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Abstract.A primary goal of Earth system modelling is to improve understanding of the interactions and feedbacks between human decision making and biophysical processes. The nexus of land use and land cover change (LULCC) and the climate system is an important example. LULCC contributes to global and regional climate change, while climate affects the functioning of terrestrial ecosystems and LULCC. However, at present, LULCC is poorly represented in global circulation models (GCMs). LULCC models that are explicit about human behaviour and decision-making processes have been developed at local to regional scales, but the principles of these approaches have not yet been applied to the global scale level in ways that deal adequately with both direct and indirect feedbacks from the climate system. In this article, we explore current knowledge about LULCC modelling and the interactions between LULCC, GCMs and dynamic global vegetation models (DGVMs). In doing so, we propose new ways forward for improving LULCC representations in Earth system models. We conclude that LULCC models need to better conceptualise the alternatives for upscaling from the local to global scale. This involves better representation of human agency, including processes such as learning, adaptation and agent evolution, formalising the role and emergence of governance structures, institutional arrangements and policy as endogenous processes and better theorising about the role of teleconnections and connectivity across global networks. Our analysis underlines the importance of observational data in global-scale assessments and the need for coordination in synthesising and assimilating available data. Published by Copernicus

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Abstract. A primary goal of Earth system modelling is to improve understanding of the interactions and feedbacks between human decision making and biophysical processes. The nexus of land use and land cover change (LULCC) and the climate system is an important example. LULCC contributes to global and regional climate change, while climate affects the functioning of terrestrial ecosystems and LULCC. However, at present, LULCC is poorly represented in Global Circulation Models (GCMs). LULCC models that are explicit about human behaviour and decision making processes have been developed at local to regional scales, but the principles of these approaches have not yet been applied to the global scale level in ways that deal adequately with both direct and indirect feedbacks from the climate system. In this article, we explore current knowledge about LULCC modelling and the interactions between LULCC, GCMs and Dynamic Global Vegetation Models (DGVMs). In doing so, we propose new ways forward for improving LULCC representations in Earth System Models. We conclude that LULCC models need to better conceptualise the alternatives for up-scaling from the local to global. This involves better representation of human agency, including processes such as learning, adaptation and agent evolution, formalising the role and emergence of governance structures, institutional arrangements and policy as endogenous processes and better theorising about the role of tele-connections and connectivity across global networks. Our analysis underlines the importance of observational data in global scale assessments and the need for coordination in synthesising and assimilating available data.

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The Australian stocks and flows framework (ASFF) is a tool for establishing a coherent historical picture of the Australian physical economy and for testing long‐term future scenarios (up to 2050 or even 2100). These scenarios can be used to investigate the long‐term physical consequences of current and future choices affecting the physical dimensions of sustainability. In this article we describe the methodology for and construction of a key component of ASFF: a dynamic physical input‐output model of material flows in the basic industries. The materials model in ASFF describes physical flows and their transformation by industrial processes. The model's structure permits scenario analysis of long‐term technological change by permitting time‐varying input‐output coefficients and vintage models of capital stocks. As a consequence, the model contains a large number of parameters, which can be left at default settings or adjusted as the modeler sees fit, in order to simulate the widest possible range of physically realizable scenarios. The materials model is built using a methodology that integrates bottom‐up process analysis with top‐down statistics on material and energy flows. We present some examples showing how the materials model has been implemented to model Australian heavy industries. Several possibilities for further developing the materials model are also described.