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Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model
Journal of Cleaner Production xxx (2016) 1e10
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Effect analysis of air pollution control in Beijing based on an odd-andeven license plate model Xiaoyao Xie a, *, Xiaodong Tou a, Li Zhang b a b
Faculty of Law, Ningbo University, China Faculty of Electrical and Computer Science, Ningbo University, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 November 2015 Received in revised form 14 September 2016 Accepted 16 September 2016 Available online xxx
Nowadays, air pollution has become a major challenge in urban management despite rapid economic development. Meanwhile, vehicle exhaust has gradually turned into the main source of air pollution in the city. To reduce air pollution, many measures have been taken including the odd-and-even license plate rule in some cities. However, it is difficult to evaluate the effectiveness of those measures. In view of this, based on the Davis method, this article has taken Beijing as its subject and built an odd-and-even license plate model by a probabilistic modelling method and the analysis of means, thus to quantify the pollution caused by vehicle exhaust emissions and the actual effect of the license plate limitation rule. This paper also examines the relationship between the license plate limitation rule and urban air pollution control and to see whether, or not, the rule exerts a positive influence on air pollution control. The results showed that the odd-and-even license plate rule has positive impacts on air pollution control in the short-term; however, the influence of the limitation policy gradually diminishes and disappears as the overall number of cars increases. Therefore, it is suggested to tackle air pollution in a broader and more effective way. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Air pollution Traffic control effect Davis method Odd-and-even license plate model Environmental benefit
1. Introduction The general public's quality of life has been greatly improved in pace with the rapid development of China's economy, therefore, there is a higher demand for a more convenient and comfortable transportation modes. Naturally, a private car is the first choice for many. It is under this background that the number of private cars has exploded in the last two decades. As we all know, the boom in private car use does not only increase traffic congestion but also aggravates air pollution. Taking Beijing as an example, its number of vehicles has exceeded 5.1 million in August 2012 and the number of drivers has exceeded 7.2 million. Besides, over 40% of major air pollutants such as NOx arise from vehicles (Zhao et al., 2010a). Beijing has adopted an odd-and-even license plate policy since 2008 with the initial aim being to alleviate traffic pressure and reduce urban environmental pollution during the preparation for the Beijing Olympics in 2008. Nevertheless, Beijing, and other municipal governments, have listed the license plate limitation rule as a key move in controlling urban air pollution and maintaining
* Corresponding author. E-mail address: [email protected] (X. Xie).
sustainable social development. Of particular note, the feasibility and effectiveness of this rule have been hotspots for academic and public discussion ever since its unveiling. Current studies of odd-and-even license plate rules are mainly concentrated around their origins, purpose, effectiveness, and so on. Most of them discuss the impacts of the policy on improving air quality. Among them, Wang et al. (2009) started from the obvious improvement in Beijing's air quality during the Beijing Olympic Games. By analysing the odd-and-even license plate rule's origin, purpose, and effect, they discussed the possibility and related issues, pros and cons, in institutionalising the policy; by using qualitative methods, Zhu (2012) focused on analysing the effects that traffic congestion has on socio-economic factors such as population, employment, GDP, energy consumption, land utilisation, environmental pollution, travel cost, and travel time. He combined a top-down system dynamics model and a bottom-up cellular automata model and started from the perspective of macro-socioeconomics and a micro-traffic model. Zhu combined the advantages that the system dynamics model has in scenario simulation and macro-driving factors together with the advantages of cellular automata models in microscopic traffic flow simulation. To explore the socio-economic impacts of urban traffic congestion, he built the
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Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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system dynamics model of the interactive relationship between urban traffic and the social economy, as well as the cellular automata model of urban traffic congestion's micro-economic costs; Chen et al. (2011) studied the Beijing Olympic Games 2008 when the odd-and-even license plate rule was one of the policies that the government implemented to improve air quality. They found that this policy brought benefits to air quality by reducing 24.9% of air pollution indices compared with the same period in 2007. Nevertheless, there are more studies pointing out the limitations of the policy, per se, and its explicit, hidden risks. Cao et al. (2014) and several other researchers studied the effects of the license plate limitation rule after Beijing's Olympics in 2008. They tracked the change of air pollution index, inhalable particles, nitrogen dioxide, and sulphur dioxide and adopted a breakpoint regression method to solve its endogenous problem. They concluded that, although the air quality in Beijing was improved during the Olympic Games, it was not as an outcome of the license plate limitation rule. Enderle et al. analysed the factors affecting traffic congestion. From an economic perspective, they pointed out that, using the odd-and-even license plate rule would only bring short-term benefits and it would not do any good to the long-term and stable development of urban economy through the analysis of urban residential demand for cars, parking lots, and the impact on the automobile industry (Enderle et al., 2012; Zhang et al., 2013). Shen et al. (2014) introduced Harbin's car ownership situation in recent years. They analysed car ownership and its exhaust components by statistical methods. It turned out that the vehicle exhaust emissions have little effect on air pollution in Harbin; but, as car ownership rises, it would also have negative impacts on air quality. Besides Beijing, the odd-and-even license plate rule has been implemented in many cities in China. Yet as it turns out, the policy does not live up to popular expectations. Taking the Guangzhou Asian Games for example, the policy has been adopted but its effectiveness is limited (Huang et al., 2012). Another case in point is Chengdu, on the whole, the limitation policy did not work as expected since its inception in 2012 (Xu and Hou, 2015). The study of this policy has led to some achievements, which will inspire our follow-up studies, however, there are still some deficiencies. First of all, a lot of studies are only conducted in a qualitative way, failing to quantify the extent of the impact of the policy. Secondly, as the air quality will be affected by the type of regional climate, studies of other cities, except Beijing, cannot be a good explanation of the merits of Beijing's odd-and-even license plate rule. Lastly, many studies use mathematical tools to analyse the policy, but most of them just compare the before, and after, effects of carrying out the odd-and-even license plate rule, or simply analyse the effects of various factors on air quality, and there is lack of predictions of air quality in the future although it is a predictable condition. In view of this, this paper attempts to use a probabilistic modelling method and analysis of means to build up an odd-andeven license plate rule simulation and analyse the change of means in this model. This paper explores the effects degree of vehicles on urban air pollution and thereby discusses the degree of improvement of the rule quantitatively. 2. Models There are a large number of qualitative studies, both domestic and overseas, of the odd-and-even license plate rule: many successful cases that have offered detailed analyses of the effects of the rule. Among them, the most exemplary is the Air Pollution Index (API) explanatory model proposed by Lucas and Davis (2008). This model took the Hoy No Circula (HNC, literally “Don't drive today”, a vehicle limitation rule) initiated by the Mexican Government in 1989 as its object of study. Davis utilised high-frequency metrical
data from the monitoring station and explored the relevance between the limitation rule and air pollution by mathematical model. To be specific, Davis used the HNC's influence 1(HNC) and a timeline covariant xt to explain the change of log(API). In the meantime, Davis employed a large number of data relating to number of cars and air pollution to analyse the change in pollutants such as sulphur dioxide, nitrous oxide, and ozone on a daily or annual basis. Furthermore, he also observed petrol use, automobile growth, and public traffic mode choice: based thereon, Davis concluded that the limitation rule did not generate a significant improvement in air quality. Besides, he added that this rule will engender a growth in automobile numbers to a certain extent. Davis's explanatory model for Mexican air pollution is as shown by the following formula (1):
logðAPIÞMexico ¼ h0 þ h1 lðHNCÞ þ h2 xt þ mt
(1)
In (1), 1(HNC) is the observable variable after the implementation of odd-and-even license plate rule, coefficient h1 is the influential factor that HNC has on air pollution. Xt is a timeline coefficient which includes the indicator variable of every month, day, and hour. So to speak, Davis' analysis and argument are quiet creative and foresighted in their partial explanation for the limitation rule's failure. Nevertheless, the model's general applicability remains open to question. For example, Davis's model cannot be applied directly to explain and judge whether, or not, Beijing's vehicle limitation rule is effective in improving air quality for several reasons. Firstly, China's air pollution is closely related to seasonal and environmental factors such as temperature, humidity, rainfall, and wind speed. Beijing has a typical temperate and monsoonal climate with four clearly distinct seasons (Li et al., 2012; Zhou et al., 2014). Therefore, seasonal factors should be critical in describing Beijing's air pollution. Secondly, Davis's study emphasis on the comparison between air qualities before and after the limitation rule, thus does not give full play to its explanatory function. Besides, Davis' model does not simulate the petrol use, vehicle growth, and public traffic before the implementation of the limitation rule. (Viard and Fu, 2015). Thirdly, Davis worked out that the license plate limitation rule has greatly boosted automobile purchases in Mexico. Yet obviously, this conclusion does not fit Beijing. Though the number of automobiles in Beijing is soaring, the limitation rule cannot strongly promote automobile purchases given citizens' income levels in Beijing. 2.1. The establishment of an air pollution model As a matter of fact, those doubts over Davis' model have not denied the model totally, but only to point out that this model is restricted. In other words, if we take full consideration of a certain area's specific conditions and transform Davis' model properly, the adapted model would still have strong explanatory power. It is under this construct that this paper has designed a model. To elaborate thereon, this paper has constructed a suitable mathematical model to explain Beijing's air pollution by taking full consideration of Beijing's conditions and introducing environmental variables such as automobile numbers, rainfall, temperature, wind speed, and so on. API, as an index measuring air quality, cannot be predicted for a specific future value since its numerical value is a random number. We get the API data for Beijing from the Ministry of Environmental Protection of the People's Republic of China (MEP). For example, the API's logarithms from 1 June 2006 to 31 May 2007 are distributed as shown in Fig. 1. It is worth noting that, the variation of air quality and the variety of factors affecting the air quality have the feature of a long time
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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Fig. 1. Log(API) of Beijing air from 1 June 2006 to 31 May 2007.
span and a large amount of data, so in a broad sense, the data belongs to the category of big data. The concept of big data is generally used to analyse vast amounts of data in the computer communication field, its utilisation, and treatment, but for the hundreds of thousands of data items on air quality collected in this paper, mathematical statistical tools can be used for processing these big data. Here we assume this stochastic process to be stable in the shortterm, and meet ergodicity requirements. In that case, we can use horal mean value to describe a certain day's random distribution of log(API) approximately. The study found that this stochastic process almost complies with the Gaussian distribution. ðxmÞ2 1 f ðxÞ ¼ pffiffiffiffiffiffi e 2 2ps
(2)
In (2), f(x) means that the daily log(API) is the probability density of x. m and s are the mean value and variance of the Gaussian distribution respectively. By the methods of moments estimation, we can get parameters m and s from the sample.
8 n > 1X > > b¼X¼ m X > > n i¼1 i < n > > 2 1X > > s¼ X X > :b n i¼1 i
(3)
However, in the long-term, the distribution of log(API)'s mean value is changing gradually while the change in the corresponding variance is smaller. The mean value in formula (2) stands for the average change of API, the variance stands for the degree of divergence of API and the average thereof. Though the mean value and variance show rheological properties over time, the change of API mean value is a factor that cannot be ignored. We suppose that the distribution of mean value is a function of time m ¼ m(t) while variance s is a constant. An existing study of Beijing's air quality shows that temperature, humidity, and wind speed are three critical factors influencing the
concentration of Beijing's air pollution[5]. Meanwhile, humidity is closely related to rainfall. Rainfall has a more obvious impact on air quality. Here we assume that the mean value m would meet the following correlativity:
m ¼ aV þ bT þ gH þ lW þ J
(4)
In (4), V stands for number of automobiles, T stands for temperature, H for precipitation, W for wind speed, while J stands for other factors, for instance, in recent years electric cars have been used more widely (Shi et al., 2016), and it has improved the areas surrounding Beijing in terms of efficiency in utilising natural resources (Zhu et al., 2016); a, b, g, l denote influence coefficients for corresponding factors. 2.2. Calculation of model parameters In (4), we introduced the number of automobiles, temperature, precipitation, wind speed, and other factors as influential over API change. To estimate the influential coefficient between different factors, we must first have a detailed understanding of how each factor changes with time. Here, for convenience when quoting data from the model, we assume 1 January 2006 as the first day. The mode functions from 1 January 2006 to 1 January 2010. 2.2.1. Change of number of automobiles For the number of automobiles, we analysed data drawn from Beijing Statistical Information Net (BSIN) (BSIN, 2007, 2009, 2011, 2013, 2015). Here we take the annual number of automobiles as the number at the end of a year, namely, the number at the beginning of the next year. We match the annual change of automobile numbers by rational polynomial. It turns out the cubic function fits this best:
V ¼ 1:432 108 t 3 þ 5:828t 2 þ 0:0567t þ 224:7
(5)
During the rule's trial period in 2007 and the 2008 Olympic Games, the government has launched a very strict odd-and-even license plate rule. As a result, the number of automobiles on the
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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road was only 0.5% of the total amount. Afterwards, the limitation rule is implemented on five days per week, excepting weekends. If we ignore the change in automobile numbers for weekdays and weekends, then the limitation effect can be represented as: 0.8 5/ 7 þ 1 2/7 ¼ 6/7, namely, the number of automobiles is 6/7 of its previous number.
2.2.2. Changes in temperature, precipitation, and wind speed Beijing has a temperate monsoon climate: it is cold and arid in winter, and hot and rainy in summer with an obvious monsoon season. Therefore, factors such as precipitation, temperature, and wind speed vary seasonally. The weather data came from the Statistical Yearbook of Beijing (Yearbook, 2011, 2008, 2009, 2010. The change of the three factors from 2006 to 2010 are shown in Fig. 2.
2.2.3. Other factors J By taking the mean value 30 days before, and after, log(API) as a statistical average analysis, we find that the average log(API) had no obvious change, and it underwent a linear variation. Thus it was assumed that the influence of other factors was linear influence:
J ¼ kt þ b
(6)
n 8 1X > x ¼ x > i > > n j¼1 ij > > > > > > n > > 1X > > y ¼ yi > > < n i¼1
> n n X X > > > > xkj xk xij xi Skj ¼ xij xi xkj ¼ > > > > i¼1 i¼1 > > > > n n > X X > > : sky ¼ xkj yj y xkj i¼1
i¼1
Here xij,(i ¼ 1,2,3,4,5,6) represent Vj,Tj,Hj,Wj,tj,bj; yi stands for mi. Then, the standard set of variance is:
8 b ¼ y b1 x1 / b5 x5 > > < 0 S11 b1 þ S12 b2 þ / þ S15 b5 ¼ s1y >/ > : S51 b1 þ S52 b2 þ / þ S55 b5 ¼ s5y
b > l ¼ 0:0727 > > > b > k ¼ 0:0001 > :b b ¼ 1:8035
bV þ b bH þ b m¼a bT þ g lW þ J þ ε bV þ b bH þ b ¼a bT þ g l W þ bkt þ bb þ ε
(7)
This is a multiple linear regression model, with which we can find out every influential coefficient by multiple regression least squares method (Feng, 2003).
(9)
In (9), b5,b4,/b0 stand for a^,b^,g^,l^,k^,b^respectively, we can use this formula to work out all influential factors. Taking the change in number of automobiles triggered by the odd-and-even license plate rule, we can get following results by using software (MATLAB™ 2012a):
8 b ¼ 0:0002 a > > >b > b ¼ 0:0008 > >
(8)
(10)
The figure of log(API)'s actual value, statistical mean value in a short period, and mean value of log(API) stimulated by model is shown as follows: from Fig. 3, the change in each simulated figure can simulate the change in log(API). From (2), (3), (7), and (10), we see that this model is a distribution function of log(API) as a distributed variable x and time t in
Fig. 2. Change in precipitation, temperature, and wind speed from 1 January 2006 to 1 January 2010.
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Fig. 3. A comparison between actual, and simulated log(API) values.
the form of their two-dimensional probability density function. ðxmðtÞÞ2 1 f ðx; tÞ ¼ pffiffiffiffiffiffi e 2 2ps
(11)
Among which:
8 n 2 1X > > > b s ¼ Xi X > > n > > i¼1 > > > > > > bV þ b bH þ b mðtÞ ¼ a bT þ g l W þ bkt þ bb > > > > > > b ¼ 0:0002 > a > < b b ¼ 0:0008 > > > > b ¼ 0:0002 > g > > > > > b > > l ¼ 0:0727 > > > > > b > k ¼ 0:0001 > > > :b b ¼ 1:8035
3.1. Verification of the air pollution model According to the modelling process in the second part, the present model is based on probability and statistics to investigate the variation of the mean value. Therefore, to verify the model, we have to: on the one hand, test the distribution of the API, to see whether, or not, it is a random distribution and whether, or not, the established distribution rule is correct. On the other hand, we verify the mean value in the probability distribution, to see whether, or not, we can successfully predict the real changes in API when there are changes in the number of vehicles, precipitation, temperature, and wind speed.
(12)
3. Verification and application of model The second part of this paper has successfully established an air pollution model, and based on the existing data about relevant factors, we have estimated the influence coefficients of each factor; however, we have to test the effectiveness of the model in practice. Thus, in this part, the argument mainly covers two aspects: firstly, we will verify the model through specific data, and test its validity, as well as its applicability in the future; secondly, based on the model, we analyse the number of vehicles because it changes yearby-year, and investigate the specific effects of the odd-and-even license plate rule. Meanwhile, we will use this model to simulate the degree of air pollution without this limitation rule, which will be compared with the actual situation. All of these steps can provide data support for further discussion of the effect of the oddand-even license plate rule.
3.1.1. Verification of the correctness of the stochastic process used in the model In Section 2.2, we used API data from 1 January 2006 to 2010; but, in the first half of 2012, the government announced a new regulation which will replace the existing API with and Air Quality Index (AQI). In Beijing, the AQI standards were applied in the air monitoring report after 2014, but due to the calculation differences between AQI and API (in calculating the AQI, main pollutants include fine particulate matter, inhalable particles, sulphur dioxide, nitrogen dioxide, ozone, and carbon monoxide, while in calculating the API, the main pollutants only include inhalable particles, sulphur dioxide, and nitrogen dioxide). Thus, when analysing the model, we have to select pre-2014 time periods: in this paper, the time period selected ran from 1 January 2012 to 1 January 2013, and we still adopted the timing methods in this model. Suppose that 1 January 2006 is the first day, so from 1 January 2012 to 1 January 2013, there are 2192e2558 days. During the period from 1 January 2012 to 1 January 2013, from the scatter diagram of the API, we can see that the distribution of log(API) is strongly random, which means that in one year, it is a random distribution. So the assumption of stochasticity in the model can still be met. Therefore, Fig. 4 shows the frequency histogram for this period and the probability distribution curve which is based on formulae (2) and (3).
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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Fig. 4. Frequency histogram and probability density function curve of log(API) from 1 January 2012 to 1 January 2013.
Above all, during that period, and based on the analysis of a stochastic process, the probability distribution models can explain the distribution change of log(API).
3.1.2. Model verification: mean change in API In 2012, the precipitation, temperature, and wind speed data are known (Yearbook, 2013), as shown in Fig. 5. In 2012, formula (5) can be applied to all estimations of the number of vehicles and thence we can predict the value of log(API) (MEP, 2016). From Fig. 6, after analysing the value of log(API), the statistical
average, and the simulated value in the model, the model was able to simulate the mean value of the air quality distribution in 2012. After 2014, the model still worked, but as is known, the quality control system is replaced by AQI, so the model cannot directly verify the air quality, but by finding some related factors and calculating the API, it can verify this indirectly.
3.2. The effect of the odd-and-even license plate rule on the number of vehicles In Section 3.1, the reasonableness of the model can be proven,
Fig. 5. Change in precipitation, temperature, and wind speed in 2012.
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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Fig. 6. Comparison between the actual distribution and simulated values of log(API).
whether it is the distribution of the API, or its predictive effect for the API. Thus, this mathematical model has measured the degree of influence of each factor on air quality, and based on the model, we can quantify and simulate the effects of a regime without a license plate limitation rule, for comparison with current effects. Furthermore, we investigated the actual number of vehicles and the variations in the API, and by predicting the value of log(API) without the odd-and-even license plate rule, we undertook a comparative analysis. 3.2.1. Comparison between the observable variables before, and after, implementation of the odd-and-even license plate rule Around July 2007, from formula (5), the number of vehicles was approximately 2.93 million, during the Lucky Beijing test events (17 August 2007 to 20 August 2007) was the first time in which the odd-and-even license plate rule came into effect. The vehicles whose license plate is odd can be driven on odd-numbered days, while others can be driven on even-numbered days. During the Olympic Games, from 20 July 2008 to 27 July 2008, the odd-andeven license plate rule remained unchanged. This rule can cut the number of vehicles on the road by one half, which thus restricts approximately 1.465 million vehicles. Under this rule, the number of vehicles being restricted is large: if we use the number of unrestricted vehicles, as shown in Fig. 7, the number of vehicles soared, under these circumstances, by limiting the number of the vehicles on the roads, the rule can only delay the time taken for the growth in traffic. In time, the high growth rate of the number of vehicles became transitory and a decreasing trend in the number of the vehicles was predicted: the number of the unrestricted vehicles will gradually reach its highest point on urban roads. That is to say, the function of the license plate limitation rule can only delay the time for the number of unrestricted vehicles to reach a maximum. From 2006 to 2010, the average growth rate has exceeded 14%, after August 2008, the effectiveness of the license plate rule will be one seventh of its original level (around 14.29%), which is the same as the annual growth rate. Thereby, we can deduce that the effectiveness of the vehicle limitation rule can only last for a year: i.e., vehicle emissions can rise to the level of a year ago. Over time, the
Fig. 7. Changes in the number of unrestricted vehicles.
positive effects brought by the rule will diminish. 3.2.2. The effect of the license plate limitation rule on the API in Beijing To analyse the influence of the license plate limitation rule on air quality in Beijing, we examined both long-, and short-term effects: in the short term, the rule can restrict a seventh of all vehicles and indeed, it can decrease the value of log(API). From the model, before 7 July 2007, the value of log(API) was 1.8977, and after the imposition of the limitation rule, its value was 1.8681, a decrease of 0.0296. That is to say, the actual value of API fell from 79 to 74. The air quality improved by about 7.1% (that is 100.02961 ¼ 7.1%). During the Olympic Games, the license plate limitation rule was significant; the concentrations of sulphur dioxide and carbon monoxide were reduced by 13% and 12%, respectively. Furthermore, the concentration of nitrogen dioxide is reduced by 43% (Witte et al., 2009). Albeit that the effort required to implement the
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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license plate limitation rule is significant, in the short-term, the effects on improving air quality are obvious, however, in the longterm, a ¼ 0.0002 is the influence factor by which vehicles affected air quality. This indicated that the contribution of every ten thousand vehicles on log(API) was 0.0002. The license plate limitation rule effect can reduce a seventh of vehicle emissions, that is, if the number of vehicles is v (in ten-thousands), the decrement of log(API) is aV/7, from 20 July 2007 to 1 January 2010, because of the effectiveness of the rule, the decrement of log(API) is between 0.022 and 0.032. Based on the model, we can conclude that the difference in the distribution of log(API) mean values was as shown in Fig. 8. From Fig. 8, in the long-term, the license plate limitation rule still works to improve air quality. Furthermore, it can reduce exhaust emissions, and to some extent, it plays a positive role in reducing air pollution. From Chart 1, the number of vehicles in Beijing is increasing at a fast pace, from 2006 to 2010, the average growth rate exceeded 14%. According to formula (5), which shows the growth rule for the number of vehicles, after September 2008, the high growth rate will make the number of vehicles exceed the threshold level before implementation of the license plate limitation rule. The absolute effect brought about by a license plate rule in reducing air pollution has been offset by the increasing number of vehicles. On the other hand, from formula (4), among various influential factors, there is also another factor J that cannot be neglected, the influence factor for the distribution of log(API)'s mean value was 0.0001, and this is a long-term linear influence, the longer the time, the stronger the influence. According to the model, since 2007, when the license plate limitation rule was implemented, the decrement in log(API) caused by J increased from 0.056 to 0.146, its effects have greatly exceeded the positive effects brought about by the rule. It is an influential factor that can reduce air pollution. In November 2009, the number of unrestricted vehicles has reached the level before the implementation of the license plate limitation rule, and J is the reason why the air quality has not deteriorated sharply with the increasing number of vehicles.
3.3. The effects of other measures on air pollution There are multiple factors giving rise to Beijing's air pollution problem. To control, and reduce, air pollution, Beijing has implemented 16 stages of different control measures between 1998 and 2012. At every stage, different control measures and implementation approaches are adopted. The 13th stage was implemented in 2007 and was aimed at restricting pollutant emissions and taking comprehensive measures to reduce emissions at the same time. The 13th stage mainly focused on the control of soot pollution, industrial pollution, fugitive dust pollution, urban and rural ecological protection and construction, perfecting hazardous weather emergency measures, and carrying out scientific research. On the basis of the 13th stage, the 14th stage of pollution control was launched in 2008. The 14th stage mainly concentrated on pushing forward five coal-burning pollution prevention projects (boiler pollutants, boiler emission standards, promotion of clean energy, abolishing the use of raw coal in urban and rural integration areas and increasing effort to deal with violations of law), controlling bullet train pollutant emissions, transforming polluting enterprises, and controlling fugitive dust pollution. The 15th stage was carried out in 2009 on the strength of the 14th stage and it promoted ‘green construction’, and prevention of fugitive dust pollution from construction sites. It also adds solid waste landfill site pollutant discharge control and emphasises the industrial upgrading of polluting enterprises. The 16th stage (started in 2010) promoted total emissions quantity control, formulating stricter environmental admittance standards, relating emission loads with factors such as regional function, air quality, emission object completion status and treatment facilities; stressing restrictions on high-emission vehicles, boosting pollution control projects and completing regulation thereof, intensifying inspections for law enforcement purposes and pollution regulation. There are many powerful measures used to control air pollution and these measures have a good effect in prohibiting the deterioration of air quality. On 9 September 2015, the United Nations Environment Programme (UNEP) and the Beijing Environmental
Fig. 8. Comparison of log(API) values before, and after, the simulated license plate rule.
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Protection Bureau announced the preliminary results of an evaluation report: “The control of air pollution in Beijing, from 1998 to 2013”. The results showed that, although there is a gap between the air quality now and the requisite national standards, the air quality has nevertheless been significantly improved. The improvements in coal-fired power plants, coal-fired boilers, and old bungalows are significant: compared with 2005, the consumption of coal in coalfired power plants has been cut by 2.56 million tons in 2013. Also, compared with 1998, the emissions of PM2.5, PM10, SO2, and NOx have been decreased by 14,500 t, 23,700 t, 25,000 t, and 30,900 t, respectively (proportional reductions of 86%, 87%, 85%, and 64%, respectively). At the same time, the transformation of the boilers used has decreased the emissions of PM2.5, PM10, SO2, and NOx decreased by 14,300 t, 24,000 t, 136,000 t, and 48,700 t, respectively. After the implementation of a series of measures, such as emission-control standards for motor vehicles, fuel quality improvements, promotion of enhanced I/M systems, the promotion of new-energy vehicles and traffic control, a total reduction of CO, THC, NOx, and PM2.5 reached 292,000 t, 32,000 t, 14,000 t, and 670 t, respectively. Furthermore, the structure of the energy generation, and travel, modes were gradually optimised, and the amount of natural gas (which is regarded as clean energy when used in power plants) has risen to 35%, because of the development of rail transportation, by the end of 2014, there would be 18 railway lines with a total length of 527 km. In addition, from 2000 to 2014, the percentage of people choosing public transportation would rise from 26% to 48% (Report summaries, 2015). All these measures have been fully explained in the model: in formula (4), the improvement effect can be shown in the variation of J. Coal-fired power plants, boilers, and polluting enterprises have discharged large amounts of PM2.5, PM10, SO2, and NOx, and other pollutants, these pollutants can all influence the value of API, during the process of governance, to some extent, all of these methods can reduce air pollution. During the process of governance, emission sources such as power plants, boilers, residential houses, and other polluting enterprises have been greatly reduced in number. Also the enrichment of rail transportation and other public transportation has reduced the number of the vehicles on the roads, and therefore reduced emissions. To some extent, these comprehensive management approaches can reduce air pollution. 4. Conclusions The particularities of the regional environment mean that air pollution in Beijing is characterised by seasonality. The model here combines the randomness of the distribution of the air pollution index and seasonality in Beijing, and after introducing precipitation, temperature, and wind speed as factors, the model can explain the air pollution found in Beijing, which can help us to analyse the extent of the influence of various factors affecting air quality; however, in this model, the reaction towards the various factors is quite smooth, and it can be difficult to predict rapid changes in air quality. Since the 2008 Olympic Games in Beijing, although license plate limitation rules have gradually become the norm, the effects thereof can be different in the long-, and short-terms. On the one hand, a license plate limitation rule can reduce the number of vehicles on the roads, therefore reduce the pollution caused by such vehicles. In the short-term, especially in the short-term under a high-intensity license plate limitation rule, the effect of improving air quality is obvious. From the model, we can see that, to some extent, during the process of implementing the limitation rule in the long-term, it can also reduce emissions and improve air quality; however, with the growing number of vehicles, after one year, the number of vehicles will reach the level it was at before the implementation of the license plate limitation rule, and the effect of
9
reducing the number of vehicles will diminish. In addition, due to the capacity of the road network, the number of vehicles on the roads is limited, when the number of vehicles reaches a certain value, even if the effect of the limitation rule still works, it cannot be effective enough to reduce the number of vehicles. When the policy can no longer reduce the number of vehicles, correspondingly, the effect of it reducing emissions will be gone. In addition to the limited effect of improving the air quality, the license plate limitation rule can also cause many socio-economic problems. The limitation rule will cause economic losses, measured by the greenhouse gas emissions, the economic losses of the license plate limitation rule are 172 times as large as its economic benefits, and its positive benefits are far below the negative economic impacts thereof. Furthermore, its legal feasibility is in doubt in the long-term: in this respect, the policy is untenable, whether it is from the perspective of the right of Constitutional equality or the rights enshrined in Road Traffic Safety Law (Zhao et al., 2010b). The increasing number of vehicles will cause serious environmental problems, and the license plate limitation rule, to a small extent, can only delay the problems. The fundamental long-term solution to reducing air pollution should be based on more efficient and reasonable methods. To improve air quality, we should attach greater importance to the following four aspects: 1) The gradual improvement of vehicle emission standards and the gradual decline of overall sewage capacity; 2) Improve traffic information networks, develop more efficient, and reasonable, motor vehicle driving rules, improve the parking environment, and enhance travel efficiency; 3) Develop and optimise an urban rail transit system and other public transportation modalities; 4) Promote technological advancement and accelerate industrial upgrading, and gradually reduce emissions from polluting enterprises. References BSIN, 2007. Possession of Civil Motor Vehicles. Retrieved 4.22.2016, from. http:// www.bjstats.gov.cn/nj/main/2007/content/mV7_1205.htm. BSIN, 2009. Possession of Civil Motor Vehicles. Retrieved 4.22.2016, from. http:// www.bjstats.gov.cn/nj/main/2009_ch1/content/mV216_1205.htm. BSIN, 2011. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2011_ch/content/mV233_1305.htm. BSIN, 2013. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2013_ch/content/mV234_1305.htm. BSIN, 2015. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2015-tjnj/zk/indexch.htm. Cao, J., Wang, X., Zhong, X.H., 2014. Can the odd-and-even license plate rule improve the air quality in Beijing? Economics 3, 1091e1126. Chen, Y., Jin, G.Z., Kumar, N., 2011. The promise of Beijing: evaluating the impact of the 2008 olympic games on air quality. J. Environ. Econ. Manag. 66 (3), 424e443. Enderle, P., Nowak, O., Kvas, J., 2012. Potential alternative for water and energy savings in the automotive industry: case study for an Austrian automotive supplier. J. Clean. Prod. 34, 146e152. Feng, T.X., 2003. Multiple Linear Regression and Least Square Method and Economic Analysis. Economist. 11,129e129. http://www.bjstats.gov.cn/nj/main/2013_ch/ content/mV92_0418.htm. Huang, F., Huang, Z.H., Wu, D., Liu, N., Song, H., 2012. The comparison between the impacts on regional air quality with and without the odd-and-even license plate rule in the the Guangzhou Asian games. South China J. Prev. Med. 2, 69e71. Li, X.F., Zhang, M.J., Wang, S.J., Zhao, A.F., Ma, Q., 2012. The analysis of API ’s (air pollution index) change characteristics and influential factors. Environ. Sci. 33 (6), 1936e1943. Lucas, W., Davis, 2008. The effect of driving restrictions on air quality in Mexico city. J. Political Econ. 116 (1), 38e81. MEP, 2016. Daily Air Quality of Key Cities. Retrieved 4.21.2016, from. http:// datacenter.mep.gov.cn/report/air_daily/air_dairy_aqi.jsp. Report summaries, 2015. Atmospheric Pollution Control Process of Beijing. Retrieved 5.5.2016, from: http://www.bjepb.gov.cn/bjepb/413526/331443/ 331937/333896/4379800/2015110910325555462.pdf.
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Shen, Y., Luo, L., Xu, C.Y., Shen, X.M., 2014. The impact of automobile exhaust on Harbin air quality. Sci. Technol. Innovation Her. 28, 116. Shi, X., Wang, X., Yang, J., Sun, Z., 2016. Electric vehicle transformation in Beijing and the comparative eco-environmental impacts: a case study of electric and gasoline powered taxis. J. Clean. Prod. 137, 449e460. Viard, V. Brian, Fu, Shihe, 2015. The effect of Beijing's driving restrictions on pollution and economic activity. J. Public Econ. 125, 98e115. Wang, M.Q., Li, R.X., Zhang, J.W., 2009. THoughts about odd-and-even license plate rule in roads. Sci. Tech. Inf. Ed. 28 (28), 31e33. Witte, J.C., Schoeberl, M.R., Douglass, A.R., Gleason, J.F., Krotkov, N.A., Gille, J.C., Livesey, N., 2009. Satellite observations of changes in air quality during the 2008 Beijing Olympics and paralympics. Geophys. Res. Lett. 36 (17), 37e44. Xu, G.H., Hou, J.Y., 2015. Research of the odd-and-even license plate Rule's impact on air quality-a case study in Chengdu. Public Econ. Policy Res. 1, 111e119. Yearbook, 2008. Weather. Retrieved 4.20.2016, from. http://www.bjstats.gov.cn/nj/ main/2008-ch/content/mV3_0103.htm. Yearbook, 2009. Weather. Retrieved 4.20.2016, from. http://www.bjstats.gov.cn/nj/ main/2009_ch1/index.htm. Yearbook, 2010. Weather. Retrieved 4.20.2016, from. http://www.bjstats.gov.cn/nj/
main/2010_ch/content/mV86_0420.htm. Yearbook, 2011. Weather. Retrieved 4.20.2016, from. http://www.bjstats.gov.cn/nj/ main/2011_ch/content/mV86_0417.htm. Yearbook, 2013. Weather. Retrieved 4.20.2016, from. http://www.bjstats.gov.cn/nj/ main/2013_ch/content/mV92_0418.htm. Zhang, F.T., Li, D.W., Yuan, K.T., 2013. The economics analysis of city car tail number limit line. Technol. Econ. Areas Commun. 15 (1), 34e36. Zhao, F.X., Xu, M., Qi, Y., 2010a. Beijing municipal environmental and economic benefit analysis of automobile limit line. Front. Forum 12, 40e44. Zhao, X.G., Xu, Z.C., Wang, X., Wang, J.N., 2010b. Analysis of the odd-and-even license plate Rule's impact on air quality in Beijing. J. Saf. Environ. 10 (4), 82e87. Zhou, Z.Y., Zhang, S.H., Gao, Q.X., et al., 2014. Meteorological factors' influence on air quality and analysis of its future trends in Beijing, Tianjin and Hebei province. Resour. Sci. 36 (1), 191e199. Zhu, M.H., 2012. Socio-economic Impact Analysis of Urban Traffic Congestion, vol. 11(1). Beijing JiaoTong University, pp. 18e22. Zhu, Q., Wu, J., Li, X., Xiong, B., 2016. China's regional natural resource allocation and utilization: a DEA-based Approach in A Big data environment. J. Clean. Prod. http://dx.doi.org/10.1016/j.jclepro.2016.02.100.
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Effect analysis of air pollution control in Beijing based on an odd-andeven license plate model Xiaoyao Xie a, *, Xiaodong Tou a, Li Zhang b a b
Faculty of Law, Ningbo University, China Faculty of Electrical and Computer Science, Ningbo University, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 November 2015 Received in revised form 14 September 2016 Accepted 16 September 2016 Available online xxx
Nowadays, air pollution has become a major challenge in urban management despite rapid economic development. Meanwhile, vehicle exhaust has gradually turned into the main source of air pollution in the city. To reduce air pollution, many measures have been taken including the odd-and-even license plate rule in some cities. However, it is difficult to evaluate the effectiveness of those measures. In view of this, based on the Davis method, this article has taken Beijing as its subject and built an odd-and-even license plate model by a probabilistic modelling method and the analysis of means, thus to quantify the pollution caused by vehicle exhaust emissions and the actual effect of the license plate limitation rule. This paper also examines the relationship between the license plate limitation rule and urban air pollution control and to see whether, or not, the rule exerts a positive influence on air pollution control. The results showed that the odd-and-even license plate rule has positive impacts on air pollution control in the short-term; however, the influence of the limitation policy gradually diminishes and disappears as the overall number of cars increases. Therefore, it is suggested to tackle air pollution in a broader and more effective way. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Air pollution Traffic control effect Davis method Odd-and-even license plate model Environmental benefit
1. Introduction The general public's quality of life has been greatly improved in pace with the rapid development of China's economy, therefore, there is a higher demand for a more convenient and comfortable transportation modes. Naturally, a private car is the first choice for many. It is under this background that the number of private cars has exploded in the last two decades. As we all know, the boom in private car use does not only increase traffic congestion but also aggravates air pollution. Taking Beijing as an example, its number of vehicles has exceeded 5.1 million in August 2012 and the number of drivers has exceeded 7.2 million. Besides, over 40% of major air pollutants such as NOx arise from vehicles (Zhao et al., 2010a). Beijing has adopted an odd-and-even license plate policy since 2008 with the initial aim being to alleviate traffic pressure and reduce urban environmental pollution during the preparation for the Beijing Olympics in 2008. Nevertheless, Beijing, and other municipal governments, have listed the license plate limitation rule as a key move in controlling urban air pollution and maintaining
* Corresponding author. E-mail address: [email protected] (X. Xie).
sustainable social development. Of particular note, the feasibility and effectiveness of this rule have been hotspots for academic and public discussion ever since its unveiling. Current studies of odd-and-even license plate rules are mainly concentrated around their origins, purpose, effectiveness, and so on. Most of them discuss the impacts of the policy on improving air quality. Among them, Wang et al. (2009) started from the obvious improvement in Beijing's air quality during the Beijing Olympic Games. By analysing the odd-and-even license plate rule's origin, purpose, and effect, they discussed the possibility and related issues, pros and cons, in institutionalising the policy; by using qualitative methods, Zhu (2012) focused on analysing the effects that traffic congestion has on socio-economic factors such as population, employment, GDP, energy consumption, land utilisation, environmental pollution, travel cost, and travel time. He combined a top-down system dynamics model and a bottom-up cellular automata model and started from the perspective of macro-socioeconomics and a micro-traffic model. Zhu combined the advantages that the system dynamics model has in scenario simulation and macro-driving factors together with the advantages of cellular automata models in microscopic traffic flow simulation. To explore the socio-economic impacts of urban traffic congestion, he built the
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system dynamics model of the interactive relationship between urban traffic and the social economy, as well as the cellular automata model of urban traffic congestion's micro-economic costs; Chen et al. (2011) studied the Beijing Olympic Games 2008 when the odd-and-even license plate rule was one of the policies that the government implemented to improve air quality. They found that this policy brought benefits to air quality by reducing 24.9% of air pollution indices compared with the same period in 2007. Nevertheless, there are more studies pointing out the limitations of the policy, per se, and its explicit, hidden risks. Cao et al. (2014) and several other researchers studied the effects of the license plate limitation rule after Beijing's Olympics in 2008. They tracked the change of air pollution index, inhalable particles, nitrogen dioxide, and sulphur dioxide and adopted a breakpoint regression method to solve its endogenous problem. They concluded that, although the air quality in Beijing was improved during the Olympic Games, it was not as an outcome of the license plate limitation rule. Enderle et al. analysed the factors affecting traffic congestion. From an economic perspective, they pointed out that, using the odd-and-even license plate rule would only bring short-term benefits and it would not do any good to the long-term and stable development of urban economy through the analysis of urban residential demand for cars, parking lots, and the impact on the automobile industry (Enderle et al., 2012; Zhang et al., 2013). Shen et al. (2014) introduced Harbin's car ownership situation in recent years. They analysed car ownership and its exhaust components by statistical methods. It turned out that the vehicle exhaust emissions have little effect on air pollution in Harbin; but, as car ownership rises, it would also have negative impacts on air quality. Besides Beijing, the odd-and-even license plate rule has been implemented in many cities in China. Yet as it turns out, the policy does not live up to popular expectations. Taking the Guangzhou Asian Games for example, the policy has been adopted but its effectiveness is limited (Huang et al., 2012). Another case in point is Chengdu, on the whole, the limitation policy did not work as expected since its inception in 2012 (Xu and Hou, 2015). The study of this policy has led to some achievements, which will inspire our follow-up studies, however, there are still some deficiencies. First of all, a lot of studies are only conducted in a qualitative way, failing to quantify the extent of the impact of the policy. Secondly, as the air quality will be affected by the type of regional climate, studies of other cities, except Beijing, cannot be a good explanation of the merits of Beijing's odd-and-even license plate rule. Lastly, many studies use mathematical tools to analyse the policy, but most of them just compare the before, and after, effects of carrying out the odd-and-even license plate rule, or simply analyse the effects of various factors on air quality, and there is lack of predictions of air quality in the future although it is a predictable condition. In view of this, this paper attempts to use a probabilistic modelling method and analysis of means to build up an odd-andeven license plate rule simulation and analyse the change of means in this model. This paper explores the effects degree of vehicles on urban air pollution and thereby discusses the degree of improvement of the rule quantitatively. 2. Models There are a large number of qualitative studies, both domestic and overseas, of the odd-and-even license plate rule: many successful cases that have offered detailed analyses of the effects of the rule. Among them, the most exemplary is the Air Pollution Index (API) explanatory model proposed by Lucas and Davis (2008). This model took the Hoy No Circula (HNC, literally “Don't drive today”, a vehicle limitation rule) initiated by the Mexican Government in 1989 as its object of study. Davis utilised high-frequency metrical
data from the monitoring station and explored the relevance between the limitation rule and air pollution by mathematical model. To be specific, Davis used the HNC's influence 1(HNC) and a timeline covariant xt to explain the change of log(API). In the meantime, Davis employed a large number of data relating to number of cars and air pollution to analyse the change in pollutants such as sulphur dioxide, nitrous oxide, and ozone on a daily or annual basis. Furthermore, he also observed petrol use, automobile growth, and public traffic mode choice: based thereon, Davis concluded that the limitation rule did not generate a significant improvement in air quality. Besides, he added that this rule will engender a growth in automobile numbers to a certain extent. Davis's explanatory model for Mexican air pollution is as shown by the following formula (1):
logðAPIÞMexico ¼ h0 þ h1 lðHNCÞ þ h2 xt þ mt
(1)
In (1), 1(HNC) is the observable variable after the implementation of odd-and-even license plate rule, coefficient h1 is the influential factor that HNC has on air pollution. Xt is a timeline coefficient which includes the indicator variable of every month, day, and hour. So to speak, Davis' analysis and argument are quiet creative and foresighted in their partial explanation for the limitation rule's failure. Nevertheless, the model's general applicability remains open to question. For example, Davis's model cannot be applied directly to explain and judge whether, or not, Beijing's vehicle limitation rule is effective in improving air quality for several reasons. Firstly, China's air pollution is closely related to seasonal and environmental factors such as temperature, humidity, rainfall, and wind speed. Beijing has a typical temperate and monsoonal climate with four clearly distinct seasons (Li et al., 2012; Zhou et al., 2014). Therefore, seasonal factors should be critical in describing Beijing's air pollution. Secondly, Davis's study emphasis on the comparison between air qualities before and after the limitation rule, thus does not give full play to its explanatory function. Besides, Davis' model does not simulate the petrol use, vehicle growth, and public traffic before the implementation of the limitation rule. (Viard and Fu, 2015). Thirdly, Davis worked out that the license plate limitation rule has greatly boosted automobile purchases in Mexico. Yet obviously, this conclusion does not fit Beijing. Though the number of automobiles in Beijing is soaring, the limitation rule cannot strongly promote automobile purchases given citizens' income levels in Beijing. 2.1. The establishment of an air pollution model As a matter of fact, those doubts over Davis' model have not denied the model totally, but only to point out that this model is restricted. In other words, if we take full consideration of a certain area's specific conditions and transform Davis' model properly, the adapted model would still have strong explanatory power. It is under this construct that this paper has designed a model. To elaborate thereon, this paper has constructed a suitable mathematical model to explain Beijing's air pollution by taking full consideration of Beijing's conditions and introducing environmental variables such as automobile numbers, rainfall, temperature, wind speed, and so on. API, as an index measuring air quality, cannot be predicted for a specific future value since its numerical value is a random number. We get the API data for Beijing from the Ministry of Environmental Protection of the People's Republic of China (MEP). For example, the API's logarithms from 1 June 2006 to 31 May 2007 are distributed as shown in Fig. 1. It is worth noting that, the variation of air quality and the variety of factors affecting the air quality have the feature of a long time
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Fig. 1. Log(API) of Beijing air from 1 June 2006 to 31 May 2007.
span and a large amount of data, so in a broad sense, the data belongs to the category of big data. The concept of big data is generally used to analyse vast amounts of data in the computer communication field, its utilisation, and treatment, but for the hundreds of thousands of data items on air quality collected in this paper, mathematical statistical tools can be used for processing these big data. Here we assume this stochastic process to be stable in the shortterm, and meet ergodicity requirements. In that case, we can use horal mean value to describe a certain day's random distribution of log(API) approximately. The study found that this stochastic process almost complies with the Gaussian distribution. ðxmÞ2 1 f ðxÞ ¼ pffiffiffiffiffiffi e 2 2ps
(2)
In (2), f(x) means that the daily log(API) is the probability density of x. m and s are the mean value and variance of the Gaussian distribution respectively. By the methods of moments estimation, we can get parameters m and s from the sample.
8 n > 1X > > b¼X¼ m X > > n i¼1 i < n > > 2 1X > > s¼ X X > :b n i¼1 i
(3)
However, in the long-term, the distribution of log(API)'s mean value is changing gradually while the change in the corresponding variance is smaller. The mean value in formula (2) stands for the average change of API, the variance stands for the degree of divergence of API and the average thereof. Though the mean value and variance show rheological properties over time, the change of API mean value is a factor that cannot be ignored. We suppose that the distribution of mean value is a function of time m ¼ m(t) while variance s is a constant. An existing study of Beijing's air quality shows that temperature, humidity, and wind speed are three critical factors influencing the
concentration of Beijing's air pollution[5]. Meanwhile, humidity is closely related to rainfall. Rainfall has a more obvious impact on air quality. Here we assume that the mean value m would meet the following correlativity:
m ¼ aV þ bT þ gH þ lW þ J
(4)
In (4), V stands for number of automobiles, T stands for temperature, H for precipitation, W for wind speed, while J stands for other factors, for instance, in recent years electric cars have been used more widely (Shi et al., 2016), and it has improved the areas surrounding Beijing in terms of efficiency in utilising natural resources (Zhu et al., 2016); a, b, g, l denote influence coefficients for corresponding factors. 2.2. Calculation of model parameters In (4), we introduced the number of automobiles, temperature, precipitation, wind speed, and other factors as influential over API change. To estimate the influential coefficient between different factors, we must first have a detailed understanding of how each factor changes with time. Here, for convenience when quoting data from the model, we assume 1 January 2006 as the first day. The mode functions from 1 January 2006 to 1 January 2010. 2.2.1. Change of number of automobiles For the number of automobiles, we analysed data drawn from Beijing Statistical Information Net (BSIN) (BSIN, 2007, 2009, 2011, 2013, 2015). Here we take the annual number of automobiles as the number at the end of a year, namely, the number at the beginning of the next year. We match the annual change of automobile numbers by rational polynomial. It turns out the cubic function fits this best:
V ¼ 1:432 108 t 3 þ 5:828t 2 þ 0:0567t þ 224:7
(5)
During the rule's trial period in 2007 and the 2008 Olympic Games, the government has launched a very strict odd-and-even license plate rule. As a result, the number of automobiles on the
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road was only 0.5% of the total amount. Afterwards, the limitation rule is implemented on five days per week, excepting weekends. If we ignore the change in automobile numbers for weekdays and weekends, then the limitation effect can be represented as: 0.8 5/ 7 þ 1 2/7 ¼ 6/7, namely, the number of automobiles is 6/7 of its previous number.
2.2.2. Changes in temperature, precipitation, and wind speed Beijing has a temperate monsoon climate: it is cold and arid in winter, and hot and rainy in summer with an obvious monsoon season. Therefore, factors such as precipitation, temperature, and wind speed vary seasonally. The weather data came from the Statistical Yearbook of Beijing (Yearbook, 2011, 2008, 2009, 2010. The change of the three factors from 2006 to 2010 are shown in Fig. 2.
2.2.3. Other factors J By taking the mean value 30 days before, and after, log(API) as a statistical average analysis, we find that the average log(API) had no obvious change, and it underwent a linear variation. Thus it was assumed that the influence of other factors was linear influence:
J ¼ kt þ b
(6)
n 8 1X > x ¼ x > i > > n j¼1 ij > > > > > > n > > 1X > > y ¼ yi > > < n i¼1
> n n X X > > > > xkj xk xij xi Skj ¼ xij xi xkj ¼ > > > > i¼1 i¼1 > > > > n n > X X > > : sky ¼ xkj yj y xkj i¼1
i¼1
Here xij,(i ¼ 1,2,3,4,5,6) represent Vj,Tj,Hj,Wj,tj,bj; yi stands for mi. Then, the standard set of variance is:
8 b ¼ y b1 x1 / b5 x5 > > < 0 S11 b1 þ S12 b2 þ / þ S15 b5 ¼ s1y >/ > : S51 b1 þ S52 b2 þ / þ S55 b5 ¼ s5y
b > l ¼ 0:0727 > > > b > k ¼ 0:0001 > :b b ¼ 1:8035
bV þ b bH þ b m¼a bT þ g lW þ J þ ε bV þ b bH þ b ¼a bT þ g l W þ bkt þ bb þ ε
(7)
This is a multiple linear regression model, with which we can find out every influential coefficient by multiple regression least squares method (Feng, 2003).
(9)
In (9), b5,b4,/b0 stand for a^,b^,g^,l^,k^,b^respectively, we can use this formula to work out all influential factors. Taking the change in number of automobiles triggered by the odd-and-even license plate rule, we can get following results by using software (MATLAB™ 2012a):
8 b ¼ 0:0002 a > > >b > b ¼ 0:0008 > >
(8)
(10)
The figure of log(API)'s actual value, statistical mean value in a short period, and mean value of log(API) stimulated by model is shown as follows: from Fig. 3, the change in each simulated figure can simulate the change in log(API). From (2), (3), (7), and (10), we see that this model is a distribution function of log(API) as a distributed variable x and time t in
Fig. 2. Change in precipitation, temperature, and wind speed from 1 January 2006 to 1 January 2010.
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Fig. 3. A comparison between actual, and simulated log(API) values.
the form of their two-dimensional probability density function. ðxmðtÞÞ2 1 f ðx; tÞ ¼ pffiffiffiffiffiffi e 2 2ps
(11)
Among which:
8 n 2 1X > > > b s ¼ Xi X > > n > > i¼1 > > > > > > bV þ b bH þ b mðtÞ ¼ a bT þ g l W þ bkt þ bb > > > > > > b ¼ 0:0002 > a > < b b ¼ 0:0008 > > > > b ¼ 0:0002 > g > > > > > b > > l ¼ 0:0727 > > > > > b > k ¼ 0:0001 > > > :b b ¼ 1:8035
3.1. Verification of the air pollution model According to the modelling process in the second part, the present model is based on probability and statistics to investigate the variation of the mean value. Therefore, to verify the model, we have to: on the one hand, test the distribution of the API, to see whether, or not, it is a random distribution and whether, or not, the established distribution rule is correct. On the other hand, we verify the mean value in the probability distribution, to see whether, or not, we can successfully predict the real changes in API when there are changes in the number of vehicles, precipitation, temperature, and wind speed.
(12)
3. Verification and application of model The second part of this paper has successfully established an air pollution model, and based on the existing data about relevant factors, we have estimated the influence coefficients of each factor; however, we have to test the effectiveness of the model in practice. Thus, in this part, the argument mainly covers two aspects: firstly, we will verify the model through specific data, and test its validity, as well as its applicability in the future; secondly, based on the model, we analyse the number of vehicles because it changes yearby-year, and investigate the specific effects of the odd-and-even license plate rule. Meanwhile, we will use this model to simulate the degree of air pollution without this limitation rule, which will be compared with the actual situation. All of these steps can provide data support for further discussion of the effect of the oddand-even license plate rule.
3.1.1. Verification of the correctness of the stochastic process used in the model In Section 2.2, we used API data from 1 January 2006 to 2010; but, in the first half of 2012, the government announced a new regulation which will replace the existing API with and Air Quality Index (AQI). In Beijing, the AQI standards were applied in the air monitoring report after 2014, but due to the calculation differences between AQI and API (in calculating the AQI, main pollutants include fine particulate matter, inhalable particles, sulphur dioxide, nitrogen dioxide, ozone, and carbon monoxide, while in calculating the API, the main pollutants only include inhalable particles, sulphur dioxide, and nitrogen dioxide). Thus, when analysing the model, we have to select pre-2014 time periods: in this paper, the time period selected ran from 1 January 2012 to 1 January 2013, and we still adopted the timing methods in this model. Suppose that 1 January 2006 is the first day, so from 1 January 2012 to 1 January 2013, there are 2192e2558 days. During the period from 1 January 2012 to 1 January 2013, from the scatter diagram of the API, we can see that the distribution of log(API) is strongly random, which means that in one year, it is a random distribution. So the assumption of stochasticity in the model can still be met. Therefore, Fig. 4 shows the frequency histogram for this period and the probability distribution curve which is based on formulae (2) and (3).
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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Fig. 4. Frequency histogram and probability density function curve of log(API) from 1 January 2012 to 1 January 2013.
Above all, during that period, and based on the analysis of a stochastic process, the probability distribution models can explain the distribution change of log(API).
3.1.2. Model verification: mean change in API In 2012, the precipitation, temperature, and wind speed data are known (Yearbook, 2013), as shown in Fig. 5. In 2012, formula (5) can be applied to all estimations of the number of vehicles and thence we can predict the value of log(API) (MEP, 2016). From Fig. 6, after analysing the value of log(API), the statistical
average, and the simulated value in the model, the model was able to simulate the mean value of the air quality distribution in 2012. After 2014, the model still worked, but as is known, the quality control system is replaced by AQI, so the model cannot directly verify the air quality, but by finding some related factors and calculating the API, it can verify this indirectly.
3.2. The effect of the odd-and-even license plate rule on the number of vehicles In Section 3.1, the reasonableness of the model can be proven,
Fig. 5. Change in precipitation, temperature, and wind speed in 2012.
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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Fig. 6. Comparison between the actual distribution and simulated values of log(API).
whether it is the distribution of the API, or its predictive effect for the API. Thus, this mathematical model has measured the degree of influence of each factor on air quality, and based on the model, we can quantify and simulate the effects of a regime without a license plate limitation rule, for comparison with current effects. Furthermore, we investigated the actual number of vehicles and the variations in the API, and by predicting the value of log(API) without the odd-and-even license plate rule, we undertook a comparative analysis. 3.2.1. Comparison between the observable variables before, and after, implementation of the odd-and-even license plate rule Around July 2007, from formula (5), the number of vehicles was approximately 2.93 million, during the Lucky Beijing test events (17 August 2007 to 20 August 2007) was the first time in which the odd-and-even license plate rule came into effect. The vehicles whose license plate is odd can be driven on odd-numbered days, while others can be driven on even-numbered days. During the Olympic Games, from 20 July 2008 to 27 July 2008, the odd-andeven license plate rule remained unchanged. This rule can cut the number of vehicles on the road by one half, which thus restricts approximately 1.465 million vehicles. Under this rule, the number of vehicles being restricted is large: if we use the number of unrestricted vehicles, as shown in Fig. 7, the number of vehicles soared, under these circumstances, by limiting the number of the vehicles on the roads, the rule can only delay the time taken for the growth in traffic. In time, the high growth rate of the number of vehicles became transitory and a decreasing trend in the number of the vehicles was predicted: the number of the unrestricted vehicles will gradually reach its highest point on urban roads. That is to say, the function of the license plate limitation rule can only delay the time for the number of unrestricted vehicles to reach a maximum. From 2006 to 2010, the average growth rate has exceeded 14%, after August 2008, the effectiveness of the license plate rule will be one seventh of its original level (around 14.29%), which is the same as the annual growth rate. Thereby, we can deduce that the effectiveness of the vehicle limitation rule can only last for a year: i.e., vehicle emissions can rise to the level of a year ago. Over time, the
Fig. 7. Changes in the number of unrestricted vehicles.
positive effects brought by the rule will diminish. 3.2.2. The effect of the license plate limitation rule on the API in Beijing To analyse the influence of the license plate limitation rule on air quality in Beijing, we examined both long-, and short-term effects: in the short term, the rule can restrict a seventh of all vehicles and indeed, it can decrease the value of log(API). From the model, before 7 July 2007, the value of log(API) was 1.8977, and after the imposition of the limitation rule, its value was 1.8681, a decrease of 0.0296. That is to say, the actual value of API fell from 79 to 74. The air quality improved by about 7.1% (that is 100.02961 ¼ 7.1%). During the Olympic Games, the license plate limitation rule was significant; the concentrations of sulphur dioxide and carbon monoxide were reduced by 13% and 12%, respectively. Furthermore, the concentration of nitrogen dioxide is reduced by 43% (Witte et al., 2009). Albeit that the effort required to implement the
Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117
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license plate limitation rule is significant, in the short-term, the effects on improving air quality are obvious, however, in the longterm, a ¼ 0.0002 is the influence factor by which vehicles affected air quality. This indicated that the contribution of every ten thousand vehicles on log(API) was 0.0002. The license plate limitation rule effect can reduce a seventh of vehicle emissions, that is, if the number of vehicles is v (in ten-thousands), the decrement of log(API) is aV/7, from 20 July 2007 to 1 January 2010, because of the effectiveness of the rule, the decrement of log(API) is between 0.022 and 0.032. Based on the model, we can conclude that the difference in the distribution of log(API) mean values was as shown in Fig. 8. From Fig. 8, in the long-term, the license plate limitation rule still works to improve air quality. Furthermore, it can reduce exhaust emissions, and to some extent, it plays a positive role in reducing air pollution. From Chart 1, the number of vehicles in Beijing is increasing at a fast pace, from 2006 to 2010, the average growth rate exceeded 14%. According to formula (5), which shows the growth rule for the number of vehicles, after September 2008, the high growth rate will make the number of vehicles exceed the threshold level before implementation of the license plate limitation rule. The absolute effect brought about by a license plate rule in reducing air pollution has been offset by the increasing number of vehicles. On the other hand, from formula (4), among various influential factors, there is also another factor J that cannot be neglected, the influence factor for the distribution of log(API)'s mean value was 0.0001, and this is a long-term linear influence, the longer the time, the stronger the influence. According to the model, since 2007, when the license plate limitation rule was implemented, the decrement in log(API) caused by J increased from 0.056 to 0.146, its effects have greatly exceeded the positive effects brought about by the rule. It is an influential factor that can reduce air pollution. In November 2009, the number of unrestricted vehicles has reached the level before the implementation of the license plate limitation rule, and J is the reason why the air quality has not deteriorated sharply with the increasing number of vehicles.
3.3. The effects of other measures on air pollution There are multiple factors giving rise to Beijing's air pollution problem. To control, and reduce, air pollution, Beijing has implemented 16 stages of different control measures between 1998 and 2012. At every stage, different control measures and implementation approaches are adopted. The 13th stage was implemented in 2007 and was aimed at restricting pollutant emissions and taking comprehensive measures to reduce emissions at the same time. The 13th stage mainly focused on the control of soot pollution, industrial pollution, fugitive dust pollution, urban and rural ecological protection and construction, perfecting hazardous weather emergency measures, and carrying out scientific research. On the basis of the 13th stage, the 14th stage of pollution control was launched in 2008. The 14th stage mainly concentrated on pushing forward five coal-burning pollution prevention projects (boiler pollutants, boiler emission standards, promotion of clean energy, abolishing the use of raw coal in urban and rural integration areas and increasing effort to deal with violations of law), controlling bullet train pollutant emissions, transforming polluting enterprises, and controlling fugitive dust pollution. The 15th stage was carried out in 2009 on the strength of the 14th stage and it promoted ‘green construction’, and prevention of fugitive dust pollution from construction sites. It also adds solid waste landfill site pollutant discharge control and emphasises the industrial upgrading of polluting enterprises. The 16th stage (started in 2010) promoted total emissions quantity control, formulating stricter environmental admittance standards, relating emission loads with factors such as regional function, air quality, emission object completion status and treatment facilities; stressing restrictions on high-emission vehicles, boosting pollution control projects and completing regulation thereof, intensifying inspections for law enforcement purposes and pollution regulation. There are many powerful measures used to control air pollution and these measures have a good effect in prohibiting the deterioration of air quality. On 9 September 2015, the United Nations Environment Programme (UNEP) and the Beijing Environmental
Fig. 8. Comparison of log(API) values before, and after, the simulated license plate rule.
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Protection Bureau announced the preliminary results of an evaluation report: “The control of air pollution in Beijing, from 1998 to 2013”. The results showed that, although there is a gap between the air quality now and the requisite national standards, the air quality has nevertheless been significantly improved. The improvements in coal-fired power plants, coal-fired boilers, and old bungalows are significant: compared with 2005, the consumption of coal in coalfired power plants has been cut by 2.56 million tons in 2013. Also, compared with 1998, the emissions of PM2.5, PM10, SO2, and NOx have been decreased by 14,500 t, 23,700 t, 25,000 t, and 30,900 t, respectively (proportional reductions of 86%, 87%, 85%, and 64%, respectively). At the same time, the transformation of the boilers used has decreased the emissions of PM2.5, PM10, SO2, and NOx decreased by 14,300 t, 24,000 t, 136,000 t, and 48,700 t, respectively. After the implementation of a series of measures, such as emission-control standards for motor vehicles, fuel quality improvements, promotion of enhanced I/M systems, the promotion of new-energy vehicles and traffic control, a total reduction of CO, THC, NOx, and PM2.5 reached 292,000 t, 32,000 t, 14,000 t, and 670 t, respectively. Furthermore, the structure of the energy generation, and travel, modes were gradually optimised, and the amount of natural gas (which is regarded as clean energy when used in power plants) has risen to 35%, because of the development of rail transportation, by the end of 2014, there would be 18 railway lines with a total length of 527 km. In addition, from 2000 to 2014, the percentage of people choosing public transportation would rise from 26% to 48% (Report summaries, 2015). All these measures have been fully explained in the model: in formula (4), the improvement effect can be shown in the variation of J. Coal-fired power plants, boilers, and polluting enterprises have discharged large amounts of PM2.5, PM10, SO2, and NOx, and other pollutants, these pollutants can all influence the value of API, during the process of governance, to some extent, all of these methods can reduce air pollution. During the process of governance, emission sources such as power plants, boilers, residential houses, and other polluting enterprises have been greatly reduced in number. Also the enrichment of rail transportation and other public transportation has reduced the number of the vehicles on the roads, and therefore reduced emissions. To some extent, these comprehensive management approaches can reduce air pollution. 4. Conclusions The particularities of the regional environment mean that air pollution in Beijing is characterised by seasonality. The model here combines the randomness of the distribution of the air pollution index and seasonality in Beijing, and after introducing precipitation, temperature, and wind speed as factors, the model can explain the air pollution found in Beijing, which can help us to analyse the extent of the influence of various factors affecting air quality; however, in this model, the reaction towards the various factors is quite smooth, and it can be difficult to predict rapid changes in air quality. Since the 2008 Olympic Games in Beijing, although license plate limitation rules have gradually become the norm, the effects thereof can be different in the long-, and short-terms. On the one hand, a license plate limitation rule can reduce the number of vehicles on the roads, therefore reduce the pollution caused by such vehicles. In the short-term, especially in the short-term under a high-intensity license plate limitation rule, the effect of improving air quality is obvious. From the model, we can see that, to some extent, during the process of implementing the limitation rule in the long-term, it can also reduce emissions and improve air quality; however, with the growing number of vehicles, after one year, the number of vehicles will reach the level it was at before the implementation of the license plate limitation rule, and the effect of
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reducing the number of vehicles will diminish. In addition, due to the capacity of the road network, the number of vehicles on the roads is limited, when the number of vehicles reaches a certain value, even if the effect of the limitation rule still works, it cannot be effective enough to reduce the number of vehicles. When the policy can no longer reduce the number of vehicles, correspondingly, the effect of it reducing emissions will be gone. In addition to the limited effect of improving the air quality, the license plate limitation rule can also cause many socio-economic problems. The limitation rule will cause economic losses, measured by the greenhouse gas emissions, the economic losses of the license plate limitation rule are 172 times as large as its economic benefits, and its positive benefits are far below the negative economic impacts thereof. Furthermore, its legal feasibility is in doubt in the long-term: in this respect, the policy is untenable, whether it is from the perspective of the right of Constitutional equality or the rights enshrined in Road Traffic Safety Law (Zhao et al., 2010b). The increasing number of vehicles will cause serious environmental problems, and the license plate limitation rule, to a small extent, can only delay the problems. The fundamental long-term solution to reducing air pollution should be based on more efficient and reasonable methods. To improve air quality, we should attach greater importance to the following four aspects: 1) The gradual improvement of vehicle emission standards and the gradual decline of overall sewage capacity; 2) Improve traffic information networks, develop more efficient, and reasonable, motor vehicle driving rules, improve the parking environment, and enhance travel efficiency; 3) Develop and optimise an urban rail transit system and other public transportation modalities; 4) Promote technological advancement and accelerate industrial upgrading, and gradually reduce emissions from polluting enterprises. References BSIN, 2007. Possession of Civil Motor Vehicles. Retrieved 4.22.2016, from. http:// www.bjstats.gov.cn/nj/main/2007/content/mV7_1205.htm. BSIN, 2009. Possession of Civil Motor Vehicles. Retrieved 4.22.2016, from. http:// www.bjstats.gov.cn/nj/main/2009_ch1/content/mV216_1205.htm. BSIN, 2011. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2011_ch/content/mV233_1305.htm. BSIN, 2013. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2013_ch/content/mV234_1305.htm. BSIN, 2015. Vehicle Ownership. Retrieved 4.22.2016, from. http://www.bjstats.gov. cn/nj/main/2015-tjnj/zk/indexch.htm. Cao, J., Wang, X., Zhong, X.H., 2014. Can the odd-and-even license plate rule improve the air quality in Beijing? Economics 3, 1091e1126. Chen, Y., Jin, G.Z., Kumar, N., 2011. The promise of Beijing: evaluating the impact of the 2008 olympic games on air quality. J. Environ. Econ. Manag. 66 (3), 424e443. Enderle, P., Nowak, O., Kvas, J., 2012. Potential alternative for water and energy savings in the automotive industry: case study for an Austrian automotive supplier. J. Clean. Prod. 34, 146e152. Feng, T.X., 2003. Multiple Linear Regression and Least Square Method and Economic Analysis. Economist. 11,129e129. http://www.bjstats.gov.cn/nj/main/2013_ch/ content/mV92_0418.htm. Huang, F., Huang, Z.H., Wu, D., Liu, N., Song, H., 2012. The comparison between the impacts on regional air quality with and without the odd-and-even license plate rule in the the Guangzhou Asian games. South China J. Prev. Med. 2, 69e71. Li, X.F., Zhang, M.J., Wang, S.J., Zhao, A.F., Ma, Q., 2012. The analysis of API ’s (air pollution index) change characteristics and influential factors. Environ. Sci. 33 (6), 1936e1943. Lucas, W., Davis, 2008. The effect of driving restrictions on air quality in Mexico city. J. Political Econ. 116 (1), 38e81. MEP, 2016. Daily Air Quality of Key Cities. Retrieved 4.21.2016, from. http:// datacenter.mep.gov.cn/report/air_daily/air_dairy_aqi.jsp. Report summaries, 2015. Atmospheric Pollution Control Process of Beijing. Retrieved 5.5.2016, from: http://www.bjepb.gov.cn/bjepb/413526/331443/ 331937/333896/4379800/2015110910325555462.pdf.
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Please cite this article in press as: Xie, X., et al., Effect analysis of air pollution control in Beijing based on an odd-and-even license plate model, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.09.117