Wildfires have demonstrated their destructive powers in several parts of the world in recent years. In an effort to mitigate the hazard of large catastrophic wildfires a common practice is to reduce fuel loads in the landscape. This can be achieved through prescribed burning or mechanically. Prioritising areas to treat is a challenge for landscape managers. To help deal with this problem we present a spatially explicit, multi-period mixed integer programming model. The model is solved to yield a plan to generate a dynamic landscape mosaic that optimally fragments the hazardous fuel continuum while meeting ecosystem considerations. We demonstrate that such a multi-period plan for fuel management is superior to a myopic strategy. We also show that a range of habitat quality values can be achieved without compromising the optimal fuel reduction objective. This suggests that fuel management plans should also strive to optimise habitat quality. We illustrate how our model can be used to achieve this even in the special case where a faunal species requires mature habitat that is also hazardous from a wildfire perspective. The challenging computational effort required to solve the model can be overcome with either a rolling horizon approach or lexicographically. Typical Australian heathland landscapes are used to illustrate the model but the approach can be implemented to prioritize treatments in any fire-prone landscape where preserving habitat connectivity is a critical constraint.

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In this paper, we propose a socially-accepted local electricity pricing mechanism for households that is dependent on the electric load of the neighbourhood and is able to flatten out the neighbourhood profile. The cost function used for this pricing mechanism is a piecewise linear approximation of a quadratic function.The motivation for using this mechanism is that the energy transition is expected to result in higher peak loads in electricity consumption as well as in renewable generation, which poses a significant challenge to the distribution grids. To tackle this problem, electricity consumption profiles need to be flattened.Following the literature, quadratic cost functions have proven their ability to achieve such a system optimization. However, their problem is that consumers find the resulting pricing mechanisms too complex and are generally unwilling to participate when offered these prices. In contrast, the socially acceptance of simple pricing mechanisms such as currently used in practice is high, but these mechanisms are hardly giving any incentive to reduce peaks in electricity profiles.Based on the feedback of consumers participating in our field test and the criteria defined in the literature, we conclude that the proposed mechanism is socially accepted. Furthermore, a numerical evaluation shows that our proposed mechanism can flatten out peaks on the neighbourhood level, albeit that its convergence is slightly slower than by using the quadratic cost function. These two findings imply that the presented pricing mechanism has the potential to be useful in practice.