Question

If we were to try to implement the economic concept of sustainability, we would face some important sources of uncertainty. Describe these areas of uncertainty and how they might limit our ability to implement sustainable policies.

Answer 1

Uncertainties pertinent to the environmental context of economies and its sustainability have implications for precautionary policies. Three types of uncertainty are acknowledged: uncertainty due to ignorance, model uncertainty and parameter uncertainty.

Consequently, four categories of uncertainty form the backbone of our framework:
Inherent uncertainties
Scientific uncertainty
Social uncertainty
Legal uncertainty
Below, we elaborate on the four categories of uncertainties, followed by an identification of
strategies for dealing with them.

1. Inherent Uncertainty—“We Cannot Know (Exactly)
The first category of uncertainty is inextricably connected to the inherent unpredictability of
the system under study. In many systems, the impact of a plan can vary because the physical size
of the potential disturbance varies per location or over time, because the systems or populations
that are affected have a diverse sensitivity to the disturbance, because other issues or trends that
are at play modify the impact of the plan under study, or because the system exhibits some level of
chaotic behavior. Note that this can relate to the environmental system,
as well as the social, legal, or political systems. However, such aspects are already largely covered
under the social and legal uncertainties in the later subsections.
Inherent uncertainties may manifest themselves in SEA in various ways, some of which are
listed below.
Because of variability of the system, the appropriate system boundaries regarding time and spatial
scales are unknown or unclear, or the vulnerability of the system(s), populations, or individuals
impacted varies. It may be possible to give “likely” bounds, but the precise impacts in practice will
vary, and outliers cannot be ruled out. Examples include variability in local weather conditions,
in local activities, or in the way local plants and animals might respond to the effects of a plan in
the environment. For SEAs, it means that the exact magnitude and full range of environmental
impacts of an activity cannot be known. Our knowledge of the natural system determines how
we represent these properties in assessments, and how we design tools to evaluate impact.
In understanding environmental processes, it is important to study the relationships between
cause and effect. Cause-and-effect mechanisms can only be established if these relationships are
well understood. In the case of very complex systems and issues, such as climate change, this is
difficult to establish, and the system may exhibit “chaotic” behavior. As a consequence, assessing
the impact of a future activity in SEAs can become very difficult, especially in complex systems
and for long-term impacts.
Uncertainties also arise in the assessment of cumulative effects.Noise pollution is a good
example for cumulative effects. If an activity takes place on a larger scale, other existing sources
of noise have to be taken into account to study the total impact of noise. Different sources of noise
reinforce each other, called accumulation. Noise increase from the assessed activity might seem
irrelevant, yet, in total, it could mean a significant increase in noise pollution in the area. It can
be difficult to understand how natural phenomena reinforce themselves. Consequently, the full
impact of an activity in an existing situation with multiple sources and burdens may not be clear,
and it can be difficult to attribute reported problems to a specific activity.
Often, inherent uncertainties exist in combination with scientific uncertainties that can be reduced through more research. For instance, the range of variability may not be known at first. The variability can then be better characterized and bounded (reducing knowledge uncertainty about the variability), but the physical source remains. For example, uncertainty around climate-change effects can be reduced by improving data analysis, models, and parameters.
However, despite improvements to models, there will always be some uncertainty inherent to the natural and
socio-economic systems involved which we cannot remove. The reducibility of uncertainty strongly
relates to determining how we deal with uncertainty .
Scientific Uncertainty—“Our Information and Understanding Could Be Wrong or Incomplete”
Scientific uncertainty entails having limited or incorrect information about phenomena.
This relates to “epistemic” or knowledge uncertainty .
Reasons include technical issues such as faults in models or data, and problems in the translation of the
practical problem into the scientific problem. This might, at least in theory, be reduced by performing
additional research , for example, the design and selection of indicators and criteria for assessment.
Technical problems are associated with data and models. More often than not, impact predictions
are made using models, rather than actual measurement, or model extrapolations based on a limited
set of measurements. This is especially the case when predicting air quality, changes in water systems,
or noise pollution in large-scale projects. Model outcomes are used to compare alternatives and
determine a preferred alternative with the least significant effects. Uncertainties may emerge in the
following ways:
Models are simplified abstractions of the real world, and are, therefore, never fully accurate .
Uncertainties can occur in the model structure, variables, and parameters . Similarly, many
assumptions are made in the modeling process, e.g., in designing a model or combining models
in a model chain, where different researchers might make different choices . That models
make simplifications and assumptions is, in itself, not necessarily bad—it is a necessary aspect
of generalizing and applying knowledge of environmental processes to evaluate new situations
(i.e., not yet existing in exactly the set-up proposed). Rather, one should relate models to model
and knowledge quality and to the fitness of the model for the purpose for which it is
Sustainability , used in the assessment . Often, generic models are developed and used in SEAs to find
consistency in the research methodology, and thus, overcome uncertainty due to limitations in
models. Interactions and variables that are unique to the situation might be overlooked.
Models use the input of data. Uncertainty about data can occur due to limited access to
information, measurement errors, type of data, and presentation of data . Also,
data might become invalid in the long term due to greater variability, depending on the time
horizon that is selected. Limitations in data seriously influence the impact prediction that is the
outcome of the model.
Data on baseline conditions is a specific issue. Baseline conditions include the developments,
impacts, and environmental dynamics that would occur without the proposed activity.
Baseline conditions are a critical starting point in SEAs, as they provide the benchmark against
which assessments are predicted. Measurement errors occur in baseline data .
The translation of problems, as defined by policy-makers and planners for scientific problems,
is a second source of scientific uncertainty:
Uncertainties can occur in the choices of data, methods, parameters, and statistics, in other words,
the assessment framework. Science is looking for measures to represent phenomena. It applies to
SEAs in the sense that indicators are selected to study environmental effects, which may not be
the best representation of the real environment .
Furthermore, projects and activities may change, and impacts that are attributable to them change
as well .
When determining change and impact, we need to determine past, present, and future activities
for the development at issue. To create an inventory of all activities, a large amount of effort
and input is needed from different stakeholders. Future activities are especially difficult to include,
since they occur over a longer time scale, influenced by many other factors.
The distinction between inherent and scientific uncertainties is not always clear, and both types of
uncertainty can be related (e.g., our inability to predict how complex systems develop or behave may
result in both types of uncertainty).

Social Uncertainty—“We Do Not Agree on What Information Is or Will be Relevant”
Social uncertainties refer to doubts or ambiguity about information by actors involved in an
SEA, or in the policy or plan at issue. It is caused by differences in human values and interpretations.
The role of social uncertainties in environmental research was only recently recognized, and the
challenge is to account for human input in the decision-making process. This relates in particularly
to the “ambiguity” type of uncertainty, as discussed in the typologies , as well as, to some
extent, to variability (e.g., variability of social values) and knowledge (limited information on the social
perception of the activities proposed, or lack of accounting for social aspects in the scientific analyses)
uncertainty. Some examples of social uncertainties in SEAs include the following:
Stakeholders, as well as decision-makers and researchers in SEAs have different values, interests,
and perceptions of environmental components . Examples are conflicts of interest regarding
the objects to be studied, and different world views regarding what is important. It influences
the framing of the problem, and therefore, the scope of the assessment. It also entails a subjective
selection of criteria and indicators. The assessment of system boundaries and impacts are a result
of negotiations between stakeholders.
The political climate influences whether an environmental problem is addressed, and which
alternatives are considered and selected . Political groups or lobbyists can have a large
influence on the outcome of the decision-making process. They can also demand to study
specific environmental aspects, such as health or sustainability. It depends on the societal
context and the period. It could also mean that politicians pursue political goals, and overrule
environmental issues.
Knowledge frames and capacities of stakeholders are strongly related to inherent and scientific
uncertainty. It entails our understanding of the environmental processes at hand, but it also
entails an understanding of what information is delivered in SEAs. This depends on the capacities
and skills of responsible persons such as policy makers and project managers. Similarly,
the frames of the analysts and competent authorities play a role in shaping the scientific analysis
in the ; issue-framing plays a key role in setting the research questions and boundaries,
strongly impacting what is analyzed and how, and consequently, the results of the analysis .
Social uncertainty can exist in the project design for the SEA process, e.g., organizational factors,
procedures, resources, and coordination among stakeholders .
The implications of social uncertainty could be legal uncertainty (see below), which suggests our
distinction in forms of uncertainty is mainly an analytical one.
4. Legal Uncertainty—“We Do Not Know What InformationWe Should (Legally) Provide”
Legal uncertainty has to do with the decision-making context. It relates to ambiguity in the
uncertainty typologies discussed, as well as, to some extent, to variability (e.g., in legal
rulings and perceptions), and knowledge (e.g., lack of clear criteria or legal precedents) uncertainty.
Decisions that are made in an SEA need to be justified, and decision-making approaches depend
on goals, performance measures, and assessment criteria . For example, new legislation
on specific environmental aspects could pose uncertainty about how to include this in the SEA
process. Legal uncertainty in that respect relates to what one “could or should have known”
before implementing the project to due diligence, as elaborated below.
The decision-making context poses uncertainty as to what information the SEA needs to deliver.
The task of supplying information is imposed on the initiator of the policy or plan . Often,
legal guidelines exist to address the type and amount of information that needs to be delivered
in SEAs to make a decision. However, uncertainty increases when the decision-making context
changes due to new (environmental) legislation or revisions of existing legislation.
The institutional context influences rights and responsibilities, and shapes the degree of power and
influence. This also relates to how responsibilities and definitions, for instance, the definition
of the “precautionary principle”, are embedded in national or European Union (EU) law or
international agreements. Such differences can lead to different levels of proof that are required
before allowing a plan, or to demanding precautionary risk-mitigation actions, and who should
bear the burden of proof .
Furthermore, De Marchi describes legal uncertainty as the future contingencies or personal
liability for actions or inactions. The people involved in an SEA process, including the initiator,
consultants, and decision-makers, are primarily concerned with making their assessments and
decisions appear defensible and politically palatable. Providing information about significant
impacts in a worst-case scenario, or uncertainties in the assessment can have consequences for the
public image, social trust, legitimacy, and political acceptability. The public can use this kind of
information to appeal to a proposal, or at least policy-makers feel that this is the case.