[en] Our fundamental premise is that energy consumption at the household level is a key indicator of standard of living. We employ state-of-the-art panel cointegration techniques to evaluate the nature of the relationship between income measures and energy consumption measures for seven East Indian Ocean countries. The general finding is that income and household electricity consumption are not cointegrated. Given this finding, we conclude that standard of living measures that rely on income measures and do not include household-level energy consumption information will necessarily miss important indications of both levels and changes of standard of living

No abstract available

[en] All energetic aspects collected within the main topic 'Energy and life' are gathered in 14 volumes. Environmental questions were devoted special attention because of public concern. The congress resolved to promote clean technologies and renewable energies with less environmental impact but without forgetting profitability. Experts in energetic topics attended the Congress

[en] Highlights: • Building energy model biases in the WECC depend on the location/number of representative cities. • Using 1 station per IECC climate zone results in a mean absolute summer temperature bias of 4.0 °C. • Using 1 station per IECC zone can lead to a 20–40% overestimate of peak loads during summer/winter. • Using all available stations reduces the mean absolute load bias by a factor of 2.5. • Using 4 stations per IECC zone reduces both temperature/load biases and computational burden. - Abstract: Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ~4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ~1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

[en] Energy models play an increasing role in the ongoing energy transition processes either as tools for forecasting potential developments or for assessments of policy and market design options. In recent years, these models have increased in scope and scale and provide a reasonable representation of the energy supply side, technological aspects and general macroeconomic interactions. However, the representation of the demand side and consumer behavior has remained rather simplistic. The objective of this paper is twofold. First, we review existing large-scale energy model approaches, namely bottom-up and top-down models, with respect to their demand-side representation. Second, we identify gaps in existing approaches and draft potential pathways to account for a more detailed demand-side and behavior representation in energy modeling.

[en] The part of the study which refers to the worldwide situation discusses the problems and aspects which are decisive for the further development of energy supplies and energy consumption, i.e. the increase in population, the economic and social development, the supply of energy to the developing countries, environmental pollution, the risk of a worldwide change in the climate, energy conservation both as regards generation and utilization, financing problems, rules and regulations, the development of new technologies and their transfer, and individual energy economics problems. At the regional level, the relevant energy supply and demand problems were analyzed by nine committees. The results obtained were verified within the overall context of the worldwide facts and uncertainties before development priorities, expectations and alternatives were fixed for the respective regions. The results of the worldwide study and of the regional studies were discussed in detail on the occasion of the 15th congress of the World Energy Council in Madrid in September 1992. (orig./UA)

[en] The substantial capital investment and the long-term nature of extension projects make it necessary, in particular for local utilities, to intensively prepare their construction projects. Resulting from this context, the PC-program MAFIOSY for calculating and optimizing the economics of pipeline extension projects has been developed to facilitate the decision-making process and to ensure an optimum decision. The optimum structure of a distribution network to be designed for a new service area is defined using the four-phase method set out below: Situation Audit; Determination of Potential; Determination of Economic and Technical Parameters; Optimization. (orig.)

[en] Uncertainties in energy demand modelling allow for the development of different models, but also leave room for different calibrations of a single model. We apply an automated model calibration procedure to analyse calibration uncertainty of residential sector energy use modelling in the TIMER 2.0 global energy model. This model simulates energy use on the basis of changes in useful energy intensity, technology development (AEEI) and price responses (PIEEI). We find that different implementations of these factors yield behavioural model results. Model calibration uncertainty is identified as influential source for variation in future projections: amounting 30% to 100% around the best estimate. Energy modellers should systematically account for this and communicate calibration uncertainty ranges. (author)

[en] The paper develops a framework to analyze energy security in an expected utility framework, where there is a risk of disruption of imported energy. The analysis shows the importance of an energy tax as a tool in maximizing expected utility, and how the level of that tax varies according to the key parameters of the system: risk aversion, probability of disruption, demand elasticity and cost of disruption. (author)

[en] Increasing land consumption and land demand particularly in mountainous regions entail further expansion of settlements to known hazard-prone areas. Potential impacts as well as regionally defined levels of 'acceptable risk' are often not transparently communicated and residual risks are not perceived by the public. Analysing past events and assessing regional damage potentials can help planners on all levels to improve comprehensive and sustainable risk management. In this letter, a geospatial and statistical approach to regional damage cost assessment is presented, integrating information on actual conditions in terms of land use disparities and recorded damage data from a documented severe flooding event. In a first step building objects are categorized according to their function and use. Tabular company information is linked to the building model via geocoded postal address data, enabling classification of building types in terms of predominant uses. For the disaster impact assessment the flood plain is delineated based on post-disaster aerial imagery and a digital terrain model distinguishing areas of long and short term flooding. Finally, four regional damage cost assessment scenarios on different levels of detail are calculated. The damage cost projection relies on available sample building-level damage records, allowing rough damage averaging for distinct building uses. Results confirm that consideration of local land use patterns is essential for optimizing regional damage cost projections.