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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

Journal of Economic Structures Cover Image
  • Research article
  • Open Access

A Waste Supply-Use Analysis of Australian Waste Flows

Journal of Economic StructuresThe Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)20143:5

https://doi.org/10.1186/s40008-014-0005-0

  • Received: 18 March 2014
  • Accepted: 19 October 2014
  • Published:

Abstract

In this paper we apply the Lenzen and Reynolds (2014) Waste Supply Use Table extension of Nakamura and Kondo’s (2002a) Waste Input–Output (WIO) framework to the 2008 Australian economy. This is the first application of any WIO-style method to Australia as a nation. We find that the Services sector has the largest direct and indirect waste generation for an intermediate sector. This is followed by the Forestry sector, for direct waste generation, and the Transport sector for direct and indirect waste generation effects.

In terms of waste treatment methods, landfill generates the smallest direct and indirect waste tonnages, but it also provides the least amount of economic activity per tonne treated, producing $US2.53 in total of economic production per tonne treated.

Keywords

  • Food waste
  • Commercial and industrial waste
  • Municipal solid waste
  • Australia input–output

1 Introduction

Waste Input–Output (WIO) analysis can provide much needed information regarding the economic impact of waste generated by sectors of the economy. A WIO (or Waste Supply Use Table (WSUT)) analysis provides the means to devise interventions and policy with a sectoral focus (Nakamura and Kondo 2002a; Kagawa 2005; Kagawa et al. 2007; Tsukui 2007; Lin 2009; Tsukui and Nakamura 2010; Matsubae et al. 2011; Tsukui et al. 2011, 2012). Yet there has never been a WIO of Australia constructed, primarily because the level of waste data required for a WIO have been too high (though previously there has been some attempted IO analysis of waste in Australia (Reynolds and Boland 2011, 2013a, 2013b; Reynolds et al. 2011). However, thanks to the high resolution waste data set of Australia discussed in Lenzen and Reynolds (2014), WIO analysis for Australia is now a reality.

In this paper we apply Lenzen and Reynolds (2014) extension of Nakamura and Kondo’s WIO framework, first published in English as (Nakamura 1999), and later (Nakamura and Kondo 2002a, 2002b, 2002c, 2006, 2008, 2009; Nakamura et al. 2007; Nakamura 2010) to the Australian economy. It is worth noting again that this is the first application of any WIO-style method to Australia as a nation.

In Sect. 2 we discuss the methodology, data sources and procedures followed to create an Australian WSUT. In Sect. 3 we present the results of a practical application of the WSUT methodology to Australia, and discuss these results in Sect. 4. Section 5 provides concluding remarks. In Appendix A (Additional files 13) we list the direct and indirect waste multipliers for 343 intermediate sectors and ten waste treatment sectors in the Australian economy as of 2008, as well as the disaggregated tonnages of industrial solid waste (ISW) and municipal solid waste (MSW). In Appendix B (Additional file 4) we provide analysis of the largest direct and indirect waste multipliers for 14 waste types. In Appendix C (Additional file 5) we provide unaltered versions of the aggregated seven sector Coefficients and Leontief matrices (Tables 2 and 3) for comparison, and a concordance matrix between the 7 and 343 sector versions of the WSUT. For completeness we also include the simplified Transactions matrix and the Gross output vector used for calculations.

2 Methodology

2.1 WSUT Notation

In this paper we will use the notation described in Lenzen and Reynolds (2014). The WSUT in balanced form is written as
$$ \left ( \begin{matrix} {{\mathbf{T}}_{11}}{} & \mathbf{T}_{12} & \mathbf{0}\\ \boldsymbol{0} & \boldsymbol{0} & \mathbf{W}_{23}\\ \mathbf{W}_{31} & \mathbf{W}_{32} & \boldsymbol{0} \end{matrix} \right ) \left ( \begin{matrix} {{\mathbf{1}}_{1}}{}\\ \mathbf{1}_{2}\\ \mathbf{1}_{3} \end{matrix} \right ) + \left ( \begin{matrix} {\mathbf{f}}{}\\ \boldsymbol{0}\\ \mathbf{W}_{{F}} \end{matrix} \right ) \mathbf{1}_{{F}}= \left ( \begin{matrix} {{\mathbf{x}}_{1}}{}\\ \mathbf{x}_{2}\\ \mathbf{x}_{3} \end{matrix} \right ) $$
(1)
where \(\mathbf{T}_{11} \in \mathbb{\mathbf{R}}^{N_{1} \times N_{1}}\) represents an intermediate demand matrix for \(N_{1}\) goods and service-producing sectors, \(\mathbf{f} \in \mathbb{R}^{N_{1} \times N_{F}}\) a final demand matrix for \(N_{F}\) final demand categories, and \(\mathbf{x}_{1} \in \mathbb{R}^{N_{1} \times 1}\) a gross output vector for \(N_{1}\) goods and service-producing sectors, respectively. Waste generation is categorised per intermediate sector as \(\mathbf{W}_{31} \in \mathbb{R}^{N_{3} \times N_{1}}\), and final demand as \(\mathbf{W}_{F} \in \mathbb{R}^{N_{3} \times N_{F}}\), distinguishing \(N_{3}\) waste types in the economy. The monetary inputs \(\mathbf{T}_{12} \in \mathbb{R}^{N_{1} \times N_{2}}\) into \(N_{2}\) waste treatment sectors (applying \(N_{2}\) treatment methods) are distinguished from the monetary inputs \(\mathbf{T}_{11}\) into other intermediate sectors. The waste produced by \(N_{2}\) waste treatment sectors is represented by \(\mathbf{W}_{32} \in \mathbb{R}^{N_{3} \times N_{2}}\) represent and the waste treated by the \(N_{2}\) waste treatment methods is contained in \(\mathbf{W}_{23} \in \mathbb{R}^{N_{2} \times N_{3}}\). The vectors \(\mathbf{x}_{3} \in \mathbb{R}^{N_{3} \times 1}\) and \(\mathbf{x}_{2} \in \mathbb{R}^{N_{2} \times 1}\) are the total output of \(N_{3}\) waste types and \(N_{2}\) waste treatment methods for the economy, respectively. \(\mathbf{1}_{1}\), \(\mathbf{1}_{2}\), \(\mathbf{1}_{3}\), and \(\mathbf{1}_{{F}}\) are column vectors of ones with appropriate dimensions.
We can express Eq. (1) in coefficient form, given by
$$ \left ( \begin{matrix} {{\mathbf{A}}_{11}} & \mathbf{A}_{12} & \boldsymbol{0}\\ \boldsymbol{0} & \boldsymbol{0} & \mathbf{S}_{23}\\ \mathbf{G}_{31} & \mathbf{G}_{32} & \boldsymbol{0} \end{matrix} \right ) \left ( \begin{matrix} {{\mathbf{x}}_{1}}{}\\ \mathbf{x}_{2}\\ \mathbf{x}_{3} \end{matrix} \right ) + \left ( \begin{matrix} {\mathbf{f}}{}\\ \boldsymbol{0}\\ \mathbf{W}_{F} \end{matrix} \right ) \mathbf{1}_{F} = \left ( \begin{matrix} {{\mathbf{x}}_{1}}{}\\ \mathbf{x}_{2}\\ \mathbf{x}_{3} \end{matrix} \right ) $$
(2)
where we define input coefficients matrices \(\mathbf{A}_{11}=\mathbf{T}_{11} \hat{\mathbf{x}}_{1}^{-1}\) ($/$), \(\mathbf{A}_{12}=\mathbf{T}_{12} \hat{\mathbf{x}}_{2}^{-1}\) ($/t), \(\mathbf{G}_{31}=\mathbf{W}_{31} \hat{\mathbf{x}}_{1}^{-1}\) (t/$), and \(\mathbf{G}_{32}=\mathbf{W}_{32} \hat{\mathbf{x}}_{2}^{-1}\) (t/t), where the “hat” over a vector x denotes a diagonal matrix with the elements of the vector along the main diagonal. For instance, if \(\mathbf{x}= \bigl [ {\scriptsize\begin{matrix}{}x_{{1}}\cr x_{{2}}\end{matrix}} \bigr]\) then \(\hat{\mathbf{x}} = \bigl[ {\scriptsize\begin{matrix}{} x_{{1}} & 0\cr 0 & x_{{2}}\end{matrix}} \bigr]\). The matrix \(\mathbf{S}_{23} \in \mathbb{\mathbf{R}}^{N_{2} \times N_{3}}\) is an \(N_{2} \times N_{3}\) version of Nakamura and Kondo’s (2009) allocation matrix (Nakamura and Kondo 2002a). The elements of \(S_{23} (i,j)\) refer to the share of waste type j that is treated by treatment method i, and are normalised according to \(\sum_{j} S_{23} (i,j) = 1\).
The Leontief inverse of the WSUT is finally formulated as follows:
$$ \left ( \begin{matrix} {{\mathbf{x}}_{1}}{}\\ \mathbf{x}_{2}\\ \mathbf{x}_{3} \end{matrix} \right ) = \left ( \begin{matrix} {\mathbf{I}- {\mathbf{A}}_{11}}{} & - \mathbf{A}_{12} & \boldsymbol{0}\\ \boldsymbol{0} & \mathbf{I} & - \mathbf{S}_{23}\\ -\mathbf{G}_{31} & -\mathbf{G}_{32} & \mathbf{I} \end{matrix} \right )^{-1} \left ( \begin{matrix} {\mathbf{f}}{}\\ \boldsymbol{0}\\ \mathbf{W}_{F} \end{matrix} \right ) \mathbf{1}_{F}. $$
(3)

2.2 Data Sources and Processing

In Lenzen and Reynolds (2014) a national Australian WSUT for 2008 was provided as a technical example. We now further describe the data sources, processing and compilation methods used to create this WSUT.

The Australian IO table was sourced from the Eora database in US dollars (Lenzen et al. 2011, 2012a, 2012b, 2013), to provide the WSUT block \(\mathbf{T}_{11}\), distinguishing \(N_{1} = 343\) industry sectors. Waste data were estimated (as discussed in Lenzen and Reynolds 2014 and Reynolds 2013), with only formal disposal accounted for. This waste estimation methodology is discussed in abridged form below.

The Australian waste treatment sector is quite small employing 3,300 persons, (0.3% of total Australian labour) and generating 9 billion AU$ gross output in 2009–2010. This can be explained by the majority of waste being sent to landfill, with much of the recycled/recovered material being sent overseas for reprocessing (Australian Bureau of Statistics 2013a). In addition to being small in size, the Australian waste (and recycling) treatment sector is not well documented, thus to create this WSUT we disaggregated the recycling parts from Eora sector #344 ‘Sanitary and garbage disposal’ into the block \(\mathbf{T}_{12}\), yielding \(N_{2} = 10\) separate treatment sectors, using data/sector labels on recycling from Department of Sustainability, Environment, Water, Population and Communities (DSEWPaC) (DSEWPaC 2012) and Hyder Consulting (Hyder Consulting 2012).1 Waste data for WSUT block \(\mathbf{W}_{31}\), distinguishing Municipal Solid Waste (MSW), Commercial and Industrial (C&I), and Construction and Demolition (C&D) origins were sourced from the Australian Bureau of Statistics (Australian Bureau of Statistics 2008, 2009, 2010, 2011, 2013b), as well as Hyder Consulting (Hyder Consulting 2012), DSEWPaC (Department of Sustainability, Environment, Water, Population and Communities 2012; Environment Protection and Heritage Council and the Department of Environment, Water, Heritage and the Arts 2010), and Inside Waste (WCS Market Intelligence 2008; Waste Management & Environment Media Pty Ltd 2011). Data on recycling (WSUT block \(\mathbf{W}_{32}\)) were sourced from DSEWPaC (DSEWPaC 2012) and Hyder Consulting (Hyder Consulting 2012). \(\mathbf{W}_{{F}}\) was estimated using MSW data (2008, 2012). This step is an assumption for simplicity, as MSW is produced by households and households are the major contributor to final demand. Finally, \(\mathbf{S}_{23}\) and \(\mathbf{W}_{23}\) were populated using data from Wright Corporate Strategy Pty Ltd and Rawtec Pty Ltd (Wright Corporate Strategy Pty Ltd and Rawtec Pty Ltd 2010) and Hyder Consulting (Hyder Consulting 2012). In our example we distinguish \(N_{3} = 14\) waste types.2 Where data were aggregated across the \(N_{1} = 344\) industry sectors, we applied a prorating procedure using sectoral economic output (x, gross output) as a proxy (see Lenzen et al. 2012a, 2012b for more details).

To disaggregate the waste generation and treatment data for \(\mathbf{W}_{31}\) we utilised a binary concordance matrix that uniquely allocates C&I and C&D waste streams to the 344 sectors of the Eora database’s Australian IOT. C&D waste producing sectors were understood to be numbered 68, 170, 218, 232, 243–249, 253–255, and 257. Fourteen waste types (Organics (Food, Green); Timber; Textiles And Clothing; Paper, Printing, And Cardboard; Plastics; Rubber; Glass; Plaster Board; Cement And Construction; Metals (Ferrous, Non-Ferrous); Electronic Waste; and Other), were mapped across the intermediate sectors using the average from three proxy vectors: total sectoral gross output per sector, employment per sector, and the amount of inputs of production per intermediate sector (Reynolds 2013; Lenzen and Reynolds 2014). This method assumes that each sector produces waste relative to its economic size and employment capacity: i.e. we distributed the total amount of waste among the waste treatment methods with the larger the economic size and employment level, the greater the volume of waste was assumed to have been treated. No assumptions were made regarding the relative technological efficiency of waste generation between sectors.

The composition of waste generated by each sector is assumed to be unique, and so was estimated by applying industry → product → waste concordance matrices to normalised versions of the matrices \(\mathbf{A}_{11}\) (the direct inputs between the intermediate sectors of the economy), and \(\mathbf{A}_{12}\) (the direct inputs between the intermediate sector of the economy, and the waste treatment sectors). These concorded A matrices were multiplied by total waste produced per sector to replicate the phenomenon of each product/industry having different waste types associated with its consumption and production. For example, the sectors of Forestry (sector number 35), Softwoods (36), and Hardwoods (37) are all sectors associated with Timber products, which is in turn associated with Timber waste. Each sector that has a part of its direct requirements (coefficient multiplier) attributed to Forestry (35), Softwoods (36), etc. will be assumed to produce a proportional amount of timber waste relative to the amount of timber products used in its production processes. For full discussion of this methodology please refer to Reynolds (2013).

To summarise, the resulting Australian WSUT model has \(N_{1}=343\), \(N_{2}=10\), and \(N_{3}=14\). Note that \(N_{1}\) has one sector less than the equivalent Eora table, as this sector has been disaggregated to become \(N_{2}\).

3 Results

Table 1 displays a simplified WSUT constructed for Australia for 2008. For the sake of readability it presents the waste flows of Australia as a 31 by 31 table (with seven aggregated intermediate industry sectors, 10 treatment sectors, and 14 waste types). A concordance matrix to convert from the 343 to the seven sector model is supplied in Appendix C. Aggregation of sectors was based on a condensed version of the Australian and New Zealand Standard Industrial Classification (Australian Bureau of Statistics 2006). Note that this is a simplified table with import, exports, and some final demand categories excluded from this table (and thus from Table 1’s gross output values).
Table 1

A simplified aggregated WSU transactions table for Australia for 2008. Compare with Eq. ( 1 ), with seven intermediate sectors

 

Ag

Mi

Fo

Ma

Ut

Tr

Se

Lf

Gl R

Ppr R

Pla R

Me

Or C

Cn R

EW R

Le R

Ru R

Food

Gdn

Tim

Txt

Ppr

Pla

Ru

Gl

Pb

Cn

M F

MNF

EW

Oth

F D

G O

Ag

47.14

0.10

0.04

9.51

0.04

0.24

11.10

0.01

0.00

0.00

0.01

0.01

0.01

0.01

0.00

0.00

0.00

              

45.02

113.25

Mi

1.71

30.92

0.09

20.16

5.86

6.41

3.96

0.10

0.02

0.03

0.06

0.04

0.08

0.06

0.01

0.01

0.01

              

7.59

77.13

Fo

0.12

0.02

0.10

1.89

0.01

0.02

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

              

0.01

2.21

Ma

14.72

7.18

0.37

114.99

2.13

12.92

79.20

0.07

0.01

0.02

0.04

0.03

0.06

0.05

0.01

0.01

0.01

              

116.24

348.04

Ut

2.54

2.91

0.00

7.22

2.44

1.92

7.72

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

              

11.01

35.78

Tr

8.03

6.17

0.04

32.76

0.93

16.77

35.26

0.05

0.01

0.02

0.03

0.02

0.04

0.04

0.01

0.00

0.01

              

22.21

122.42

Se

15.85

13.03

0.28

79.89

6.69

37.00

263.16

0.08

0.01

0.03

0.05

0.04

0.07

0.06

0.01

0.01

0.01

              

235.16

651.42

Lf

                 

 

Gl R

                 

 

Ppr R

                 

 

Pla R

                 

 

Me

                 

 

Or C

                 

 

Cn R

                 

 

EW R

                 

 

Le R

                 

 

Ru R

                 

 

Food

              

Gdn

              

Tim

              

Txt

              

Ppr

              

Pla

              

Ru

              

Gl

              

Pb

              

Cn

              

M F

              

MNF

              

EW

              

Oth

              

Monetary values in units of $US1,000,000,000 waste flows (grey areas) in units of 100,000 tonnes. Waste values <0.005 are not displayed for the sake of readability. An unaltered version of this table is available in Appendix C

Waste volumes are reported in physical units of one million tonnes and monetary units of one billion US dollars. US dollars were used to keep consistency with the Eora tables, which use USD as a base currency.

The \(\mathbf{W}_{31}\) section of Table 1 presents a breakdown of waste type generated by each intermediate sector of the Australian economy. Waste generation by treatment sector is listed in the \(\mathbf{W}_{32}\) section. The \(\mathbf{W}_{23}\) section provides the waste volumes processed by each treatment type. The \(\mathbf{W}_{F}\) section provides the Australian MSW data. The \(\mathbf{x}_{2}\) and \(\mathbf{x}_{3}\) sections give the total tonnages of waste sorted by treatment type or waste type.

Tables 2 and 3 display the aggregated direct and total waste generation multipliers created from the full Australian WSUT that is presented as simplified in Table 1. Monetary gross output values were sourced directly from the EORA database and so contain full imports and export data, these values can be found in Appendix C. Caution should be used when reading Tables 2 and 3, as there are multiple scales presented on the one table (millions of dollars per millions of dollars; dollars per tonne; kilograms per tonne and tonnes per millions of dollars), Table 2 uses an additional scale for the coefficients of \(\mathbf{S}_{23}\)—tonnes per tonnes—in order to illustrate the integration of the \(\mathbf{S}_{23}\) matrix into the WSUT.
Table 2

The aggregated coefficients matrix of the WSUT, for Australia for the year 2008. Compare with Eq. ( 2 )

 

Ag

Mi

Fo

Ma

Ut

Tr

Se

Lf

Gl R

Ppr R

Pla R

Me

Or C

Cn R

EW R

Le R

Ru R

Food

Gdn

Tim

Txt

Ppr

Pla

Ru

Gl

Pb

Cn

M F

MNF

EW

Oth

Ag

Mi

Fo

Ma

Ut

Tr

Se

Lf

Gl R

Ppr R

Pla R

Me

Or C

Cn R

EW R

Le R

Ru R

Food

0.02

0.45

0.05

0.40

0.10

0.32

0.03

1.57

0.15

4.57

Gdn

0.01

0.21

0.03

0.19

0.05

0.15

0.01

0.75

0.07

2.19

Tim

0.01

0.18

0.02

0.16

0.04

0.13

0.01

0.64

0.06

1.86

Txt

 

0.06

0.01

0.05

0.01

0.04

 

0.20

0.02

0.59

Ppr

0.01

0.36

0.04

0.32

0.08

0.26

0.02

1.26

0.12

3.67

Pla

0.05

1.42

0.17

1.26

0.30

1.02

0.08

4.97

0.47

14.46

Ru

 

0.04

 

0.03

0.01

0.03

 

0.13

0.01

0.37

Gl

 

0.02

 

0.01

 

0.01

 

0.06

0.01

0.17

Pb

Cn

0.01

0.16

0.02

0.15

0.03

0.12

0.01

0.57

0.05

1.67

M F

0.01

0.22

0.03

0.19

0.05

0.16

0.01

0.77

0.07

2.23

MNF

 

0.06

0.01

0.05

0.01

0.04

 

0.20

0.02

0.59

EW

 

0.10

0.01

0.09

0.02

0.07

0.01

0.36

0.03

1.06

Oth

0.02

0.46

0.05

0.41

0.10

0.33

0.03

1.61

0.15

4.68

Million $ per Million $; Dollar $ per Tonne; Tonnes per Tonne; Tonnes per Million dollar $; Kilograms per Tonne

Values <0.005 are not displayed for the sake of readability. An unaltered version of this table is available in Appendix C

Table 3

Aggregated total waste generation multipliers of the WSUT, for Australia for the year 2008. Compare with Eq. ( 3 )

 

Ag

Mi

Fo

Ma

Ut

Tr

Se

Lf

Gl R

Ppr R

Pla R

Me

Or C

Cn R

EW R

Le R

Ru R

Food

Gdn

Tim

Txt

Ppr

Pla

Ru

Gl

Pb

Cn

M F

MNF

EW

Oth

Ag

Mi

Fo

Ma

Ut

Tr