Architekturmodelle und Datenkonzepte im multimodalen Energiesystem

Dieser Bericht fasst die Ergebnisse der Sichtung und Bewertung existierender Beschreibungen und Architekturen des Systems Energie/IKT sowie Vorschläge zu nötigen Erweiterungen gemäß Task 3.1 „Architekturmodelle des Energiesystems“ und zu bestehenden und zukünftigen Datenflüssen gemäß Task 3.2. „Kommunikationsarchitekturen“ zusammen. Dazu werden zunächst eine Beschreibung sowie eine Analyse der relevanten Architekturbestandteile und der beteiligten Rollen und Akteure durchgeführt. Darauf aufbauend wird dann ein Überblick existierender Anwendungsfälle von IKT im Kontext Smart Grid gegeben und exemplarisch einzelne Fälle detaillierter analysiert

The effect of forest owner decision-making, climatic change and societal demands on land-use change and ecosystem service provision in Sweden

The uncertain effects of climatic change and changing demands for ecosystem services on the distribution of forests and their levels of service provision require assessments of future land-use change, ecosystem service provision, and how ecosystem service demands may be met. We present CRAFTY-Sweden, an agent-based, land-use model that incorporates land owner behaviour and decision-making in modelling future ecosystem service provision in the Swedish forestry sector. Future changes were simulated under scenarios of socio-economic and climatic change between 2010 and 2100. The simulations indicate that the influence of climatic change (on land productivities) may be less important than that of socio-economic change or behavioural differences. Simulations further demonstrate that the variability in land owner and societal behaviour has a substantial role in determining the direction and impact of land-use change. The results indicate a sizeable increase in timber harvesting in coming decades, which together with a substantial decoupling between supply and demand for forest ecosystem services highlights the challenge of continuously meeting demands for ecosystem services over long periods of time. There is a clear need for model applications of this kind to better understand the variation in ecosystem service provision in the forestry sector, and other associated land-use changes

Dynamic Social Networks in Agent-based Modelling. Increasingly Detailed Approaches of Network Initialisation and Network Dynamics.

Zugleich: Dissertation, Universität Kassel, 201

CRAFTY EU28 Land Use | version C001-G9-C3-I1 | IPCC Scenario A1 | runID 3

CRAFTY (Competition for Resources between Agent Functional Types) is a large-scale ABM (Agent-based model) framework of land use change. It has been designed to allow efficient but powerful simulation of a wide range of land uses and the goods and services they produce. These simulation outputs show land use patterns in 2010, 2020, 2030, and 2040 emerging from competition under IPCC scenario A1.Holzhauer, Sascha; Calum, Brown. (2016). CRAFTY EU28 Land Use | version C001-G9-C3-I1 | IPCC Scenario A1 | runID 3, 2010-2040 [dataset]. University of Edinburgh. School of GeoSciences. Institute of Geography. http://dx.doi.org/10.7488/ds/1423

Land managers’ behaviours modulate pathways to visions of future land systems

Attempts to influence the development of land systems are often based on detailed scenarios that constrain relevant factors, describe a range of divergent but plausible futures and identify potential pathways to visions of desirable conditions. However, a number of assumptions are usually made during this process, and one of the most substantial is that land managers display homogeneous, economically rational behaviour across space, time and scenarios. This assumption precludes the consideration of important behavioural effects and limits understanding of the feasibility of scenario-based pathways towards visions. We use an agent-based land use model to examine broad forms of behavioural variation within defined scenarios in theoretical contexts. We relate model results to stakeholder-developed visions of desired future land systems in Europe and so assess the scope for behavioural pathways towards these normative futures. We find that the achievability of visions is determined by internal inconsistencies, scenario conditions and the multifunctional potential of land uses, with a fundamental tension between large-scale land use productivity and small-scale diversity (i.e. land sparing and land sharing). Trading conditions affect this balance most strongly and represent an obvious target for governance strategies concerned with achieving multi- functional land use. However, within specific circumstances behavioural effects are strong and diverse, and can accelerate, counteract or mitigate the impacts of other drivers. This suggests that visions for the land system should focus on trade-offs, identifying those that are least strong, most acceptable and most susceptible to adjustment through behavioural or other influences

Identifying data challenges to representing human decision-making in large-scale land-use models

Land-use models are by now an accepted method in scientific research, both to increase our understanding of land-use change processes and to project future land-use trajectories. Many of these models simulate changes as a function of spatial data layers, such as elevation, accessibility and soil type. However, land-use changes are ultimately the result of human decisions. Therefore representing human decision-making processes in models is essential to advance our understanding of land-use change processes as well as our capacity to support policy making. Agent-based models allow human decision-making to be represented explicitly. However, their application is constrained by the availability of data about actors and their decision-making processes. Empirical data can be obtained from case studies, but the geographic extent of these case studies is constrained by time and resources. Therefore we argue that we need new sources of data to support model representation of these processes. In this chapter, we further specify this data demand and discuss potential methods of data acquisition. Data acquisition methods include metastudies, aligning with various ongoing large-scale data collection efforts, dedicated projects and crowdsourcing