Once all the important impacts have been identified, their potential size and characteristics can be predicted. Impact prediction or forecasting is a technical exercise. It utilises physical, biological, socio-economic and cultural data to estimate the likely characteristics and parameters of impacts (e.g. magnitude, spatial occurrence etc.). A range of methods and techniques may be employed. These can be a continuum from simple methods for impact identification (described earlier) to advanced methods, often involving the application of mathematical models. Examples of commonly used impact prediction tools are given in the table below.
|GIS and computer expert systems||
In many cases, this work will need to be carried out by specialists in the disciplines involved or in the application of models and techniques. However, the sophistication of prediction methods used should be in proportion to the scope of the EIA and relevant to the importance of the particular impact. Specialists may become involved in research and methodology that is of interest to them rather than directly related to the impact of the proposal. This can be avoided by making sure the programme of research and data collection is focused on addressing the concerns outlined in the terms of reference.
Where possible, impacts should be predicted quantitatively. This makes comparison among alternatives and with baseline conditions easier and facilitates impact monitoring and auditing later in the EIA process. If quantification is difficult, then it is important to use methods that allow the impacts to be estimated and compared systematically. Rating techniques, for example, can be used to assist impact estimation (as well as assign values) where there is insufficient data, a high level of uncertainty and/or limited time and money (all common in many EIAs). The results of qualitative analysis should be communicated clearly, for example in the form of a range of graded â€˜dot sizesâ€™ presented in a table.
Quantification means using numbers to indicate the impact. In most societies numbers have come to be important ways of communicating. The precision they infer gives the impression that they are â€˜trueâ€™, or at least as accurate as possible. However, when dealing with the bio-physical environment and social issues there are still many situations where our understanding of causes and effects (our models) are not perfect representations of what happens in all situations. So our models are likely to provide incomplete (possibly false) information. Yet the numbers that these models produce give the impression of accuracy.
It cannot be the responsibility of the EIA developers to conduct the research, development and testing of models to ensure they are perfect. But it is their responsibility to be aware of any possible inaccuracies, and to estimate the uncertainty created in the modelsâ€™ results. It is also their responsibility to ensure that these uncertainties are clear to anyone reading the EIA reports.
Sometimes there are few or no alternatives to qualitative description, as is in the case of scenic quality, amenity, sense of place or other landscape characteristics. Wherever possible, description should be based on some type of classification and the impacts summarised in appropriate form, for example, maps, cross-sections and/or photomontages.
In most cases, an multi-disciplinary team will conduct the EIA study. The terms of reference will dictate the composition of the team and the knowledge base and skills required. When organising different specialists to address a common task or problem, it is important for the study manager to establish a clear process of communication with, and amongst, them. This should extend to communication with those responsible for overall project management, as it is often possible for design changes to be incorporated to reduce environmental impacts well before the production of the EIA report. This can result in savings of money and time to the proposal in the long run.
Impact predictions are made against a â€˜baselineâ€™ established by the existing environment (or by its future state). Known as baseline studies, the collection of data on relevant biophysical, social and economic aspects provides a reference point against which the characteristics and parameters of impact related changes are analysed and evaluated. In many cases, it is likely that the current baseline conditions will still exist when a project is implemented. However, certain projects have long lead times. In these cases, predictions may need to be made about the future state of the environment (the baseline condition for the no-development option).
When establishing a baseline, information is gathered on:
- current environmental conditions;
- current and expected trends;
- effects of proposals already being implemented; and
- effects of other foreseeable proposals.
In practice, assembling baseline information can be time consuming and expensive. There can be difficulties in collecting appropriate and sufficient information. For example, there may be unforeseen circumstances in which the collection of data cannot be completed as required by the Terms of Reference. In such cases, the EIA team may have to revise the study strategy and/or use their judgement to make predictions. When this occurs it should be indicated in the EIA report with a short explanation of the reasons.
Specialised knowledge is usually required to oversee, and, where necessary, set limits on the collection of data required for impact analysis and monitoring. EIA project managers also need to ensure that time and effort is not spent on unnecessary data collection or that excessive space in the EIA report is not occupied by a description of baseline conditions. For example, the baseline chapter or section could be limited to not more than ten per cent of the total number of pages in the report. This issue is discussed further in Section 8 â€“ Reporting.
An impact or effect can be described as the change in an environmental parameter, which results from a particular activity or intervention. The change is the difference between the environmental parameter with the project compared to that without the project (as represented pictorially in the figure above). It is predicted or measured over a specified period and within a defined area.