1. Introduction
In recent years, China’s rapid economic growth has correlated with huge energy consumption, especially for the road transport sector (for example, in recent years, China’s road transport sector has had enormous growth in the road transport infrastructure investment (see
Figure 1), road (and highway) mileage (see
Figure 1), the stock of vehicles (see
Figure 2), freight and passenger traffic volumes (see
Figure 3), etc.). In 2012, China’s total consumption of gasoline and diesel reached 81,410,000 tonnes and 169,660,000 tonnes respectively. The gasoline and diesel consumption of road transport sector was 9,000,000 tonnes and 9,980,000 tonnes in 1994; while, in 2012, they increased to 37,530,000 tonnes and 107,270,000 tonnes respectively, which accounted for 46% and 63% in the road transport sector in 2012 (see
Figure 4).
As a consequence, China’s rapid economic growth and energy consumption resulted in serious environmental issues, especially air pollution emissions, which caused substantial losses to economic development and public health [
1,
2,
3,
4,
5]. It has been shown that about two-thirds of China’s cities have not attained the ambient air quality standards applicable to urban residential areas [
1]. According to The World Bank [
6], the economic burden of premature mortality and morbidity associated with air pollution in China accounted for 157.3 billion RMB yuan (1.16% of GDP) in 2003. In addition, Chen et al. estimate that total suspended particles (TSPs), one of the major air pollution emissions, is causing the 500 million residents in Northern China to lose more than 2.5 billion life years of life expectancy [
7].
Due to the large-scale investment driven by huge transport demand (see
Figure 1), the civil vehicle stock, passenger and freight volumes have been increasing drastically during the past three decades (see
Figure 2 and
Figure 3).
As a result, the road transport sector is found to be one of the major emitters and responsible for serious air pollution and huge public health losses in China [
4,
8,
9,
10,
11], especially in urban areas [
1,
12,
13,
14]. In 2012, the total vehicle emissions reached to 46.12 million tonnes. Specifically, the emissions of
,
, HC (hydrocarbon) and
were 6.4, 0.622, 4.382 and 34.71 million tonnes, respectively (source: China Vehicle Emission Control Annual Report 2013). For urban areas such as Beijing, Has and Wang find that 74% of the ground
in Beijing was attributed to vehicles, while only 2% and 13% were emitted by power plants and industry respectively [
1]. They also find that vehicle exhaust accounted for 46% of the total VOCs emission in Beijing. In addition, Guo et al. [
13] estimate that the total economic costs of health impacts due to air pollution contributed from transport in Beijing during 2004 to 2008 was 272, 297, 310, 323, 298 million U.S. dollars (mean values), respectively, which accounted for 0.52%, 0.57%, 0.60%, 0.62% and 0.58% of annual local GDP.
After the 2008 financial crisis, crude oil prices experienced drastic fluctuations. For example, from 2008 to 2009, the annual average crude oil prices of WTI (West Texas Intermediate) and Brent fell by 38% and 36%, respectively. In 2010, crude oil prices of WTI and Brent increased 28% and 29% than those in 2009, respectively. However, WTI crude oil price in 2013 was only 4% higher than that in 2012; in contrast, Brent crude oil price decreased by 3% (source: U.S. Energy Information Administration (EIA),
http://www.eia.gov; we can also see this trend from
Figure 5). Similarly, since 2008, China’s gasoline and diesel prices, which are based on international crude oil price, also experienced sharp volatility (see
Figure 5) (according to the “Oil price management (tentative) of China”, the gasoline and diesel retail prices are usually determined by the following mechanism in China. The National Development and Reform Commission (NDRC) sets the gasoline and diesel highest retail prices, which are mainly determined on the basis of the international crude oil price, for provinces (autonomous regions and municipalities) or central cities; therefore, the international crude oil price is the benchmark for China’s oil price, and China is a price taker rather than a price maker). From August 2014, as international crude oil price fell sharply from $100 per barrel to around $50 per barrel in February 2015, nearly a 50 percent decline (source: U.S. Energy Information Administration (EIA),
http://www.eia.gov), China’s gasoline and diesel retail prices appeared as a rare “thirteen losing streak”.
The role of oil prices in the macroeconomy has been the focus of a large number of economic studies [
15,
16,
17,
18,
19,
20,
21]. One important parameter for determining the consequences of crude oil price shocks for the macroeconomy is the price elasticity of the demand for gasoline [
22]. As shown in
Table 1, the demand for gasoline has been investigated extensively using different methodologies.
In recent years, more studies have used flexible demand system methods to estimate the gasoline demand. For example, the normalized quadratic (NQ) method is applied to examine inter-fuel substitution elasticities at the sector and aggregate levels [
23,
24]. By employing Canadian household survey data and using three of the flexible functional forms (the Almost Ideal Demand System (AIDS), the Quadratic AIDS (QUAIDS) and the Minflex Laurent model), Chang and Serletis estimate that the own-price elasticity for gasoline demand in the transportation sector is between
and
[
22].
On the one hand, as mentioned earlier, China’s rapid economic growth and huge energy consumption resulted in serious air pollution emissions. Especially, the road transport sector has been blamed as one of the major emitters, which caused huge losses to public health in China. On the other hand, after the 2008 financial crisis, oil prices experienced more frequent and dramatic fluctuations. Therefore, there are some extremely important issues to be investigated: What effects do oil price changes have on the road transport sector in China? Furthermore, how do oil prices affect the road transport pollution emissions and public health? Unfortunately, by now, there are still no clear and satisfactory solutions to these issues.
In this paper, we use the road transport panel data from the majority of prefecture-level cities in China and estimate the road transport fuel (i.e., gasoline and diesel) demand through two flexible demand systems (the AIDS and the QUAIDS models). After that, we investigate the impact of pollution emissions and public health from oil price shocks by means of pollution emission elasticities, as well as air quality and health effects evaluation models. Our framework, to the best of our knowledge, is the first attempt to study the transmission mechanisms between the oil price changes and public health effects in China.
The rest of the paper is organized as follows.
Section 2 provides a discussion of the framework of this study;
Section 3 discusses the data and some key parameters;
Section 4 presents the empirical results for road transport fuel demand system and health effects;
Section 5 concludes the paper.
3. Data and Key Parameters Description
In this paper, we investigate 306 cities in China over the period of 2002 to 2012. The data used in this paper include vehicle population, fuel (gasoline and diesel) prices, total population and overall mortality rate of China, air concentrations and exposure-response (ER) coefficients, annual average vehicle mileage traveled (VMT), fuel economy and pollution emission factors of different vehicles types, etc. (see
Table 2 and
Table 3 for a summary). Given that much of the data is not publicly available, a great effort has been made here in terms of the data collection and process.
Firstly, we use vehicle population data, which are specifically divided into ten vehicle categories (this paper uses the classification system of the Ministry of Public Security, which is used both for official statistical reporting by the National Bureau of Statistics of China (NBSC) [
4,
27]) (see
Table 3). The panel dataset covers approximately 33,660 data from 306 cities in 25 provinces of mainland China from 2002 to 2012 (Because of data limitations, we are unable to obtain the data from the following areas: Xinjiang, Qinghai, Hong Kong, Macau and Taiwan. Moreover, we exclude municipalities’ data, namely Beijing, Tianjin, Shanghai and Chongqing. Data source: the NBSC,
http://data.stats.gov.cn/index).
Secondly, we use the annual average vehicle mileage traveled (VMT) data in China. There have been some estimates on China’s VMT data (for example, see [
51,
54]). In addition, the VMT is related to the economic development level and road traffic infrastructure [
54]). (In addition, the VMT is also affected by some other factors, such as vehicle age [
51,
54]. Due to data availability limitations, these factors are not considered in this study.). It is widely known that there are huge differences between different regions (cities) of China in geographic conditions, economic development and transportation infrastructure, etc. However, China’s existing VMT estimates in different cities are not readily available or substantially insufficient. Therefore, we use the national average VMT data in 2002 as the baseline data. Then, we estimate the provincial annual average VMT data for each type of vehicle from 2002 to 2012 by using the provincial average passenger data and freight transport distance data. Specifically, we calculate the ratio of provincial average passenger data to national average passenger data from 2002 to 2012. According to the ratio and national average VMT baseline data, we can further estimate the provincial VMT data for each type of passenger vehicle (i.e., large passenger vehicles (LPV)-D, medium PV (MPV)-G, small PV (SPV)-G, mini PV (MNPV)-G, public bus (PB)-D and Taxi-G in this paper) in every year. Similarly, for all kinds of truck (i.e., heavy duty truck (HDT)-D, medium duty truck (MDT-D), light duty truck (LDT-D) and mini truck (MNT-G) in this study), we use the ratio of provincial freight transport distance data to national freight transport distance data. On this basis, we assume that all of the cities within the same provincial region have the same VMT data. Our VMT estimates are supported by many survey papers and empirical studies in the existing literature (please see
Table 3), indicating that our estimated results are reasonably acceptable.
Thirdly, the gasoline and diesel annual average prices, from 2002 to 2012, are used in this paper (source: Wind database,
http://www.wind.com.cn). Since the international oil prices are the benchmarks of China’s oil prices and fundamentally important in China’s gasoline and diesel pricing mechanisms, the road transport sector is merely a price taker rather than a price maker. In this paper, we use the provincial gasoline and diesel annual average retail prices because gasoline and diesel prices in different cities within the same province are basically the same.
Finally, in 2012, the number of China’s total population was 1,354,040,000, while the overall mortality rate was 0.715%. According to Zhang et al. [
41], the Chinese national average VOSL, which was calculated after inflation and exchange rate adjustment, was 855,642.81 RMB yuan in 2012 (for the inflation rate and exchange rate of RMB yuan to USD, see the NBSC,
http://data.stats.gov.cn/index).
5. Conclusions
In recent years, China’s rapid economic growth and huge energy consumption resulted in serious air pollution emissions, which caused substantial losses to public health. In particular, the road transport sector has been blamed as one of the major emitters in China. After the 2008 financial crisis, frequent and dramatic fluctuations in oil prices have had some significant impacts on China’s road transport sector, which is as a price-taker. Therefore, it is extremely important to study the effects of oil price shocks on public health in China. In this study, we estimate China’s road transport fuel demand system by using the AIDS and the QUAIDS models and investigate the impacts of pollution emissions and public health losses from the road transport sector under four scenarios of different oil price shocks.
We find that all of the own-price elasticities of fuel demand are negative and statistically significant, and they vary across different vehicle categories, which range from to based on the AIDS model and from to based on the QUAIDS model. In addition, expenditure elasticities are positive and statistically significant, which range from 0.286 to 1.239 based on the AIDS model and from 0.233 to 1.214 based on the QUAIDS model. In particular, results estimated by the AIDS model show that except for medium passenger vehicles, the own-price elasticities for all vehicle categories are inelastic, which indicate that Chinese drivers are not sensitive to fuel price changes, especially for public buses with the own-price elasticity of . Furthermore, estimates by the QUAIDS model are similar to results from the AIDS model except that the own-price elasticities of small passenger vehicles () and taxi () are inelastic.
Furthermore, price elasticities of air pollution emissions are also all negative, while pollution emission expenditure elasticities are positive. Specifically, results from both the AIDS and the QUAIDS models suggest that, for and emissions, the emission price elasticities of small passenger vehicles, heavy duty trucks and light duty trucks are comparatively larger than those of other vehicles. emission price elasticities of diesel vehicles are significantly greater than those of gasoline vehicles, implying that emissions are more sensitive to diesel price than gasoline price.
Finally, this study estimates the air pollutant emissions and health losses under different oil price shocks. Our results show that, when the increase of gasoline and diesel prices reduces the road transport fuel demand, public health losses caused by road transport will decrease. In contrast, when gasoline and diesel prices decline, meaning an increase in road transport fuel demand, the corresponding health losses rise. For instance, based on the AIDS model and the linear health effect model, we find that when gasoline and diesel prices rise 30% simultaneously, the total reduction of air pollution emissions from road transport sector reaches 1,147,270 tonnes. The total number of premature deaths decreases by 16,149, and the total economic loss is reduced by 13,817.953 million RMB; while based on the non-linear health effect model, the premature deaths and total economic loss decrease by 15,534 and 13,291.4 million RMB yuan respectively. According to the results from the linear health effect model, when gasoline and diesel prices decrease 40% simultaneously, pollution emissions increase by 1,529,690 tonnes; the total number of premature deaths and corresponding economic loss rise by 21,532 cases and 18,423.937 million RMB yuan respectively; while based on the non-linear health effect model, the premature deaths and economic loss rise by 20,690 cases and 17,703.193 million RMB yuan respectively. Furthermore, applying the QUAIDS model, we find that a 30% increase in fuel price will reduce air pollution emissions, premature deaths and economic losses in China by 902,810 tonnes, 11,604 cases (the linear health effect model) and 9928.509 million RMB yuan (the linear health effect model) respectively.
This paper is the first study that proposes a transmission mechanism between fuel demand and health damages in China using pollution emission elasticities. It investigates the impact of increasing demand for fuel from the road transport sector on the Chinese health and economic losses under four scenarios of oil price shocks. According to the Energy Information Administration, China is the world’s largest energy consumer and became the largest net importer of petroleum since 2014. Given its serious air pollution emission and substantial health damages, this paper provides important insights for policy makers in terms of persistent increasing in fuel consumption and the associated health and economic losses.