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EIA Essay Assignment
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what implications does uncertainty have for decision-making in EIA and how it might be managed in practise? The aim of environmental impact assessment (EIA) is to generate information on various impacts on the environment likely to result from human activity (Norwegian Ministry of the Environment, 2003). This means that an EIA should forecast with a high degree of accuracy. However, post-audit studies conclude that the real impacts of human activity differ from those predicted by EIA (Flyvbjerg et al, 2003; Wood et al, 2000). In this essay, I aim to discuss the implications of uncertainty for decision-making and to list a range of approaches to minimizing the risk of uncertainty in EIA. Theoretically, EIA involves predictions of all the possible options and impacts of human activity, but in practice these cannot be known (e.g. Ozdemir and Saaty, 2006). To avoid 'knowledge gaps' (Gustavsson, 2011), there is a clear need to consider uncertainty during all stages of EIA process: identifying potential options and their impacts for a project; identifying criteria to assess these options; choosing an option, or set of options; identifying management actions to carry out the chosen option; enacting the selected management actions; managerial review and judgement (Marier et. Al., 2008). When identifying options and their impacts, it is crucial to make sure that these are not overlooked, since stakeholders might be willing to optimise the long process (Hage et. al., 2010). Bryant and Lempert (2010), for example, suggest an approach for option generation that helps in identifying risks and impacts by encouraging a range of stakeholders to involve in the process, using modelling or simulation modelling. Similarly, considering the judgements of multiple experts has a great weight in achieving accuracy in predictions (Refsgaard et al., 2007). Although expert judgement may be seen as subjective, it can be balanced by using the knowledge gained from professional experience. Mathematical models can also be used in EIA. Ozdemir and Saaty’s (2006) approach – a method of modelling unknowns – aims to compare the impact of a project of unknown options or actions with those predicted before (Ayyub, 2001). Identifying criteria for the success of the project is by far the most challenging part of the EIA practise. Subjective expected utility (SEU) theory can be taken into account in decision analysis. However, limitations related to disparate values can cause problems (e.g. Bojórquez-Tapia et al., 2005). To overcome these limitations, sensitivity analysis (SA) integrated with a network-line analysis can be used (Lempert and Collins, 2007). The use of 1
SA-based values was first introduced by Lempert and Collins (2007) for a process of Lake Eutrophication. In addition, a wide range of modelling approaches that are discussed by Kelly et al. (2013) could much improve the accuracy in EIA. For example, in the case of groundwater protection project in Denmark, Bayesian Networks (BNs) were successfully used to make accurate predictions related to surface and groundwater quality, biodiversity, and the recreational value of water. Issues may arise when selecting the options as well. However, Kalra et al. (2014) proposed a new ground-breaking approach, Low Regret Decisions and Robust Optimization methods, which states that stakeholder’s preferences change in the decision-making process. This particular method makes inaccuracy explicit, allowing to select options that are robust for everyone. In addition, when there is a need for time flexible tools, Real options (RO) method can be used. The advantage of RO analysis is that uncertainties in such method can be reduced as more information becomes available as a project develops (McManus and Hastings, 2006). One of the aims of EIA is to identify measures to manage the impacts of an action. According to literature (e.g. Aven and Nøkland, 2010), importance analysis and different modelling approaches can be used to achieve this goal. They help to deal with the uncertainty attached to environmental variables. The current EIA process can also be improved by introducing and using additional attributes that exhaustively characterize impacts and help set priorities, as discussed by Kristensen et al. (2006). Another widely used tool in EIA is the impact checklist (Canter et al., 1998). However, critics argue for more realistic approaches. In line with this, Kelly et al. (2013), for example, said that there is a need to use models that capture such aspects as biophysics, society, economy, and, most importantly, their interactions. One way to minimize this problem is to constantly update environmental models. The BN approach is extremely helpful way of updating information. Importantly, the use of interaction matrices are widely used in traditional EIA process, such methods still fail to provide efficient information. For example, when there is interaction between only two independent variables, there are no issues with predictions. However, traditional used interaction matrices are not able to process the qualitative information when it comes to more advanced calculations.
The management of impacts is far more than just identifying critical factors. It is crucial that specific values and measures are put in place. Due to the one-off nature of projects, there is uncertainty about the functioning of the measures (Leung et al., 2016). In
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other words, management measures are completely situation-specific. With this in mind, it is important to come up with an exhaustive document of measures. Although the implementation of uncertain management actions has not been reported in depth with case studies in the context of projects producing environmental impacts (e.g. Canter and Atkinson, 2010), it is possible to refer to some options which link implementation of actions with project monitoring activities. When there is uncertainty about the functioning of management actions, we would advocate an approach that can be implemented in a timely fashion, and in which there is the opportunity to take a series of sequential measures, revising them over time and resetting priorities as more information becomes available from early warning and sensitive monitoring (Paté-Cornell, 1986). The managerial review and judgement stage was created to highlight the disadvantages and limitations of a given decision analysis (Walker et al., 2003). In this approach, six pillars of a process are tackled: ‘ ‘issues to be resolved’, ‘control’, ‘knowledge’, ‘performance’, ‘sub-processes’, and ‘environment’. To improve the method, an approach by Kloprogge et al. (2011) to prioritize assumptions by complex model-based scientific assessments can be used. However, this method still needs to be further investigated and improved. Importantly, it is not only the low prediction accuracy of EIA, but also the on-time presentation on uncertainty itself that needs to be considered. On-time communication with decision-makers about uncertainty is necessary for decision-making bodies to understand which predictions are uncertain, the ways in which they are uncertain, the possible magnitude of the uncertainty, and, most importantly, the implications of uncertainties. The most straightforward solution to this would be a procedural approach discussed by Glasson et al (1999), which suggests requiring ‘an uncertainty report as a step in the EIA process’. It is important to note, however, that both lack of information and glut of information will negatively affect the environment. Consequently, it is crucial that information is easily delivered, understandable and constructive in EIA. With this and today’s challenges of reliance on disclosing uncertainties in mind, are there any alternative options for coping with uncertainty in EIA? As a new possible solution to address uncertainties, Partidario and Sheate suggest opening up new institutions in Impact Assessment (IA), including EIA, that can equally exert power and lean between stakeholders. They believe that the current linear model of knowledge transfer is not able to deal with today’s issues in IA. Another option, according to EIA specialists, is to improve public participation in addressing uncertainty in EIA, as often the local knowledge can be significantly beyond the experts’ knowledge and judgement. 3
The need to better communicate uncertainties in EIA practice in general has been stated on a number of occasions (Duncan; 2013). Nevertheless, Tennoy, Kvaener, and Gjerstad (2006) concluded that uncertainty is not fully disclosed in EIA practice. Their case studies show that context, data and model errors were the types of uncertainty most often disclosed. However, none of these uncertainties was discussed in depth in EIA practise. According to Lees et al. (2016), even if uncertainty is addressed in EIA practice, there is a lack of clear pattern in the documents, with a lack of ‘standard practise, procedure, and terminology’. To conclude, the main approaches to coping with uncertainty are summarised below (Lipshitz and Strauss, 1997). First, there must be a structured involvement of stakeholders throughout the whole EIA process. Stakeholders’ involvement in the process will result in better understanding of the entire project. Secondly, robustness-based decision-making actions should be taken to achieve success. This approach aims to compromise and satisfy all scenarios. Thirdly, assumption evaluation (i.e. modelling and monitoring) can play a crucial role in achieving the desired goals with minimum impacts. Fourthly, sensitivity and uncertainty analyses should be performed, which are, unfortunately, often ignored in evaluating options, identifying criteria, and setting priorities. Moreover, the use of structured multi-expert judgement can be used to bridge the gap between hard evidence and unknown characteristics of a system. Furthermore, multi-dimensional characterization of impacts can be used to identify management actions. For example, attributes such as violation of equity or potential for mobilization can be introduced in the EIA process. Importantly, modelling techniques such as Bayesian networks with any kind of network analysis can help to support decisions. Additionally, it is important to not just make a personal judgment, but rather to consider multiple management actions based on the comprehensive analysis of information and case studies from elsewhere. Also, it is important for uncertainty to be managed at different EIA stages. The particular method creates a possibility to make decisions in a timely manner and to be flexible as more information becomes available later in the project. Finally, there must be better communication and presentation of uncertainty during the entire EIA process, so that decision-makers are aware of it at all times.
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