gao Q, Zhang T, Ahmed MA, Sutamihardja TRM and Gregory R (2008) Mitigation of Global

Greenhouse Gas Emissions from Waste: Conclusions and Strategies from the

Intergovernmental Panel in Climate Change (IPCC) Fourth Assessment Report. Working Group

III (Mitigation). Waste Management and Research, Issue 26, pp. 11-32

Bogner J, landfill academic, personal communication, presentation to the Waste Management

Association of Victoria, Melbourne, 16 July 2009)

Bramyrd T, Johansson M and Tham G (2007) The Role of Landfills as Accumulators of Organic

Carbon to Compensate for CO2 Emissions to the Atmosphere. Eleventh International Waste

Management and Landfill Symposium, October.

Bridgewater E and Parfitt J (2009) Evaluation of the WRAP Separate Food Waste Collection

Trials, Final Report – Updated June 2009. Prepared for WRAP UK.

Brown P, Cottrell A, Wibberley L and Scaife P (2006) A Life Cycle Assessment of the Victorian

Electricity Grid. Technology Assessment Report 57, Cooperative Research Centre for Coal in

Sustainable Development, QCAT Technology Transfer Centre, Pullenvale, Queensland,

October.



BSI (British Standards Institution 2008) Specification for the Assessment of the Life Cycle

Greenhouse Gas Emissions of Goods and Services. Publicly Available Specification PAS

2050:2008, prepared by Grahams Sinden, October.

Centre for Design at RMIT University (2001) Stage 2 Report for Life Cycle Assessment for

Paper and Packaging Waste Management Scenarios in Victoria. Prepared for EcoRecycle

Victoria, January.

Centre for Design at RMIT University and Nolan-ITU (2003) Life Cycle Assessment of Waste

and Resource Recovery Options (Including Energy from Waste). Prepared for EcoRecycle

Victoria, Final Report, Version 1- 19 April

Centre for Design at RMIT University (2007) LCA of Waste Strategy Options. Appendix to a

2007 report prepared by Hyder Consulting for Sustainability Victoria titled 'Modelling and

analysis of options for the metropolitan waste and resource recovery strategic plan', Version

4.1, December.

Chanton JP, Powelson DK and Green RB (2009) Methane oxidation in landfill cover soils; is a

10% default value reasonable? Journal of Environmental Quality, Volume 38, pp. 654-663

DCC (Department of Climate Change 2007) Technical Guidelines for the Estimation of

Greenhouse Emissions and Energy at Facility Level - Energy, Industrial Process and Waste

Sectors in Australia, Discussion Paper.

DCC (Department of Climate Change 2009a) National Greenhouse Accounts (NGA) Factors.

DCC (Department of Climate Change 2009b) National Greenhouse and Energy Reporting

System Measurement - Technical Guidelines for the Estimation of Greenhouse Gas Emissions

by Facilities in Australia.

Dessus B and Laponche B (2008) Reducing Methane Emissions: The Other Climate Change

Challenge. Working Paper 68, Agence Française de Dévelopment, August.

De Gioannis G, Muntoni A, Cappai G and Milia S (2008) Landfill gas generation after

mechanical biological treatment of municipal solid waste. Estimation of gas generation rate

constants. Waste Management, Issue 29, pp. 1026-1034

Dever S and Swarbrick G (2008) How to Achieve Sustainable Management of Landfill Gas in

Australia. Paper presented at Enviro08, Southbank, Melbourne.

Dever S, environmental consultant, personal communication. Email headed Response to Hyder

and dated 9 October forwarded by HLS on 12 October 2009.

EcoRecycle Victoria (2005a) Disposal Based Waste Survey. Prepared by WasteAudit and

Golder Associates, June.

EcoRecycle Victoria (2005b) Disposal Based Waste Survey - Site Report, Hanson Wollert.

Prepared by WasteAudit and Golder Associates, June.

Eklind Y, Sundberg C, Smars S, Steger K, Sundh I, Kirchmann H and Johnsson H (2007)

Carbon turnover and ammonia emissions during composting of biowaste at different

temperatures. Journal of Environmental Quality 36 pp. 1512-1520.

EnviroCom Australia (2006a) City of Banyule Kerbside Domestic Waste Stream Audit.

EnviroCom Australia (2006b) City of Darebin Kerbside Domestic Waste Stream Audit.

EnviroCom Australia (2006c) Hume City Council Kerbside Domestic Waste Stream Audit.

EnviroCom Australia (2006d) Moreland City Council Kerbside Domestic Waste Stream Audit.



Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywood J, Lean J, Lowe

DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M and Van Dorland R (2007) Changes in

Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical

Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis

M, Avery KB, Tignor M and Miller HL (eds.)]. Cambridge University Press, Cambridge, United

Kingdom and New York, NY, USA.

Gardner WD, Ximenes F, Cowie A, Marchant JF, Mann S and Dods K (2002) Decomposition of

Wood Products in the Lucas Heights Landfill Facility. 3rd Australian Conference on LCA, July.

GHD (2009) Report for Hanson Landfill Services: Flux Testing at Wollert Landfill. January.

Gibson TS, Chan KY, Sharma G and Shearman R (2002) Soil Carbon Sequestration Utilising

Recycled Organics: A Review of the Scientific Literature. Project 00/01R-3.2.6A, Prepared by

the Organic Waste Recycling Unit of NSW Agriculture for Resource NSW, August.

Gómez KE, Gonzalez-Gil G, Lazzaro A and Schroth MH (2009) Quantifying methane oxidation

in a landfill-cover soil by gas push-pull tests. Waste Management, Issue 29, pp. 2158-2526

Hansen J and Sato M (2007) Global Warming: East-West Connections. NASA Goddard Institute

for Space Studies and Columbia University Earth Institute.

Hellebrand HJ (1997) Emission of Nitrous Oxide and other Trace Gases during Composting of

Grass and Green Waste. Journal of Agricultural Engineering Research, Issue 69, pp. 365-375.

Hogg D, Baddeley A, Gibbs A, North J, Curry R and Maguire C (2008) Greenhouse Gas

Balances of Waste Management Scenarios - Report for the Greater London Authority. Prepared

by Eunomia and EnviroCentre, January.

Hyder Consulting (2008) Greenhouse Emissions from Waste Management at Three Melbourne

Municipalities. Prepared for the Metropolitan Waste Management Group.

IPCC (Intergovernmental Panel on Climate Change) 2006 IPCC Guidelines for National

Greenhouse Gas Inventories, Volume 5, Waste.

Inman M (2008) Carbon is forever. Nature Reports: Climate Change. Published online 20.11.09

at www.nature.com.

Jensen JEF and Pipatti R (2002) CH4 Emissions from Solid Waste Disposal. Background paper,

good practice guidance and uncertainty management in national greenhouse gas inventories,

pp. 419-439.

Lou XF and Nair J (2009) The impact of landfilling and composting on greenhouse gas

emissions - A review. Bioresource Technology, Issue 100, pp. 3792-3798

Manfredi S, Tonini D, Christensen TH (2009) Landfilling of waste: accounting for greenhouse

gases and global warming contributions. Waste Management & Research published online 6

October.

Micales JA and Skog KE (1996) The decomposition of forest products in landfills. International

Biodeterioration and Biodegradation, Volume 39, pp. 145-158

Nolan-ITU (2004), National Benefits of Implementation of UR-3R Process, A Triple Bottom Line

Assessment. Prepared for Global Renewables, July.

Park S, Lee CH, Ryu CR and Sung K (2007) Biofiltration for reducing methane emissions from

modern sanitary landfills at the low methane generation stage. Water Air Soil Pollution, Issue

196, pp. 19-27

Partl H, environmental consultant, personal communication, 26 September 2009a.



Partl H, environmental consultant, personal communication, 26 September 2009b, translating

from a German language publication: Cuhls C and Clemens J (2005) Suitability of Biofilters for

Waste Gas Purification. Proceedings of International Symposium MBT, Hanover, November.

Pittle L, Nillumbik Shire Council, personal communication. Phone conversation, 28 August 2009.

Reilly JM, Jacoby HD and Prinn RG (2003) Multi-Gas Contributors to Global Climate Change:

Climate Impacts and Mitigation Costs of Non-CO2 Gases. Prepared by Massachusetts Institute

of Technology for the Pew Center on Global Climate Change, February.

Rinne J, Pihlatie M, Lohila A, Thum T, Aurela M, Tuovinen JP, Laurila T and Vesala T (2005)

Nitrous Oxide Emissions from a Municipal Landfill. Environmental Science and Technology,

Volume 39, pp. 7790-7793

ROU (Recycled Organics Unit 2001) Greenhouse Gas Emissions from Composting Facilities.

Prepared for NSW Waste Boards, 27 September.

ROU (Recycled Organics Unit 2003) Life Cycle Inventory and Life Cycle Assessment for

Windrow Composting Systems. Prepared for NSW Department of Environment and

Conservation, October.

Savanne D, Cassini P, Pokryszka Z, Tauziede C, Tregoures A, Berne P, Sabroux JC, Cellier P

and Laville P (1995), A Comparison of Methods for Estimating Methane Emissions from MSW

Landfills. Fifth International Landfill Symposium, October.

Scharff H and Jacobs J (2006) Applying guidance for methane emission estimation for landfills.

Waste Management, Issue 26, pp. 417-429

Schuetz C, Bogner J, Chanton J, Blake D, Morcet M and Kjeldsen P (2003) Comparative

oxidation and net emissions of methane and selected non-methane organic compounds in

landfill cover soils. Environmental Science and Technology, Volume 37, pp. 5150-5158

Shackley S and Wynee B (1997) Global Warming Potentials: ambiguity or precision as an aid to

policy? Climate Research Vol. 8 pp.89-106.

Singh, SP, Chonhenchob, V & Singh, J. (2006) Life Cycle Inventory and Analysis of Re-usable

Plastic Containers and Display-ready Corrugated Containers Used for Packaging Fresh Fruits

and Vegetables. Packaging Technology Science 2006; 19: 279-293.

Smith A, Brown K, Bates J, Ogilvie S and Rushton K (2001) Waste Management Options and

Climate Change. Prepared by Prepared by AEA Technology for the European Commission, DG

Environment. Abingdon UK.

Spokas K, Bogner J, Chanton JP, Morcet M, Aran C, Graff C, Moreau-Le Golvan Y and Hebe I

(2005) Methane mass balance at three landfill sites: What is the efficiency of capture by gas

collection systems? Waste Management, Issue 26, pp. 516-525

Standards Australia (2009) Stationary source emissions - Area source sampling - Flux chamber

technique. AS/NZS 4323.4:2009.

Streese J and Stegmann R (2003) Microbial oxidation of methane from old landfill sin biofilters.

Waste Management, Issue 23, pp. 573-580

Themelis NJ and Ulloa PA (2006) Methane generation ain landfills. Renewable Energy, Issue

32, pp. 1243-1257

Thompson S, Sawyer J, Bonam R and Valdivia JE (2009) Building a better methane generation

model: Validation models with methane recovery rates from 35 Canadian landfills. Waste

Management, Issue 29, pp. 2085-2091



Trégourès A, Beneito A, Berne P, Gonze MA, Sabroux JC, Savanne D, Pokryszka Z, Tauziède

C, Cellier P, Laville P, Milward R, Arnaud A, Levy F and Burkhalter R (1999) Comparison of

seven methods for measuring methane flux at a municipal solid waste landfill site. Waste

Management and Research, issue 17, pp. 453-458

UK Environment Agency (2004) Guidance on Monitoring landfill Gas Surface Emissions.

September.

US EPA (US Environmental Protection Agency 2006) Greenhouse Gas Emissions from

Management of Selected Materials in Municipal Solid Waste.

Warnken Ise (2007) Potential for Greenhouse Gas Abatement from Waste Management and

Resource Recovery Activities in Australia. Prepared for SITA Environmental Solutions,

September.

Wastemin (2006a) Nillumbik Shire Council Domestic Organics, Recycling and Residual Waste

Audit.

Wastemin (2006b) City of Whittlesea Domestic Organics, Recycling and Residual Waste Audit.

Weitz KA, Thorneloe SA, Nishtala SR, Yarkosky S and Zannes M (2002) The impact of

municipal solid waste management on greenhouse gas emission in the United States. Journal

of the Air and Waste Management Association, September, pp. 1000-1012



Appendix A

Peer review and responses

Note: This peer review was undertaken prior to the issue of the draft report to the client. The document has

changed significantly since the review but the general methods and most data are unchanged.









Responses to draft peer review

Email from Joe Pickin to Hannes Partl, 13 October 2009

Dear Hannes, thank you for your peer review of our preliminary draft report 'Comparative

Greenhouse Life Cycle Assessment of Wollert Landfill'. This email summarises Hyder's

response to your review. The responses are given below in dot points that correspond to

headings within your draft review report.

� General. No response needed.

� Scenarios selection. Agree. No further response needed.

� Emissions over time. Thank you for the guidance on how to express emissions over time.

Our existing approach weights emissions according to how long they are present in the

atmosphere within the assessment period. This is not discounting in the typical economic

sense because we place the same value on warming impacts no matter when they occur

within the assessment period (but, in line with the standard IPCC approach, we place no

value on that warming that occurs outside of the assessment period). I accept that use

of multiple assessment periods complicates matters but do not agree that this does

not 'assist in understanding the results or providing a basis for decision making'.

However, I accept that we did not make an adequate case for why it is worth using

multiple assessment periods. In response to your comments we intend to (a) revise the

report to provide such a case and (b) give primacy to the assessment over the 100 year

assessment period.

� Inclusion of biogenic carbon. Thank you for the reference on N2O emissions from landfills

- I will include reference to this but do not intend to change the assumption of zero

emissions.

� GHG impact of landfills. I understand that the GHD gas emission survey included areas of

the site where waste was deposited but not capped and areas under temporary cover but

not yet connected to the gas collection system. Consequently we consider that pre-

collection emissions to be encompassed within the extrapolations from that survey. We

will refer in the text to the anecdotal evidence of collection efficiencies reducing over time,

but note that when methane generation rates decline the potential effects of this

phenomenon on emission rates would be counteracted by: (a) higher oxidation rates due

to lower fluxes through the cap and (b) the diminishing proportion of the total waste that is

uncapped as cells are filled.

� Assumptions on AWTs/MBTs. Thanks for the reference on carbon sequestration - I will

seek it out. We will increase the assumed N2O emissions from processing of waste

including food to be in line with the reference you provide. I will check the wording and

calculations in relation to the potential for confusion between landfill gas and methane.

� Results. No response needed.



Appendix B

An improved method of estimating warming effects of waste management scenarios over limited timeframes



An improved method of estimating warming effects of waste management scenarios over limited timeframes

A rationale for assessing warming impacts using multiple GWP assessment periods is set out in

Section 1.2. The method used takes into account the timeframe of emissions as well as the

assessment period. This was introduced in Section 2.3.5 and is described more fully here.

The methane generated from a unit of waste in a landfill does not occur in a pulse but is emitted over

many years. Most LCAs compare these emissions using the standard 100 year assessment period.

This is logically incorrect. If we care only about warming effects for 100 years from today then, for

emissions that arise in say 40 years, we should care about only 60 years of warming effects.

Similarly, carbon is stored in compost and stabilised organics and gradually released over many

years after deposition. The greenhouse gas benefit of this storage is real but is partially overlooked if

we are using the standard 100 year assessment timeframe. The standard approach would attribute a

benefit only to carbon that remains stored after 100 years. This effectively assumes that, over the

100 year period that we care about, the warming effects of carbon dioxide emitted in the first year

and the 99th year are identical. This clearly cannot be.

In this study we pioneer an approach for assessing warming effects to a particular date taking into

account the different timeframe over which emissions occur. We estimate warming effects from the

year of deposition (2008) to 2027, to 2047 and to 2107. Due to data limitations we restrict our

application of this approach to the relevant major greenhouse gas fluxes – i.e. methane emissions

from landfill and storage of carbon in compost. We ignore time issues associated with less significant

life cycle elements such as minor emissions of methane and nitrous oxide and time delays in green

energy offsets.

For our revised approach we would ideally use data on the annual warming effects of methane and

carbon dioxide during their atmospheric life. This data was not available to the project team. Instead

we apply data on global warming potentials over different time periods. The IPCC (Forster et al 2007)

publishes the GWP of methane over only 20, 100 and 500 year timeframes but Dessus and

Laponche (2008) apply the IPCC calculation method to derive a GWP values over a range of

timeframes as shown in the table below. These calculations can be readily extended to approximate

GWPs on an annual basis.

Table 19 Global warming potential values for methane over a range of timeframes

Source: Dessus and Laponche (2008 p.46)

Timeframe 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

GWP 101 90 80 72 64 58 53 49 45 42 39 37 35 33 31 30 28 27 26 25

This data set can be used to estimate total warming effects over a given time period by assuming

that the annual warming effect of carbon dioxide is constant over the assessment periods under

consideration. The error in applying this assumption is small since a) the average atmospheric

residency time of carbon dioxide is considerably longer than the assessment timeframes used and b)

most emissions arise early in the assessment period. We subtract one unit from Dessus and

Laponche’s GWP values to account for the biogenic nature of the carbon dioxide to which methane is

oxidised in the atmosphere.

The total warming effect (WEx) of the emissions between 2008 and the final year (X) relative to a pulse of equal mass of CO2

emitted in 2008 can be estimated from the following algorithm.

WEx ≈ ∑−=

=

1

1

Xn

n

Emn * (GWPX-n-1) * (X-n)/X

Where Emn = the estimated emissions in year n

GWPn = the estimated global warming potential of methane in year n.



Appendix C

Uncertainty estimates – description of applied distribution types



Uncertainty estimates – description of applied distribution types

Distribution type Description Graphical representation

Lognormal The most commonly applied distribution for the

modelling undertaken in this study. This

distribution is determined by an estimated

standard deviation and a best estimate most

likely value. The distribution that most closely

approximates natural systems.

Triangular This distribution returns a value covering a range

specified by a minimum and a maximum, the

shape of the distribution is then determined by

the size of the most likely value relative to the

minimum and maximum, and is not necessarily

symmetrical as for the example shown.

Uniform (range) This distribution is applied where there is an

equal probability that a value lies between a

minimum and a maximum value.



Appendix D

Summary of the parameter values and uncertainty distributions used in the model



Summary of the parameter values and uncertainty distributions used in the model

Name Value Distribution SD^2 Low High Comment

1 Area00_EC 10000 Lognormal 1.3 0 0 NoEstimated area of the enclosed composting facility for the base case, and scenarios 1 &

2 (unit is m2). Assumed pedigree matrix is (4,5,1,1,1,na).

2 Area01_MBT1 35000 Lognormal 1.3 0 0 NoEstimated area of the aerobic MBT for scenario 1 (unit is m2). Assumed pedigree matrix

is (4,5,1,1,1,na).

3 Area02_MBT2 35000 Lognormal 1.3 0 0 NoEstimated area of the anaerobic MBT for scenario 2 (unit is m2). Assumed pedigree

matrix is (4,5,1,1,1,na).

4 Area03a_EC 22000 Lognormal 1.3 0 0 NoEstimated area of the enclosed composting facility for scenario 3a (unit is m2). Assumed

pedigree matrix is (4,5,1,1,1,na).

5 Area03b_EC 20000 Lognormal 1.3 0 0 NoEstimated area of the enclosed composting facility for scenario 3b (unit is m2). Assumed

pedigree matrix is (4,5,1,1,1,na).

6 CH4_EnContent 55.5 Uniform 0 55.5 55.5 No Energy content of methane (unit is MJ/kg).

7 Dist_EC_Use 20 Lognormal 1.5 0 0 NoDistance from enclosed composting facility to location of application of compost (unit is

km). Assumed pedigree matrix is (5,1,1,1,1,na)

8 Dist_Gar_0 103980 Lognormal 1.05 0 0 NoCollection and transfer truck hours for the garbage collection for scenario 0, 1 and 2 (unit

is hours of truck operation/year). Pedigree matrix is (1,1,1,1,1,1).

9 Dist_Gar_3a 57046 Lognormal 1.05 0 0 No

Collection and transfer truck hours for the garbage collection for scenario 3a, for all

councils - fortnightly collection (unit is hours of truck operation/year). Pedigree matrix is

(1,1,1,1,1,1).

10 Dist_Gar_3b 98164 Lognormal 1.05 0 0 No

Collection and transfer truck hours for the garbage collection for scenario 3b, for all

councils - weekly collection (unit is hours of truck operation/year). Pedigree matrix is

(1,1,1,1,1,1).

11 Dist_Org_0 27197 Lognormal 1.05 0 0 No

Collection and transfer truck hours for the organics collection for scenarios 0, 1 and 2,

also including Nillumbik food collection truck hours (unit is hours of truck operation/year).

Pedigree matrix is (1,1,1,1,1,1).

12 Dist_Org_3a 87482 Lognormal 1.05 0 0 No

Recovery rate of steel at the landfill face (45% of total of 1,831.5 tonnes, rest recovered at

transfer station, which is excluded), also excludes steel in C&D (42.1%), (unit is kg

recovered in 2008-09).

13 Dist_Org_3b 86356 Lognormal 1.05 0 0 NoCollection and transfer truck hours for the organics collection for scenario 3b, for all

councils (unit is hours of truck operation/year). Pedigree matrix is (1,1,1,1,1,1).

14 Div_Food 0.55 Lognormal 1.3 0 0 NoDiversion rate of food from garbage to organics collection, for scenarios 3a & 3b (unit is a

proportion). Estimated pedigree matrix is (4,5,1,1,1,1).

15 EC_Carbon 0.4 Lognormal 1.3 0 0 NoAssumed proportion of dry compost product that is carbon (unit is a proportion).

Assumed pedigree matrix is (5,5,na,na,na,na).

16 EC_Comp_3a 0.437 Lognormal 1.24 0 0 NoIncremental quantity of compost in scenario 3a that has an applied fertiliser offset value

(unit is a proportion). Assumed pedigree matrix is (3,1,1,1,1,5).

17 EC_Comp_3b 0.391 Lognormal 1.24 0 0 NoIncremental quantity of compost in scenario 3b that has an applied fertiliser offset value

(unit is a proportion). Assumed pedigree matrix is (3,1,1,1,1,5).

18 EC_Emis_CH4 0.00003 Triangle 0 0 0.00006 NoQuantity of CH4 emitted during the composting of 1 kg of incoming organic material, in

enclosed composting (unit is kg CH4/kg of organic waste).

19 EC_Emis_N2O 0.00006 Triangle 0 0 0.00012 NoQuantity of N2O emitted during the composting of 1 kg of incoming organic material, in

enclosed composting (unit is kg N2O/kg of organic waste).

20 EC_In_Dies_lit 0.001 Lognormal 1.23 0 0 No

Volume of diesel fuel consumed by operations to process 1 kg of incoming organics, in

enclosed composting (unit is litres of diesel/kg of incoming organics). Assumed pedigree

matrix is (4,3,2,2,1,3).

21 EC_In_Elec 0.05 Lognormal 1.24 0 0 No

Quantity of electricity consumed by operations to process 1 kg of incoming organics, in

enclosed composting (unit is kWh/kg of incoming organics). Assumed pedigree matrix is

(4,1,1,3,1,4).

22 EC_Out_Com 0.57 Lognormal 1.21 0 0 No

Quantity of compost produced from 1 kg of incoming organics (unit is kg compost/kg of

incoming organics). Based upon a 60% conversion mass of compost of the 95% of

incoming material that is recovered. Assumed pedigree matrix is (4,1,1,1,1,na).

23 EC_Out_LF 0.05 Lognormal 1.3 0 0 NoQuantity of residual waste from enclosed composting per 1 kg of incoming organics (unit

is kg residual waste/kg incoming organics). Assumed pedigree matrix is (4,5,1,1,na,na).

24 EC_Trans_Comp 20 Triangle 0 10 30 NoTransport distance of compost product from enclosed composting operations, assumed

to be an average of 20 km, which is consistent with the SV modelling (unit is km).

25 EC_Water 0.5 Uniform 0 0.5 0.5 No Assumed proportion of compost product that is water (unit is a proportion).

26 Gen_Dies_EC 38.6 Uniform 0 38.6 38.6 No General parameter - Energy content of diesel (unit is MJ/litre).

27 Hhs_Coun_Nill 19887 Uniform 0 19887 19887 NoNumber of serviced properties (residential + non-residential) in Nillumbik (unit is number of

households).

28 Hhs_Coun_Other 249428 Uniform 0 249428 249428 NoNumber of serviced properties (residential + non-residential) in councils other than

Nillumbik (unit is number of households).

29 LF_CH4_Vent 0.015 Uniform 0 0.01 0.02 No

Collected methane that is vented (directly to atmosphere) due to simultaneous turbine

and flare downtime (1% of the time), plus the small proportion of methane that is not

oxidised during burning through the gas turbines (0.5% of methane into the turbines) (unit

is proportion of collected methane).

30 LF_D00_Inert 0 Triangle 0 0 0 NoDegradable organic carbon (DOC) 'methane factor' for inert materials in landfill (unit is

proportion of material that is degradable carbon). This covers plastics, glass, steel and Al.

31 LF_D01_Food 0.15 Triangle 0 0.12 0.18 NoDegradable organic carbon (DOC) 'methane factor' for food in landfill (unit is proportion of

material that is degradable carbon).

32 LF_D02_PpCB 0.4 Triangle 0 0.32 0.48 NoDegradable organic carbon (DOC) 'methane factor' for paper & cardboard in landfill (unit is

proportion of material that is degradable carbon).

33 LF_D03_GWs 0.2 Triangle 0 0.16 0.24 NoDegradable organic carbon (DOC) 'methane factor' for soft garden waste in landfill (unit is


34 LF_D04_GWh 0.2 Triangle 0 0.16 0.24 NoDegradable organic carbon (DOC) 'methane factor' for hard garden waste in landfill (unit is


35 LF_D05_Nap 0.24 Triangle 0 0.19 0.29 NoDegradable organic carbon (DOC) 'methane factor' for disposable nappies in landfill (unit

is proportion of material that is degradable carbon).

36 LF_D14_Tmbr 0.43 Triangle 0 0.34 0.52 NoDegradable organic carbon (DOC) 'methane factor' for timber in landfill (unit is proportion

of material that is degradable carbon).

37 LF_D15_Tex 0.24 Triangle 0 0.19 0.29 NoDegradable organic carbon (DOC) 'methane factor' for textiles in landfill (unit is proportion

of material that is degradable carbon).

38 LF_D16_Rub 0.39 Triangle 0 0.31 0.47 NoDegradable organic carbon (DOC) 'methane factor' for rubber & leather in landfill (unit is


39 LF_D17_AlOt 0.1 Triangle 0 0.08 0.12 No

Degradable organic carbon (DOC) 'methane factor' for 'all other' materials in landfill (unit is

proportion of material that is degradable carbon). Uncertainty values estimated based

upon the typical uncertainty ranges for other materials.

40 LF_D18_EC 0.2 Triangle 0 0.15 0.25 NoDegradable organic carbon (DOC) 'methane factor' for enclosed composting residual

material in landfill (unit is proportion of material that is degradable carbon).

41 LF_D19_MBT 0.2 Triangle 0 0.15 0.25 NoDegradable organic carbon (DOC) 'methane factor' for ARRT (both aerobic and anaerobic)

residual material in landfill (unit is proportion of material that is degradable carbon).

42 LF_Df00_Inert 0 Triangle 0 0 0 NoDissimilable fraction (DOCf) 'methane factor' for inert materials in landfill (unit is proportion

of DOC that actually degrades in landfill). This covers plastics, glass, steel and Al.




43 LF_Df01_Food 0.795 Triangle 0 0.75 0.84 NoDissimilable fraction (DOCf) 'methane factor' for food in landfill (unit is proportion of DOC

that actually degrades in landfill).

44 LF_Df02_PpCB 0.4 Triangle 0 0.35 0.45 NoDissimilable fraction (DOCf) 'methane factor' for paper & cardboard in landfill (unit is

proportion of DOC that actually degrades in landfill).

45 LF_Df03_GWs 0.375 Triangle 0 0.25 0.5 NoDissimilable fraction (DOCf) 'methane factor' for soft garden waste in landfill (unit is


46 LF_Df04_GWh 0.375 Triangle 0 0.25 0.5 NoDissimilable fraction (DOCf) 'methane factor' for hard garden waste in landfill (unit is


47 LF_Df05_Nap 0.5 Triangle 0 0.4 0.6 NoDissimilable fraction (DOCf) 'methane factor' for disposable nappies in landfill (unit is


48 LF_Df14_Tmbr 0.2 Triangle 0 0.05 0.35 NoDissimilable fraction (DOCf) 'methane factor' for timber in landfill (unit is proportion of DOC

that actually degrades in landfill).

49 LF_Df15_Tex 0.3 Triangle 0 0.24 0.36 No

Dissimilable fraction (DOCf) 'methane factor' for textiles in landfill (unit is proportion of

DOC that actually degrades in landfill). **UNCERTAINTY RANGE ASSUMED TO BE +/-

20%**

50 LF_Df16_Rub 0.15 Triangle 0 0.12 0.18 No

Dissimilable fraction (DOCf) 'methane factor' for rubber & leather in landfill (unit is

proportion of DOC that actually degrades in landfill). **UNCERTAINTY RANGE

ASSUMED TO BE +/-20%**

51 LF_Df17_AlOt 0.075 Triangle 0 0.05 0.1 No

Dissimilable fraction (DOCf) 'methane factor' for 'all other' materials in landfill (unit is

proportion of DOC that actually degrades in landfill). Assumed the same as the MBT

value.

52 LF_Df18_EC 0.075 Triangle 0 0.05 0.1 NoDissimilable fraction (DOCf) 'methane factor' for enclosed composting residual material in

landfill (unit is proportion of DOC that actually degrades in landfill).

53 LF_Df19_MBT 0.075 Triangle 0 0.05 0.1 NoDissimilable fraction (DOCf) 'methane factor' for MBT (both aerobic and anaerobic)

residual material in landfill (unit is proportion of DOC that actually degrades in landfill).

54 LF_Eo 4.7 Lognormal 1.05 0 0 No

Electricity offset value (Eo) is the greenhouse gas benefit per unit waste, associated with

offsetting the generation of electricity using conventional fuels (unit is kWh/kg CH4

recovered). Assumed pedigree matrix is (1,1,1,1,1,2).

55 LF_F 0.5 Triangle 0 0.45 0.55 NoMethane fraction (F) 'methane factor', which is the proportion of methane in the generated

gas (unit is a proportion).

56 LF_In_Dies_Lit 0.001369 Lognormal 1.05 0 0 No

Volume of diesel fuel consumed by landfill operations to process 1 kg of incoming mixed

waste (unit is litres of diesel/kg of incoming material). Assumed pedigree matrix is

(1,1,1,1,1,na).

57 LF_In_Elec 0.000047 Lognormal 1.05 0 0 NoQuantity of electricity consumed by landfill operations to process 1 kg of incoming mixed

waste (unit is kWh/kg of incoming material). Assumed pedigree matrix is (1,1,1,1,1,na).

58 LF_Liner_HDPE 1.85 Triangle 0 0.9 2.8 No HDPE liner CO2e impact (unit is kg CO2e / tonne waste landfilled).

59 LF_MCF 0.95 Triangle 0 0.9 1 No

Methane correction factor (MCF) 'methane factor', which is the proportion of organic

degradation that occurs in an anaerobic environment, so that carbon is emitted as CH4

rather than CO2 (unit is proportion).

60 LF_R_Coll_Rate 1105 Uniform 0 1105 1105 NoMeasure collection rate of methane at Wollert, includes methane that is not oxidises

through the turbine and losses during downtime of turbines and flare (unit is m3/hr).

61 LF_R_Emis_Rate 187.5 Uniform 0 129 246 No Measured methane emission (to atmosphere) for the landfill (unit is m3/hr).

62 LF_R_Meas_Err 1 Triangle 0 0.5 1.24 No Instrument measurement error range, from Standards Australia 2009 (unit is a proportion).

63 LF_R_NoColl 0.1 Uniform 0 0.05 0.15 NoMethane that is generated after the gas collection system is operational (unit is a

proportion).

64 LF_R_Ox 0.2 Uniform 0 0.1 0.3 No

Methane oxidation factor (Ox ) 'methane factor', which is the proportion of methane that is

oxidise by methanotrophic bacteria (from CH4 to CO2) as it passes through the landfill

cover or cap (unit is a proportion).

65 LF_R_value_0 0.74 Undefined 0 0 0 No

**SENSITIVITY ANALYSIS PARAMETER**

Methane recovery rate for all time periods (R* value) (unit is a proportion). Low value is

0.600, mid value is 0.740, high value is 0.880. No uncertainty values (high/low) inserted

here as impact of high/low values is assessed using sensitivity analysis (i.e. changing the

mid-point value directly).

66 LF_Rec_Steel 477000 Uniform 0 477000 477000 No

Recovery rate of steel at the landfill face (45% of total of 1,831.5 tonnes, rest recovered at

transfer station, which is excluded), also excludes steel in C&D (42.1%), (unit is kg

recovered in 2008-09).

67 MBT_Emis_CH4 0.00003 Triangle 0 0 0.00006 NoQuantity of CH4 emitted during the processing of 1 kg of incoming organic material, for

both aerobic and anaerobic MBT (unit is kg CH4/kg of organic waste).

68 MBT_Emis_N2O 0.00013 Triangle 0 0 0.00026 NoQuantity of N2O emitted during the processing of 1 kg of incoming organic material, for

both aerobic and anaerobic MBT (unit is kg N2O/kg of organic waste).

69 MBT_FES_06_PET 0.5 Lognormal 1.3 0 0 No

Front end sorting recovery rate from both aerobic and anaerobic MBTs for rigid PET

packaging (unit is a proportion of the incoming material). Assumed pedigree matrix is

(4,5,1,1,1,1).

70 MBT_FES_07_HDPE 0.5 Lognormal 1.3 0 0 No

Front end sorting recovery rate from both aerobic and anaerobic MBTs for rigid HDPE


(4,5,1,1,1,1).

71 MBT_FES_08_PVC 0.5 Lognormal 1.3 0 0 No

Front end sorting recovery rate from both aerobic and anaerobic MBTs for rigid PVC


(4,5,1,1,1,1).

72 MBT_FES_09_4to7 0.5 Lognormal 1.3 0 0 No

Front end sorting recovery rate from both aerobic and anaerobic MBTs for rigid 4 to 7

plastic types packaging (unit is a proportion of the incoming material). Assumed pedigree

matrix is (4,5,1,1,1,1).

73 MBT_FES_12_Stl 0.75 Lognormal 1.3 0 0 NoFront end sorting recovery rate from both aerobic and anaerobic MBTs for steel (unit is a

proportion of the incoming material). Assumed pedigree matrix is (4,5,1,1,1,1).

74 MBT_FES_13_Al 0.75 Lognormal 1.3 0 0 NoFront end sorting recovery rate from both aerobic and anaerobic MBTs for aluminium (unit

is a proportion of the incoming material). Assumed pedigree matrix is (4,5,1,1,1,1).

75 MBT1_In_Dies 0.0579 Lognormal 1.23 0 0 No

Diesel consumption per kg of household and C&I garbage processed in an aerobic MBT

(unit is MJ/kg incoming waste). Value is a Hyder estimate. Assumed pedigree matrix is

(4,3,2,2,1,3).

76 MBT1_In_Elec 0.075 Lognormal 1.24 0 0 No

Quantity of electricity consumed by aerobic MBT operations to process 1 kg of incoming

garbage (unit is kWh/kg of incoming organics). Value is a Hyder estimate. Assumed

pedigree matrix is (4,1,1,3,1,4).

77 MBT1_Out_Com 0.29 Lognormal 1.62 0 0 No

Proportion of mass input to aerobic MBT that goes to low quality compost applications

(unit is kg waste to compost/kg incoming material). Assumed pedigree matrix is

(4,5,1,na,4,na).

78 MBT1_Out_LF 0.3 Lognormal 1.62 0 0 NoProportion of mass input to aerobic MBT that goes to landfill (unit is kg waste to

Wollert/kg incoming material). Assumed pedigree matrix is (4,5,1,na,4,na).

79 MBT2_In_Dies 0.0579 Lognormal 1.23 0 0 No

Diesel consumption per kg of household and C&I garbage processed in an anaerobic

MBT (unit is MJ/kg incoming waste). Value is a Hyder estimate. Assumed pedigree

matrix is (4,3,2,2,1,3).

80 MBT2_In_Elec 0.1 Lognormal 1.24 0 0 No

Quantity of electricity consumed by anaerobic MBT operations to process 1 kg of

incoming garbage (unit is kWh/kg of incoming organics). Value is a Hyder estimate.

Assumed pedigree matrix is (4,1,1,3,1,4).

81 MBT2_Out_Com 0.23 Lognormal 1.62 0 0 No

Proportion of mass input to anaerobic MBT that goes to low quality compost applications

(unit is kg waste to compost/kg incoming material). Assumed pedigree matrix is

(4,5,1,na,4,na).

5.0




82 MBT2_Out_Elec 0.134 Undefined 0 0 0 No

**SENSITIVITY ANALYSIS PARAMETER**

Quantity of electricity generated by anaerobic MBT operations from the processing of 1

kg of incoming garbage (unit is kWh/kg of incoming organics). Value is a Hyder estimate.

83 MBT2_Out_LF 0.34 Lognormal 1.62 0 0 NoProportion of mass input to anaerobic MBT that goes to landfill (unit is kg waste to

Wollert/kg incoming material). Assumed pedigree matrix is (4,5,1,na,4,na).

84 Prp_CI01_Food 0.294 Undefined 0 0 0 YesProportion of C&I garbage that was food waste in 2007-08 (unit is a fraction of total waste

in garbage bin).

85 Prp_CI02_PpCB 0.161 Undefined 0 0 0 YesProportion of C&I garbage that was paper and cardboard in 2007-08 (unit is a fraction of

total waste in garbage bin).

86 Prp_CI03_GWs 0.037 Undefined 0 0 0 YesProportion of C&I garbage that was soft garden waste in 2007-08 (unit is a fraction of total

waste in garbage bin).

87 Prp_CI04_GWh 0.009 Undefined 0 0 0 YesProportion of C&I garbage that was hard garden waste in 2007-08 (unit is a fraction of


88 Prp_CI05_Nap 0 Undefined 0 0 0 YesProportion of C&I garbage that was disposable nappies in 2007-08 (unit is a fraction of


89 Prp_CI06_PET 0.012 Undefined 0 0 0 YesProportion of C&I garbage that was rigid PET in 2007-08 (unit is a fraction of total waste in

garbage bin).

90 Prp_CI07_HDPE 0.023 Undefined 0 0 0 YesProportion of C&I garbage that was rigid HDPE in 2007-08 (unit is a fraction of total waste

in garbage bin).

91 Prp_CI08_PVC 0.004 Undefined 0 0 0 YesProportion of C&I garbage that was rigid PVC in 2007-08 (unit is a fraction of total waste

in garbage bin).

92 Prp_CI09_4to7 0.007 Undefined 0 0 0 YesProportion of C&I garbage that was rigid plastic types 4 to 7 in 2007-08 (unit is a fraction

of total waste in garbage bin).

93 Prp_CI10_Film 0.08 Undefined 0 0 0 YesProportion of C&I garbage that was plastic film in 2007-08 (unit is a fraction of total waste

in garbage bin).

94 Prp_CI11_Gla 0.017 Undefined 0 0 0 YesProportion of C&I garbage that was glass bottles in 2007-08 (unit is a fraction of total


95 Prp_CI12_Stl 0.016 Undefined 0 0 0 YesProportion of C&I garbage that was steel packaging in 2007-08 (unit is a fraction of total


96 Prp_CI13_Al 0.011 Undefined 0 0 0 YesProportion of C&I garbage that was aluminium packaging in 2007-08 (unit is a fraction of


97 Prp_CI14_Tmbr 0.108 Undefined 0 0 0 YesProportion of C&I garbage that was timber in 2007-08 (unit is a fraction of total waste in

garbage bin).

98 Prp_CI15_Tex 0.076 Undefined 0 0 0 YesProportion of C&I garbage that was textiles in 2007-08 (unit is a fraction of total waste in

garbage bin).

99 Prp_CI16_Rub 0.005 Undefined 0 0 0 YesProportion of C&I garbage that was rubber and leather in 2007-08 (unit is a fraction of total


100 Prp_CI17_AlOt 0.14 Undefined 0 0 0 YesProportion of C&I garbage that was 'other' (uncategorised) waste in 2007-08 (unit is a

fraction of total waste in garbage bin).

101 Prp_GW01_Food0 0.053 Undefined 0 0 0 YesProportion of green waste that was food waste in 2007-08, averaged across all councils,

for scenarios 0, 1 & 2 (unit is a fraction of total waste in the green waste bin).

102 Prp_GW01_Food3a 0.456 Undefined 0 0 0 YesProportion of green waste that was food waste in 2007-08, averaged across all councils,

for scenario 3a (unit is a fraction of total waste in the green waste bin).

103 Prp_GW01_Food3b 0.412 Undefined 0 0 0 YesProportion of green waste that was food waste in 2007-08, averaged across all councils,

for scenario 3b (unit is a fraction of total waste in the green waste bin).

104 Prp_GW02_PpCB0 0.002 Undefined 0 0 0 YesProportion of green waste that was paper & cardboard in 2007-08, averaged across all

councils, for scenarios 0, 1 & 2 (unit is a fraction of total waste in the green waste bin).

105 Prp_GW02_PpCB3a 0.001 Undefined 0 0 0 YesProportion of green waste that was paper & cardboard in 2007-08, averaged across all

councils, for scenario 3a (unit is a fraction of total waste in the green waste bin).

106 Prp_GW02_PpCB3b 0.001 Undefined 0 0 0 YesProportion of green waste that was paper & cardboard in 2007-08, averaged across all

councils, for scenario 3b (unit is a fraction of total waste in the green waste bin).

107 Prp_GW03_GWs0 0.777 Undefined 0 0 0 YesProportion of green waste that was soft garden waste in 2007-08, averaged across all


108 Prp_GW03_GWs3a 0.44 Undefined 0 0 0 YesProportion of green waste that was soft garden waste in 2007-08, averaged across all


109 Prp_GW03_GWs3b 0.476 Undefined 0 0 0 YesProportion of green waste that was soft garden waste in 2007-08, averaged across all


110 Prp_GW04_GWh0 0.158 Undefined 0 0 0 YesProportion of green waste that was hard garden waste in 2007-08, averaged across all


111 Prp_GW04_GWh3a 0.083 Undefined 0 0 0 YesProportion of green waste that was hard garden waste in 2007-08, averaged across all


112 Prp_GW04_GWh3b 0.09 Undefined 0 0 0 YesProportion of green waste that was hard garden waste in 2007-08, averaged across all


113 Prp_GW05_Nap0 0.001 Undefined 0 0 0 YesProportion of green waste that was nappies in 2007-08, averaged across all councils, for

scenarios 0, 1 & 2 (unit is a fraction of total waste in the green waste bin).

114 Prp_GW05_Nap3a 0 Undefined 0 0 0 YesProportion of green waste that was nappies in 2007-08, averaged across all councils, for

scenario 3a (unit is a fraction of total waste in the green waste bin).

115 Prp_GW05_Nap3b 0.001 Undefined 0 0 0 YesProportion of green waste that was nappies in 2007-08, averaged across all councils, for

scenario 3b (unit is a fraction of total waste in the green waste bin).

116 Prp_GW14_Tmbr0 0.008 Undefined 0 0 0 YesProportion of green waste that was timber in 2007-08, averaged across all councils, for

scenarios 0, 1 & 2 (unit is a fraction of total waste in the green waste bin).

117 Prp_GW14_Tmbr3a 0.007 Undefined 0 0 0 NoProportion of green waste that was timber in 2007-08, averaged across all councils, for

scenario 3a (unit is a fraction of total waste in the green waste bin).

118 Prp_GW14_Tmbr3b 0.007 Undefined 0 0 0 YesProportion of green waste that was timber in 2007-08, averaged across all councils, for

scenario 3b (unit is a fraction of total waste in the green waste bin).

119 Prp_GW17_AlOt0 0.001 Undefined 0 0 0 Yes

Proportion of green waste that was 'other' (uncategorised) waste in 2007-08, averaged

across all councils, for scenarios 0, 1 & 2 (unit is a fraction of total waste in the green

waste bin).

120 Prp_GW17_AlOt3a 0.012 Undefined 0 0 0 Yes

Proportion of green waste that was 'other' (uncategorised) waste in 2007-08, averaged

across all councils, for scenario 3a (unit is a fraction of total waste in the green waste

bin).

121 Prp_GW17_AlOt3b 0.013 Undefined 0 0 0 Yes

Proportion of green waste that was 'other'(uncategorised) waste in 2007-08, averaged

across all councils, for scenario 3b (unit is a fraction of total waste in the green waste

bin).

122 Prp_HG01_Food0 0.462 Undefined 0 0 0 YesProportion of household garbage that was food waste in 2007-08, for scenarios 0, 1 & 2

(unit is a fraction of total waste in garbage bin).

123 Prp_HG01_Food3a 0.27 Undefined 0 0 0 YesProportion of household garbage that was food waste in 2007-08, for scenario 3a (unit is a


124 Prp_HG01_Food3b 0.313 Undefined 0 0 0 YesProportion of household garbage that was food waste in 2007-08, for scenario 3b (unit is a


125 Prp_HG02_PpCB0 0.084 Undefined 0 0 0 YesProportion of household garbage that was paper and cardboard in 2007-08, for scenarios

0, 1 & 2 (unit is a fraction of total waste in garbage bin).

126 Prp_HG02_PpCB3a 0.12 Undefined 0 0 0 YesProportion of household garbage that was paper and cardboard in 2007-08, for scenario

3a (unit is a fraction of total waste in garbage bin).

127 Prp_HG02_PpCB3b 0.112 Undefined 0 0 0 YesProportion of household garbage that was paper and cardboard in 2007-08, for scenario

3b (unit is a fraction of total waste in garbage bin).

128 Prp_HG03_GWs0 0.055 Undefined 0 0 0 YesProportion of household garbage that was soft garden waste in 2007-08, for scenarios 0, 1

& 2 (unit is a fraction of total waste in garbage bin).

129 Prp_HG03_GWs3a 0.049 Undefined 0 0 0 YesProportion of household garbage that was soft garden waste in 2007-08, for scenario 3a


0.125




130 Prp_HG03_GWs3b 0.05 Undefined 0 0 0 YesProportion of household garbage that was soft garden waste in 2007-08, for scenario 3b


131 Prp_HG04_GWh0 0.014 Undefined 0 0 0 YesProportion of household garbage that was hard garden waste in 2007-08, for scenarios 0,

1 & 2 (unit is a fraction of total waste in garbage bin).

132 Prp_HG04_GWh3a 0.012 Undefined 0 0 0 YesProportion of household garbage that was hard garden waste in 2007-08, for scenario 3a


133 Prp_HG04_GWh3b 0.013 Undefined 0 0 0 YesProportion of household garbage that was hard garden waste in 2007-08, for scenario 3b


134 Prp_HG05_Nap0 0.069 Undefined 0 0 0 YesProportion of household garbage that was nappies in 2007-08, for scenarios 0, 1 & 2 (unit

is a fraction of total waste in garbage bin).

135 Prp_HG05_Nap3a 0.098 Undefined 0 0 0 YesProportion of household garbage that was nappies in 2007-08, for scenario 3a (unit is a


136 Prp_HG05_Nap3b 0.092 Undefined 0 0 0 YesProportion of household garbage that was nappies in 2007-08, for scenario 3b (unit is a


137 Prp_HG06_PET0 0.002 Undefined 0 0 0 YesProportion of household garbage that was rigid PET in 2007-08, for scenarios 0, 1 & 2


138 Prp_HG06_PET3a 0.003 Undefined 0 0 0 YesProportion of household garbage that was rigid PET in 2007-08, for scenario 3a (unit is a


139 Prp_HG06_PET3b 0.003 Undefined 0 0 0 YesProportion of household garbage that was rigid PET in 2007-08, for scenario 3b (unit is a


140 Prp_HG07_HDPE0 0.005 Undefined 0 0 0 YesProportion of household garbage that was rigid HDPE in 2007-08, for scenarios 0, 1 & 2


141 Prp_HG07_HDPE3a 0.007 Undefined 0 0 0 YesProportion of household garbage that was rigid HDPE in 2007-08, for scenario 3a (unit is

a fraction of total waste in garbage bin).

142 Prp_HG07_HDPE3b 0.006 Undefined 0 0 0 YesProportion of household garbage that was rigid HDPE in 2007-08, for scenario 3b (unit is


143 Prp_HG08_PVC0 0.001 Undefined 0 0 0 YesProportion of household garbage that was rigid PVC in 2007-08, for scenarios 0, 1 & 2


144 Prp_HG08_PVC3a 0.001 Undefined 0 0 0 YesProportion of household garbage that was rigid PVC in 2007-08, for scenario 3a (unit is a


145 Prp_HG08_PVC3b 0.001 Undefined 0 0 0 YesProportion of household garbage that was rigid PVC in 2007-08, for scenario 3b (unit is a


146 Prp_HG09_4to70 0.003 Undefined 0 0 0 YesProportion of household garbage that was rigid plastic types 4 to 7 in 2007-08, for

scenarios 0, 1 & 2 (unit is a fraction of total waste in garbage bin).

147 Prp_HG09_4to73a 0.004 Undefined 0 0 0 YesProportion of household garbage that was rigid plastic types 4 to 7 in 2007-08, for

scenario 3a (unit is a fraction of total waste in garbage bin).

148 Prp_HG09_4to73b 0.004 Undefined 0 0 0 YesProportion of household garbage that was rigid plastic types 4 to 7 in 2007-08, for

scenario 3b (unit is a fraction of total waste in garbage bin).

149 Prp_HG10_Film0 0.015 Undefined 0 0 0 YesProportion of household garbage that was plastic film in 2007-08, for scenarios 0, 1 & 2


150 Prp_HG10_Film3a 0.022 Undefined 0 0 0 YesProportion of household garbage that was plastic film in 2007-08, for scenario 3a (unit is a


151 Prp_HG10_Film3b 0.021 Undefined 0 0 0 YesProportion of household garbage that was plastic film in 2007-08, for scenario 3b (unit is a


152 Prp_HG11_Gla0 0.019 Undefined 0 0 0 YesProportion of household garbage that was glass bottles in 2007-08, for scenarios 0, 1 & 2


153 Prp_HG11_Gla3a 0.027 Undefined 0 0 0 YesProportion of household garbage that was glass bottles in 2007-08, for scenario 3a (unit is


154 Prp_HG11_Gla3b 0.025 Undefined 0 0 0 YesProportion of household garbage that was glass bottles in 2007-08, for scenario 3b (unit is


155 Prp_HG12_Stl0 0.017 Undefined 0 0 0 YesProportion of household garbage that was steel packaging in 2007-08, for scenarios 0, 1

& 2 (unit is a fraction of total waste in garbage bin).

156 Prp_HG12_Stl3a 0.025 Undefined 0 0 0 YesProportion of household garbage that was steel packaging in 2007-08, for scenario 3a


157 Prp_HG12_Stl3b 0.023 Undefined 0 0 0 YesProportion of household garbage that was steel packaging in 2007-08, for scenario 3b


158 Prp_HG13_Al0 0.001 Undefined 0 0 0 YesProportion of household garbage that was aluminium packaging in 2007-08, for scenarios

0, 1 & 2 (unit is a fraction of total waste in garbage bin).

159 Prp_HG13_Al3a 0.002 Undefined 0 0 0 YesProportion of household garbage that was aluminium packaging in 2007-08, for scenario

3a (unit is a fraction of total waste in garbage bin).

160 Prp_HG13_Al3b 0.002 Undefined 0 0 0 YesProportion of household garbage that was aluminium packaging in 2007-08, for scenario

3b (unit is a fraction of total waste in garbage bin).

161 Prp_HG14_Tmbr0 0.02 Undefined 0 0 0 YesProportion of household garbage that was timber in 2007-08, for scenarios 0, 1 & 2 (unit


162 Prp_HG14_Tmbr3a 0.029 Undefined 0 0 0 YesProportion of household garbage that was timber in 2007-08, for scenario 3a (unit is a


163 Prp_HG14_Tmbr3b 0.027 Undefined 0 0 0 YesProportion of household garbage that was timber in 2007-08, for scenario 3b (unit is a


164 Prp_HG15_Tex0 0.02 Undefined 0 0 0 YesProportion of household garbage that was textiles in 2007-08, for scenarios 0, 1 & 2 (unit


165 Prp_HG15_Tex3a 0.029 Undefined 0 0 0 YesProportion of household garbage that was textiles in 2007-08, for scenario 3a (unit is a


166 Prp_HG15_Tex3b 0.027 Undefined 0 0 0 YesProportion of household garbage that was textiles in 2007-08, for scenario 3b (unit is a


167 Prp_HG16_Rub0 0.01 Undefined 0 0 0 YesProportion of household garbage that was rubber and leather in 2007-08, for scenarios 0,

1 & 2 (unit is a fraction of total waste in garbage bin).

168 Prp_HG16_Rub3a 0.014 Undefined 0 0 0 YesProportion of household garbage that was rubber and leather in 2007-08, for scenario 3a


169 Prp_HG16_Rub3b 0.013 Undefined 0 0 0 YesProportion of household garbage that was rubber and leather in 2007-08, for scenario 3b


170 Prp_HG17_AlOt0 0.203 Undefined 0 0 0 YesProportion of household garbage that was 'other' (uncategorised) waste in 2007-08, for

scenarios 0, 1 & 2 (unit is a fraction of total waste in garbage bin).

171 Prp_HG17_AlOt3a 0.288 Undefined 0 0 0 YesProportion of household garbage that was 'other' (uncategorised) waste in 2007-08, for

scenario 3a (unit is a fraction of total waste in garbage bin).

172 Prp_HG17_AlOt3b 0.268 Undefined 0 0 0 YesProportion of household garbage that was 'other' (uncategorised) waste in 2007-08, for

scenario 3b (unit is a fraction of total waste in garbage bin).

173 Wt_CI_All 96352000 Undefined 0 0 0 YesMass of C&I garbage generated in 2007-08, this value remains the same for all scenarios

(unit is kg).

174 Wt_GW_All_0 33280000 Undefined 0 0 0 YesMass of household green waste, including Nillumbik food recovery, for scenarios 0, 1 & 2

(unit is kg/yr).

175 Wt_GW_All_3a 90166000 Undefined 0 0 0 YesMass of household green waste, including Nillumbik food recovery, for scenarios 3a (unit

is kg/yr).

176 Wt_GW_All_3b 83342000 Undefined 0 0 0 YesMass of household green waste, including Nillumbik food recovery, for scenarios 3b (unit

is kg/yr).

177 Wt_HG_All_0 144402000 Undefined 0 0 0 Yes Mass of household garbage, including food waste, for scenarios 0, 1 & 2 (unit is kg/yr).

178 Wt_HG_All_3a 101291000 Undefined 0 0 0 Yes Mass of household garbage, including food waste, for scenarios 3a (unit is kg/yr).

179 Wt_HG_All_3b 108215000 Undefined 0 0 0 Yes Mass of household garbage, including food waste, for scenarios 3b (unit is kg/yr).

180 Yrs_Horizon 100 Undefined 0 0 0 NoThis time value is used to modify the proportion of carbon that remains in compost (unit is

years).



Appendix E

Review of three significant international models of greenhouse gas emissions from waste



Review of three significant international models of greenhouse gas emissions from waste

IPCC (2006)

The Intergovernmental Panel on Climate Change produces guidance on how countries should

estimate emissions from waste in their national greenhouse gas inventories (IPCC 2006). The

method for estimating emissions from landfills and composting operations has set the agenda

for many other studies that investigate emissions from waste, including this study. The methods

are reviewed below.

Methane emissions from landfills are estimated by a first-order decay model, in which emissions

are related to the amount of carbon remaining in the waste over time. The approach is similar to

earlier models created to estimate landfill gas yields for commercial harvesting, but builds on

these to link emissions to particular types of organic wastes. The model provides some flexibility

in terms of parameter values but provides defaults that must be used unless an adequate

justification can be provided.

Each major type is allocated a specified degradable organic carbon content, a fixed percentage

of which is assumed to degrade (the default is 50%). The remainder of the carbon is assumed

to remain in the landfill. The carbon degrades at a constant rate linked to climate. Half of the

carbon (by default) is assumed to degrade to methane, the remainder to CO2. CO2 emissions

are ignored because ‘biogenic’ carbon balances are accounted for under a different category of

the IPCC approach. A ‘methane correction factor’ is applied to the calculated emissions where

landfills are unmanaged or where they are managed to be semi-aerobic. Any methane

recovered from the landfill is subtracted, and a portion of the emitted methane is assumed to be

oxidised to CO2 as it passes through the aerobic capping and upper layers of the waste. In

many countries with well-regulated landfills, this proportion is assumed to be 10%.

The IPCC method for composting provides range factors for estimating methane and nitrous

oxide emissions. On a net basis, the net higher value of the range is 18x the lower value, so the

method allows for a wide range of estimates. The mid-range value is given more emphasis,

suggesting that it should be used as the default value should no additional information be

available. As with landfills, biogenic CO2 emissions are ignored.

Smith et al (2001)

This was a major LCA study undertaken for the European Community. It found that “overall,

source segregation of municipal waste followed by recycling (for paper, metals, textiles and

plastics) and composting / [anaerobic digestion] (for putrescible wastes) gives the lowest net

flux of greenhouse gases …” (Smith et al 2001 p.iii). Much of this benefit resulted from

avoidance of emissions from landfills.

An average EU gas recovery rate of 33% was estimated based on the proportion of waste

thought to be sent to landfills having gas recovery, country-specific estimates of operational gas

recovery rates and an estimated proportion of emissions that occur before or after gas recovery

systems are installed. The study noted, however, that operational gas recovery of 70% to 90%

of the methane is achievable, and undertook sensitivity analysis on higher rates. This showed

that the rate of recovery of methane for reuse is important and determines whether or not

composting and paper recycling provided a net greenhouse gas benefit.

Carbon sequestration was also found to be a key issue for landfills, where anaerobic conditions

enhance carbon storage. Carbon sequestration has a relatively small role in relation to

composting because of the rapid rate of decomposition after application to soils.



The study also found that emissions associated with transport, residual waste and recovered

materials were relatively small. Nitrous oxide emissions from landfill were unlikely to be

significant. Comparisons were all carried out over a 100 year assessment timeframe.

US EPA (2006)

This is another major LCA that assessed waste management scenarios in the US from a carbon

perspective. It did not report an overall ‘winner’ in its scenarios assessment, instead focusing on

scenarios that particular types of settlements might take to reduce net emissions. Like Smith et

al (2001), it found that “the results for landfills are very sensitive” to the methane oxidation rate

and gas collection system efficiency (ibid p.ES16). Based on previous US EPA research, it was

assumed that landfills with gas recovery systems collect 75% of the methane generated (but

only 59% of methane is generated in such sites). Carbon stored in the landfill was assumed to

be sequestered.

The researchers conducted a literature review of the emissions from composting, concluding

that “CH4 generation from centralised compost piles is essentially zero” (ibid p.50). The carbon

flux associated with composting – including soil carbon restoration and increased humus

formation – was estimated at around 0.06 tonnes of carbon equivalent per tonne of waste39

.

39 Results are converted from the reported carbon equivalent per short ton of waste (about 1.1 tonnes).



Appendix F

Further detail on the analysis results



Further detail on the analysis results - overview using time sensitive and instantaneous emissions models, and different emission assessment timeframes

Emissions using mid-range values (kt CO2-e)

Wollert landfill

1 - Aerobic

MBT

2 - Anaerobic MBT



weekly garbage

Time sensitive, 20 years -57 -77 -73 -83 -75

Time sensitive, 100 years -81 -51 -87 -82 -79

Instantaneous emissions, 100 years -72 -33 -70 -67 -66

Instantaneous emissions, 500 years -118 -30 -68 -100 -100

Emissions indexed to 100 to display relativities

Four tables showing emissions by life cycle element (using mid-range values) (kt CO2-e) 1. Assessment period = 100 years; model type = time sensitive

Wollert landfill

1 - Aerobic

MBT

2 - Anaerobic

MBT



weekly garbage

Transport 7 8 8 8 10

Product offsets (electricity, recyclables & compost) -61 -30 -70 -48 -50

Emissions from other organic degradation processes 1 7 7 2 2

Process inputs 4 35 43 8 8

Methane from landfill 60 3 3 51 51

Carbon storage -92 -74 -78 -102 -99

Net emissions -81 -51 -87 -82 -79

-100

-80

-60

-40

-20

0

Time sensitive,

20 years

Instantaneous

emissions, 100

years

Instantaneous

emissions, 500

years

Time sensitive,

100 years3b - Food separation,

weekly garbage


fortnightly garbage

2 - Anaerobic MBT

1 - Aerobic MBT

Wollert landfill



2. Assessment period = 100 years; model type = instantaneous emissions

Wollert landfill

1 - Aerobic

MBT

2 - Anaerobic

MBT



weekly garbage






Carbon storage -88 -56 -61 -86 -86

Net emissions -72 -33 -70 -67 -66

3. Assessment period = 20 years; model type = time sensitive

Wollert landfill

1 - Aerobic

MBT

2 - Anaerobic

MBT



weekly garbage






Carbon storage -107 -102 -106 -127 -122

Net emissions -57 -77 -73 -83 -75

4. Assessment period = 500 years; model type = instantaneous emissions

Wollert landfill

1 - Aerobic

MBT

2 - Anaerobic

MBT



weekly garbage






Carbon storage -87 -50 -57 -83 -84

Net emissions -118 -30 -68 -100 -100

Hanson Landfill Services / City of Whittlesea - · PDF fileHanson Landfill Services / City of Whittlesea Comparative Greenhouse Gas Life Cycle Assessment of Wollert Landfill Final

Documents