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
The misinterpretation of variables of national accounts and especially of GNP has long been a
subject of discussion and controversy. The follow- ing statements suggest that the misuse of environ-
mental accounting systems will be even greater.
1 There is no simple justifiable valuation sys- tem for environmental accounting, such as the
market prices employed for SNA. For different aspects of environmental problems different valu-
ation procedures are used: prevention and restora- tion costs, contingent valuations, etc. The results
of these different valuations can be great Pearce et al., 1989; Hausman, 1993, so that the choice of
valuation system totally dominates the results. As each of these valuation systems is only appropri-
ate for the analysis of certain specific questions, the mixing of different systems results in signifi-
cant problems with regard to interpretation.
2 The necessity for ‘consistent’ pricing of all environmental aspects in monetary terms has
many serious consequences. Either the accounting system is restricted to those aspects which are
relatively easy to monetize, thereby reducing the range of the accounting system, or, if ‘complete-
ness’ is sought, increasingly dubious valuation methods must be tolerated. In any case the selec-
tion norms are not set by the goal of the account- ing system but rather by the obligation to
monetize.
3 Environmental conditions expressed in monetary variables implicitly give the impression
that they are easily comparable with other mon- etary variables, such as yields of economic in-
vestments. However,
due to
the fictitious
character of monetized environmental variables, such comparisons necessarily lead to serious mis-
understandings. 4 Finally, a monetization of variables and
the consequent concentration on only a few ag- gregates results in such a drastic reduction of
the analytical potential of the accounting system that it cannot possibly be of use for policy-mak-
ing see also Lintott, 1996.
The advocates of monetization of environmen- tal variables argue that in present-day society
only monetary terms attract sufficient attention: ‘‘Information in physical units is scarcely ab-
sorbed in economic policy and literature. As we are living in a monetary society, it seems as if
only monetary terms play a role in economic decision making with respect to our environmen-
tal problems. Politicians can only deal with monetary numbers…’’ Richter, 1991, p. 9.
These considerations point directly to the fun- damental problem of SEEA. SEEA is a politi-
cally important
accounting system,
focusing international attention on the necessity to take
into consideration environmental damages as well as to maintain environmental quality. Such
a politically motivated system is invariably a compromise between differing opinions and rec-
ommendations. In addition, the system is ex- pected to allow acceptable entries for countries
with different quality levels of national statistics.
These merits
of SEEA
are undisputed.
Nonetheless, criticism of an important account- ing system, propagating the valuable idea of en-
vironmental protection, is justifiable if, from a scientific point of view, the method chosen
seems to be misleading or even dangerous. The argument that it is better to have a problematic
system with the right intention than to have no system at all, is just as unacceptable as saying
that it is better to have wrong data than no data.
5. What can be done?
