Chapter 6 only Flashcards
Prediction in EA is about what
Prediction in EA is about understanding, to the extent possible, the relationship between the environment(s) potentially affected and the source of the impact or activity that may be harmful or pose risk.
Understanding change in _____________ and ____________ is important to measure the impacts associated with a project.
baseline conditions and the range of normal variability
For example, if employment in the area for which the project is proposed has been steadily increasing over the previous five years, then ____________________
this baseline trend should be predicted in the absence of the project to ensure a more informed decision about the significance of the predicted employment impact of the proposed project.
There are three main categories of impact predictions in EA:
i) impacts of the project on the environment (biophysical and human); ii) impacts of the environment on the project; and iii) the cumulative impacts of the project.
Predictions about _______________ constitute the bulk of predictions in EA. Such predictions have been at the heart of EA since its inception under the US NEPA, but the scope and complexity of impact predictions has broadened considerably to include more holistic and integrated concepts, ____________________
the impacts of a project on VCs of the biophysical and human environment
ranging from ecosystem services and climate change to impacts on well-being and Indigenous rights.
At the most basic level, predictions in EA concern how ___________________
a VC of the biophysical (e.g., water quality) or human (e.g., employment) environment might change under project conditions compared to future conditions in the absence of the project.
These predictions are usually based on
indicator of change in the condition of a VC or indicator of the level of stress to a VC
condition-based indicators
An indicator (e.g., phosphorus concentrations, benthic invertebrate abundance) that provides direct, measurable information about the condition or state of a valued component.
may be used to identify potential impacts on water quality.
stress-based indicators
(e.g., stream crossing density, riparian area disturbance) stress-based indicator Also referred to as a disturbance-based indicator, such as human access or linear feature density, typically used as a proxy for effects on or level of risk to environmental components.
community health and well-being
A holistic concept comprised of a series of social, economic, health, environmental, and other indicators that provides an overall indication of the state or functioning of a local community.
determinants of health and well-being
Underlying factors, such as physical health, education, mental health, health services, coping skills, and social support networks, that collectively provide an indication of an individual’s health.
climate-resilient projects
Projects designed to adapt to changing climate conditions and climate risks.
climate risk
the need to consider the impacts of climate change on projects
Predicting and understanding climate risk is about understanding the ___, _______, and ______
climate hazard, exposure, and vulnerability of the project
The vulnerability of a project to climate risk is a function of ________________, or the degree to which the project and its associated infrastructure are affected by climate change, and ______________, or the ability to adapt project design or project characteristics to changing climatic conditions.
sensitivity, adaptive capacity
Projects with ____________ infrastructure, such as shipping or supply terminals, for example, must be able to adapt to the impacts of accelerated sea-level rise, storm surges, and coastal erosion. For projects in ___________, thawing permafrost (Figure 6.4) poses significant risk to infrastructure, and shortening ice road seasons means that the costs of transporting project goods, construction materials, or resupply are likely to increase over time. If the project is not adaptable, there is high risk of _____________________
coastal; Arctic; stranded assets whereby projects become no longer economically viable because of increasing climate-related costs
A project’s impacts do not occur in isolation—they often __________
add to, interact with, or amplify the impacts of other projects, activities, or disturbances in the project’s regional environment.
cumulative effect
A change in the environment caused by the combined or interacting effects of multiple actions, including natural disturbances, that accumulate across space and time.
In the absence of assessing cumulative impacts, a project’s impacts may be _______________________
mistaken for the cumulative impacts of other activities, or the significance of the project’s additional impacts to a VC may be underestimated.
cautionary threshold
The level of change or set condition at which monitoring efforts should be increased to more closely monitor valued component or indicator conditions to prevent any further adverse change.
target threshold
Typically, a politically or socially defined limit, a margin of safety, and a mandatory trigger for management action.
Morris and Therivel (2001) suggest that impact prediction requires five basic elements, including:
i) sound understanding of the nature of the proposed undertaking;
ii) knowledge of the outcomes of similar projects;
iii) knowledge of past, present, or approved projects whose impacts may interact with the proposed undertaking;
iv) predictions of the project’s impacts on other environmental and socio-economic components that may interact with those directly affected by the project; and
v) information about environmental and socio-economic receptors and how they have in the past and might in the future respond to change.
the quality of an impact prediction depends on the skill and experience of the _______, the nature and availability of ____________, the quality of the predictive ________ used, and the degree of complexity or uncertainty in the environmental or socio-economic system of concern.
practitioner; baseline data; tool or model;
Accuracy
refers to the extent of system-wide bias in a prediction, or the closeness of a predicted value to its “true” value.
