Landscape futures analysis: Assessing the impacts of environmental targets under alternative spatial policy options and future scenarios

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Abstract

Environmental targets are often used in planning for sustainable agricultural landscapes but their impacts are rarely known. In this paper we introduce landscape futures analysis as a method which combines linear programming optimisation with scenario analysis in quantifying the environmental, economic, and social impacts associated with achieving environmental targets, on a landscape scale. We applied the technique in the Lower Murray in southern Australia. Landscape futures models were used to identify specific geographic locations in the landscape for six natural resource management (NRM) actions such that regional environmental targets are achieved. The six potential NRM actions that may be undertaken to achieve environmental targets include remnant vegetation management, ecological restoration, conservation farming, deep-rooted perennials, and the production of biomass and biofuels feedstock for renewable energy generation. We developed landscape futures under four alternative spatial prioritisation policy options and four future climate and commodity price scenarios. The impacts of each landscape future were calculated across a range of environmental, economic, and social indicators. The external drivers, climate change and commodity prices, and internal decisions such as policy options for spatially prioritising NRM actions, both have a strong influence on the costs and benefits of achieving environmental targets. Illustrative results for the cleared agricultural areas in the Mallee region indicate that whilst achieving targets can have substantial environmental benefits, it requires large areas of land use and land management change, and is likely to be costly (up to $348.5 M per year) with flow-on impacts on the regional economy and communities. Environmental targets can be achieved more cost-effectively through spatial planning. Costs can be further reduced if markets are established for carbon, biomass, and biofuels to enable landholders to generate income from undertaking NRM. Landscape futures analysis is an effective tool for supporting the strategic regional NRM policy and planning decisions of how best to set and achieve environmental targets.

Introduction

Conservation and environmental management agencies commonly set targets for guaranteeing minimum levels of natural capital in planning for the sustainability of agricultural landscapes (MacDonald et al., 1999, Carwardine et al., 2009). In Australia and elsewhere, regional agencies have adopted this strong sustainability approach and developed plans for conserving and managing natural capital assets including land, water, and biodiversity, and for addressing climate change (Farrelly and Conacher, 2007). Regional plans typically enumerate a range of environmental targets and propose natural resource management (NRM) actions for their achievement. Achieving environmental targets often requires substantial change in land use and management over the long term. Whilst significant investment is directed at achieving these environmental targets (Hajkowicz, 2009), the potentially substantial environmental, economic, and social impacts of their achievement are rarely known. Integrated assessment is required to quantify the impacts of alternative ways of achieving environmental targets under a range of future scenarios. This can help regional agencies understand the costs, benefits and trade-offs associated with internal NRM planning and policy decisions given uncertainty in external drivers (Prato, 2007).

Internal decisions by environmental agencies, especially the question of where to locate conservation and environmental management in the landscape, can significantly affect the environmental, economic, and social impact of achieving environmental targets (Bryan and Crossman, 2008, Crossman and Bryan, 2009). Often, spatial prioritisation of conservation and environmental management is either non-existent, or inefficient, based on either costs or benefits only (Newburn et al., 2005). Spatial prioritisation considering both cost and benefit has been shown to achieve environmental targets efficiently (Ando et al., 1998, Polasky et al., 2008). This has been demonstrated in planning for conservation (Wilson et al., 2009, Moilanen et al., 2009), ecological restoration (Crossman and Bryan, 2006), managing sediment and nutrients (van Walsum et al., 2008), land retirement (Yang et al., 2003), and enhancing natural capital (Crossman and Bryan, 2009). Spatial priorities for conservation and environmental management are typically identified using numerical allocation techniques (Moilanen et al., 2009). These techniques locate NRM actions in the landscape to optimise an objective function (e.g. maximise cost-effectiveness) subject to the constraint that environmental targets are achieved. Techniques include a range of exact (Haight and Snyder, 2009), approximate (McDonnell et al., 2002), and heuristic (e.g. Moilanen, 2007) optimisers. Bryan and Crossman (2008) developed the concept of systematic regional planning for NRM as a quantitative technique that utilises integer programming within a multi-criteria decision analysis framework. Systematic regional planning locates NRM actions in the landscape to efficiently achieve multiple environmental and economic objectives (Crossman and Bryan, 2006, Crossman and Bryan, 2009, Crossman et al., 2007).

External drivers such as changes in climate, commodity prices, and social preferences can also affect the environmental, economic, and social impacts of achieving environmental targets. Scenario analysis has been widely used to guide strategic planning for conservation and environmental management (Peterson et al., 2003, Xiang and Clarke, 2003, Mahmoud et al., 2009). Scenarios are plausible and internally consistent, but not necessarily probable, futures (IPCC, 2001). There have been many applications of scenario analysis on a landscape scale (Steinitz et al., 2003, Baker et al., 2004, Berger and Bolte, 2004, Hulse et al., 2004, Santelmann et al., 2004; Shearer, 2005, Walz et al., 2007). Many studies have defined landscape scale scenarios using qualitative techniques based on participation of stakeholder groups (Hulse et al., 2004, Patel et al., 2007). Others have combined participatory approaches with quantitative scenario modelling in the analysis of landscape futures (Walz et al., 2007). Quantitative modelling techniques have included spatial multi-criteria analysis (Pettit and Pullar, 2004, Berger, 2006), agent-based modelling (Happe et al., 2006), actor-based modelling (Bolte et al., 2004), and integrated assessment and modelling (Carmichael et al., 2004, van Ittersum et al., 2008, Wei et al., 2009). Some studies (Liu et al., 2007, Meyer and Grabaum, 2008) have found optimisation and scenario analysis to be a valuable combination for selecting land use and management alternatives under uncertainty.

In this study, we build upon the systematic regional planning approach in combining linear programming with scenario analysis on a landscape scale to quantify the impact of regional environmental targets under alternative spatial policy options and future scenarios. We call this landscape futures analysis. We generated landscape futures for the Lower Murray in southern Australia and present illustrative results for the cleared agricultural areas of the Mallee region. We identified from regional plans and stakeholder participation environmental objectives with specific targets and NRM actions for achieving these targets. Four spatial prioritisation strategies called policy options, and four future scenarios characterised by changes in climate and commodity prices were also defined. Layers quantifying the spatial distribution of net economic cost of each NRM action and the benefits across multiple environmental objectives were modelled under each scenario as input into landscape futures analysis. Each landscape future describes the optimum spatial arrangement of NRM actions in the landscape for each policy option (e.g. minimum cost, maximum benefit) under each scenario, subject to the achievement of environmental targets. The environmental, economic, and social impacts of landscape futures were then quantified. Landscape futures can inform key internal decisions in regional planning such as the benefits of alternative spatial policy options, and quantify the impacts of achieving regional targets given uncertainty in external drivers.

Section snippets

Materials and methods

Landscape futures analysis follows a defined sequence, with each stage underpinned by close stakeholder consultation and participation. Broadly, the sequence involves:

  • 1.

    Identifying environmental objectives and potential NRM actions for achieving these

  • 2.

    Defining spatial prioritisation policy options

  • 3.

    Defining future scenarios

  • 4.

    Modelling the spatial distribution of the range of costs and benefits of NRM actions at a landscape scale under the baseline and future scenarios

  • 5.

    Specifying alternative landscape

Results

The general results of landscape futures analysis for the Lower Murray study area are discussed with quantitative examples given for the illustrative example of the cleared agricultural areas of the Mallee region. A detailed description of the results for the entire study area can be found in Bryan et al. (2007b).

Landscape futures generated through systematic regional planning models and their associated environmental, economic, and social impacts varied substantially between spatial policy

Discussion

Targets are an effective and widely used tool in taking a strong sustainability approach to regional environmental planning but are seldom evaluated a priori. Landscape futures analysis enables the integrated, quantitative assessment of the environmental, economic, and social impacts of environmental targets. The technique also enables the evaluation of the influence of internal decisions and their performance under changes in external drivers framed as future scenarios. Landscape futures

Conclusion

Landscape futures analysis enables the quantitative, integrated assessment and comparison of the environmental, economic, and social impacts of achieving environmental targets through alternative spatial policy options under future scenarios. The results of this study highlighted the sheer magnitude of the changes in land use and management required to achieve environmental targets. Whilst the achievement of environmental targets may have substantial benefits for multiple environmental

Acknowledgements

We are grateful for the funding of the Commonwealth’s National Action Plan for Salinity and Water Quality, and CSIRO’s Water for a Healthy Country Flagship and Sustainable Agriculture Flagship. We are also grateful for the guidance of the project partners under the Land Technologies Alliance and the contributions of more than 40 researchers over 5 years.

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