Technical Description of the IIASA Model Cluster

The quantitative analysis of REDD supply schedules were carried out in a global total land-use context. The Global Model cluster combines geographically explicit biophysical models with economic modeling. The model cluster covers all land-use types and thus allows for fully integrated analysis of competitive interactions between different land uses and land use change types. Combining the different models allows for geographic explicit analysis of REDD policies in a global context. The geographic explicit analysis of REDD policy options is carried out using the G4M (former DIMA) model (e.g. Rokitiansky et al., 2007; Kindermann et al., 2006, 2008b). G4M is driven by exogenous market price assumptions for land and commodities without taking market feedbacks into account. The partial equilibrium model GLOBIOM generates endogenous prices. GLOBIOM has global geographic coverage and accounts for all land uses and thus allows for REDD policy analysis in a wider land use and global change context. When the two models are coupled the G4M model serves a double purpose. First it informs GLOBIOM on basic biophysical forest growth information and engineering costing of various forest management options. Second, results from GLOBIOM, such as endogenous commodity and land prices and trade, are used as exogenous drivers for the geographically explicit modeling using G4M. In the latter G4M becomes a "sophisticated" downscaling algorithm for GLOBIOM results facilitating "visual validation" of results and geographic REDD hot spotting. In the following the two models are described. In the description of G4M we provide a detailed description of the improved carbon accounting and calibration methods departing from (Kindermann et al., 2006). Changes in the calibration methodology have necessarily created considerable differences in baseline emissions and thus REDD costs as published in (Kindermann et al., 2006). Baselines in (Kindermann et al., 2006) are determined mainly by future GDP and population development assuming low institutional barriers for expansion of the agricultural and forestry sectors whereas the latter is mainly driven by the continuation of historical emissions and the continuation of institutional barriers of agricultural and forestry sector development. The version of G4M presented in this document was calibrated to the global emissions estimates provided by the IPCC while the one in (Kindermann et al., 2006) was calibrated to the estimates provided by global analysis using remote sensing methods. Differences in the results of these two model versions of G4M provide valuable insights on the impact of changes in methodologies on REDD costs. The description of GLOBIOM is provided with less detail due to space limitations

Defining New Global Land-use Map in 2050 by Including Environmental Flow Requirements

Allocation of agricultural commodities and water resources is subject to changes in climate, demographics and dietary patterns. The use of integrated assessment modeling frameworks that combine climate, hydrological, crop and economic models anticipate those future changes. Results from previous integrated assessments have almost always neglected water resources or included them only in a broad way. The focus of this study is on how the inclusion of water resources affects future land use and, in particular, how global change will influence repartition of irrigated and rainfed lands at global scale. We used two general circulation model (GCM) simulations of climate change scenario including a radiative forcing of 8.5 W/m2 (RCP8.5), the socio-economic scenario (SSP2: middle-of-road), and the Variable Monthly Flow (VMF) method to calculate environmental flow requirements (EFRs). Irrigation withdrawals were adjusted to a monthly time-step to account for biophysical water limitations at finer time resolution. Re-allocation of rainfed and irrigated land might be useful information for land-use planners and water managers at an international level. For example, some countries are likely to adopt measures to increase their water use efficiencies (irrigation system, soil and water conservation practices) to face water shortages, while others might consider improving their trade policy to avoid food shortage and to protect freshwater ecosystems

Uncertainties in global land cover data and its implications for climate change mitigation policies assessment

Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how high will be competition for land between food and bioenergy in the future as well as how much land is there available for afforestation projects? These questions can only be answered if reliable maps of land cover exist. We put this research in the framework of GEOSS, examine how modeling tools can be used for benefit assessment and design an assessment framework. We illustrate the importance of good quality global land cover maps by using cropland extend from the currently best global maps of land cover namely GLC-2000, MODIS, GlobCover and CropLikelyhood as input for the EPIC model (to model crop yields) and global economic land use model GLOBIOM. We use all of the 4 maps and create a maximum crop extend and map. Based on a baseline map and the maximum crop extend map e model effects of climate policies (e.g. the potentials of substitution of fossil fuels with biofuels)

The development of a resource-efficient photovoltaic system

This paper presents the measures taken in the demonstration of the photovoltaic case study developed within the European project ‘Towards zero waste in industrial networks’ (Zerowin), integrating the D4R (Design for recycling, repair, refurbishment and reuse) criteria at both system and industrial network level. The demonstration is divided into three phases. The first phase concerns the development of a D4R photovoltaic concept, the second phase focused on the development of a specific component of photovoltaic systems and the third phase was the demonstration of the D4R design in two complete photovoltaic systems (grid-connected and stand-alone). This paper includes a description of the installed photovoltaic systems, including a brief summary at component level of the lithium ion battery system and the D4R power conditioning system developed for the pilot installations. Additionally, industrial symbioses within the network associated with the photovoltaic systems and the production model for the network are described

Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014). We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results

The sensitivity of the costs of reducing emissions from deforestation and degradation (REDD) to future socioeconomic drivers and its implications for mitigation policy design

Climate change mitigation policies for the land use, land use change, and forestry (LULUCF) sector are commonly assessed based on marginal abatement cost curves (MACC) derived from optimization models or engineering approaches. Yet, little is known about the space of validity of MACCs and how they are influenced by changes in main underlying drivers. In this study, we apply the Global Forest Model (G4M) to explore the sensitivity of MACCs to variation of socioeconomic drivers of deforestation, afforestation, and forest management activities. Particularly, three key factors are considered: (I) wood price, as an indicator of timber market developments; (II) agricultural land price, as a proxy representing the developments on agricultural markets; and (III) corruption coefficient, representing the progress in institutional development and measuring abatement costs use efficiency. The results indicate that the MACCs are more sensitive to the corruption coefficient than to agricultural land price and wood price. Furthermore, we find that the MACCs are more robust with high carbon dioxide (CO2) price and that the sensitivity of the MACCs is higher at low CO2 prices. In general, it can be concluded that when assessing medium-term mitigation policies characterized by low CO2 prices, MACCs need to be developed in-line with institutions currently in place. When designing long-term mitigation policy characterized by high CO2 prices, the role of the analyzed drivers in MACCs estimation is less important