Predicting Effects

The AB RSDS illustrates that regional SEA must not only be future-focused but also dynamic in that its assumptions and products should be updated and adapted on an ongoing basis in order to respond to shifting regional conditions.

3.4.2 Predicting Effects

An ongoing debate in SEA concerns the role of impact prediction and the extent to which it is useful or even possible in a strategic setting. Impact prediction has been a foundational element of project-based EA since its inception; however, strategic assessment contexts are typically associated with a higher degree of uncertainty than project-level assessment (Fischer 2007;

Partidário and Fischer 2004). In each of the frameworks examined there was considerably less emphasis on precise prediction of impacts and increased emphasis on approaches to establish thresholds, limits, and targets for future development. In other words, the emphasis was on

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establishing ‘goalposts’ to guide future regional development, rather than on making precise impact predictions per se.

In the case of the GSH RES, for example, the goal of modeling was ultimately to balance the competing objectives of environmental protection and economic development rather than to predict specific stressor-VEC responses into the future. The framework was designed to

systematically evaluate the cumulative effects of multi-sector land uses and surface disturbances under different future scenarios. Using MARXAN and asking ‘what if’ questions about potential future change, the assessment team created future images of the region accounting for natural change and cumulative development change so as to identify a preferred future and the means to achieve it. One of the practitioners involved explains:

When analyzing scenarios, we were trying to find appropriate targets whereby

cumulative disturbance could happen but we would still meet our conservation targets and maintain connectedness across the landscape. When we found those targets that allowed us to maintain a degree of ecological connectivity then we were happy with the targets.

While the team was successful in identifying a preferred regional development scenario, this approach was very exploratory in that the team did not know at the outset what valued ecosystem components (VECs) or impacts were most significant, what the real drivers of change in the region were, or if those drivers of change were the primary reason for observed impacts. These factors represent, in part, a shifting environmental baseline; a phenomenon that has always been present in impact assessment but which can be a particular challenge in regional, strategic

exercises that extend over several years. This shifting regional baseline also presented challenges in the implementation of the AB RSDS framework, as noted above. Similar to the GSH RES, the

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AB RSDS framework focused heavily on establishing environmental thresholds, indicators, and minimum standards of performance as a basis for future monitoring, and to guide future regional development.

The TCCMP also found that a shifting regional baseline created difficulties, ultimately leading to an approach that focused on establishing regional development thresholds, limits and targets. The TCCMP framework was built around the ALCES model. Once ALCES is

populated with data, it can compose various land use development scenarios, although it is not spatially explicit. In other words, it generates output for the ecosystem a whole but is unable to disaggregate results to a specific region within the ecosystem. This was troublesome for land managers who wanted to know what might be predicted within their own jurisdiction. The model was also found to be too broad with so many scenario possibilities that it was difficult for land managers to pinpoint what it was that they specifically wanted the model to produce.

The number of jurisdictions involved, the different scales of data, and different standards of reporting, along with different levels of understanding around the issues, made the data standardization process extremely complex. Only a small portion of data was known and this known data was heavily supplemented with trajectories, predictions, and educated guesses – large data gaps were very time intensive to fill. In addition, stakeholders were not always forthcoming with data and were often uncomfortable about knowing the results of predictions.

Thus, over time, the focus of the TCCMP shifted away from using predictive modeling, and placed greater emphasis on identifying what to track within the region, including those VECs that are potential indicators of ecosystem health. In this way, environmental targets could be set and land managers could find ways to meet them that made sense within their own jurisdictional context. This was found to be a much easier way to address regional cumulative

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effects rather than focusing on predicting specific impacts on specific VECs in a complex regional environment. Land managers also felt more comfortable communicating common development goals to their jurisdictional counterparts rather than having to respond to or explain the potentially controversial results that could come from strategic modeling exercises. Two members of the administrative team for the TCCMP explain: “They’re worried that maybe it will change their job, but not in a positive way. ‘Is that going to give a political message that I don’t want to hear, or that my bosses don’t want to hear?’ We think a lot of the failure (of the

modeling exercise) was driven by not wanting to know the answers to some of these things.”