Environmental Engineering Reference
In-Depth Information
agreements with farmers to test new crops, including NIS. Although we can
nd no evi-
dence that these tests are designed in part so as to quantify the risk that these crop species
will become invasive, this would seem justi
fi
fi
ed.
(2006) argues that, due to our incomplete understanding of which species
pose the largest risk, countries should take a precautionary approach with regard to per-
missible imports of NIS. However, research on learning under uncertainty suggests that,
when damages and control costs are uncertain and can only be learned through experi-
ence, erring on the side of caution may be advisable (Kolstad, 1996). In terms of policy
prescriptions, this suggests that countries may want to employ grey lists instead of white
or black lists: rather than either banning (black list) or granting unimpeded entry (white
list) to unknown species, countries may want to grant restricted entry to unknown species
so that more can be learned about them (as was practiced by the aforementioned US
policy on avocado imports from Mexico).
Learning can also take place by observing the outcomes of a given introduction in other
countries. Calzolari and Immordino (2005) examine this possibility with respect to new
products with uncertain human health e
Simberlo
ff
ed crops) which
can be learned over time - through experience. They conclude that each country wants
the other to allow the new product and provide the safety information. This incentive to
free-ride results in an ine
ff
ects (such as genetically modi
fi
ciently low level of learning globally.
Damage
Without doubt, reactive policy, particularly eradication and control, is essen-
tial to managing NIS damages. In some instances reactive and pre-emptive policy can be
analyzed separately. For example, when budgets are unconstrained, research on the
optimal pre-emption program can treat the damages from allowing entry of
X
individu-
als as an exogenous function
D
(
X
), where
D
(
X
) measures the expected loss arising when
the optimal post-introduction control policy is applied.
X
may represent a vector of traits:
e.g. if individuals vary genetically, then
X
will measure the invading population's genetic
composition. Alternately, if the structure of the host economy varies with the type of
instrument used, then
X
will include information on domestic and international prices.
The function
D
(
X
) then embeds an optimal post-introduction response. While not the
purpose of this review, it is noteworthy that the post-introduction response literature
includes analysis of both deterministic and stochastic growth (see Olson and Roy, 2002).
Finally, if there is uncertainty regarding the number of individuals introduced,
X
may be
a random variable. Olson and Roy (2005) show that the (relative) bene
ts of control and
prevention depend only on the expected introduction rate if marginal damages are con-
stant; however, when damages are variable, additional information is needed.
However, when agency budgets for preventing and controlling NIS are jointly con-
strained,
D
(
X
) cannot be treated as exogenous: the revenue-generating/depleting proper-
ties of pre-emptive policies such as tari
fi
ff
s and inspections will impact damages from
actual introductions.
Finno
et al. (2006) look at the preferences of managers for prevention relative to
control of invasive species; they observe that managers tend to prefer control because its
productivity is less variable. Leung et al. (2005) evaluate prevention versus control with
an application to zebra mussel invasions into lakes with power plants. Finno
ff
et al.
(2005a, 2005b) examine a framework wherein a government agency chooses optimal pre-
vention and control of a zebra mussel invasion in a Midwest lake, taking into account
ff
