A logical and systematic approach needs to be taken to impact identification. The aim is to take account of all of the important environmental/project impacts and interactions, making sure that indirect and cumulative effects, which may be potentially significant, are not inadvertently omitted. This process begins during screening and continues through scoping, which identifies the key issues and classifies them into impact categories for further study. In the next phase, the likely impacts are analyzed in greater detail in accordance with terms of reference specifically established for this purpose.
Over time, a number of EIA methodologies and tools have been developed for use in impact identification. Some are also useful for scoping and/or presenting the results of the EIA or assigning significance. In practice, relatively simple methodologies and tools are applied to impact identification (as compared to more complex, data-demanding methods which may be used in impact prediction). Experience indicates these simple methods are of proven value for undertaking a systematic approach to impact identification.
The most common formal methods used for impact identification are:
overlays and geographic information systems (GIS);
expert systems; and
More advanced EIA tools
Cost-Benefit Analysis (CBA)
Multi-Criteria Analysis (MCA)
Risk Assessment (RA)
Simulation modeling (SM)
Checklists annotate the environmental features or factors that need to be addressed when identifying the impacts of projects and activities. They can vary in complexity and purpose, from a simple checklist to a structured methodology or system that also assigns significance by scaling and weighting the impacts (such as the Battelle Environmental Evaluation System). Both simple and descriptive checklists can be improved and adapted to suit local conditions as experience with their use is gained.
Checklists provide a systematized means of identifying impacts. They also have been developed for application to particular types of projects and categories of impacts (such as dams or road building). Sectoral checklists often are useful when proponents specialise in one particular area of development. However, checklists are not as effective in identifying higher order impacts or the inter-relationships between impacts, and therefore, when using them, consider whether impacts other than those listed may be important.
Four General types of Checklists are:
Simple Checklist: a list of environmental parameters with no guidelines on how they are to be measured and interpreted.
Descriptive Checklist: includes an identification of environmental parameters and guidelines on how to measure data on particular parameters.
Scaling Checklist: similar to a descriptive checklist, but with additional information on subjective scaling of the parameters.
Scaling Weighting Checklist: similar to a scaling checklist, with additional information for the subjective evaluation of each parameter with respect to all the other parameters.
There are several major reasons for using checklists:
They are useful in summarizing information to make it accessible to specialists from other fields, or to decision makers who may have a limited amount of technical knowledge;
Scaling checklists provide a preliminary level of analysis; and
Weighting is a mechanism for incorporating information about ecosystem functions.
Problems with Checklists
They are too general or incomplete;
They do not illustrate interactions between effects;
The number of categories to be reviewed can be immense, thus distracting from the most significant impacts; and
The identification of effects is qualitative and subjective.
The Battelle method
The Battelle Environmental Evaluation System (BEES) is a methodology for conducting environmental impact analysis developed at Battelle Columbus Laboratories by an interdisciplinary research team under contract with the U.S. Bureau of Reclamation (Dee et al., 1972; Dee et al., 1973). BEES is based on a hierarchical assessment of environmental quality indicators.
The system is based on a classification consisting of four levels:
Level I: Categories,4 Major categories: ecology, pollution, aesthetics and human interest
Level II: Components eighteen (18) components ? Level III: Parameters seventy-eight (78) parameters ? Level IV: Measurements.
Each category (Level I) is divided into several components, each component (Level II) into several parameters, and each parameter (Level III) into one or more measurements. The BEES identifies a total of four (4) categories, eighteen (18) components and seventy-eight (78) parameters.
First designed for water resource development, the Battelle method can easily be used in other projects. The principle lies in splitting the environmental impacts in four (4) major categories: ecology, pollution, aesthetics and human interest. These categories are divided into thematic data as shown:
1. Ecology (3 components)
? Species and populations, ? Habitats and communities, ? Ecosystems.
2. Pollution (4 components)
3. Aesthetics (6 components)
Man-made objects, ? Composition.
Human interest (6 components)
Historical packages, ? Cultures,
Life patterns, ? Composition.
These thematic data are further divided into environmental indicators. For example in the pulp and paper industry, water pollution could be represented by: BOD, dissolved oxygen, faecal coliforms, inorganic carbon, pH, temperature, total dissolved solids, turbidity, etc. Once the environmental indicators are chosen, the method follows three steps:
At this stage, the goal of the method is to transform environmental indicators into environmental quality. The notation table defines a number from 0 to 1 (0 for poor quality and 1 for good quality). Thus it is possible to quantify evolution both in the wrong or right direction (environmental deterioration or improvement).
A total of a 1,000 points (or Parameter Importance Units: PIU) are shared among the indicators by the authors of the EIA. They reflect the relative importance of each parameter.
The comparison between the situation with and without the project is done in Environmental Impact Units (EIU). It can even reflect benefits or losses in terms of environmental conditions.
(Vi) 1 = Environmental quality for indicator “i” in the project conditions, (Vi)2 = Environmental quality for indicator “i” without the project, wi = Relative weight of the indicator “i” (PIU), m = total number of indicators.
The principal advantage of this method is that it gives a comparative analysis between several situations, thus it is particularly efficient when effecting choices between alternatives.
A matrix is a grid-like table that is used to identify the interaction between project activities, which are displayed along one axis, and environmental characteristics, which are displayed along the other axis.
The Leopold matrix is the best known matrix methodology available for predicting the impact of a project on the environment.
It is a two dimensional matrix cross-referencing:
The activities linked to the project that is supposed to have an impact on man and the environment.
The existing environmental and social conditions that could possibly be affected by the project.
The activities linked to the project are listed on one axis: raw material production, building construction, water supply, energy supply, raw material preparation, pulp and paper mills processing, gaseous emissions, liquid effluents, cooling water discharges, noise, solid wastes treatment and disposal, transportation.
The environmental and social conditions are listed on the other axis, and divided in three major groups:
Physical conditions: soil, water, air…,
Biological conditions: fauna, flora, ecosystems…,
Social and cultural conditions: land use, historical and cultural issues, populations, economy…
The Leopold matrix proposes a three-step process to estimate the impact:
First step: For all the interactions considered significant by the authors, the first step is to mark the corresponding boxes in the matrix with a diagonal line.
Second step: Once the boxes with supposed significant interactions are slashed, the author evaluates each box by applying a number from 1 to 10 (1 is the minimum and 10 the maximum) to register the magnitude of the interaction. This number is transferred to the upper left hand corner. It represents the scale of the action and its theoretical extent.
Third step: The final step for this method is to mark (from 1 to 10), in the lower right hand corner, the real importance of the phenomenon for the given project. It then gives an evaluation of the extent of the environmental impact according to the assessor’s judgment.
Once the matrix is established the EIA gives a precise description of each important impact in the matrix (with the larger numerical values for magnitude and importance). The discussion must also address columns and rows with large numbers of interactions. They show activities, or elements, in connection with the environment which are particularly significant or sensitive. “Entries’ are made in the cells to highlight impact severity or other features related to the nature of the impact, for instance:
ticks or symbols can identify impact type (such as direct, indirect, cumulative) pictorially;
numbers or a range of dot sizes can indicate scale; or ? Descriptive comments can be made.
The Leopold matrix proposes a framework for all developers but, on one hand, it is too detailed for pulp and paper projects and on the other not precise enough for such projects. It is generally more efficient to accommodate it as needed and to develop a customized matrix for the project. An example of a possible matrix for the pulp and paper industry is given in Figure 4.
The Leopold interaction matrix is a comprehensive matrix, which has 88 environmental characteristics along the top axis and 100 project actions in the left hand column. Potential impacts are marked with a diagonal line in the appropriate cell and a numerical value can be assigned to indicate their magnitude and importance. Use of the Leopold matrix is less common than its adaptation to develop other, less complex matrices.
Sample of the Leopard Marix
Example: Matrix of magnitude of the impact of factors on environmental components for an Energy project
Impact factors have been evaluated separately for each environmental component relevant for the scope of this study, and scored on a scale from 0 to 5 for impact magnitude, according to the following scale:
0 – No observable effect; 1 – Low effect; 2 – Tolerable effect; 3 – Medium high effect; 4 – High effect; 5 – Very high effect (devastation).
In addition to the standard form of the Leopold matrix, the following criteria have also been used: Impact significance with designations from L to M, according to the following scale: L – limited impact on location; O – impact of importance for municipality; R – impact of regional character; N – impact of national character; M – impact of cross-border character.
Impact probability with designations from M to I, according to the following scale: M – impact is possible probability of less than 50%); V – impact is probable (probability of over 50%); I – impact is certain (100% probability).
Impact duration with designation P (occasional/temporary) and D (long-term/permanent). Furthermore, physical, biological and socio-cultural environmental characteristics of the subject location have been separated and, within them, 16 environmental components have been defined.
Using the table, environment-activity interactions can be noted in the appropriate cells or intersecting points in the grid. Entries are made in the cells to highlight impact severity or other features related to the nature of the impact, for instance:
ticks or symbols can identify impact type (such as direct, indirect, cumulative) pictorially;
numbers or a range of dot sizes can indicate scale; or ? Descriptive comments can be made.
Matrices require information about both the environmental components and project activities. The cells of the matrix are filled in using subjective (expert) judgment, or by using extensive data bases.
There are two general types of matrices:
1) Simple interaction matrices;
(Simple matrix methods simply identify the potential for interaction) 2) Significance or importance-rated matrices.
Significance or importance-rated methods require either more extensive data bases or more experience to prepare. Values assigned to each cell in the matrix are based on scores or assigned ratings, not on measurement and experimentation.
For example, the significance or importance of impact may be categorized (no impact, insignificant impact, significant impact, or uncertain). Alternatively, it may be assigned a numerical score (for example, 0 is no impact, 10 is maximum impact).
The assessment matrix involves testing specific elements of a project proposal (i.e. design, siting, construction, operations) against a series of environmental indicators. The matrix has indictors in the columns and elements of the project proposal in the rows. Each cell of the matrix is given a rating criteria based on the level of impact of each element of the proposal on the corresponding environmental indicator. The rating criteria is determined by the person undertaking the assessment and is visually represented by a symbol/colour in the cell. Further comments can be added to the assessment matrix providing suggestions for alternatives or mitigation proposals.
The assessment matrix highlights which elements of the project proposal will have an impact and it is often used in early appraisal of project proposals and assist in the development of mitigation measures.
Network diagrams provide a means for displaying first, secondary, tertiary, and higher order impacts.
Networks illustrate the cause – effect relationship of project activities and environmental characteristics. They are, therefore, particularly useful in identifying and depicting secondary impacts (indirect, cumulative, etc).
Simplified networks, used in conjunction with other methods, help to ensure that important second order impacts are not omitted from the investigation. More detailed networks are visually complicated; time consuming and difficult to produce unless a computer programme is used for the task. However, they can be a useful aid for establishing ‘impact hypotheses’ and other structured science used approaches to EIA.
To develop a network, a series of questions related to each project activity (such as what are the primary impact areas, the primary impacts within these areas, the secondary impact areas, the secondary impacts within these areas, and so on) must be answered.
In developing a network diagram, the first step is to identify the first order changes in environmental components.
The secondary changes in other environmental components that will result from the first order changes are then identified.
In turn, third order charges resulting from secondary changes are identified. This process is continued until the network diagram is completed to the practitioner’s satisfaction. The network helps in exploring and understanding the underlying relationships between environmental components that produce higher order changes that are often overlooked by simpler approaches.
Networks or systems diagrams overcome the limitations of matrices by accommodating higher order impacts. They are also far better at explicitly identifying the causal basis for impacts.
In addition, they are well suited to identifying the interaction between a number of activities, components, and a single target resource.
As an assessment tool, they are capable of making qualitative predictions of the cumulative impact of a number of activities on a single target resource.
However, they neither formally integrate over the spatial and temporal dimensions, nor do they integrate across target resources.
While networks and systems diagrams can be communicated well and are easy to develop using expert judgment, scientific documentation of complex systems diagrams require a considerable amount of human and financial resources.
? Flow diagrams are sometimes used to identify action-effect- impact relationships. An example is given in Figure 4.1 (Sorensen, 1971), which shows the connection between a particular environmental impact (decrease in growth rate and size of commercial shellfish) and coastal urban development.
Figure 4.1 Example of a flow-chart used for impact identification (Sorensen, 1971)
Spatially Based Methods Overlays
An overlay is based on a set of transparent maps, each of which represents the spatial distribution of an environmental characteristic (for example, susceptibility to erosion). Information for an array of variables is collected for standard geographical units within the study area, and recorded on a series of maps, typically one for each variable. These maps are overlaid to produce a composite.
The resulting composite maps characterize the area’s physical, social, ecological, land use and other relevant characteristics, relative to the location of the proposed development.
One way is to use before and after maps to assess visually the changes to the landscape.
The other way is to combine mapping with an analysis of sensitive areas or ecological carrying capacity.
Their limitations relate to:
Lack of causal explanation of impact pathways; and
Lack of predictive capability with respect to population effects. However, some sophisticated versions can make predictions about potential habitat loss.
Geographic Information Systems
Traditionally, the overlays have been produced by hand. As a result of recent developments, Geographical Information Systems (GIS) are becoming popular in situations where the computer technology and trained personnel are available. Computers are also used routinely to do cluster analyses of complex overlays.
GIS is a powerful management tool for resource managers and planners. Its applications are limited only by the quality, quantity, and coverage of data that are fed into the system. Some of the standard GIS applications are integrating maps made at different scales; overlaying different types of maps which show different attributes; and identifying required areas within a given distance from roads or rivers.
For instance, by overlaying maps of vegetation and soils, a new map on land suitability can be generated and the impact of proposed projects can be studied.
The farm-to-market transport economics can be considered in determining the planting of specific areas on a commercial scale. Similarly, the most favorable zones for the development of shrimp farming outside mangroves can be located.
Rapid Assessment Procedure
The rapid assessment procedure allows for quick estimation of releases of pollutants to the environment.
The rapid assessment procedure may be used to assess the environmental impacts of developments.
The use of waste load factors enables prediction of the approximate pollutant loadings generated by a new development project. This, in conjunction with knowledge about existing pollutant concentrations, allows a preliminary assessment of the degree to which the project would adversely affect the prevailing conditions of the proposed site.
On a local basis, rapid assessment studies can provide the following contributions to environmental management agencies (WHO, 1983):
define high priority control actions;
organize effective detailed source survey programs;
organize appropriate environmental monitoring programs;
assess and evaluate the impacts of proposed pollution control strategies;
assess impacts of new industrial development projects; and
Help site selection and determination of proper control measures.
Expert or knowledge based systems are used to assist diagnosis, problem solving and decision – making. A number of such computerized systems have been developed for use in EIA, primarily at the early stages of the process. For example, screening and scoping procedures have been automated using a number of rules and a data system, which encodes expert knowledge and judgment. The user has to answer a series of questions that have been systematically developed to identify impacts and determine their ‘mitigability’ and significance. Based on the answer given to each question, the expert system moves to the next appropriate question. Like GIS systems, expert systems are an information intensive, high investment method of analysis. As such, they are limited in their current use and application, especially by many developing countries. However, they also have the potential to be a powerful aid to systematic EIA in the future, not least because they can provide an efficient means of impact identification.
Although not strictly a formal method, professional judgment or expert opinion is widely used in EIA. Knowledge and expertise gained in EIA work can be used to systematically develop data banks, technical manuals and expert systems, thereby assisting in future projects. The successful application of the formal methods of impact identification described above rests upon professional experience and judgment. Expert opinion and professional judgment can be focused by the use of interactive methods, such as Delphi techniques and science workshops, to identify impacts, model cause effect relationships and establish impact hypotheses as noted earlier, all methods of analysis involve professional judgment and the use of advanced tools and models will require expert knowledge. Sole reliance on ‘best estimate’ professional judgment may be unavoidable when there is a lack of data to support more rigorous analyses or there is a lack of predictive methodology (as in the analysis of certain social impacts). Examples include the prediction of the effect of a water supply proposal on: ? The activities of women or community interaction; and ? The loss of a communal place or sacred site.
Such predictions should be made by specialists, who are familiar with the type of proposal, the geographic region and/or similar cases that are analogous to the situation. Where professional judgment is used without also employing other methods, the judgment and values of the specialist concerned may be open to challenge. Peer review and the use of agreed concepts and frameworks can be useful to corroborate findings.
Quantitative mathematical models
Quantitative models express cause-effect relationships as mathematical functions, derived from deterministic or probabilistic relationships. A number of such models are used in EIA to predict certain types of impacts, for example, on air, water, soil and habitat. More complex computerbased simulations are data demanding and often their use in EIA requires certain simplifying assumptions to be made.
The choice and use of quantitative models for impact prediction should be suited to the particular cause-effect relationship being studied; for example, transport and fate of oil spills, sediment loadings and fish growth and pesticide pollution of groundwater aquifers. Attention also needs to be given to the consistency, reliability and adaptability of models. Usually operational changes are made to the input conditions for the model to see how the outputs are affected. For instance, differences in air pollution can be calculated by changing the height of a stack or the rate of output of emissions.
Examples of the use of quantitative models include:
Air dispersion models to predict emissions and pollution concentrations at various locations resulting from the operation of a coal-fired power plant;
Hydrological models to predict changes in the flow regime of rivers resulting from the construction of a reservoir; and
Ecological models to predict changes in aquatic biota (e.g. benthos, fish) resulting from discharge of toxic substances.
Although traditionally this type of analysis has been carried out for physical impacts, there is increasing use of mathematical models to analyse biological, social/demographic and economic impacts.
When interpreting the results of quantitative mathematical models it should be remembered that all models are simplifications of the real world. They require the specialist to make a number of assumptions in both their development and their use. If these assumptions are inappropriate then there can be significant implications for the accuracy and usefulness of the output data. EIA project managers should ask all specialists carrying out mathematical analyses to clearly state the assumptions inherent in the use of their models, together with any qualifications to be placed on the results.
Experiments and physical models
Experiments and scale models can be used to test and analyse the effects of project-related activities and the effectiveness of proposed mitigation techniques. These methods have not been used extensively in impact prediction. However, they can be appropriate, depending upon the nature of the impact and the resources available, and providing certain cautions are remembered. When using the results of experiments or models, note that unpredicted outcomes can occur when the data are ‘scaled up’ to life size.
Experiments can be undertaken directly in the field or under laboratory conditions. Examples of their use include:
the exposure of fish in a laboratory to concentrations of pollutants to determine mortality levels; and
Field trials of the effectiveness of different methods of erosion control.
Physical models can be built to predict the behaviour and effect of the actual project on the environment. For example, a physical model could be used to simulate changes to patterns of sand or sediment deposition resulting from port and harbour works.
Reviewing case studies of projects in similar environments can inform and assist impact prediction and analysis. Comparisons will be especially helpful if impact monitoring and auditing data are available. Otherwise, the results obtained by a comparable use of EIA methodology should be consulted. Sometimes, relevant case material will not be readily accessible or available. In that event, there is a large body of general information on the impact ‘footprints’ of major types of projects, such as dams, roads, airports and power stations.
However, this should be read with care as to its source and provenance.
Uncertainty is a pervasive issue at all stages of the EIA process but is especially important for impact prediction. Put simply, uncertainty is a state of relative knowledge or ignorance. Where cause-effect relationships are ‘known’ and understood, however imperfectly, impacts can be forecast (or at least described). Certain impacts are unknown until they occur; for example, ozone depletion caused by release of CFCs and inter-species transmission of the human variant of Bovine Spongiform Encephalopathy (BSE) or ‘mad cow’ disease.
Sources of uncertainty in impact prediction include:
scientific uncertainty – limited understanding of an ecosystem (or community) and the processes that govern change;
data uncertainty – restrictions introduced by incomplete or non-comparable information, or by insufficient measurement techniques; and
Policy uncertainty – unclear or disputed objectives, standards or guidelines for managing potential hazards and effects.
There are a number of approaches that can be used to address uncertainty in impact prediction, including:
‘best’ and ‘worst’ case prediction to illustrate the spread of uncertainty;
attaching confidence limits to impact predictions; and
Sensitivity analysis to determine the effect of small changes in impact magnitude.
The relationship between impact, size and severity may not be linear. Small changes in impact magnitude may cause larger than expected increases or decreases in the severity of environmental change. Where necessary, an assessment should be made of the effect that small changes in the magnitude of the impact (say less than 10 per cent) have on the environment, particularly if significant or valued resources are potentially affected. This is referred to as a sensitivity analysis.
A broader range of impacts and interrelationships are now routinely integrated into EIA. These include the social, economic and health aspects of environmental change. In comparison to biophysical impacts, less experience has been gained in analysing these and other nonbiophysical impacts.
Social Impact Assessment
People are an integral part of the environment. Human activity alters the biophysical environment and, in turn, these impacts are translated into social effects. In many EIA systems the immediate and direct social impacts of a proposal always should be analysed as an integral component of an EIA.
Social impacts include changes that affect individuals, groups, communities and populations as well as the interactions between them. They are alterations in the way people live, work, play, relate to each other and organize their communities and institutions to meet their needs and guide their collective actions, as well as changes in their characteristic values, beliefs, norms, traditions and perceptions of quality of life and well-being.
Social impacts can be divided into four main types:
demographic impacts such as changes in population numbers and characteristics (such as sex ratio, age structure, in-and-out migration rates and resultant demand for social services, hospital beds, school places, housing etc);
cultural impacts including changes to shared customs, traditions and value systems (e.g. language, dress, religious beliefs and rituals) archaeological, historical and cultural artifacts and to structures and environmental features with religious or ritual significance;
community impacts including changes in social structures, organizations and relationships and their accompanying effect on cohesion, stability, identity and provision of services; and
Socio-psychological impacts including changes to individual quality of life and wellbeing, sense of security or belonging and perceptions of amenity or hazard.
Often, local people are not the beneficiaries of proposed development. Rather they bear the brunt of the adverse impacts. These effects are especially acute in developing countries when projects displace people whose security and subsistence depends on the land and resources that will be affected. World Bank environmental and social assessment procedures give particular attention to the impact on indigenous peoples and other vulnerable ethnic and cultural groups whose lifestyle, value and tenure systems may be disrupted or lost.
A comprehensive social impact assessment (SIA) will be required in such cases. In other circumstances, adding a relevant specialist to the EIA team may suffice to address social impacts. However, it should be emphasized that there is little consensus on the social impacts that should be included as part of an EIA process. Other than agreeing that the scope is too limited, SIA practitioners themselves differ on the aspects to be studied and the framework within which they should be analyzed.
Table 1: Main advantages and disadvantages of impact identification methods
Read the paper by Bosko Josimovic, Jasna Petric & Sasa Milijic with the title “The Use of the Leopold Matrix in Carrying out the EIA for Wind Farms in Serbia” and give a summary of the paper.
Choose a project and apply the Leopold Matrix to “identification and predict the impacts” of a project of your choice.
Due date: After 14 days