Download EIOA/EEIOA swot - Reviewed

CALCAS SWOT Analysis framework
Draft 2: November 2006
Basic information: [1 page]

Name of the assessment tool: Environmental Input-Output Analysis / Environmentally
Extended Input-Output Analysis

Acronym: Env-IOA, E-IOA or EIOA / EE-IOA or EEIOA

Author of SWOT evaluation (name, organisation, address, e-mail):
Kiti Suomalainen, IST,
Centre for Innovation, Technology and Policy Research,
Departamento de Mecanica II,
Av. Rovisco Pais,
1049-001 Lisbon,
Portugal
[email protected]

Level of assessment:
micro (e.g. household, company, product level),
meso (e.g. sectors, material flow systems, branches),
macro (e.g. countries, economies).
Mainly macro, can be applied to meso-level in some cases depending on the level of
detail of available data and purpose of assessment. Bottom-up approaches of IO-based
LCA allow micro level assessment.

Assessed aspects of sustainability: Environmental, generally national environmental
accounting.

Main purpose of the assessment: To account for environmental burdens and
understand interdependencies of environmental impacts at a national level, related to
transactions between national industries/sectors.

Description of the methodology: EIOA/EEIOA are expansions of conventional IOA
introducing the environmental dimension to the conventionally monetary analysis.
EIOA uses physical units to describe the activities between sectors, added as extra
lines to the conventional IO-table. EEIOA defines a separate so called intervention
matrix, which shows the amount of pollutants emitted and natural resources consumed
to produce one unit monetary output of each industry. In addition to flows (input
materials) corresponding to monetary trade, quantifiable externalities such as pollution
are typically also included.
Detailed description [1-3 pages]
Input-output analysis accounts for the level of output of each sector corresponding to that
level of activity in all the other sectors in a national economy. By-products, mostly negative
from the environmental view point (e.g. pollution), are often linked directly to the network of
physical flows that build up an IO-table. This technical interdependence can be described in
terms of structural coefficients similar to those already used to trace the interdependence of
production between the sectors of the economy (Leontief, 1970).
EIOA
In EIOA pollutants can be integrated to the conventional IO-tables as their own sectors; each
sector in its production may cause a certain amount of pollution that is accounted for as an
input to that sector. Thus the vectors and matrices are grouped by whether the figure refers to
goods or pollutants;
1,2,3, .. . i. .. j .. . m, m+i,m+2,. .. g .. . k .. . n
goods
pollutants
The technical coefficients become;
aij – input of good i per unit of output of good j
aig – input of good i per unit of eliminated pollutant g (eliminated by sector g)
agi – output of pollutant g per unit of output of good i (produced by sector i)
agk – output of pollutant g per unit of eliminated pollutant k (eliminated by sector k)
rgi, rgk – proportion of pollutant g generated by industry i or k eliminated at the expense of that
industry.
Variables are denoted by;
xi – total output of good i,
xg – total amount of pollutant g eliminated,
yi – final demand of good i,
yg – final delivery of pollutant g,
pi – price of good i,
pg – price (cost) of eliminating one unit of pollutant g,
vi – value-added of industry i per unit of good i produced by it,
vg – value-added in anti-pollution sector g per unit of pollutant g eliminated by it
With this notation the matrices and vectors become;
A11 = [aij]
A21 = [agi]
A12 = [aig]
A22 = [agk]
Q21 = [qgi]
Q22 = [qgk]
i, j = 1, 2, 3,…, m
g, k = m+1, m+2,…, n
qgi = rgi agi
qgk = rgk agk
y1
x1
X 1=
x2
⋮
xm
Y 1=
;
x m+2
⋮
xm
⋮
ym
V 1=
;
y m+1
x m+1
X 2=
y2
v1
Y 2=
;
y m+2
⋮
ym
v2
⋮
vm
;
v m+1
;
V 2=
v m+2
⋮
vm
;
Once the appropriate technical input and output coefficients have been identified, pollution
and its mitigation can be included in the IOA as integral parts of the economic processes. The
physical input-output balance becomes;
[
][ ] [ ] [ ] [
I − A11 − A12 X 1
Y
X
I − A11 − A12
= 1 ⇔ 1 =
A21
− I+A22 X 2
Y2
X2
A21
− I+A22
−1
][ ]
Y1
Y2
The input-output balance between prices and values-added (in monetary units) is described as;
[
I− A
'
−A
' 11
12
' 21
−Q
'
I− Q
22
][ ] [ ] [ ] [
P1
V
P
I− A
= 1 ⇔ 1 =
'
P2
V2
P2
−A
' 11
12
' 21
−Q
'
I− Q
22
−1
][ ]
V1
V2
EEIOA
In EEIOA, on the other hand, a separate interventions matrix is compiled to represent
environmental interventions for each industry involved. As for technologies in IO-tables,
linearity is assumed also for environmental interventions, i.e. that the amount of
environmental intervention associated with an industry is proportional to the amount of output
of that industry. If B (dimension qxm) is the intervention matrix, showing the amount of
pollutants emitted and natural resources consumed to produce one monetary unit of output of
each industry, then the total direct and indirect pollutant emissions and natural resources
corresponding to satisfying a certain amount of final demand y is given by
m = B(I-A)-1y
where m (dimension q) is the total domestic direct and indirect vector of environmental
burdens and A is the conventional Leontief technology matrix (Tukker at al, 2006).
This is closely related to National Accounting Matrices with Environmental Accounts
(NAMEAs), which were introduced by the Dutch Central Bureau of Statistics in the 1990's
and were soon after adopted by Eurostat and spread to all EU countries. NAMEAs integrate
economic accounts with environmental data providing a picture of where exactly in the
economy emissions, waste and resources are used or generated.
In Europe EEIOA has been used with NAMEAs to identify hotspots within the European
production and consumption patterns and reductions in per capita resource use and emission
outputs. This provides a tool for developing policies for sustainable consumption and
production (SCP). Such a tool can answer questions on which features of European
consumption and production are mainly responsible for the high energy and resources use,
and where the greatest gains in sustainability can be made. EEIOA with NAMEAs is a
method for identifying the environmental pressures resulting both from national production
and consumption; it may be used to identify key intervention points and to help EU and
national policy makers to take SCP a step further. EEIOA and NAMEAs offer complement to
existing inventories on air emissions and energy, and allows for a consumption-oriented view
of the economy; EEIO methods present real possibilities for estimating indirect pressures
associated with national consumption.
However, the NAMEAs are still limited in scope and are yet to be developed to include
several more categories such as land-use, waste flows, water flows and toxic emissions. Also
time-series of NAMEAs are not yet widely available.
Other similar methods
Several other IO-approaches have been developed to support traditional LCA studies. Since
all processes in an economy are directly or indirectly linked with each other, a process
analysis based LCI is always truncated to a certain degree, limited by the practically and
viably set scope of the study. This problem has led to the use of IOA in LCA. IO-based LCI
uses basically the structure of EEIOA for tracking material flows and pollutants. It may be
argued that the system boundary of an IO-based LCI is generally more complete than that of a
process analysis, since it includes all transaction activities within the country. However, the
IOA method can provide LCIs only for the pre-consumer stages, thus not including use and
end-of-life phases. Also the IO approach is suitable only if the amount of imported
commodities is negligible in the studied system. Finally, the available data for IO-based LCIs
is normally older than that available for process-based analysis, since it typically takes several
years to collect and publish IO tables (Suh & Huppes, 2005).
The best choice for future seems to be the so called hybrid analysis, which links processbased and IO-based analysis to combine their strengths. Three main ways of doing it are
tiered hybrid analysis, IO-based hybrid analysis and integrated hybrid analysis. Tiered hybrid
analysis uses process-based analysis for use, end-of-life and the most significant upstream
phases and IO-based LCI for the remaining input requirements. This offers an easy extension
on simple partial LCA systems in filling in the gaps. IO-based hybrid analysis increases the
IO resolution by disaggregating industry sectors and uses this augmented IO-table for the
upstream processes. Process based analysis is used for use and end-of-life phases. This IO
approach avoids the problem of overlapping calculations for upstream processes. Both tiered
and IO-based LCI use external linking between the IO approach and the process based LCA
approach. In integrated hybrid analysis the IO table is interconnected with the matrix
representation of the physical product system both at upstream and downstream cut-offs,
where detailed, case specific data are not available. This appears to be the most seamless
approach of the hybrid alternatives. It has an advantage in its quality of results especially
when is comes to system completeness (Suh & Huppes, 2005).
Applications
The input-output approach has been applied on many different types of environmental
analysis, but at a national level the general question to be answered is of the form “how much
production is needed in various parts of the economy to satisfy a final demand of a certain
product worth x monetary units”. From an environmental perspective, EIOA can be used e.g.
to calculate energy consumption or emissions related to the production of a certain good, in
total or per a monetary unit of production, or to analyse emissions due to imports (Miller &
Blair 1985) or more generally the environmental impacts of trade (Hubacek & Giljum 2003).
EIOA has also been used to evaluate the emissions of CO2, SO2 and NOx in relation to a
governmental long-term scenario for the national economy (Östblom, 1996), or to build
indicators for emissions per product group, e.g. identifying the CO2-intensities for different
goods (Alfredsson, 2002). At the process level IO-approaches have generally been used as a
complementing tool for LCA and provide systematic tools that can be used for integrated
environmental analysis and planning (Pan & Kraines, 2001).
Strength: [1 page]
a) relevance of assessment (what is being assessed, usefulness)
EIOA/EEIOA are useful for identifying the flows that account for the most significant
environmental impacts at the level of study, and serve as a sound basis for mitigation
policy.
These approaches bring together economic and environmental data in a consistent,
related sectoral framework.
EEIOA may be used
 to identify key intervention points,
 to help EU and national policy makers to take SCP a step further,
 as a complement to existing inventories on air emissions and energy, allowing for
a consumption-oriented view of the economy,
 to present real possibilities for estimating indirect pressures associated with
national consumption,
 to evaluate environmental performance at the meso and macro dimensions,
especially where higher resolution at sectoral levels has been achieved.
b) Methodology
EIOA/EEIOA is computationally compatible with LCA and flexible at integrating
other data sources. It has the computational advantage of being a transparent tool, all
flows can easily be tracked to their sources. EIOA also has the additional flexibility to
include all relevant flows (not based on existing standardized formats) and can be
applied on all levels, if relevant data is available. Thus as long as the used data is valid
for the studied flows, the method is highly robust and reliable.
EEIOA has the methodological advantage of a coherent framework where
environmental, economic and social data can be inventoried. Such standardized data
sets can be used for multiple purposes from environmental monitoring to policy
analysis.
Weaknesses/Limitations: [1 page]
a) relevance of assessment (what is being assessed, usefulness)
EIOA/EEIOA cannot be used to draw very detailed conclusions from a macro-level
study, but more specific analysis is usually required.
EIOA/EEIOA is typically based on static accounting, which makes it less suitable for
long term sustainability analysis. It is difficult to include consequences of
technological change in the system unless a more complex model is built e.g. in timeseries. I addition, EIOA/EEIOA gives the average effect of a change in the system,
which may be an understatement. It is the marginal effect that would give the real
effect of a given change, and thus allow identifying where in the economy responding
to that change would be least expensive.
Currently the data is often indirect and/or based on partial measurements of resource
extraction and emissions corresponding to economic inputs and output, and thus
statistical procedures have to be used for producing the data sets required for deriving
EEIO tables. Also the underlying IO tables used still differ considerably among
countries both in structure and level of differentiation, making comparison of EEIO
analyses difficult.
At a European level EEIO tables are still limited to few emissions to air (GHG
emissions) and comprehensive EU-wide EEIO tables are still non-existent (Tukker et.
al., 2006). It will take several years until EEIO tables with a few dozen emissions to
air and water, and the most important extractions of natural resources for EU-25 will
be available.
b) Methodology
At macro-level much detail is omitted and the assumption of homogeneity between
products of a sector may be a disadvantage when trying to identify specific sources of
pollutants. Also all interdependencies are assumed linear, which brings the problem of
scaling – linear interdependencies are typically an acceptable approximation only for
small changes. Data collection and model construction may be cost-intensive and
time-consuming tasks.
There is also a gap between theoretical and practical application of the methodology.
Data gathering on products are currently in different and only partially linked
classification systems; one for domestic product flows and another for imports and
exports. Data gathering and processing becomes unnecessarily complex.
Opportunities for broadening life cycle approaches: [1 page]
e.g. benefits for the economy, scientific progress, policy-making
EIOA/EEIOA can be used at EU or national level to identify spots where a more detailed
study is necessary with a well defined LC approach. It can be used to help identify areas for
policy-targeting. Whereas IOA informs us about the economic interconnections of an
economy, a general framework for EIOA/EEIOA could inform policy makers about the
general environmental performance of that economy.
EU research can take advantage of standardized EU statistics to identify coherent EU level
patterns and construct an entire snapshot of physical flows (including pollutants) within the
EU or beyond. Such results can be used as input for Sustainable Consumption and Production
(SCP) policies as well as for knowledge gathering for the Thematic Strategy on Sustainable
Use of Natural Resources.
In order to work through the inconsistency between IO- and EIO-tables among countries and
regions, a combination of ESA95 and NAMEAs to a consolidated EU table has been
proposed (Tukker et. al., 2006). Three pathways have been identified differing in structure,
data and costs, of which the medium resolution EEIO tables , so called IO/NAMEA++, was
suggested as most favourable for short-term developments. This approach is a European EEIO
model based on existing procedures and using only European data with easier updatability
than what current procedures allow for.
IO-LCI and hybrid analysis clearly offer systematic ways of broadening the scope of LCA,
both in completeness and number of aspects taken in to account. Whereas traditional LCA
inevitably has to cut-off the scope at some level, hybrid analysis allows all the national
interconnectedness to be included. Also the matrix form allows easy inclusion of additional
aspects; an economic evaluation of the life cycle becomes quite straight-forward.
Risks for broadening life cycle approaches: [1 page]
e.g. risks due to insufficient data, cost-intensiveness, lack of salience or credibility
Gathering data from all EU member countries has the risk of lack of validation of data. Using
data only from established data sources may be limited. For comprehensive long-term
conclusions the collection and analysis of time-series is necessary, which is a cost-intensive
and time-consuming task.
In addition EIOA lacks clear definition of the methodology, especially in scope, which may
hinder broader use of the tool. EU research can avoid risks that are harmful to EIOA by
defining a standard model and the statistical data that should be provided by member
countries to build a robust and reliable tool for sustainability assessment at the EU-level.
Whereas EIOA is quite flexible in defining its scope, it is essential to standardise these
procedures for EEIOA used at EU-level. Not doing so risks the credibility of this
methodology. A standardised “all-inclusive” EU-wide table also avoids the risk of omitting
some relevant environmental aspects.
Although EEIOA methods and models have been developed in a unified framework much due
to the UN-directed standardisation and classifications of sectors and products, it is not
compatible with the currently used European ESA95 data sets. Due to the lack of European
standardised classification systems and procedures, data gathering is currently much more
expensive than necessary – by better coordination and alignment the current effort in
manpower could produce greatly improved data for supporting sustainability policies.
Literature/Internet links:
Alfredsson, E., 2002, Green consumption, energy use and carbon dioxide emissions. Thesis,
Department of Social and Economic Geography, Spatial Modelling Centre, Umeå University,
Umeå.
Finnveden, G., Moberg, Å., 2005, Environmental systems analysis tools – an overview,
Journal of Cleaner Production 13, 1165-1173.
Grêt-Regamey, A., Kytzia, S., 2007, Integrating the valuation of ecosystem services into the
Input-Output economics of an Alpine region, Ecological Economics 63, pp. 786-798.
Hoekstra, R., Bergh, J.C.J.M van den, 2006, Constructing physical input-output tables for
environmental modeling and accounting: Framework and illustrations, Ecological Economics
59, pp. 375-393.
Hubacek, K., Giljum, S., 2003, Applying physical input-output analysis to estimate land
appropriation (ecological footprint) of international trade activities, Ecological Economics
44, pp 137-151.
Leontief, W., 1970, Environmental Repercussions and the Economic Structure: An InputOutput Approach, The Review of Economics and Statistics, Vol. 52, No. 3., pp. 262-271.
Leontief, W., 1986, Input-Output Economics, Oxford University Press.
Miller, R., Blair, P., 1985, Input-Output analysis – foundations and extensions, Prentice-Hall,
Englewood Cliffs NJ.
Pan, X., Kraines, S., 2001, Environmental Input-Output Models for Life cycle Analysis,
Environmental and Resource Economics 20, pp. 61-72.
Suh, S., Huppes, G., 2005, Methods for Life Cycle Inventory of a product, Journal of Cleaner
Production 13, pp. 687-697.
Tukker, A., Huppes, G., Oers, L. van, Heijungs, R., 2006, Technical Report Series:
Environmentally extended input-output tables and models for Europe, Institute for
Prospective Technological Studies, European Commission, Directorate-General Joint
Research Centre.
Östblom, G., 1996, Emissions to air and the allocation of GDP: Medium term projections for
Sweden in conflict with the goals of CO2, SO2 and NOx emissions for year 2000. Working
Paper no.54, Konjunkturinstitutet, Stockholm.