Abstract: Climate change is causing substantial disruption of socio-economic systems around the world. Climate mitigation and adaptation measures are vital and require careful analysis of economic costs and benefits. Integrated Assessment Models (IAMs) are widely applied for these purposes in an attempt to support policy decisions. Despite their recent advancements, IAMs still suffer from substantial methodological limitations risking to mislead policy makers (Stern, 2016). It is argued that IAMs should further improve on the following assumptions: (i) a rough assessment of impacts on broad aggregated regions assuming a representative economic agent, (ii) a focus primarily on the reduction of economic output due to climate impacts ignoring destruction of livelihoods, loss of life and migration, (iii) assuming an incremental change in the GDP loss as temperatures increase ignoring the presence of potential tipping points, (iv) absence of learning, (v) simplified representation of technological change in the energy sector (Pindyck, 2013; Farmer et.al. 2015; Burke et.al. 2016). Together with the traditional problems with intergenerational discounting these issues form a significant challenge for the scientific community. Significant research effort is being invested currently in improving (iv) and (v), which both alter the estimation of costs of mitigation. Estimation of benefits of mitigation ? i.e. avoided losses (i-iii) ? did not yet received enough attention. The main issue here is that adverse impacts of climate change are quite unevenly distributed between regions and various socio-economic groups within them. Global damage estimates are likely to be significantly different if this heterogeneity, interactions between various entities, endogenous learning and non-linearity in economies reactions to climate is accounted for. While a myriad of local empirical studies provides an ample evidence that it matters for the overall damage assessment, the task of aggregating and finding general patterns and processes in this body of knowledge is not easy. We discuss the potential ways to address this problem.Firstly, we review the general approaches to estimate climate-driven economic impacts and damages in IAMs on the macro level. Then we outline how regional and local studies approach the same issue accounting for micro-level processes. We finally discuss few alternative approaches to bridge the two. To what extent can this gap be covered by adjusting the existing IAMs design? Should a completely different approach be taken by constructing the entire global economy from the bottom up? What are the risks and advantages of using a suit of models or nested model as a compromise solution? During the conference we would like to discuss these issues and corresponding pros and cons of each of the alternatives. References:Burke, M., et.al. Opportunities for advances in climate change economics. Science, 2016. 352(6283): p. 292-293.Farmer, J.D., C. Hepburn, P. Mealy, and A. Teytelboym, A Third Wave in the Economics of Climate Change. Environmental Resource Economics, 2015. 62: p. 329?357.Pindyck, R.S., Climate Change Policy: What Do the Models Tell Us? Journal of Economic Literature, 2013. 51(3): p. 860?872.Stern, N., Current climate models are grossly misleading. Nature, 2016. 530: p. 407-409.

Abstract: Human agency is a main determinant of land use and global land use change emerges from individual land use decisions in response to driving factors operating across multiple spatial and temporal scales. However, in global integrated models used for climate assessments, land use change is either presented by simplified aggregate decision making using profit optimization assumptions or by heuristics accounting for location suitability. There is a common understanding that there is a need to better represent (variation in) human agency in such large scale land use assessments (Rounsevell et al., 2014; Verburg et al., 2016).In this presentation I will discuss how a systematic review of case-studies on land use change is used to characterize different human land use decision mechanisms and how these are linked to contextual conditions. Based on this empirical knowledge land change models may vary their behavioral assumptions across time and space to better represent variation and evolvement of human agency. In the context of climate change a representation of human agency should capture feedbacks between land use change and climate change beyond the physical responses, i.e. in terms of mitigation and adaptation behavior. A first idea on including such feedbacks and constraints in model implementation is discussed.References:Rounsevell MDA, Arneth A, Alexander P, Brown DG, De Noblet-Ducoudr? N, Ellis E, Finnigan J, Galvin K, Grigg N, Harman I, Lennox J, Magliocca N, Parker D, O'Neill BC, Verburg PH, Young O. 2014. Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dynam., 5, 117-137. http://dx.doi.org/10.5194/esd-5-117-2014Verburg PH, Dearing J, Dyke J, Leeuw Svd, Seitzinger S, Steffen W, Syvitski J. 2015. Methods and Approaches to Modelling the Anthropocene. Global Environmental Change doi: 10.1016/j.gloenvcha.2015.08.007

Abstract: Until recently, projections of future climate have been obtained by first projecting the evolution of society ? in particular human energy and land-use activities ? and then examining the implications of those activities on climate and ecosystems within Earth system models.Typically, these assessments have not considered feedbacks from climate and ecosystem change onto the evolution of society and therefore energy and land-use, which are the primary anthropogenic drivers of climate change. Here we present the rationale for and theoretical basis of a coupled model framework known as the Integrated Earth System Model (iESM) that combines the physical representations of an Earth System Model with the human system representations of an Integrated Assessment Model. We demonstrate a previously unexplored feedback whereby future climatic, biological, and geochemical changes influence the productivity of land resources and therefore the evolution of land-use, agriculture, bioenergy use, and fossil fuel emissions. We further decompose this effect into the separate influences of climate change and the elevated CO2 concentrations, and we examine the role of climate mitigation policies in affecting the magnitude and nature of this feedback. The synchronous coupling of human and Earth systems in the iESM is enabled by a generalized software framework that could in principle accommodate a wide variety of human and Earth system models with varying levels of spatial and temporal resolution, as well as process detail. We discuss key lessons learned in building such an architecture and highlight the theoretical and practical challenges encountered in coupling models that represent linked processes at fundamentally different scales.

Abstract: This paper presents an agent based model for the study of coupled economic and climate dynamics that endogenously co-evolve across a range of different scenarios. The model offers a flexible laboratory to test various combinations of macroeconomic, industrial and climate policies both in the context of long run economic growth and medium run transition towards a greener economy. Furthermore, we propose a stochastic description of the feedbacks stemming from a warming and more volatile climate and study how such negative shocks propagate through the economy. For this reason, the model is particularly well suited for the study of extreme climate events, which are usually forgotten by standard integrated assessment models.

Abstract: Integrated Assessment Models (such as, notably, GCAM) use a regional approach: the planet is subdivided into some number of regions (for GCAM, 283 regions for agricultural production and 32 regions for other commodities), and these are used as the units of analysis when the model is run. This has a number of advantages, most notably computational tractability and data availability, but also that the units match those in which policies are established (e.g. trade between countries or blocks of countries). However, data availability and computational power are both changing; moreover, the impacts of a changing climate are being felt- and decisions about responses to them made- at smaller scales. We can reasonably ask if a different resolution might not be more appropriate for modeling climate-human feedbacks.One potential avenue is to take the city as the unit of analysis. Some advantages to this approach are presented and discussed here. Urbanism is increasing, and cities are representing daily life for a higher proportion of the world's population each day. Cities represent the economic drivers of the planet: the changes in consumption and production, even when that plays out across agricultural landscapes far removed from cities, are increasingly driven by cities. Responses to climate challenges will play out in cities, whether this occurs by rising water levels or by changes in demand for agricultural products, or by altering the efficiency and distribution of consumption of energy for buildings and transportation.In this presentation, I sketch a proposed modeling framework in which cities are central, and represent nodes on a network. The modeled network of cities has a number of dimensions along which adaptation in response to climate challenges is possible, and these modify the human system's impact on the climate system in a true feedback loop. I outline what this modeling program might look like, given the current state of modeling urban systems in the social science work, and especially with respect to agent-based modeling, considering both the prospect of cities as agents and of cities comprising simulated human agents. An important component will be asking how the results of such a modeling activity might usefully be compared to results from IAMs such as GCAM, and whether the change in focal point leads to genuinely novel insight or whether the approaches might mutually reinforce one another. I additionally consider the data, technical, and computational challenges of a truly agent-based approach at global scales.

Abstract: Food production is responsible for over 70% of freshwater use by humans and is the primary cause of land conversion globally. The global system of food production and trade is complex, with interdependencies between natural and socioeconomic conditions in importing and exporting regions, as well as the physical and socioeconomic infrastructure linking the two. Given this complexity, policy or environmental changes can have non-linear and cascading impacts for water and land resources along the supply chain. As the world becomes more globalised and more urbanised our dependence on this complex system increases. Thus, in order to achieve sustainable food security, we require an understanding of the complexity of the food production and trade system.In this paper we set out a framework for modelling the complex feedbacks between food production policy and water and land resource use and the how these are linked globally via trade. Our framework couples a multi-agent policy network with a model of the physical environment based on the global hydrological model PCR-GLOBWB and the dynamic vegetation model LPJ-GUESS. Cities are nodes in our network and are linked via physical trade infrastructure. Our framework provides a template for new type of Earth System model that captures the complex feedbacks between policy and environmental change and how these are linked globally via trade.

Abstract: One of the crucial aspects of global climate change, and global change more generally is the representation of ecosystems. Forest in particular plays a critical role in the regulation of atmospheric CO2. However, the dynamics of vegetated systems depends upon the presence of animals: current earth system models may include a global vegetation model, but the herbivores and the carnivores that depend upon them and help to shape them are typically absent. At the same time the ability of humans to create long-term sustainable societies arguably requires the maintenance of healthy ecosystems, and this needs a consideration of the whole ecosystem dynamics rather just the vegetated part. The ability to maintain wild-capture fisheries, for example, depends upon being able to assess the health of the carnivorous fish that are the main focus of fishing fleets, yet we have little idea of how fishing affects the resilience or long term stability of ecosystems in the ocean, especially in the face of increasing acidification from CO2 input. The Madingley model is the first model to include in a general way all of the living systems on the earth, including the animal component. The model is agent-based, but because of the vast numbers of animals that exist in certain categories (zooplankton for example) it uses a cohort representation in which a single agent may represent just one individual or many millions. In order to keep the model as general as possible, the cohorts may also represent multiple species that occupy the same functional group, so that our limited knowledge of the specifics does not prevent a meaningful representation of the dynamics. Grid-based climate driving fields and land-surface representations are used to link the individuals to environmental factors, with a variety of configurations that allow for the exploration of different scales. This also allows for scenario-based experiments to be performed in which the effects of human harvesting of natural capital on the ecosystem dynamics can be explored. However, since the model is already agent-based it also forms an ideal platform onto which global-scale agent-based dynamical human systems can be built while including all the relevant feedbacks between the atmosphere, ocean, biosphere and human systems. The latter present challenges both in coupling the model to existing climate GCMs, but also in exploring sufficiently generic but computationally tractable societal dynamics that go beyond the simple economic and equilibrium assumptions that still dominate most integrated assessments. The presentation will give a description the current state of the model and that ways it is currently being developed to include human factors.