The spatial distribution of commuting CO2 emissions and the influential factors: A case study in Xi'an, China

Abstract

As the transport sector is a major source of greenhouse gas emissions, the effect of urbanization on transport CO₂ emissions in developing cities has become a key issue under global climate change. Examining the case of Xi'an, this paper aims to explore the spatial distribution of commuting CO₂ emissions and influencing factors in the new, urban industry zones and city centers considering Xi'ans transition from a monocentric to a polycentric city in the process of urbanization. Based on household survey data from 1501 respondents, there are obvious differences in commuting CO₂ emissions between new industry zones and city centers: City centers feature lower household emissions of 2.86 kg CO₂ per week, whereas new industry zones generally have higher household emissions of 3.20 kg CO₂ per week. Contrary to previous research results, not all new industry zones have high levels of CO₂ emissions; with the rapid development of various types of industries, even a minimum level of household emissions of 2.53 kg CO₂ per week is possible. The uneven distribution of commuting CO₂ emissions is not uniformly affected by spatial parameters such as job–housing balance, residential density, employment density, and land use diversity. Optimum combination of the spatial parameters and travel pattern along with corresponding transport infrastructure construction may be an appropriate path to reduction and control of emissions from commuting.

Keywords

Sustainable urbanization ; Spatial distribution ; Factors ; Commuting CO2 emissions ; Xi'an

1. Introduction

Many recent researches have noted that urbanization has impacts on carbon emissions (Cole and Neumayer, 2004 , Jones, 1991 , Parikh and Shukla, 1995 , York, 2007 , Dhakal, 2010  and Martínez-Zarzoso and Maruotti, 2011 ). The IPCC (2007) reported that more than 75% of CO₂ emissions are emitted from cities, and this statistic will increase exponentially in the foreseeable future with projections of an increase to 83% by 2030 (IEA, 2008 ).

As cities are the center for economic development and associated with continuous population growth as well as ever-increasing urbanization rates (Banister, 2005 ), carbon emissions have increased rapidly especially in developing countries featuring booming economic development and unprecedented urbanization (WB, 2005  and Dhakal, 2009 ). China, one of the important developing countries in which carbon emission reductions are necessary, experienced an increase in its urbanization rate of 36.85% in the last 36 years from 17.92% in 1978 (NBSC, 2014 ).

With the rapid urbanization resulting from Chinas booming economic development, motorization developed rapidly, fostering a significant increase in vehicle kilometers traveled (VKT) and greenhouse gas (GHG) emissions (Zhao et al., 2011 ). The effect of urban expansion on transportation in growing megacities has become a key issue in the context of global climate change, as motorized mobility is a major source of domestic GHG emissions (Zhao, 2010 ). The transport sector contributes to 27% of energy-related CO₂ emissions (IEA, 2012 ) and is responsible for approximately 13% of GHG emissions. In developing countries, it is the fastest growing sector in terms of GHG emissions, driven mainly by rapid motorization. If no aggressive and sustained mitigation policies are implemented, CO₂ emissions from transport sector are expected to almost double by 2050 (IPCC, 2014 ). Moreover, in the context of rising motorization, the environmental effects of transport tend to be more detrimental (Zhao, 2010 ). Estimates from the United Nations in 2011 suggested that developing countries' share in global ownership of cars will rise from 25% in 1995 to 48% in 2050. During the period of 2001–2005, cars accounted for 7% of domestic CO₂ emissions; this percentage is forecasted to increase due to the rapid process of motorization (WB, 2007 ).

The process of urbanization has changed the spatial distribution of occupations, industries, and land use, which means that the larger the urban scale, the greater the reorganization of industries and the redistribution of residences and employment (Zhao, 2010  and Zhao et al., 2011 ). Along with these changes, associated activities caused problems, such as backward infrastructure construction, uneven land development, a lower mix of land use (Deng and Huang, 2004  and Pan et al., 2008 ), a higher use of motor vehicles (Cervero and Kockelman, 1997 ), and the consequent higher carbon emissions (King, 2007  and Glaeser and Kahn, 2010 ).

Researchers have claimed that there is spatial difference in household transport carbon emissions between different regions in a city. In Adelaide, Australia, the families located in the outer suburbs generated more carbon emissions than those who lived near the center of the city (Troy et al., 2003 ). Similarly, in the capital area of Korea, the highest transport carbon emissions are from the families living in the urban fringe (Ko et al., 2011 ). Not all the results were consistent, however. In France, compared with the families in rural and suburb areas, those living in the city center and near the city generated more transport carbon emissions (Nicolas and David, 2009 ). However, in comparing transport energy consumption and transport carbon emissions between the inner and the outer suburbs in the five largest cities in Australia, the results showed that the level of transport energy consumption and transport carbon emissions of the two areas in each city are similar (Moriarty, 2002 ). Researches on the relationship between household transport carbon emissions and household locations in the city have suggested that household locations have impacts on the transport carbon emissions, but the impacts are not consistent.

In studies on urban spaces, the results showed that especially for transport energy consumption, there is a strong correlation between the urban form and energy consumption, and thus carbon emissions. There are many measurements for urban form spatial parameters such as job–housing balance, residential density, employment density and land use diversity. It is almost a foregone conclusion that when the job–housing balance is uneven, the longer the residential commuting distance and the greater travel demand will be (Cervero, 1989 , Cervero, 1991  and Cervero and Duncan, 2006 ). A city of higher residential density is most likely a compact city, which may be a preferable city development pattern for decreasing transport CO₂ emissions by shortening commuting distance and commuting time (Cervero and Kockelman, 1997  and Zheng et al., 2010 ). The higher employment density is usually related to shorter commuting distance, thus allowing for low-carbon travel modes, such as walking and cycling. The higher land use diversity (Zhao et al., 2007 ) is usually associated with the greater probability of short distance travel, which may help in reducing the use of high-carbon vehicles such as cars.

Realignment in the arrangement of urban spatial parameters could reduce GHG emissions because urban form influences the volume of GHG emissions generated by households (Grazi et al., 2008  and Hankey and Marshall, 2010 ). This influence is felt in part through the way that compact urban forms are associated with lower transport energy use and carbon emissions (Wang et al., 2013  and Hankey and Marshall, 2010 ). There is general acknowledgement among researchers that land use change can influence the use of certain transportation modes (Zhang, 2004  and Zahabi et al., 2012 ), and that the reduction of travel and the modal split are relevant to GHG emissions control (Mishalani et al., 2014a  and Mishalani et al., 2014b ).

In fast urbanizing countries such as China, urban forms evolve rapidly as they develop rings of new suburbs and new industry zones. The question of urbanization with relation to travel and transport emissions, and particularly, the relative impact of spatial parameters on household transport carbon emissions, needs further empirical research in different spatial context.

Many monocentric cities, such as Xi'an, the case city in this paper, are gradually developing into polycentric cities as urbanization increases, with the formation of new districts to share functions and deflect pressure from the city center. Meanwhile, urbanization has brought about uneven development areas in a city, with concomitant uneven transport carbon emissions according to the above review of the relationship between the household locations and transport carbon emissions. Given the scale of the task of addressing global warming, no means of reducing CO₂ emissions (and by implication, GHG emissions) should be overlooked. Considering the uneven development and carbon emissions caused by the formation of new districts in the process of urbanization for monocentric cities, Xi'an is used as a case study to answer the following three questions:

• How are transport carbon emissions distributed in urban spaces with the formation of new districts in the process of urbanization for monocentric cities?
• What is the cause of this distribution?
• What should we do to decrease and control the transport carbon emissions in new urban spaces?

2. Methods and data

2.1. Methods

2.1.1. Commuting CO₂ emissions calculation

The commuting CO₂ emissions are emissions from the commuting vehicles operation of commuters for their commuting travel activities from home to work place or from work place to home by modes of car, bus, taxi and others. In this paper, only the issue of household commuting carbon emissions is considered. Considering the residential commuting distance is usually fixed, the distance-based simplified method is used to calculate for CO₂ emissions, which has been proven to produce only minor differences from complicated calculation methods (Stead, 1999 ). In addition, on a local scale, the simplified calculation method is adequate with the support of large sample data.

The commuting frequency is introduced into the distance-based calculation method, given the different residential commuting times every day. The weekday is a calculation unit as shown in Eq. (1) , but week is an analysis unit of which emissions are the sum in the five weekdays (Monday to Friday). The commuting CO₂ emissions calculation for a commuter per weekday is

$e_{i}=l_{i}\cdot f_{i}\cdot m_{i},$

( 1)

where e_i is in kilogram; l_i is the mean distance of single travel (km); f_i is commuting frequency; m_i is CO₂ emission factor (kg km⁻¹ ).

There are two kinds of commuting distance l_i used in this paper: one distance is calculated using Shortest Route Method based on the Urban Transportation Planning System (UTPS) (Mekky, 1995 ), according to the work origin and destination of commuters derived from the survey; another is gained based on the travel time and average speed of the transport mode, if commuters of samples do not respond their work destinations.

Emission factor m_i is a key parameter in calculating commuting CO₂ emissions and the factor in vehicle operation is related to engine size, vehicle type, travel condition and others. This paper assumes that the type and size of each mode is the same and ignores the difference in driver skill and external environment. Using the method given in Stead (1999) , this paper modified and recalibrated factors of each transport mode to represent the actual situation of Xi'an. The CO₂ emission factor of modes in Stead (1999) are not only dependent on the mode, fuel, vehicle speed, engine size and temperature of the journey, but also the average occupancy which is crucial for calculating emissions from a single person, a commuter in the paper. Referenced to Stead (1999) , emission factor is a result of emissions per vehicle divided by average occupancy. This paper chose emissions per car and emissions per shuttle bus of similar characteristic as Xi'an in Stead (1999) as the emissions per vehicle of the same mode, and then emission factors of car and shuttle bus in Xi'an were got respectively by using local average occupancy to divide the emissions per vehicle. There is difference in calculating the emission factors of bus and taxi. Emissions per vehicle is not referenced directly from Stead (1999) , but is calculated by the fuel consumption volumes per kilometer (m³  km⁻¹ ) and the emissions of per volume (kg m⁻³ ) of fuel, because natural gas is fuel both for bus and taxi in Xi'an while petrol for Stead (1999) . Similar to Stead (1999) , after emissions per vehicle of bus and taxi being divided respectively by local average occupancy of corresponding mode, the emission factors of bus and taxi are obtained. The results are shown in Table 1 .

Table 1. CO₂ emission factors of transport modes in Xi'an.

Transport mode	Fuel type	Average occupancy (persons)	CO2 emissions per capita (kg km−1 )
Walking			0
Bicycle & electric bicycle			0
Bus	Natural gas	42.0	0.024
Car	Gasoline	2.5	0.068
Shuttle bus	Diesel	38.0	0.033
Taxi	Natural gas	3.8	0.028

Note: This paper assumes high values of average occupancy because of commuting in morning–evening rush hours are considered.

2.1.2. Aggregation into traffic zones

Traffic zones are the basic units for travel surveys and travel data collection for further analysis in UTPS, usually bounded by roads, residential building and the compass of sub-district administrative office. The compass is especially used for its advantages in traffic zone division to assure convenient data acquisition and accurate data. There are 1305 traffic zones in UTPS in 2012. In UTPS, traffic zones are usually used to record the household survey data of traffic generation and attraction. In addition, data of land use, population, employment and other socioeconomic information can be coded into the attributes of traffic zones. With the homogeneous characteristics of the resident population in a traffic zone, the average CO₂ emissions of samples in a traffic zone can be reasonably calculated. For better comparison of CO₂ emissions between different sizes of traffic zones, the zone-level commuting CO₂ emissions intensity ratio is introduced. The related zone-level CO₂ emissions calculation formulas are shown in Table 2 .

Table 2. Calculation formula for zone-level CO₂ emissions.

Zone-level CO2 emissions type	Formula	Instruction
Total of each zone (kg per week)	${\textstyle C_{n}=\sum _{h}C_{hn}}$	Chn is total CO2 emissions of household h in traffic zone n
Per capita of each zone (kg per week per person)	${\textstyle A_{n}={\frac {C_{n}}{Q_{n}}}}$	An is per capita CO2 emissions in traffic zone n ;Qn is the number of commuters in traffic zone n
CO2 emissions intensity (kg (100 m2 )−1 per week)	${\textstyle I_{n}={\frac {C_{pn}}{S_{n}}}}$	In is the CO2 emissions intensity in traffic zone n ;Sn is the area of traffic zone n ;Cpn is the total CO2 emissions of total employees in traffic zone n ,Cpn  = An ·Pn , Pn is the number of employees in traffic zone n

2.2. Data

2.2.1. Research region

As shown in Fig. 1 a, the built-up area of Xi'an consisted of six administrative areas, in which the permanent urban population reached 4.43 million in 2013 and accounted for 71.7% of total permanent urban population in Xi'an (XMBS, 2014 ). Three administrative areas (Xincheng, Beilin, and Lianhu) comprise the central business district (CBD) and the inner area of the city extending to the second ring road, whereas the suburbs are made up of Weiyang and Yanta mainly inside the third ring road. An outer fringe comprises Baqiao and the rest of Weiyang. The area within the third ring road is the main urban area, a core area for economic development and residential activities of the city.

Fig. 1. (a) Location of built-up areas in Xi'an, (b) distribution of industries in Xi'an (the areas marked in bold are examined in this paper).

After years of evolution and urban construction, the main urban area is located around the road network of layers radiating from a single center and the industrial development pattern of the old city surrounded by new districts with a clear separation in function. In Xi'an, there is a firmer link between the administrative districts and the industrial districts. The location of administrative districts and industrial districts investigated in this paper are shown in Fig. 1 b.

Table 3 shows the main socioeconomic indexes of the administrative districts in main urban area. The GDP of the permanent population of the three administrative districts in the city center (Xincheng, Beilin, and Lianhu) increased by 6%–10% annually during 2002–2013. It means that there is still an agglomeration of the population in central areas, which is related to the developed infrastructure and rich land use. Compared with the administrative districts in center area, the districts outside the center area (Yanta, Weiyang, and Baqiao), where new industries have been established, have experienced a rapid increase in population, especially in Weiyang and Yanta, the two districts of the fastest economic development, with GDP increasing annually by 10.1% and 10.7% respectively. The growth of population of Weiyang was brought about by the construction of an Economic & Technological Development Zone and the movement of the city administrative center into the area. Further, the operation of Metro Line 2 attracted many residents moving to Weiyang. As for Yanta, the most populous administrative district, the fastest population increase is mainly because of the gathering of high-tech industries in the zone, which offered many jobs and attracted many employees. The pleasant environment of the Qujiang New District in Yanta also spurred growth in population.

Table 3. Indexes of socioeconomic characteristics of administrative districts in main urban areas.

Administrative district	Main industry zone	Land areaa (km2 )	GDPb (¥billion)	Annual growth rate (%)	Permanent urban population (million)	Population changec (%)
Xincheng		30	5.06	9.0	59.64	5.9
Beilin		24	5.92	10.8	62.23	10.2
Lianhu		43	5.53	9.0	70.43	10.5
Baqiao	Chanba Ecological District	325	2.93	11.1	56.52	16.2
Weiyang	Xi'an Economic & Technological Development Zone	262	6.75	10.1	75.32	31.9
Yanta	Xi'an High-Tech Zone; Qujiang New District	149	10.66	10.7	119.29	30.0

a. Data sourced from XTMC (2013) .

b. GDP and population sourced from XMBS (2014) .

c. This data was processed based on population data for 2013 and 2002 from XMBS, 2003  and XMBS, 2014 .

2.2.2. Data collection

Socioeconomic data, geographical and commuter travel information are main three categories of data used in this paper. Socioeconomic data such as GDP and population are collected from statistical year book and sub-district administrative offices. Geographical data of region boundary, road attributes are mainly based on the urban transportation planning model calibrated based on the data from planning departments. A household questionnaire survey was used to collect commuter travel data. Four groups of information were sought, including household information, energy consumption associated with commuting, the urban built environment, and the use of electricity and gas at home. The energy consumption associated with commuting is the focus of this paper. Major data items include distance from home to places of employment and the location of both places; mode of commuting including walking, cycling, public transport, company shuttle bus, car/motorcycle and taxi; and the time required using the given mode. Three members of each household were required to provide the details specified above.

A spatial stratified random sampling procedure was applied for sample selection based on the traffic zones in UTPS. The population in each traffic zone in the main urban area can be directly calculated based on the data from sub-district administrative offices and roughly indirectly estimated based on the area ratio of the traffic zone to the total area of the sub-district, then based on the total population in the main urban area and ratio of population of each traffic zone to total population, the total sample in main urban area and the sample in each traffic zone are determined. A total of 1501 valid questionnaires were collected.

3. Results

3.1. Distribution of zone-level total commuting CO₂ emissions

Using the traffic zone as a statistical unit, Fig. 2 a shows that there is obviously uneven spatial distribution of commuting CO₂ emissions in the main urban area of Xi'an. The commuting CO₂ emissions in the overall area of the city center are low. The high carbon emissions are mainly concentrated in the areas along the second ring road. The probability of high commuting CO₂ emissions in the western and southern areas is greater than that in the eastern and northern areas.

Fig. 2. (a) Zone-level total commuting CO2 emissions in Xi'an (unit: 103  kg per week), (b) zone-level population density (unit: persons in 100 m2 ).

Notably, the new districts, the High-Tech Zone in the southwest, the Qujiang New District in the southeast and the Economic & Technological Development Zone in the north, are the areas where the high total commuting CO₂ emissions are found. On the whole, the total commuting CO₂ emissions gradually increase outward with the expansion of a monocentric city. However, with less urbanization in the outlying areas, the spread of high total commuting CO₂ emissions is curbed; that is, at the edge of the city, the total commuting CO₂ emissions are not actually high at all.

What was interesting to note was that there is almost the opposite of spatial distribution between the population density and total commuting CO₂ emissions. As shown in Fig. 2 b, population density in the first ring road reached a very high level, but the total commuting CO₂ emissions in the same area are at a very low level. In the belt area between the first ring road and the second ring road, the high population density in the northern region of the east–west axis does not lead to high total commuting CO₂ emissions. By contrast, the high population density in the southern region of the east–west axis shows high total commuting CO₂ emissions. The region outside the second ring road, especially in the south, with a greater population density, shows higher total commuting CO₂ emissions. With reference to Fig. 2 a and b, the analysis revealed that a handful of families and individuals generate greater levels of commuting CO₂ emissions. According to statistics in this paper, the families making up 25% of the population generated 75% total household commuting CO₂ emissions, and the individuals constituting 15% generated 75% total individual commuting CO₂ emissions.

3.2. Distribution of zone-level per capita commuting CO₂ emissions

As shown in Fig. 3 , the spatial distribution and changing tendency in the expansion zones show similar levels of individual commuting CO₂ emissions to the zone-level household commuting CO₂ emissions. The high average individual commuting CO₂ emissions were unevenly distributed over the entire main urban area. On the whole, the emissions are low in the city center. The high emissions are mainly distributed in the new industrial districts in the north, southwest, and southeast.

Fig. 3. Zone-level per capita commuting CO2 emissions in Xi'an (unit: kg per week).

3.3. Distribution of zone-level CO₂ emissions intensity

As Fig. 4 indicates, inside the first ring road, the commuting CO₂ intensity is low, while in the second ring road, the commuting CO₂ intensity increases. It is also apparent that the intensity in the south of the city is higher than that in the north, and there is a higher intensity in the city center than that in new industrial districts. The spatial distribution of the intensity is consistent with the degree of land development of Xi'an; that is where there is a higher degree of land use and land development, there is a higher commuting intensity. To some extent, the difference in spatial distribution of commuting CO₂ emissions of different regions is related to the industry type, the development order, and the land development policy of each region.

Fig. 4. Zone-level CO2 emissions intensity in Xi'an (unit: kg (100 m2 )−1 per week).

3.4. Commuting CO₂ emissions in industry districts

As mentioned earlier, in Xi'an, there is high coupling between the administrative districts and industrial districts. In order to more easily obtain the data, this paper made statistics of the indicators mentioned above to explore the impact factors of commuting CO₂ emissions.

As shown in Table 4 , Yanta District generated the highest household commuting CO₂ emissions and Baqiao generated the lowest emissions. From the point of the order and degree of industrial development, the emissions of administrative districts where the new industries are located are higher than those of the administrative districts in the city center.

Table 4. Spatial parameters and commuting CO₂ emissions of the six administrative districts.

Administrative district	Household commuting CO2 emissions (kg per week)	Spatial parameter
		Job–housing balance	Residential density	Employment density	Land use diversity
Beilin	2.70	0.542	0.332	0.023	1.031
Xincheng	2.89	0.512	0.296	0.021	1.032
Lianhu	2.99	0.562	0.109	0.014	1.125
Average (Center)	2.86	0.539	0.246	0.019	1.063
Baqiao	2.53	0.537	0.248	0.001	0.955
Weiyang	3.25	0.521	0.280	0.002	0.991
Yanta	3.81	0.497	0.101	0.005	0.975
Average (New Industry Zone)	3.20	0.518	0.210	0.003	0.974
Correlation coefficienta		−0.642	−0.608	−0.323	−0.134

a. Correlation between each spatial parameter and the household commuting CO₂ emissions.

The results of correlation analysis on household carbon emissions with four indicators showed that all four indicators did not satisfy the significance test, which indicated that there is no significant correlation. In other words, on the citys scale, the increase or decrease of job–housing balance, residential density, employment density, and land use diversity is not accompanied by an increase or decrease of carbon emissions.

Nevertheless, it is important to note the polarization between the carbon emissions in the administrative districts and the new industrial districts. The carbon emissions of Baqiao are the lowest, with the level of 2.53 kg per week. However, in similar administrative districts where the new industrial districts are located, the carbon emissions of Weiyang and Yanta are high in the city, with the value of 3.25 kg per week and 3.81 kg per week, respectively. In order of the development of the three districts, Yanta is the earliest, followed by Weiyang and Baqiao. The rank of employment density in the three districts is consistent with this order.

Beilin, Xincheng, and Lianhu, where the administrative and industrial areas are located in the city center, have moderate levels of commuting CO₂ emissions. The residential density, employment density, and land use diversity of the three districts are higher than those of the new industrial districts as a whole. The employment density of the new industry districts is only one-sixth of the density of the city center, which means that many employees still living in new districts undergone a long-distance commuting travel to work. Furthermore, in the city center, transport infrastructure, especially for low-carbon modes of public transit and metro, is more prefect and provides reasonable transport conditions and environment.

As shown in Table 4 , the commuting CO₂ emissions of Lianhu are the highest in the city center. The land use diversity in Lianhu is also the highest, but its residential density and employment density are the lowest. Moreover, the greatest job–housing balance indicated that many residents living in this district work across districts, which increased the commuting distance. Consequently, Lianhu district shows different levels of commuting CO₂ emissions.

Most importantly, Yanta generated the highest commuting CO₂ emissions in new industry zones, but its job–housing balance is the lowest, appearing inconsistent with Lianhu. The reason is that Qujiang New District is a high-tech development zone where economic conditions are better. Here, families can easily afford one or more cars, and are more likely to use their car for commuting. Although their commuting distance and commuting time are not long, their preference for car-based travel still contributed to the high commuting CO₂ emissions.

4. Conclusions and discussion

It was found that in the process of urbanization of monocentric cities, high commuting CO₂ emissions emanated from the new industrial districts where the high technology industries and advanced manufacturing industries are located. The commuting CO₂ emissions in the city center are generally lower than those of the new industrial districts on the urban fringe in Xi'an, as evidenced in previous researches, which reminded us that the reasonable planning and construction of the urban fringe area should be the focus of carbon emission reductions. Importantly, carbon emissions are not always at a high level as indicated in previous researches results, but with the difference of industry type and development level, the commuting CO₂ emissions between new industrial districts are different; in fact, even a minimum level of carbon emissions can be generated. The interesting results are not entirely unexpected as previous researches had already shown that the distribution of high transport carbon emissions in the city center and in urban fringe areas was inconsistent. The inconsistency reflected that distribution of the transport carbon emissions for a city may be important one of the key characteristics to be carefully considered in policy making for sustainable urbanization.

The uneven development of regions in the process of urbanization caused a series of problems with imbalances between employment, travel demand, and supply. All the problems arose out of the region-specific characteristics of travel mode choices and uneven spatial commuting CO₂ emissions. Analysis of the factors impacting commuting CO₂ emissions in administrative districts means that uniformity in spatial impact parameters is unsupported by the evidences. The spatial parameters are to some extent related to the travel distance, and in fact the travel mode is also an important cause of commuting emissions. Yanta District of the lowest job–housing balance generated the highest emissions because of high car usage even for short travel distance. Optimum combination of the spatial parameters and travel pattern along with corresponding transport infrastructure construction may be an appropriate path to emission reductions of commuting. In new districts, to curb the use of cars as well as to increase the land use intensity should be encouraged while in the city center, to improve the bus service and further guided bus usage should be continued.

This leaves us with a question of whether, at some time in the future, Baqiao, which is dominated by tourism development, will become a high carbon emissions region in Xi'an like Weiyang and Yanta where rapid development has taken place. The second question is whether, with the economic improvement and increase in residential density, employment density, and land use diversity, the commuting CO₂ emissions of Weiyang and Yanta will gradually decrease. Both these uncertainties about commuting CO₂ emissions in the process of urbanization need further and long-term research.

Acknowledgements

This study was funded by National Natural Science Foundation of China (51178055 ) and Asia Pacific Network for Global Change Research (1094801 ).

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