What can we learn about the role and importance of scoping in the context of environmental impact assessment?
“Closed” versus “open” scoping
I am intrigued by the highly variable approaches to scoping practice in environmental impact assessment and the considerable range between “closed” approaches and more ambitious and open exercises. Closed approaches to scoping tend to narrow the range of questions, possibilities and alternatives that may be considered in environmental impact assessment, while limiting or precluding meaningful public input. Of course, the possibility of more open scoping is sometimes precluded beforehand by narrow terms of reference determined by regulators.
When scoping is not done well, it inevitably compromises subsequent steps in the process.
It seems simple enough to say that community values and aspirations should be central to informing government decisions that affect them. But simple things can turn out to be complex.
In particular, when research to inform land and water policy was guided by what the community valued and aspired to rather than solely technical considerations, a much broader array of desirable outcomes was considered and the limitations of what science can measure and predict were usefully exposed.
What is deep uncertainty? And how can scenarios help deal with it?
Deep uncertainty refers to ‘unknown unknowns’, which simulation models are fundamentally unsuited to address. Any model is a representation of a system, based on what we know about that system. We can’t model something that nobody knows about—so the capabilities of any model (even a participatory model) are bounded by our collective knowledge.
One of the ways we handle unknown unknowns is by using scenarios. Scenarios are stories about the future, meant to guide our decision-making in the present.
By David Brunckhorst, Jamie Trammell and Ian Reeve
Landscapes are the stage for the theatre of human-nature interactions. What does ‘landscape’ mean and what integrative function does it perform?
What is landscape?
Consider a painting of a landscape or look out a window. We imagine, interpret and construct an image of the ‘landscape’ that we see. It’s not surprising that landscapes (like the paintings of them) are valued through human perceptions, and evolve through closely interdependent human-nature relationships. Landscapes are co-constructed by society and the biophysical environment. Landscape change is, therefore, a continuous reflection of the evolving coupled responses of environment and institutions. Landscapes are especially meaningful to those who live in them.
Prediction under uncertainty is typically seen as a daunting task. It conjures up images of clouded crystal balls and mysterious oracles in shadowy temples. In a modelling context, it might raise concerns about conclusions built on doubtful assumptions about the future, or about the difficulty in making sense of the many sources of uncertainty affecting highly complex models.
However, prediction under uncertainty can be made tractable depending on the type of prediction. Here I describe ways of making predictions under uncertainty for testing which conclusion is correct. Suppose, for example, that you want to predict whether objectives will be met. There are two possible conclusions – Yes and No, so prediction in this case involves testing which of these competing conclusions is plausible.