Dr Mona Chitnis
Mona is a Senior Lecturer in Energy Economics and Director of Surrey Energy Economics Centre (SEEC) at the School of Economics, University of Surrey where she joined as a Lecturer in 2014. She was a Lecturer in Economics at the Department of Economics, University of Aberdeen in 2013-2014. Mona was a Research Fellow in the multidisciplinary Sustainable Lifestyle Research Group (SLRG) and Research Group on Lifestyles, Values and Environment (RESOLVE) at the Centre for Environmental Strategy (CES), University of Surrey in 2006-2013. Before joining CES, she worked for almost ten years in the Macroeconomics Department of Management and Planning Organisation (Government) in Tehran as an Expert in Economics and State Planning, and lectured at Alzahra University in Tehran. Mona obtained her BSc, MSc and PhD (2005) all in Economics from the University of Tehran.
General: energy economics, environmental economics, applied econometrics, applied microeconomics.
Current research interest: energy demand modelling, rebound effects, energy efficiency and emissions.
Energy Economics and Technology (ECOM026-PGT)
Energy Economics (ECO3012-UG)
Director of Surrey Energy Economics Centre (SEEC)
Member of the Faculty Ethics Committee
Member of the Faculty Equality, Diversity and Inclusion Committee (EDIC)
Faculty EC Teacher of the Year Award 2017
"Investigating the market for green heat", Surrey Living Lab.
"Free clean solar energy by 2035", Surrey Living Lab.
Given the increasing importance of the wastewater sector in terms of energy us-age, the understanding of the level of energy efficiency of wastewater treatment plants (WWTPs) is useful to both the industry itself as well as policy makers. Here, based on economic foundations, we apply a Stochastic Frontier Analysis (SFA) approach for energy demand modelling to estimate energy efficiency in the wastewater sector. Using specific SFA models and panel data from 183 Swiss WWTPs over the period 2001 to 2015, the paper illustrates that distinguishing between persistent and transient inefficiency is essential to deduce appropriate en-ergy efficiency diagnosis in WWTPs. In this respect, persistent energy inefficiency is found to be more severe than transient energy inefficiency. Furthermore, it is shown that the age of the equipment influences the demand for energy and the energy savings due to technological innovation are quantified. Finally, economies of output density and scale are estimated demonstrating that for plants operating below optimal scale significant energy savings can be achieved if plants would be operated at higher size. Moreover, our analysis reveals also that for plants larger than 100,000 Population Equivalent, at least from an energy efficiency point of view, it would be no more beneficial to increase their scale.
This article attempts to quantify the contributions of economic and noneconomic factors that drive UK consumer expenditure for 12 COICOP categories of goods and services using the structural time series model (STSM) over the period 1964Q1 to 2006Q1. This approach allows for the relative quantification of the impact of noneconomic factors on UK household expenditure demand (via a stochastic trend and stochastic seasonal) in addition to the economic factors (income and price). The results suggest that the contribution of the noneconomic factors is generally higher for ‘housing, water, electricity, gas and other fuels’, ‘health’, ‘communication’ and ‘education’; hence, they have an important role to play in these sectors. The message for policymakers is therefore that, in addition to economic incentives such as taxes which might be needed if they wish to restrain future expenditure, other policies that attempt to influence lifestyles might also need to be considered.
This study estimates the combined direct and indirect rebound effects from various types of energy efficiency improvement and behavioural change by UK households and explores how these effects vary with total expenditure. The methodology is based upon estimates of the expenditure elasticity and GHG intensity of 16 categories of goods and services, and allows for the capital cost and embodied emissions of the energy efficiency measures themselves. The study finds that rebound effects, in GHG terms, are modest (0-32%) for measures affecting domestic energy use, larger (25-65%) for measures affecting vehicle fuel use and very large (66-106%) for measures that reduce food waste. Furthermore, measures undertaken by low income households are associated with the largest rebound effects, with direct emissions forming a larger proportion of the total rebound effect for those households. Measures that are subsidised or affect highly taxed energy commodities may be less effective in reducing aggregate emissions. These findings highlight the importance of allowing for rebound effects within policy appraisals, as well as reinforcing the case for economy-wide carbon pricing. © 2014 Elsevier B.V.
Given the increasing importance of the wastewater sector in terms of energy usage, the understanding of the level of energy efficiency of wastewater treatment plants (WWTPs) is useful to both the industry itself as well as policy makers. Here, based on economic foundations, we apply a Stochastic Frontier Analysis (SFA) approach for energy demand modelling to estimate energy efficiency in the wastewater sector. Using specific SFA models and panel data from 183 Swiss WWTPs over the period 2001 to 2015, the paper illustrates that distinguishing between persistent and transient inefficiency is essential to deduce appropriate energy efficiency diagnosis in WWTPs. In this respect, persistent energy inefficiency is found to be more severe than transient energy inefficiency. Furthermore, it is shown that the age of the equipment influences the demand for energy and the energy savings due to technological innovation are quantified. Finally, economies of output density and scale are estimated demonstrating that for plants operating below optimal scale significant energy savings can be achieved if plants would be operated at higher size. Instead, our analysis reveals also that for plants larger than 100000 Population Equivalent, at least from an energy efficiency point of view, it would be no more beneficial to increase their scale.
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, as well as the capital cost of the measures. 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. © 2012 Elsevier Ltd.
This study estimates the combined direct and indirect rebound effects from energy efficiency improvements in the delivery of six energy services to UK households, namely: heating; lighting; cooking; refrigeration and clothes washing; entertainment and computing; and private vehicle travel. We use a unique database on the price and quantity demanded of these energy services over the past half century. We estimate a two-stage almost ideal demand system for household expenditure, using these energy services as expenditure categories. We estimate rebound effects in terms of carbon emissions and only include the ‘direct’ emissions associated with energy consumption. Our results suggest direct rebound effects of 70% for heating, 54% for private vehicle travel and ~90% for the other energy services. However, these effects are offset by negative indirect rebound effects – that is, indirect rebounds contribute additional emission savings. As a result, our estimates of combined rebound effects are generally smaller, namely 54% for lighting, 55% for heating, 41% for refrigeration and clothes washing, -12% for entertainment and computing, 44% for cooking and 69% for vehicle travel. We also find some evidence that rebound effects have declined over time. We provide some important caveats to these results, and indicate priorities for future research.
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.
Using the modelling tool ELESA (Econometric Lifestyle Environment Scenario Analysis), this paper describes forecast scenarios to 2030 for UK household expenditure and associated (direct and indirect) greenhouse gas (GHG) emissions for 16 expenditure categories. Using assumptions for real household disposable income, real prices, ‘exogenous non-economic factors’ (ExNEF), average UK temperatures and GHG intensities, three future scenarios are constructed. In each scenario, real expenditure for almost all categories of UK expenditure continues to grow up to 2030; the exceptions being ‘alcoholic beverages and tobacco’ and ‘other fuels’ (and ‘gas’ and ‘electricity’ in the ‘low’ scenario) leading to an increase in associated GHG emissions for most of the categories in the ‘reference’ and ‘high’ scenarios other than ‘food and non-alcoholic beverages’, ‘alcoholic beverages and tobacco’, ‘electricity’, ‘other fuels’ and ‘recreation and culture’. Of the future GHG emissions, about 30% is attributed to ‘direct energy’ use by households and nearly 70% attributable to ‘indirect energy’. UK policy makers therefore need to consider a range of policies if they wish to curtail emissions associated with household expenditure, including, for example, economic measures such as taxes alongside measures that reflect the important contribution of ExNEF to changes in expenditure for most categories of consumption.
This study estimates the combined direct and indirect rebound effects from various types of energy efficiency improvement by UK households. In contrast to most studies of this topic, we base our estimates on cross-price elasticities and therefore capture both the income and substitution effects of energy efficiency improvements. Our approach involves estimating a household demand model to obtain price and expenditure elasticities of different goods and services, utilising a multiregional input–output model to estimate the GHG emission intensities of those goods and services, combining the two to estimate direct and indirect rebound effects, and decomposing those effects to reveal the relative contribution of different mechanisms and commodities. We estimate that the total rebound effects are 41% for measures that improve the efficiency of domestic gas use, 48% for electricity use and 78% for vehicle fuel use. The primary source of this rebound is increased consumption of the cheaper energy service (i.e. direct rebound) and this is primarily driven by substitution effects. Our results suggest that the neglect of substitution effects may have led prior research to underestimate the total rebound effect. However, we provide a number of caveats to this conclusion, as well as indicating priorities for future research.
This study estimates the gasoline demand function for the Islamic Republic of 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.
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 proposing a testing procedure for the UEDT and APR in energy demand models within both a panel context (consistent with the Huntington, 2006 approach) and the structural time seriesmodelling framework. A set of tests across a range of specifications using time-series and panel data are therefore suggested in order to try and ascertainwhether 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 annualwhole economy data for 17 OECD countries over the period 1960–2006 the results suggest that for most of the countries the UEDT is preferred to APR, whereas for another group the UEDT and APR are complements, and for another group they are substitutes. 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.