the integration of genuine ecological viewpoints. We cannot, therefore, agree with the statement
that ‘‘Using the SNA as a starting-point for the SEEA does not necessarily lead to a purely eco-
nomic view of environmental concerns. Rather, it permits the introduction of ecological elements
into economic thinking…’’ United Nations, 1993, S.23.
The apparent dominance of economics certainly cannot be explained as a disregard for ecology,
but in fact results from the fundamental incom- patibility of the scales used in economics and
ecology. If this fact is recognized, unfounded ex- pectations with regard to SEEA can be avoided.
3. The preponderance of artificially generated data
SEEA implicitly assumes comprehensive knowl- edge of economic and ecological facts and their
mutual relations. Among other things it contains the following broad assumptions.
It is possible to attribute at least those immis- sions resulting directly from production and con-
sumption to specific economic activities with sufficient exactness over longer periods of time:
1 the relations between emissions and immis- sions are so precisely clarified that the transfer of
pollution from its site of origin to the site of its effect can be clearly traced;
2 the dynamics of ecosystems are so well understood that damage induced by pollution can
be estimated with sufficient precision; 3 it is even possible to estimate these damages
with such precision that they can be expressed in monetary equivalents.
It is obvious that the above assumptions cannot be based on empirical observations but can only
be artificially ‘constructed’ with the help of hy- potheses and models. The recognition of this fact
leads to the fundamental question about the con- sequences of the existence of such model hypothe-
ses in ex-post accounting systems.
Holub and Tappeiner 1997 have shown that SNA 1968 as well as SNA 1993, and all national
accounting systems contain, besides directly ob- servable variables, many variables that are gener-
ated with the help of theoretical models. Such model-generated variables are used at micro lev-
els, for instance micro data at the level of single units
2
, up to complex variables at higher aggrega- tion levels, for instance coefficient matrices of
input – output tables.
3
In principle these artificially generated data could in some cases be observed but, because of
the high costs engendered by unreliable informa- tion from respondents, they are only estimated.
Some of these data cannot be observed, for in- stance information about regional imports or fac-
tor incomes. The hypotheses applied extend from simple proportional estimates on the basis of key
variables to incisive assumptions about produc- tion processes.
SEEA contains an even greater number of such hypothesis-generated variables than those in exist-
ing national accounts. 1 To connect environmental data directly with
data of existing national accounts it is necessary to assign environmental pollution loads to the
appropriate economic activities. On the one hand these activities must form the basis for environ-
mental policies United Nations, 1993, p. 25; on the other, the costs of avoiding pollution can only
be determined if its origins are identifiable.
2 Another problem concerns the connections between emissions and immissions. These connec-
tions are indispensable for determining the rela- tionship between environmental pollution and
environmental damage. In addition, without an understanding of these connections no spatially
delimited accounting system can be developed. Because in many cases one special pollutant load
may suffice to severely influence a whole ecosys- tem regardless of the quantities of other pollu-
tants, and because of the regeneration potential in nature, as well as our ignorance of the decomposi-
tion pathways of many factors in environmental
2
For instance, the production of small firms is artificially estimated with the help of key variables derived from produc-
tion data of larger firms.
3
These quadratic input-coefficient tables are generated with the help of make and use matrices based on industry technol-
ogy or commodity technology assumptions. Both assumptions are incisive hypotheses with respect to production processes.
pollution, the gap between necessary information and observable facts is practically insurmountable
and has therefore to be bridged by modeling assumptions.
A further point refers to the connections be- tween immissions and environmental decay. This
connection is the basis of every assignment of restoration costs to the economic activities re-
sponsible. However, this connection is obscured by at least four problem areas. For many types of
environmental damage the causes are not clear. Examples are provided by hazards to almost all
impairments of human health induced by pollu- tion levels below currently established thresholds.
Secondly there is the problem of multi-causality: if several pollution factors are necessary for dam-
age to occur, the assignment of this damage must be highly arbitrary. A third problem arises from
the fact that some of the consequences of environ- mental pollution only become visible after a long
delay for instance, genetic consequences of ra- dioactive pollution. Consideration of only the
immediate consequences would lead to false prior- ities in environmental policies. Fourthly, the visi-
ble consequences of cumulative pollution factors do not necessarily increase monotonically with the
cumulation process. An impressive example of this is seen in the considerable temporal deviation
in forest damage resulting from diverse natural causes.
However, most strongly dependent on theoreti- cal assumptions in SEEA are the valuations for
evaluation see Hueting, 1991. Prevention and restoration costs and especially contingent valua-
tions based on surveys are exclusively theoretical constructions. They are only valid against the
background of a special and rather arbitrary model world.
Why does the existence of model-generated data in an accounting system like SEEA pose
such problems? The most cogent argument against model-generated data is that the hypothe-
ses used to generate these data may be incorrect. This implies that the resulting data are subject to
errors which differ fundamentally from the well- known errors in empirical surveys deficient defin-
itions, sample errors, etc.. Moreover, the extent of these errors cannot be adequately assessed by
users. As a result of the implicit theoretical as- sumptions of hypotheses contained in the data,
users lose degrees of freedom for their own theo- rizing. In any case, the results of personal model-
ing cannot be adequately interpreted.
In most cases users of the data have no means of eliminating the effects of built-in hypotheses,
nor can their impact on the data be estimated with the help of sensitivity analyses. Thus obser-
vation and fiction can no longer be separated. The problem becomes even worse when model-gener-
ated data are aggregated with empirically ob- served data into an inhomogeneous conglomerate
which users are unable to sort out.
4. The danger of a misuse of SEEA data
