Mona Chitnis

Dr Mona Chitnis


Senior Lecturer in Energy Economics
PhD, MSc, BSc
+44 (0)1483 689923
11 AD 00
Mondays 15.30-17.30 (semester 1, weeks 1-11)

Biography

Biography

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.

Research interests

General: energy economics, environmental economics, applied econometrics, applied microeconomics.

Current research interest: energy demand modelling, rebound effects, energy efficiency and emissions.

Teaching

Energy Economics and Technology (ECOM026-PGT)

Energy Economics (ECO3012-UG)

Departmental duties

Director of Surrey Energy Economics Centre (SEEC)

Awards

Faculty EC Teacher of the Year Award 2017

Research projects

My publications

Publications

Chitnis M, Sorrell S (2015) Living up to expectations: Estimating direct and indirect rebound effects for UK households, Energy Economics 52 (Supp 1) pp. S100-S116
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.
Chitnis M, Hunt LC (2010) Contribution of economic versus non-economic drivers of UK household expenditure, 03-10
Chitnis M (1998) Disequilibrium model analysis of labour market in the large manufacturing workshops in Iran,
Chitnis M, Druckman A, Hunt LC, Jackson T (2011) Predicting UK Household Expenditure and associated GHG Emissions: Outlook to 2030,
Chitnis M, Hunt LC (2011) Modelling UK household expenditure and the contribution of economic versus non-economic factors,
Chitnis M, Druckman A, Hunt LC, Jackson T, Milne S (2009) Analysing The Role of Lifestyles in Determining UK Household Energy Demand and GHG Emissions: Predictions and Scenarios,
Chitnis M, Sorrell S, Druckman A, Firth S, Jackson TD (2014) Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups., SLRG Working Paper Sesries 01-2014
Chitnis M, Hunt LC (2010) Modelling UK household expenditure: economic versus non-economic drivers, Applied Economics Letters 18 (8) pp. 753-767 Taylor & Francis
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.
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.
Chitnis M, Druckman A, Sorrell S, Jackson T (2010) An investigation into the rebound and backfire effects from abatement actions by UK households, In: RESOLVE Working Paper Series 05-10
Chitnis M, Hunt LC (2008) UK Household Energy Expenditure and CO2 Emissions,
Chitnis M, Sorrell S, Druckman A, Firth SK, Jackson T (2014) Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups, Ecological Economics 106 pp. 12-32
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.
Chitnis M, Druckman A, Hunt LC, Jackson T, Milne S (2012) Forecasting scenarios for UK household expenditure and associated GHG emissions:
Outlook to 2030,
Ecological Economics 84 pp. 129-141 Elsevier
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.
Adeyemi OI, Broadstock DC, Chitnis M, Hunt LC, Judge G (2010) Asymmetric price responses and the underlying energy demand trend: Are they substitutes or complements? Evidence from modelling OECD aggregate energy demand, Energy Economics 32 (5) pp. 1157-1164 Elsevier
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.
Druckman A, Chitnis M, Sorrell S, Jackson T (2010) Shifting sands? Exploring rebound and backfire in a changing economy,
Chitnis M, Hunt LC (2008) Structural Time Series Model Estimation of UK Household Energy Expenditure and Future CO2 Emissions,
Chitnis M (2009) What drives the change in UK household energy expenditure and associated CO2 emissions, economic or non-economic factors?, In: RESOLVE Working Paper Series 08-09
Chitnis M, Sorrell S, Druckman A, Firth S, Jackson T (2012) Estimating direct and indirect rebound effects for UK households, In: SLRG Working Paper 01-12
Ahmadian M, Chitnis M, Hunt LC (2007) Gasoline Demand, Pricing Policy and Social Welfare in the Islamic Republic of Iran, OPEC Review 31 (2) pp. 105-124 Wiley
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.
Adeyemi OI, Broadstock DC, Chitnis M, Hunt LC (2007) Modeling OECD aggregate energy demand: asymmetric price responses and the underlying energy demand trend: are they substitutes or complements?,
Chitnis M, Sorrell S, Druckman A, Jackson T (2011) Estimating rebound effects from technical energy efficiency improvements by UK Households,
Chitnis M, Sorrell S, Druckman A, Firth SK, Jackson T (2013) Turning lights into flights: Estimating direct and indirect rebound effects for UK households, Energy Policy 55 pp. 234-250
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.
Druckman A, Chitnis M, Sorrell S, Jackson T (2012) Missing carbon reductions? Exploring rebound and backfire effects in UK households, Energy Policy 49 pp. 778-778 Elsevier
Households are expected to play a pivotal role in reducing the UK?s greenhouse gas (GHG) emissions, and the UK Government is encouraging specific household actions to help meet its targets. However, due to there bound effect, only a portion of the GHG emission reductions estimated by simple engineering calculations are generally achieved in practice. For example, replacing short car journeys by walking or cycling reduces consumption of motorfuels. But this frees up money that may be spent on, for example, purchasing extra clothes or flying on vacation. Alternatively, the money may be put into savings. Since all of these options lead to GHG emissions, total GHG savings may be less than anticipated. Indeed, in some instances, emissions may increase ? a phenomenon known as ?backfire?. We estimate that there bound effect for a combination of three abatement actions by UK households is approximately 34%. Targeting re-spending on goods and services with a low GHG intensity reduces this to a minimum of around 12%, while re-spending on goods and services with a high GHG intensity leads to backfire. Our study highlights the importance of shifting consumption to lower GHG intensive categories and investing in low carbon investments.
Chitnis M, Druckman A, Hunt LC, Jackson T, Milne S (2012) Forecasting UK household expenditure and associated GHG emissions: outlook to 2030, In: RESOLVE Working Paper 02-12
Chitnis M, Hunt LC (2009) Modelling UK household expenditure: economic versus non-economic drivers, In: RESOLVE Working Paper Series 07-09
Chitnis M, Druckman A, Hunt LC, Jackson T (2009) Predicting energy expenditure and associated GHG emissions for UK households: where will it be in 2020?,
Chitnis M, Hunt LC (2009) UK Household Disaggregated Energy Expenditure Demand: Contributions of Economic and Non-Economic Factors,