Preparation of Economy Wide -Material Flows Accounts using ...

Jim West International Study Tour– Black Mountain – September 2013

CSIRO ECOSYSTEM SCIENCES

Preparation of Economy Wide - Material Flows Accounts using

International Data.

Disclaimer and request

Out of material and energy flows accounting, and emissions accounting, the focus here will be on materials flows because:

• Energy flows largely are already effectively accounted for in the IEA or EIA databases (or can be trivially derived from them).

• Emissions data has come directly from that available directly from the World Bank’s WDI database

Preparation of Economy Wide - Material Flows Accounts using International Data. – Black Mountain – September 2013 | Jim West2 |

from the World Bank’s WDI database

In contrast, the work we’ve done on material flows has been quite major, and having individual countries become involved would improve it still further.

PLEASE FEEL FREE TO ASK QUESTIONS AS WE GO

What is Economy Wide – Material Flows Accounting (EW-

MFA) ?

EW-MFA deal with material inputs and outputs from a national economy, using physical rather than monetary terms.

Covers domestic extraction of materials from the natural environment (excluding water and air), and international trade of materials.

Only flows crossing the system boundary between the environment and the economy are counted. “Hidden” flows are not counted.


and the economy are counted. “Hidden” flows are not counted.

The key final metric is Domestic Material Consumption (DMC), arrived at via Domestic Extraction (DE) , and Physical Trade Balance (PTB).

PTB = Imports – Exports

DMC = DE + PTB

How do we compile EW-MFA accounts?

Refer to the Eurostat EW-MFA Compilation Guide (2012) for how these statistics should be compiled at national level.

This is the “Standard”, (that said, some departures may be


said, some departures may be forced, or warranted for other reasons)

What use is EW-MFA ?

The “Guide” says that it is “to describe the interaction of the domestic economy with the natural environment and the rest of the world economy (ROW) in terms of flows of materials.

Determine how much “stuff” needs to be extracted from the environment to support a certain material standard of living.

Determine how much waste needs to be sunk back into the


Determine how much waste needs to be sunk back into the environment (even infrastructure eventually ends up as waste).

** Provide basic information necessary to determine Resource Efficiency**

Improved RE = Lower Environmental Impacts (ceteris paribus)

Examples 1: National level DMC trajectories in the Asia-Pacific

region


Individual trajectories in Latin America do not fit the classic pattern of

socio-metabolic transitions well

Contrasting socio-metabolic transitions for two world regions - ISIE Ulsan 2013 | Jim West, Heinz Schandl7 |

Examples 2: World regional level trajectories for DMC,

material intensity, and GDP/capita


How suited to purpose is DMC? Depends on commodity.

For allocating responsibility for resources “use” and resource efficiency:

Minerals : Poor - Reasonable. Less biased against resource producers / exporters than TMI (which includes hidden flows), or DE alone, but material footprint far superior.


Fossil Fuels : Good (though misses embodied energy)

For determining where environmental loads accrue efficiency:

Good. Captures where much of the material and energy intensive processing takes place. Better than MF, but less comprehensive than TMI.

DMC Vs. Material Footprint.


From: Wiedmann, Schandl, Lenzen, Moran, Suh, West, and Kanemoto. (2013). “The material footprint of nations”. Proceedings of the National Academy of Sciences.

Using international data sets for EW-MFA

“The Guide” is aimed at best practice for compilation of EW-MFA accounts by individual countries, preferably by national statistical agencies.

CSIRO EW-MFA databases needed to cover many countries, had to rely on comprehensive international databases.

Inevitable that some quality lost due to use of generalized international coefficients rather than nation specific ones.


coefficients rather than nation specific ones.

However (we believe) that some our modelling used is superior to the default options suggested in the guide (notably for biomass and metal ores). Also much less biased towards industrialized nations.

Note: beware some misleading wording in “The Guide” e.g. mis-statement of ore grades.

Key material categories.

Ultimately, four materials categories were defined, with further detail within these in 11 sub-categories. Similar to “The Guide” top level divisions, but not identical

Category Sub-category

Biomass Crops

Crop residues

Grazed biomass


Wood

Fossil fuels Coal

Petroleum

Natural gas

Metal ores and industrial minerals Ferrous ores

Non-ferrous ores

Industrial minerals

Construction minerals Construction minerals

Compilation level often more detailed.

Initial compilation of DE done into 35 categories, conform to The Guide’s categories at 2 to 4 digit level.

Country EWMFACat EWMFAName 1970 1971 1972

Australia A.1.1.1 Cereals 12904533 14840092 10781927

Australia A.1.1.10 Other crops 20979 20536 17861

Australia A.1.1.2 Roots and tubers 763149 775769 824603

Australia A.1.1.3 Sugar crops 17644800 19390500 18928300

.. .. .. .. .. ..

Australia A.1.2.1 Crop residues (used) 22236442 24991770 21413062


Australia A.1.2.1 Crop residues (used) 22236442 24991770 21413062

Australia A.1.2.2.2 Grazed biomass 57746580 61653104 70415892

Australia A.1.3.1 Timber (Industrial roundwood 5989240 6295400 6044480

Australia A.1.3.2 Wood fuel and other extraction 1796720 1798338 1799840

Australia A.2.1 Iron Ores 51186080 62096904 64398144

Australia A.2.2.1 Copper ores - gross ore 16037299 18289790 19558434

Australia A.2.2.2 Nickel ores - gross ore 538510.5 848992.9 1138581

.. .. .. .. .. ..

Australia A.3.1.4 Chemical and fertilizer minerals 174269.8 241905.1 264879.8

.. .. .. .. .. ..

Australia A.4.2.2 Natural gas 1029096 1532984 2208486

Main raw data sources for DE estimation.

Colour indicates degree of confidence in estimates (green high, red low)

Sub-category Main Raw Data source Post Processing

Crops FAO Crop Production Statistics Minimal

Crop residues FAO Crop Production Statistics Moderate modelling

Grazed biomass FAO Food balance sheets Extensive modelling, large assumptions

Wood FAO Forestry Minor modelling


Coal IEA (and EIA) Minimal

Petroleum IEA (and EIA) Minimal

Natural gas IEA (and EIA) Minimal (Energy to weight conversion)

Ferrous ores USGS, UN Industrial Commodities Minimal, moderate assumptions

Non-ferrous ores USGS, UN Industrial Commodities Simple modelling, large assumptions

Industrial minerals USGS, UN Industrial Commodities Simple modelling, large assumptions

Construction minerals USGS, UN Industrial Commodities Moderate modelling, large assumptions

Main raw data sources for Trade estimation.

Colour indicates degree of confidence in estimates (green high, red low)

Sub-category Main Raw Data source Post Processing

Crops FAO Trade Statistics Minimal

Crop residues FAO Trade Statistics , UN Comtrade Minimal

Grazed biomass FAO Trade Statistics , UN Comtrade Minimal

Wood FAO Forestry Minimal


Wood FAO Forestry Minimal

Coal IEA (and EIA) Minimal

Petroleum IEA (and EIA) Minimal

Natural gas IEA (and EIA) Minimal (Energy to weight conversion)

Ferrous ores UN Comtrade Minimal

Non-ferrous ores UN Comtrade Minimal

Industrial minerals UN Comtrade Minimal

Construction minerals UN Comtrade Minimal

Raw data is only a very rough starting point for some

categories of material - 1

EW-MFA is interested in determining the quantity of raw material as extracted from the environment

Fossil fuel statistics are excellent, and most mass is retained in traded products.


Basic crop data is good and generally in the units / on the basis we require.

Forestry data also reasonably good, roundwood basis is what we want, and weight conversions not too difficult.

In Contrast:

Raw data is only a very rough starting point for some

categories of material - 2

Statistics on Metals usually on contained metal or concentrates basis. We want ore.

Statistics on crop residues need to be calculated from crops produced. We are only interested in that portion that enters economy. Statistics here are poor.

Construction materials are rarely well recorded. The best we can do is


Construction materials are rarely well recorded. The best we can do is get the figure for cement (which is recorded), and apply factors to that.

Grazed biomass is almost never recorded. The figure is calculated from complex modelling based on other figures, some of which are poorly determined.

DMC and the problem of double counting.

Double counting can be a major problem in determining DMC. A compromise between missing major flow volumes and double counting was reached.

We used different scopes for DE and Trade.

For DE, only primary materials as extracted from the environment were counted, as double counting of mass occurs when we include


counted, as double counting of mass occurs when we include processed goods. e.g. Crude oil extracted + gasoline refined = double counting

For Trade, products which had undergone considerable processing were included in volumes e.g. exports of roundwood + wood chips + paper are independent. Elaborately transformed, multi-material items are excluded (of necessity).

Example 1 - Grazed biomass - complex modelling, many

assumptions.

No nation measures how much grass its herds eat. HOWEVER:

Production of animal products is generally well recorded - FAO.

Portion of crops and fishmeal going to animal feed is recorded (to some degree) – FAO.

Studies have been undertaken on the feed energy required to produce


Studies have been undertaken on the feed energy required to produce different animal products (most notably Wirsenius 2000).

(Animal products x required feed energy/kg) – feed energy supplied from crops = “Feed Gap”

Missing energy required for ruminant products must (we hope) come from grass.

Grazed biomass (cont.) - Hierarchically model different

animals’ claims on feed crops.

Country Item MJ/kg

Afghanistan Cattle meat 499

Afghanistan Cow milk, whole, fresh 13.82

Afghanistan Eggs Primary + (Total) 53

Afghanistan Goat meat 998

Afghanistan Goat milk, whole, fresh 27.64

.. .. ..

Albania Cattle meat 160

Albania Cow milk, whole, fresh 9.95

Albania Eggs Primary + (Total) 42


Albania Eggs Primary + (Total) 42

Crop NEmCattle NEgCattle TotNExCattle DEPig MEPig MEChicken

wheat_grains 9.35 6.49 15.85 16.3 15.6 14.7

rice_grains 9.35 6.49 15.85 16.3 15.6 14.7

maize_grains 9.35 6.49 15.85 16.4 15.7 15

.. .. .. .. .. .. ..

sorghum_grains 8.59 5.82 14.42 15.5 14.9 15

cassava_tubers 8.16 5.43 13.6 14.5 13.9 0

Example 2 – Metal ores - simple modelling, huge

assumptions.Statistics on Metals usually on contained metal or concentrates basis. We want ore tonnages.

Conceptually very simple, Ore = contained metal / grade

Unfortunately:

• Ore grades can vary enormously between deposits and countries.


• Ore grades can vary enormously between deposits and countries.

• poly-metallic deposits –> coupled production -> double counting*.

• Trade data does not distinguish well between metal ores, concentrates.

*In a sense, there is no fully satisfactory answer to the coupled production problem. The Guide’s allocation by value is perhaps as good as any.

What can be done by individual countries to improve EW-

MFA.

• Compile key mining statistics by commodity, especially tonnes and grade of ore (mine by mine).

• Better record production of construction aggregates.

• Report mineral imports / exports in more disaggregated form i.e.


• Report mineral imports / exports in more disaggregated form i.e. separate ores from concentrates, and attach a weighted average metal content to each.

• Determine what non-grazed feeds are actually received by individual classes of animal.

Links and further reading.

CSIRO and UNEP REEO reports and online material flows databases :http://www.csiro.au/Outcomes/Climate/Adapting/Resource-Efficiency-Asia-Pacific.aspxwww.csiro.au/AsiaPacificResourceFlows, www.csiro.au/LatinAmericaCaribbeanResourceFlows(see also the technical annexes linked from these pages)

“The Guide”: just Google “Eurostat EW-MFA Compilation Guide” to get latest.

Context setting:


Context setting: Krausmann, F., S. Gingrich, N. Eisenmenger, K.-H. Erb, H. Haberl, and M. Fischer-Kowalski. 2009. Growth in global materials use, GDP and population during the 20th century. Ecological Economics 68: 2696 - 2705.

DMC and Trade issues: Schandl, H. and J. West. 2012. Material Flows and Material Productivity in China, Australia, and Japan. Journal of Industrial Ecology 16(3): 352-364.

Material Footprint: Wiedmann, T. O., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, and K. Kanemoto. 2013. The material footprint of nations. Proceedings of the National Academy of Sciences.

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