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Your nostalgia has createdA nonexistent country, with lawsAlien to earth and man.George Seferis,The Return of the Exile(Keeley and Sherrard, 1981, pp. 224-25)The history of Greece's first century as an independent nation-state is in many ways a history of the interplay among urban space, nationalism, and identity. It is also a history of nostalgia: Western European nostalgia for one specific past, Greek nostalgia for another, and the tension between the two.

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This study describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

Natural hazards and climate-related disasters disregard political borders, where additional barriers can complicate mitigation, response and recovery efforts within and between the sectors of Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR). The ESPREssO Project (Enhancing Synergies for Disaster Prevention in the European Union) aims to improve management of transboundary disasters by encouraging closer synergies between the CCA and DRR communities. Using targeted stakeholder interviews, questionnaires, Think Tank discussions and purpose-built serious games, ESPREssO draws on both CCA and DRR stakeholder experiences and informed perspectives in order to identify current gaps. Set within a fictitious border zone, ESPREssO's RAMSETE II serious game challenges CCA and DRR stakeholders in making coordinated decisions before, during and after a simulated disaster, in protection of population and critical infrastructure. Results highlight the essential role of local governance mechanisms as the sharp end of the policy wedge, with current examples of proactivity that require to be championed and supported at national level in order to thrive. These good practice examples reflect the fact that transboundary settings, despite their challenges, act as fertile ground for mutual growth, offering opportunities for CCA and DRR communities to find innovative ways to cooperate and unite in developing synergies and strengthening their mutual efforts towards resilience. Stakeholders emphasise a need to invest more resources in informal cooperation and call on policy makers to recognise that each border zone raises its own unique set of complex challenges that requires flexibility and special consideration by transboundary authorities in management of disasters.

The ESPREssO Project set out to propose ways to inform more coherent national and European approaches on Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA). A critical step in this process is the identification of existing barriers to effective collaboration, finding new areas of common ground, and ways to enhance co-operation with regards to CCA and DRR policymaking in Europe. This is particularly important considering the potential relationships between CCA and DRR activities at the regional, national, European and global levels. Serious games have emerged as a valuable tool to communicate information and catalyse discussion in many policy arenas. The games have the power to inform, mainly by exposing strengths and weaknesses of a system but not necessarily create policy choices. This paper presents the development process and rationale behind creation of RAMSETE I, a serious game developed by and for the ESPREssO Project to elicit information from its stakeholders in aiming to inform synergies between CCA and DRR sectors. The results assess its application as a device to frame discussions during an international Think Tank workshop. The serious game focused on three particular aspects of CCA and DRR policy interactions: (1) separation of administrative responsibilities and the use of different terminology, (2) the ongoing competition for funding and political will as well as (3) difficulties regarding the top-down implementation of policies. The rules and design process are presented briefly, before going in-depth into the information gleaned during its application in the workshop.

Abstract. Both aleatory and epistemic uncertainties associated with different sources and components of risk (hazard, exposure, vulnerability) are present at each step of seismic risk assessments. All individual sources of uncertainty contribute to the total uncertainty, which might be very high and, within the decision-making context, may therefore lead to either very conservative and expensive decisions or the perception of considerable risk. When anatomizing the structure of the total uncertainty, it is therefore important to propagate the different individual uncertainties through the computational chain and to quantify their contribution to the total value of risk. The present study analyses different uncertainties associated with the hazard, vulnerability and loss components by the use of logic trees. The emphasis is on the analysis of epistemic uncertainties, which represent the reducible part of the total uncertainty, including a sensitivity analysis of the resulting seismic risk assessments with regard to the different uncertainty sources. This investigation, being a part of the EU FP7 project MATRIX (New Multi-Hazard and Multi-Risk Assessment Methods for Europe), is carried out for the example of, and with reference to, the conditions of the city of Cologne, Germany, which is one of the MATRIX test cases. At the same time, this particular study does not aim to revise nor to refine the hazard and risk level for Cologne; it is rather to show how large are the existing uncertainties and how they can influence seismic risk estimates, especially in less well-studied areas, if hazard and risk models adapted from other regions are used.