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Die weltweit anstehende sozial-ökologische Transformation wird für die Industrienation Deutschland weitreichende Konsequenzen haben. Vertreter von Post Growth-Ansätzen empfehlen für die unmittelbare Zukunft einen Übergang zu Null-Wachstum. Wir analysieren drei verschiedene Entwicklungspfade: Business as usual, No Growth und Green Growth. Dazu unterscheiden wir zwischen braunem, grünem und grauem Kapital (z.B. Kohlekraftwerke, Solarpanels und Schienennetze). Business as usual ist mit der vom Bundesverfassungsgericht angemahnten zügigen Entwicklung in Richtung Klimaneutralität schlicht unvereinbar, da der braune Kapitalstock viel zu langsam abgebaut wird. Green Growth kombiniert hingegen eine genügend schnelle Abschreibung des braunen Kapitals mit einem noch schnelleren Ausbau des grünen Kapitalstocks und kann dadurch Klimaneutralität bis 2045 erreichen. Der damit verbundene Investitionsschub bewirkt ein höheres, aber anders geartetes Wachstum als im Business as usual. Auch Null-Wachstum kann Klimaneutralität nur durch den Übergang von braunem zu grünem Kapital erreichen. Dieser Übergang wird jedoch so forciert, dass er politisch weder wünschenswert noch realisierbar ist: Die Einkommen großer Teile der Bevölkerung würden keinerlei Verbesserung erfahren. Eine Green Growth-Strategie bietet also gegenüber den anderen beiden Szenarien klare Vorteile. Für eine erfolgreiche und sozial gerechte Umsetzung dieser Strategie sind öffentliche Investitionen zentral.

The UN sustainable development goals contain environmental, economic, and social objectives. They may only be reached, or at least it would be easier to reach them, if instead of a trade-off between these objectives that implies a need for balancing them, there are synergies to be reaped. This paper discusses how the structures of economic models typically used in policy analysis influence whether win-win strategies for the environment and the economy can be conceptualised and analysed. With a focus on climate policy modelling, the paper points out how, by construction, commonly used model structures find mitigation costs rather than benefits. This paper describes mechanisms that, when added to these model structures, can bring win- win options into a model's solution horizon, and which provide a spectrum of alternative modelling approaches that allow for the identification of such options.

Klimawandel, Hungersnöte, Weltwirtschaftskrisen: Viele Risiken sind heute systemisch, vernetzt und nicht über geläufige Ursache-Wirkungs-Modelle erklärbar. Natürliche, technische und sozioökonomische Prozesse interagieren mit gesellschaftlichen. Die Risikoforschung braucht einen systemischen Blick.

Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

Rich countries have developed a historically unprecedented capability to manage conventional risks - fire, floods, earthquakes etc., but also car accidents, many workplace risks, and more. It is based on two institutions - insurance markets and public risk governance - supported by a powerful theory: the expected utility approach to risk. Expected utility refines the utilitarian paradigm of rational action by combining the concept of utility functions with the concept of probability distributions, using subjective probabilities where required. One might think that future progress in risk research will consist mainly in refining this approach and spreading it to emerging and less developed countries. However, greater progress is necessary and possible. It is necessary because the global economy and technostructure we live in have generated new systemic risks - including financial crises, pandemics, climate change, nuclear war. These risks exceed the coping capacity of conventional risk management and call for new forms of integrated risk governance. Greater progress is possible because recent research has developed ways to address the basic difficulties of expected utility without loosing its valuable insights. They involve three major advances. First, to introduce a risk function that generalizes expected utility so as to overcome well-known difficulties like the Allais paradox. Second, to embed expected utility in a framework of iterated network games so as to take into account the social learning processes that are essential for real world risk governance. And third, to accommodate the logic of complementary descriptions called for by the new systemic risks of the 21st century. The coming breakthrough in risk research may best be achieved by bringing these advances to bear on practical efforts aiming at integrated risk governance.

This study models maximum temperatures in Switzerland monitored in twelve locations using the Generalised Extreme Value (GEV) distribution. The parameters of the GEV distribution are determined within a Bayesian framework. We find that the parameters of the underlying distribution underwent a substantial change in the beginning of the 1980s. This change is characterised by an increase both in the level and the variability. We assess the likelihood of a heat wave of the Summer of 2003 using the fitted GEV distribution by accounting for the presence of a structural break. The estimation results do suggest that the heat wave of 2003 appears not that statistically improbable event as it is generally accepted in the relevant literature.

An integrated assessment (IA) model combines knowledge from very different disciplines in view of a practical problem. Most models developed so far are rather monolithic in the sense that it is difficult to combine components from different models for purposes of new assessments. We propose to develop a modular approach to IA based on advances in knowledge management as well as in object oriented software engineering. The incentive structure of modular IA is based on turning the knowledge produced neither into public nor into private, but rather into club goods. Competition amongst modelers becomes a process of discovery at the level of module design and module coupling, with strong synergies between competing teams. Together they develop a community pool of IAM-modules, along with software and know-how for running them in varying combinations.