Household Energy Demand in Urban China: Accounting for Regional Prices and Rapid Income Change.

AuthorCao, Jing
  1. INTRODUCTION

    Due to its rapid urbanization and economic growth, China's energy consumption is rising at one of the fastest rates in the world--at nearly 8% per year over the 2000-2011 period--and residential energy consumption has grown even more rapidly. Specifically, household electricity and natural gas use rose at annual rates of 12.5% and 19.4%, respectively, over the last decade. (1) Although household energy consumption per capita remains low compared with developed countries, it is rapidly closing that gap. For instance, total energy use for cooking and heating has more than doubled during this period, from 123 kilograms standard coal equivalent (SCE) in 2000 to 278 kilograms in 2011. (2) Household gasoline consumption increased at an annual rate of 17% during the 2000-2010 period due to rapidly increasing motor vehicle use. (3) The International Energy Agency (IEA 2011) projects that China will dramatically increase its share of global oil consumption, and Chinese household energy consumption patterns are converging on those of the western world. These changes will have a significant impact on China's total energy consumption, which, in turn, will have important implications for urban air quality.

    Air pollution from past energy use has already led to serious damage. Utilizing conservative assumptions, the World Bank and SEPA (2007) has estimated that the health damage caused by air pollution alone amounted to 1.16% of GDP in 2003, in addition to another 0.26% worth of damage to agriculture and buildings. Higher numbers of household-owned vehicles are clearly a source of higher NOx emissions, even as reduced coal use by households has contributed to reduced levels of certain pollutants, such as particulate matter (PM). Nevertheless, most northern cities continue to rely heavily on coal for heating, which has maintained high PM levels. Current and projected levels of PM and ozone pose a severe public health challenge. Successful strategies to reduce pollution from household energy use require a solid understanding of the factors that drive residential energy demand, i.e., how households respond to changes in income, prices, technology and urban structure, given that demographic profiles are also changing. Nonetheless, given the importance of this topic, research on urban household energy consumption using Chinese microdata are surprisingly scarce. Most recent studies of Chinese household energy consumption have concentrated on modeling aggregate demand because individual household data are generally unavailable (Shonali Pachauri and Leiwen Jiang, 2008; Li et al., 2011; Zhen et al., 2011). Since preferences for energy differ based on household characteristics, including age, employment status, household size, and stock of durables, energy consumption behavior is not estimated particularly well with aggregate data (Baker and Blundell, 1991).

    Another group of papers on Chinese household energy demand has studied the demand for particular types of energy use based on household data using single equation models (Xu, 2012; Zheng et al., 2011; Murata et al., 2008). Such models impose strong separability restrictions and are thus unable to estimate the cross-price effects between different energy commodities (Labandeira et al., 2006). Current empirical research on Chinese household energy demand thus does not allow for accurate and comprehensive prediction of consumer responses to government policies.

    One of the more sophisticated methods of modeling household energy demand consists of multiple equation systems that include all energy types and also allow for individual households to have different energy consumption patterns based on birth and education cohort, employment status, household size, stock of durables, etc. The availability of a long time-series of household data enables us to recover more precise price and income responses that take into account these differences in household characteristics. Jorgenson, Slesnick and Stoker (1988) estimated residential energy demand for electricity, natural gas, fuel oil, and gasoline at the household level, while Baker, Blundell, and Micklewright (1989) and Baker and Blundell (1991) estimated household energy demand for electricity, gas, and other energy sources that accounted for cross-price effects. More recent papers, such as Labandeira and Labeaga (1999), Tiezzi (2005), Labandeira et al. (2006), and Gundimeda and Kohlin (2008), have also estimated household demand for different types of energy using multiple equation modeling.

    The main objective of this paper is to fill a gap in the literature and provide a better estimate of the income and price elasticities of household demand for various types of energy in urban China, while accounting for the vast differences in regional prices and incomes using microdata. Most of the current studies estimate China's energy demand using more macro-level price and quantity data such as provincial level data, and are thus unable to control for household characteristics. Although more advanced methods such as quasi-experiments or experiments have been applied on energy demand elasticities in developed countries, there are no such studies published in China yet partly due to the data availability issues. Considering big differences upon institutions and consumer patterns between China and other developed countries, directly referencing these existing elasticities from developed countries would be quite misleading, even with the latter using more advanced state-of-the-art experimental or quasi-experimental methods. China is experiencing big changes, in terms of household demographics, income level as well as behavior changes, such rapid transition has not been experienced by other countries at such as speed. We might expect price elasticities of demand on energy maybe higher in China, considering rising energy prices may co-change with the urban infrastructure shift, while most recent studies in western countries often locked in certain infrastructure environment. So our paper fills the gap in the literature and help us better understand the underlying factors determining household energy demand in China.

    It is well established that household demand for energy services depends on appliance and housing stocks (McFadden et al., 1977; Hausman et al., 1979). Dennerlein and Flaig (1987), Baker and Blundell (1991), Zweifel et al. (1997), Alberini et al. (2011), and Fell et al. (2012) introduce appliance dummies to control for the effects of durables on energy consumption, whereas Garbacz (1984) and Tiwari (2000) define an appliance stock index. Dwelling characteristics have also been shown to affect price and income elasticities (Baker and Blundell, 1991; Reiss and White, 2005; Labandeira et al., 2006). Our household data allow us to consider conditional demand in greater detail than previous research on Chinese demand; in particular, the detailed information on the stocks of household appliances and housing characteristics.

    It is essential to have accurate measurements of household income and prices to estimate elasticities. The quality and coverage of the consumption data in China have been widely discussed and debated, including the lack of estimates for owner-occupied housing (e.g., Benjamin, Brandt, Giles and Wang 2008). A secondary objective of this paper is to develop a more complete measure of housing expenditures (and related imputed incomes) and prices.

    We use a two-stage budgeting approach in which total expenditures are allocated to energy and nonenergy consumption in the first stage. We must thus construct prices for the energy and nonenergy bundles. Since prices vary substantially across provinces in China, we follow Brandt and Holz (2006) in constructing provincial energy and nonenergy price indices, in addition to the values of the consumption baskets in the base year. We are able to estimate price and income elasticities more precisely with such wide spatial price differences.

    Past research has indicated that energy preferences shift with household income (West and Williams, 2004; Gundimeda and Kohlin, 2008), with the gender of the head of the household (Somani, 2013), the education, employment status, age and birth cohort of the head of the household (Baker and Blundell, 1991; Labandeira et al., 2006), and the age of children (Labandeira et al., 2006). To control for this observable heterogeneity, we divide households into three groups based on expenditure levels (low, middle and high income), and we include dummies for the gender, education level, birth cohort and employment status of the head of the household, in addition to age-group dummies for children.

    Our data set--the China Urban Household Survey (CUHS)--was collected by the National Bureau of Statistics (NBS) over the 2002-2009 period and included nearly 15,000 households each year with detailed data on energy consumption. The CUHS is used by the NBS to compute both the CPI and the consumption component in the National Accounts.

    The remainder of this paper is structured as follows. Section 2 begins with the two-stage budgeting model of household energy demand, specifying all the household characteristics discussed above. In section 3 we describe the data, the construction of the spatial price indices, the appliance stocks, and imputation of owner-occupied housing, and we discuss the household demographic characteristics we utilize. In section 4 we present the empirical results, and we conclude the paper in section 5 by summarizing our main findings and the corresponding policy implications.

  2. MODEL OF CONSUMER BEHAVIOR

    2.1. Two-stage Budgeting

    The two-stage budgeting approach dates to Gorman (1959, 1971), and Jorgenson and Slesnick (1988) and Baker, Blundell and Mickelwright (1989) are some of the earlier papers to apply the method to household energy demand. In recent applications, households are assumed to behave as individual consuming...

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