Isabel Horta

Abstract
Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.

Abstract
Local actions are seen as of major importance for the achievement of climate change mitigation targets. In the past few years, the number of local action plans towards climate change mitigation has been increasing, and it is essential to analyze their contribution to the achievement of global targets. Even if the relationship between local action plans and the reduction of energy use and GHG emissions is often assumed, this has not yet been validated nor quantified by empirical studies involving a large number of municipalities. Thus, the aim of this paper is to. perform an empirical analysis on the link between local action plans and energy use and GHG emissions. The analysis is composed by a test of hypothesis and a regression analysis, performed for the municipalities of three European countries Portugal, Sweden and United Kingdom. The main conclusion is that, in the context of these three countries, the analysis performed was not able to detect a significant impact related to the existence of local plans on GHG emissions. From the panel data regression analysis, it was possible to confirm that external factors, not directly related to local climate change mitigation actions, have a significant impact on GHG emissions.

Abstract
The heating energy demand stated in energy performance certificates (EPC) and in other instruments used in the of evaluation of building's energy performance is usually determined assuming very specific (reference) indoor behavioral/heating patterns. Particularly, they tend to assume that households heat (nearly) the entire house to a "comfort" temperature during (nearly) all the heating season. However, several field studies have shown that there are major niches of the housing stock which do not follow this pattern (even the majority, in some geographical areas). Considering this matter, it would be interesting to build models able to estimate heating energy use values resultant from occupation and heating patterns different from those considered as "reference". This work aimed at producing tools to assess the relationship between heating energy use and indoor temperatures at different levels of occupant behavior (in terms of where, when and at what temperature households heat their dwellings). This relationship was expressed through models while still takes advantage of the information from the certificates. The work developed artificial neural networks (ANN) that characterize the relationship between heating energy use, indoor temperatures and the heating energy demand under reference conditions (typically available from energy rating/certificates) in the residential buildings, for different occupant behaviors heating patterns. Theoretically, these models can be applicable to any national geographical context. The data for building the ANNs was obtained from dynamic thermal building simulations using ESP-r, considering a large number of housing types and hypothetical occupation and heating patterns (i.e., which parts of the house are heated, when and at what temperature). From the analysis performed, it was possible to conclude that the developed ANN models proved to perform well (R-2 > 0.93) in estimating either heating energy use or indoor temperature, both at an individual and at the building stock level. This work may have important contributions in the energy planning practices regarding the residential building stock.

Abstract

Abstract
Urban form is an important driver of energy demand and therefore of GHG emissions in urban areas. Yet, research on urban form and energy remains sectorial and hasn't been able to deliver a full understanding of the impact of the physical structure of cities upon their energy demand. Most common approaches feature engineering models in buildings, and statistical models in transports. This study aims at contributing to the characterization of the link between urban form and energy considering altogether three distinct energy uses: ambient heating and cooling in buildings, and travel. A high-resolution methodology is proposed. It applies GIS to provide the analysis with a spatially-explicit character, and neural networks to model energy demand based on a set of relevant urban form indicators. The results confirm that the effect of urban form indicators on the overall energy needs is far from being negligible. In particular, the number of floors, the diversity of activities within a walking reach, the floor area and the subdivision of blocks evidenced a significant impact on the overall energy demand of the case study analyzed.