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

Abstract

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.

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

“…Future GHG intensities for the 'reference' scenario are based upon the future trends of the 16 STSM equations, similar to Hunt and Ninomiya (2005) and Chitnis and Hunt (2012), with appropriate variation around these for the 'low' and 'high' scenarios; as shown in Table 2. When looking at these estimated future GHG intensities it must be remembered that these figures are a result of historic changes in both the real expenditure in each category and the emissions in the same category.…”

“…GHG intensities for each of the 16 expenditure categories are modelled in a similar way to that in Hunt and Ninomiya (2005), again using the STSM as presented in Chitnis and Hunt (2012). Historical GHG emissions 9 (1992-2004) attributed to household final demand are estimated using the Surrey Environmental Lifestyle MApping (SELMA) framework Jackson, 2008 and2009b).…”

A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China

“…It was found that, although these associations vary considerably across emission domains, high emissions were more likely to be from low income, unemployed, and elderly households. Chitnis and Hunt (2012) estimated that CO 2 attributable to households would not fall by 29 % (or 40 %) by 2020 compared with that in 1990. Rosas-Flores et al (2011) focused on the estimation of energy consumption, energy savings, and reduction of emissions of CO 2 related to the use of urban and rural household appliances in Mexico between 1996 and 2021, which can be useful to policy makers as well as household appliance users.…”

Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces

“…All three of these studies were conducted at the country level in spite of the huge disparities in income and climate across regions and provinces. Household energy service demand is often associated with income and expenditure [11][12][13][14]. Zhao et al [4] surveyed household energy consumption in four kinds of Chinese regions and examined the impact of the population scale and per capita income on per household energy consumption.…”