What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020

Given the amount of direct and indirect CO2 emissions attributable to UK households, policy makers need a good understanding of the structure of household energy expenditure and the impact of both economic and non-economic factors when considering policies to reduce future emissions. To help achieve this, the Structural Time Series Model is used here to estimate UK ‘transport’ and ‘housing’ energy expenditure equations for 1964-2009. This allows for the estimation of a stochastic trend to measure the underlying energy expenditure trend and hence capture the non-trivial impact of ‘non-economic factors’ on household ‘transport’ and ‘housing’ energy expenditure; as well as the impact of the traditional ‘economic factors’ of income and price. The estimated equations are used to show that given current expectations, CO2 attributable to ‘transport’ and ‘housing’ expenditures will not fall by 29% (or 40%) in 2020 compared to 1990, and is therefore not consistent with the latest UK total CO2 reduction target. Hence, the message for policy makers is that in addition to economic incentives such as taxes, which might be needed to help restrain future energy expenditure, other policies that attempt to influence lifestyles and behaviours also need to be considered.Household energy expenditure; CO2 emissions; Structural Time Series Model

Gasoline Demand, Pricing Policy and Social Welfare in Iran

This study estimates a gasoline demand function for Iran using the structural time series model over the period 1968-2002 and uses it to estimate the change in social welfare for 2003 and 2004 of a higher gasoline price policy. It is found that short and long run demand price elasticities are inelastic, although the response is greater in the long run. Hence, social welfare is estimated to fall because of the higher gasoline price (ceteris paribus). However, allowing all variables in the model to change, social welfare is estimated to increase since the changes in the other variables more than compensate for the negative effects of the policy.

Estimating direct and indirect rebound effects for UK households

Energy efficiency improvements by households lead to rebound effects that offset the potential energy and emissions savings. Direct rebound effects result from increased demand for cheaper energy services, while indirect rebound effects result from increased demand for other goods and services that also require energy to provide. Research to date has focused upon the former, but both are important for climate change. This study estimates the combined direct and indirect rebound effects from seven measures that improve the energy efficiency of UK dwellings. The methodology is based upon estimates of the income elasticity and greenhouse gas (GHG) intensity of 16 categories of household goods and services, and allows for the embodied emissions of the energy efficiency measures themselves. Rebound effects are measured in GHG terms and relate to the adoption of these measures by an average UK household. The study finds that the rebound effects from these measures are typically in the range 5-15% and arise mostly from indirect effects. This is largely because expenditure on gas and electricity is more GHG-intensive than expenditure on other goods and services. However, the anticipated shift towards a low carbon electricity system in the UK may lead to much larger rebound effects

Rebound effects for household energy services in the UK

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Estimating rebound effects from technical energy efficiency improvements by UK households

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Population Expansion for Training Language Models with Private Federated Learning

Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large population of devices, FL with DP produces a performant model in a timely manner. However, for applications with a smaller population, not only does the model utility degrade as the DP noise is inversely proportional to population, but also the training latency increases since waiting for enough clients to become available from a smaller pool is slower. In this work, we thus propose expanding the population based on domain adaptation techniques to speed up the training and improves the final model quality when training with small populations. We empirically demonstrate that our techniques can improve the utility by 13% to 30% on real-world language modeling datasets

Asymmetric Price Responses and the Underlying Energy Demand Trend: Are they Substitutes or Complements? Evidence from Modelling OECD Aggregate Energy Demand

A number of energy demand studies have considered the importance of modelling Asymmetric Price Responses (APR), for example, the often-cited work of Gately and Huntington (2002). Griffin and Schulman (2005) questioned the asymmetric approach arguing that this is only capturing energy saving technical progress. Huntington (2006), however, showed that for whole economy aggregate energy and oil demand there is a role statistically for both APR and exogenous energy saving technical change. In a separate strand of the literature the idea of the Underlying Energy Demand Trend (UEDT) has been developed, see for example Hunt et al. (2003a and 2003b) and Dimitropoulos et al. (2005). They argue that it is important, in time series energy demand models, to allow for stochastic trends (or UEDTs) based upon the structural time series/dynamic regression methodology recommended by Harvey (1989, 1997). This paper attempts to bring these strands of the literature together by conducting tests for the UEDT and APR in energy demand models within both a panel context (consistent with the Huntington, 2006 approach) and the structural time series modelling framework. A set of tests across a range of specifications using time-series and panel data are therefore undertaken in order to ascertain whether energy saving technical change (or the more general UEDT) and APR are substitutes for each other when modelling energy demand or whether they are actually picking up different influences and are therefore complements. Using annual whole economy data for 17 OECD countries over the period 1960 – 2004 the results suggest that in general the UEDT and ARP are complementary estimation methodologies when modelling aggregate energy demand. It is argued therefore that energy demand modellers should not assume at the outset that one method is superior to the other. Moreover, wherever possible, a general model (be it in a time series or panel context) that includes a ‘non linear UEDT’ and APR should be initially estimated, and only if accepted by the data should symmetry and/or a more restrictive UEDT be imposed.Energy Demand, OECD, Asymmetric Price Responses, Underlying Energy Demand Trend.

Comparative energy efficiency of Swiss wastewater treatment plants based on economic foundations

IAEE Conference, Groningen, June 10-13, 2018As the number of wastewater treatment plants (WWTPs) increases worldwide and the effluent quality requirements become more demanding, the issue of energy efficiency has been attracting increasing attention from an environmental and economic point of view. Earlier approaches to measuring WWTP energy efficiency such as Data Envelopment Analysis (DEA) have recently focused on controlling for exogenous variables ignoring the possible presence of omitted (not observed) variables. This omission can lead to biased efficiency index. Moreover, since the level of efficiency can be decomposed in two parts, one persistent and one transient, based on such approaches, water utilities may decide to invest in new machines and infrastructure, when instead the origins of inefficiency come from a non-optimal use of some machines or vice versa. The objective of this paper is to investigate how overall inefficiency of WWTPs is decomposed to persistent and transient inefficiency. This allows better evaluation of energy saving measures since both components convey different types of information. While persistent inefficiency reflects long-term structural problems due to, e.g., energy inefficient equipment used for wastewater treatment, transient inefficiency is associated with process operational practices or decisions that take place in the short term. Distinguish between persistent and transient inefficiency, while controlling for exogenous factors, is thus essential to deduce appropriate energy diagnosis and design useful energy efficiency strategies for WWTPs. This research applies a novel approach of Stochastic Frontier Analysis (SFA) for energy demand modelling to estimate the comparative energy efficiency of a comprehensive panel of WWTPs in Switzerland, as far as in known, for the first tim

Forecasting scenarios for UK household expenditure and associated GHG emissions : Outlook to 2030

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