GIS-based Backcasting: An innovative method for parameterisation of sustainable spatial planning and resource management

Mankind has been making predictions since the earliest recorded history. From the astrologers of antiquity to the climate specialists of today, all have attempted to make predictions about future events. These attempts include assessments of whether or not these outcomes are probable or possible, desirable or undesirable. This paper concentrates on describing a methodology for the development and analysis of scenarios by refining and expanding existing methods. The approach is called Backcasting and was conceptually developed to support sustainable decisions in the energy sector. Backcasting works backwards from the envisioned future goals to the present, setting milestones to achieve the desired objective. These milestones are small interim scenarios along the way between the future scenario, usually 20–50 years ahead, and the present situation. Our Backcasting methodology is implemented in a modelling environment based on Geoinformation-System (GIS-based) using the scripting language Python. The methodology is demonstrated for an example of urban sprawl in rural areas, which often results in high infrastructure costs, large commuting distances and long travel times. To act against environmental degradation of rural areas, sustainable planning has to be the “overarching goal” [5], and will be supported with the developed approach.

► We develop a Backcasting method to support sustainable spatial planning and resource management. ► We demonstrate the Backcasting method for the example of urban sprawl. ► Urban sprawl often results in high infrastructure costs, long travel times and large commuting distances. ► Urban sprawl often causes environmental degradation of rural areas which has to be counteracted. ► This innovative framework will firstly link Backcasting with Geoinformation Systems (GIS).