Construction of coarse-grained order-parameters in non-equilibrium systems

We develop a renormalization group (RG) procedure that includes important system-specific features. The key ingredient is to systematize the coarse graining procedure that generates the RG flow. The coarse graining technology comes from control and operator theoretic model reduction. The resulting "generalized" RG is a consistent generalization of the Wilsonian RG. We derive the form of the projection operator from the dynamics of a nonlinear wave equation and renormalize the distribution of initial conditions. The probability density of the initial conditions is chosen to be the Boltzmann weight for a standard $\phi^4$-theory. In our calculation, we find that in contrast to conventional implementations of the RG, na\"ive power counting breaks down. The RG-equations that we derive are different from those derived from the conventional RG.Comment: 13 pages, 0 figure

Demand, services and social aspects of mitigation

Assessment of the social science literature and regional case studies reveals how social norms, culture, and individual choices, interact with infrastructure and other structural changes over time. This provides new insight into climate change mitigation strategies, and how economic and social activity might be organised across sectors to support emission reductions. To enhance well-being, people demand services and not primary energy and physical resources per se. Focusing on demand for services and the different social and political roles people play broadens the participation in climate action

Reviewing the scope and thematic focus of 100 000 publications on energy consumption, services and social aspects of climate change: A big data approach to demand-side mitigation

As current action remains insufficient to meet the goals of the Paris agreement let alone to stabilize the climate, there is increasing hope that solutions related to demand, services and social aspects of climate change mitigation can close the gap. However, given these topics are not investigated by a single epistemic community, the literature base underpinning the associated research continues to be undefined. Here, we aim to delineate a plausible body of literature capturing a comprehensive spectrum of demand, services and social aspects of climate change mitigation. As method we use a novel double-stacked expert-machine learning research architecture and expert evaluation to develop a typology and map key messages relevant for climate change mitigation within this body of literature. First, relying on the official key words provided to the Intergovernmental Panel on Climate Change by governments (across 17 queries), and on specific investigations of domain experts (27 queries), we identify 121 165 non-unique and 99 065 unique academic publications covering issues relevant for demand-side mitigation. Second, we identify a literature typology with four key clusters: policy, housing, mobility, and food/consumption. Third, we systematically extract key content-based insights finding that the housing literature emphasizes social and collective action, whereas the food/consumption literatures highlight behavioral change, but insights also demonstrate the dynamic relationship between behavioral change and social norms. All clusters point to the possibility of improved public health as a result of demand-side solutions. The centrality of the policy cluster suggests that political actions are what bring the different specific approaches together. Fourth, by mapping the underlying epistemic communities we find that researchers are already highly interconnected, glued together by common interests in sustainability and energy demand. We conclude by outlining avenues for interdisciplinary collaboration, synthetic analysis, community building, and by suggesting next steps for evaluating this body of literature