RESEARCH ARTICLE

Neighborhood form and CO2 emission: evidence from 23neighborhoods in Jinan, China

Jiaxing GUO1, Huan LIU (✉)1, Yang JIANG2,3, Dongquan HE2, Qidong WANG1, Fei MENG2, Kebin HE (✉)1

1 State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China2 The Energy Foundation, Beijing 100004, China

3 School of Architecture, Tsinghua University, Beijing 100084, China

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2013

Abstract To understand the household CO2 emissionlevel in China, as well as how much the neighborhoods’socio-economic or design factors could influence the CO2

emission, 23 neighborhoods in Jinan were investigated in2009 and 2010. These neighborhoods fall into fourdifferent types: superblock, enclave, grid and traditional.The household CO2 emission includes sources of both in-home energy use and passenger transportation. Theaverage CO2 emission per household is 7.66 t$a–1,including 6.87 t in-home operational emission and 792kg transportation emission. The household CO2 emissionby neighborhood categories is 10.97, 5.65, 6.49, 5.40t$household–1$a–1 for superblock, enclave, grid and tradi-tional respectively. Superblock has the highest averageemission and also the highest percent (more than 25%) oftransportation emission among four different types ofneighborhoods. The residential CO2 emission of superb-lock neighborhoods in Jinan has already reached the levelin developed countries nearly ten years ago. It ispredictable that more superblock neighborhoods wouldbe built in China with the fast urbanization. How to avoidthe rapid household CO2 emission growth in the futurewould be a systematic issue. The study also found that inaddition to income and apartment area, household density,land use mix and accessibility to public transportation arethree primary factors which have significant impacts onCO2 emission. High density, mixed land use andconvenient accessibility to public transportation tend toreduce household CO2 emission.

Keywords CO2 emission, neighborhood type, transporta-tion, household energy, China

1 Introduction

With the increasing concern on the global climate change,the idea to reduce CO2 emissions by improving energyefficiency has been used in several aspects. Notably, morethan half of today’s world population live in urbanizedarea, consuming 75% of the world’s energy consumptionand generating 80% of total greenhouse gas emission [1–3]. China is undergoing a rapid urbanization. Chineseurban population has expanded to 690.79 million in theyear 2011, exceeding the population of 656.56 million inrural area for the first time [4]. Energy consumption incities accounts for 81% of China total energy consumptionin 2004, and increased to 84% in 2006 [5]. According tostatistics, the annual household Greenhouse Gases (GHG)emission takes about 30% of the whole country’s GHGemission. It is predicted that China’s urbanization rate willreach to 65%–75% and the total urban area will nearlydouble in 2030 [6]. Therefore, a low carbon pattern inurban development will bring great potential to reduceemissions for future, as well as to alleviate air pollution andto resolve energy shortage problems in China.Several studies on how urban form could impact energy

and pollution results have been carried out in developedcountries [7–10]. Although scholars have drawn anincreasing concern upon low-carbon cities, the presentwork is mainly about describing the concept and designingprinciples. There is a lack of empirical research takingneighborhood as basic dwelling unit to find out thecorresponding relationship among neighborhood design,residents’ behaviors and the CO2 emissions. Furthermore,the urban form and personal behavior in western countriesare quite different from China. It is an uneasy fact that thereis very little empirical research on the issues concerningChinese cities.Instead of study on low-carbon cities, this research

directly takes neighborhoods as study targets. In Jinan, theReceived September 17, 2012; accepted April 1, 2013

E-mail: liu_env@tsinghua.edu.cn, hekb@tsinghua.edu.cn

Front. Environ. Sci. Eng.DOI 10.1007/s11783-013-0516-1

capital city of Shandong province, 23 neighborhoods wereselected and 4169 samples of households were surveyed.This research collects data using both bottom-up and top-down methods. The household energy use and activity dataare mainly from household questionnaire survey. We alsocollected official statistics on residential water, electricityand nature gas usage. The household energy use and CO2

emission are calculated using both the survey data as wellas the official statistics. Combined with GeographicInformation System (GIS) information, the effect ofneighborhood planning parameters and socio-economiccharacteristics has been half-quantitatively evaluated uponhousehold CO2 emission. The index parameters for furtherquantitative study have been put forwarded to provideempirical evidence support for CO2 emission estimation inurban area.

2 Methods

2.1 Neighborhood selection

Jinan, a typical medium-sized city undergoing rapidurbanization and motorization, is also one of the mostfamous historical and cultural cities in China. It is expectedthat after long-term development, Jinan concentratesvarious Chinese typical neighborhood forms. In thisresearch, these neighborhood types can be classified intofour categories: traditional, grid, enclave and superblock[10].1) Traditional neighborhoodsTraditional neighborhoods in our research belong to

hutong style, are the earliest existing kind of neighbor-hood. This neighborhood typology could go back to Mingand Qing dynasty. These neighborhoods were mainlylocated in Jinan’s old town and village-in-city around theold town, characterized by the courtyard-style of one tothree floors and narrow alleys.2) Grid neighborhoodsGrid neighborhoods in Jinan can trace their history back

to the early 1920s, and were mainly constructed in thecolonial period as commercial district. This kind ofneighborhoods lied on the south of Jinan’s old railwaystation, where the district consisted of several typicalblocks that were mainly composed of traditional court-yards, but have gradually evolved into various buildingforms during the renewal.3) Enclave neighborhoodsEnclave neighborhoods were prevailed in the socialist

era of mid-1980s. This neighborhood type established atraditional relation between housing and jobs in a localwork unit system. Here the job-housing balance is realizedby integrating the urban productive activities and social lifein the same location.4) Superblock neighborhoods

Since the 1980s, the housing reforms have beenconducted in the new market process aimed to transformthe socialist welfare-oriented housing via the privatizationof housing ownership. The job-housing balance wasbroken, for residents and businesses have more freedomon the space site-selection with China’s increasingly activeland and real-estate markets. This kind of neighborhoods isthe single living space, featuring high-rise building andlandscaping.In summary, these four different categories of neighbor-

hoods have their own distinct design factors, and thelifestyles and traffic activity patterns of these four are alsodifferent from each other. In terms of traditional neighbor-hoods, because of their narrow internal street space and thecomplex layout structure, it is difficult for vehicles to enterthese neighborhoods or park inside there. To the grid, thepublic streets connecting between small blocks haveimproved the district’s openness and accessibility, provid-ing some parking lots on streets. Retails and trees along thestreets create a comfortable pedestrian environment. To theenclave, job-housing balance is its most outstandingfeature. Apart from this, “traffic calming” measures andthe pedestrian-and-vehicle dividing system are usuallyincluding in these neighborhoods. Finally, superblockneighborhoods are quite different from the other threebecause of their tower blocks and modern facilities. Theauto-oriented internal roads network and sufficient parkingspaces for private cars are two of the traits characterizingsuperblock’s internal traffic environment.Totally 23 neighborhoods in Jinan representing four

typologies mentioned above are selected in this study.Table 1 illustrates the names, types, locations andpopulation sizes of these 23 neighborhoods. A clusteranalysis, based on a series of disaggregated neighborhoodform indicators (i.e. building height, parking spaces, etc.),generally confirms our hypothesis that the 23 neighbor-hoods selected can be grouped into the four neighborhoodtypologies (Fig. 1). The analysis was run in the StatisticalProduct and Service Solutions (SPSS) software, whichcalculates “distances” between data points of our formindicators, and produces a hierarchical tree diagram tovisualize how the neighborhood cases are distinct to eachother at integrated level. Superblock and traditional typesof neighborhoods can be distinguished from the others fortheir own distinct characteristics. However, the other tenneighborhoods belonging to grid and enclave typologiesare failed to differentiate by this method. Actually, thereare many differences in the urban design between thesetwo kinds of neighborhoods. Internal roads in gridneighborhoods are connected with streets outside allowingbuses to pass through. Whereas enclave is a kind of close-end area, this type of neighborhoods tends to concentrateall transit stops around their entrances and no bus stop canbe located inside. If adding “transit accessibility” into thecluster analysis as another indicator, grid and enclave maybe identified as independent categories.

2 Front. Environ. Sci. Eng.

2.2 Data collection

Data were collected by 4169 households’ questionnairesurveys in 2009 and 2010 in Jinan. Eligible respondentswere adults, aged 20 to 65, who resided in theseneighborhoods, and were interviewed at home bysurveyors. The questions were organized to collect dataon household traffic behaviors, in-house energy uses andresidential characteristics. Table 1 presents the charactersof neighborhoods and samples.

2.3 Method and data

2.3.1 Household in-home energy consumption

The in-home energy consumption includes four parts:electricity, gas, coal and centralized heating (Eq. (1)) [11].The equations for each part are listed as follows.

HE ¼ Ee þ Eg þ Ec þ Eh, (1)

where:HE— energy use, household in-home, MJ

Ee — energy use, electricity, MJEg — energy use, gas, MJEc — energy use, coal, MJEh — energy use, centralized heating, MJEe, Eg, Ec, Eh were calculated by the Eqs. (2)–(5), as

follows:

Ee ¼efee

eunitprice� eheatvalue, (2)

efee — household electricity charge, CNYeunitprice — unit price of electricity, CNY$kW–1$h–1,

0.5469 CNY$kW–1$h–1 in Jinaneheatvalue — heat value of electricity, MJ$kW–1$h–1, 3.60

MJ$kW–1$h–1

Eg ¼ Vg � gheatvalue, (3)

Vg — gas use volume, m3

gheatvalue — heat value of gas, MJ$m–3, 34.91MJ$m–3

Ec ¼cfee

cunitprice� cheatvalue, (4)

cfee — coal cost, CNY

Table 1 Statistics of 23 neighborhoods investigated in Jinan

typology neighborhood namenear Bus Rapid Transit

(BRT) (yes or no)distance to city center/km neighborhood population/person sample number/household

traditional 1. Zhang Village yes 3.0 11190 309

2. Dikou no 5.0 7789 92

3. Furong no 0.7 7594 125

grid 4. Commercial District no 3.6 11720 296

5. Commercial South no 2.4 8439 117

6. Commercial North yes 2.7 5526 122

enclave 7. Wuyingtan yes 4.6 16130 305

8. Yanzishan no 3.5 21000 304

9. Dongcang yes 2.3 5600 296

10. Foshanyuan no 0.8 5300 281

11. Gannan yes 1.6 9205 123

12. Kuangshan yes 6.3 17977 172

13. Taoyuan no 8.7 6541 110

superblock 14. Shanghai Garden no 7.3 6400 305

15. Sunshine 100 no 4.8 19000 303

16. Lvjingjiayuan yes 2.8 2500 230

17. Mingshi yes 6.1 6049 88

18. Quancheng Garden yes 7.2 11147 188

19. Weidong yes 4.9 4759 108

20. Jixiangyuan no 4.3 4127 92

21. Digital Bay no 3.5 1546 49

22. Feicuijun yes 4.2 14400 81

23. New World no 4.3 3072 73

Source: derived and extended from Ref. [10]

Jiaxing GUO et al. Neighborhood form and CO2 emission: evidence from 23 neighborhoods in Jinan 3

cunitprice — unit price of coal, CNY$kg–1, 0.8 CNY$kg–1

in Jinancheatvalue — heat value of coal, MJ$kg–1, 25.12MJ$kg–1

Eh ¼ 86:4� N � A� qh �ti – tati – to,h

, (5)

N — heating days, 140 days in JinanA— house area, m2

qh — design space-heating load, 33.16 W$m–2 in Jinanti— indoor design temperature for heating, 18°C as

designedta —winter outdoor average temperature, –0.9°C in

Jinanto,h—winter heating outside design temperature, – 7°C

as designed

2.3.2 Household transportation energy consumption

We collected detailed traffic activities for one week of allfamily members by the questionnaire. The travel distancesare separately summarized by different traffic modes andcorrected with practical occupancy of vehicles (Eq. (6)).Considering the fuel efficiency, the trip distances can beconverted to energy consumption (Eq. (7)). The fuelefficiency is derived from vehicles fuel economy and fuelenergy content factor (Eq. (8)) [10].

ETi ¼

XmEmi , m 2 fcar,taxi,bus,motorcycle,ebikeg,

(6)

Emi ¼

Xj

Xk

FRmi,j,k �

TDmi,j,k

OCmi,j,k

!� EIm, (7)

EIm ¼ FUm � ECm, (8)

where,ETi — total household weekly traffic energy use, by

household i, MJ$household (HH)–1$week–1

Emi —weekly household traffic energy use, by house-

hold i using trip mode m, MJ$HH–1$week–1

FRmi,j,k — trip frequency, by mode m, for purpose k, made

by person j in household i, trips$week–1

TDmi,j,k — average trip distance, by mode m, for purpose

k, made by person j in household i, km$trip–1

OCmi,j,k — trip occupancy, by mode m, for purpose k,

made by person j in household i, occupancy$trip–1

EIm — energy intensity factor for mode m, MJ$km–1

FUm — fuel economy factor associated with mode m,L$km–1; kW$h$km–1

ECm — energy content factor of the fuel type consumedby mode m, MJ$L–1

In the calculation of household transportation CO2

emission, we convert unit of ETi from MJ$week–1 to

MJ$year–1. The parameters for calculating transportationenergy consumption are summarized in Table 2.

2.3.3 CO2 emission calculation

The recommend methodology developed by the Inter-

Fig. 1 Hierarchical cluster analysis result of 23 neighborhoods in Jinan (Horizontal coordinate indicates the difference amongneighborhoods, and the neighborhoods are arranged mainly by groups in vertical coordinate)

4 Front. Environ. Sci. Eng.

governmental Panel on Climate Change (IPCC) was usedto calculate CO2 emissions (Eq. (9)). A set of values forCO2 emission factors (EFfuel) is summarized in Table 3:

CO2fuel ¼ Efuel � EFfuel, (9)

where,CO2fuel —CO2 emission of a certain fuel, tEfuel — energy consumption amount of this fuel, TJEFfuel — default CO2 emission factor, t$TJ–1

2.3.4 Key indexes

Several indexes were generated to investigate theirrelationship with energy consumption. These indexesinclude neighborhood design parameters, residents’ activ-ity parameters and economy parameters. There are threekey indexes were evaluated in our research, including:household density, land use mix and accessibility to transit.Household density was defined by Eq. (10) as below:

HHdensity ¼ HHnumber

TotalArea, (10)

HHdensity— household density, HH$km–2

HHnumber— household number, HHTotalArea— neighborhood area, km2

MIX ¼ 1 –r

T–1

5

��������þ c

T–1

5

��������þ i

T–1

5

��������þ a

T–1

5

��������

�

þ o

T–1

5

�������� �� 5

8, (11)

MIX — index of land use mixr— residential area (or land area), m2

c— commercial/business/office floor space (or landarea), m2

i— industrial floor space (or land area), m2

a— administrative/institutional floor space (or landarea), m2

o— open-space/recreational floor space (or land area),m2

T — total area of residential, commercial, industrial,administrative and open-space, m2

Index of land use mix was calculated by differentfunctional areas showed in Eq. (11) [15].In this paper, whether the household is near to BRTwas

selected to be a proxy of the accessibility to transit.

2.3.5 Summary of activity and energy use data in Jinan

As a summary, the parameters mentioned in energyconsumption calculation in section 2.3 are presented inthis section. Table 4 gives a concise view of the energy uselevel in all neighborhoods.

3 Results and discussion

3.1 Neighborhoods energy consumption and CO2 emission

According to Eqs. (1)–(5), the in-home energy wascalculated by the sum of electricity use, coal use, gas useand heating energy, and the transportation energy wascalculated separately. The average value of each typeneighborhoods was further calculated based on theweighted average of neighborhood samples.As a result, Jinan residential annual energy consumption

is 60 GJ$household–1, CO2 emission is 7.66 t$house-hold–1$a–1. Among the total CO2 emission, the in-homeoperational emission is 6.87 t$household–1$a–1 and thetransportation emission is 792 kg$household–1$a–1. Differ-ent neighborhoods have a different composition of energyconsumption and CO2 emission (Fig. 2(a)). Superblock is

Table 2 Transportation mode, energy type, fuel economy and CO2 emission factors

item car bus taxi motorcycle e-bike

energy type gasoline diesel gasoline gasoline electricity

EI m /(MJ$km–1) 2.962 10.680 2.673 0.612 0.076

CO2 EFfuel /(t$TJ–1) 69.3 74.1 69.3 69.3 –

FUm /(L$km–1) a) 0.092 0.3b) 0.083 0.019 0.021c)

Notes: a) derived from [10], [12]; b) derived from [13]; c) derived from [14]; and its unit is kW$km–1

Table 3 Household energy type and CO2 emission factors

fuel bituminous coalanthracite

coalnatural gas

liquefied petroleumgas (LPG)

coal gas diesel gasoline

IPCC category energy industries residential residential residential Residential transportation transportation

associated HHenergy source

electricity centralizedheating

household coal gas gas Gas transit car/motor

CO2 EFfuel /(t$TJ–1) 94.6 98.3 56.1 63.1 44.4 74.1 69.3

Jiaxing GUO et al. Neighborhood form and CO2 emission: evidence from 23 neighborhoods in Jinan 5

the biggest consumer among four different types ofneighborhoods (Fig. 2(b)). Its annually energy consump-tion reaches 80 GJ$household–1. Traffic energy consump-tion accounts for more than 25%, which is also the highestratio among the four neighborhood categories. Traditionalneighborhoods have the lowest energy consumption rate,34.6 GJ$household–1. The energy consumption per house-hold for grid and enclave is 40.9 and 38.6 GJ$year–1.Figure 2(c) shows the CO2 emission of different types ofneighborhoods. The figure result is similar to the energyconsumption result illustrated in Fig. 2(b). Household CO2

emission by neighborhood categories is 10.97, 5.65, 6.49,5.40 t$a–1 for superblock, enclave, grid and traditionalrespectively.The household CO2 emission in Jinan was compared

with Beijing and other cities in developed countries [17–20] (Fig. 3). The residential CO2 emission of superblockneighborhoods in Jinan has already reached the level ofdeveloped countries nearly ten years ago. The averageemission in Jinan is comparable with that in Beijing, whichmeans the results in this research is typical for Chinesecities. With the fast urbanization, more superblockneighborhoods would be built in Jinan. Thus, it ispredictable that the household CO2 emission will growrapidly in the future.

3.2 Impact from socioeconomic and demographic ofneighborhoods

Sections 3.2 and 3.3 aim to investigate the relationshipbetween emissions and different parameters, such as theeconomic, design and traffic parameters. Family incomeand living area of each apartment are selected as two majordemographics indicators. With different levels of incomeand area in four types of neighborhoods, the householdCO2 emission is quite different (Fig. 4). The averagehousehold CO2 emission in every different level of incomeand house area is summarized in Table 5. Generally, theCO2 emission increases with the increasing of house areaand household income. However, the fitting curves of twofigures belong to different types. CO2 emission is linearcorrelated with house area. Index correlated with incomemeans when the income reaches a certain level, the CO2

emission would not be impacted by income any more.In particular, household central heating’s annual cost is

charged according to house area. Besides, the electricitydemand will be greater in larger apartments. Income isrelated to household consuming behaviors (i.e., electricity,gas and coal use) and transportation behaviors. Householdsocioeconomic and demographics characteristics are twoof the important influencing factors of household trans-

Table 4 Household operational and transportation survey data in Jinan

item of survey sample number mean standard deviation max min

operational /(CNY$month–1) electricity 2400 122 92 900 3

gas 1000 45 35 500 10

coal 445 41 31 400 10

heating 1633 549 230 2150 50

transportation frequency /(trips$week–1)

car 2627 8.34 5.17 40 0.125

taxi 247 5.61 4.54 28 1.000

motor 241 8.27 5.35 30 1.000

e-bike 1252 9.73 5.89 60 1.000

bus 3618 7.84 5.21 40 0.125

length /(km$trip–1) car 2566 12.38 11.14 90 0.250

taxi 223 9.01 7.54 40 1.000

motor 241 7.00 6.17 40 0.425

e-bike 1208 7.18 6.89 60 0.200

bus 3484 10.22 8.68 300 0.100

occupancy /(occu-pancy$trip–1)

car 2713 1.53 0.75 7 1.000

taxi 261 1.52 0.77 4 1.000

motor 241 1.15 0.35 2 1.000

e-bike 1275 1.19 0.43 4 1.000

bus – 30a) – – –

Notes: a) derived from [16], the bus occupancy was not included in the questionnaire survey data

6 Front. Environ. Sci. Eng.

portation CO2 emission [10,21,22]. For instance, house-hold income has a conclusive influence on private carownership. In this research, superblock neighborhoods’transportation CO2 emission is the highest, correspondingto its highest household annual income.

Comparing the family-based distribution, most offamilies in superblock have much higher emission ratesthan traditional neighborhoods. There’s no significantdifferent distribution between enclave and grid neighbor-hoods of CO2 emissions.

3.3 Impact from neighborhood form and design

3.3.1 Household density

Newman and Kenworthy concluded that increasing theurban population density can save a significant amount ofenergy [23], which was proved in this study (Fig. 5). Inthese four different categories of neighborhoods, enclavehas the highest household density, while its CO2 emissionis the lowest. The household density has an oppositetendency to the residential CO2 emission. The householddensity has an effect both on in-home and transportationCO2 emission. On one hand, higher household density isassociated with smaller spaces per unit and less area ofexternal wall, with which it is more efficient in storingcool/heat and thus reducing CO2 emission [24]. On theother hand, density has a great influence on householdtransportation behaviors. Residents living in higher density

Fig. 2 Household operational and transportation energy consumption and CO2 emission in Jinan: (a) by each neighborhood; (b)comparison of household energy consumption by neighborhood categories; (c) comparison of household CO2 emission by neighborhoodcategories

Fig. 3 Comparisons on household annual CO2 emission amongdifferent cities

Jiaxing GUO et al. Neighborhood form and CO2 emission: evidence from 23 neighborhoods in Jinan 7

neighborhoods might be more likely to take public transit,walk or ride to destinations. The percentage of privatetransportation is higher in lower density neighborhoods[25].

3.3.2 Land use mix

In general, the mixed-use neighborhoods would providenearby jobs and services, and lead to lower household CO2

emission [26,27]. In this study, residential, commercial,industrial, administrative and recreational functional areasare selected to define the level of land use mix of 23neighborhoods respectively as shown in Eq. (11).Land use mix and household CO2 emission by four

neighborhood categories were compared (Fig. 6). The mix-use level of superblock is the lowest, while the householdCO2 emission is the highest. Land uses of the other three

types of neighborhoods are more mixed, and their emissionis lower. For example, in traditional and grid neighbor-hoods, there are a lot of shops along the streets. Thecommercial and residential functions are well mixed inthese kinds of neighborhoods. However, individual coalstoves are still used commonly for cooking and heating,which leads to higher CO2 emission compared with naturalgas and so on. In summary, neighborhood land use mixindex seems to have a negative effect on household CO2

emission, but more rigorous analysis to exclude confound-ing factors is warranted.

3.3.3 Distance to public transit

In this section, household transportation CO2 emissions

Table 5 Distribution of household income and CO2 emission

income /(10000 CNY$a–1)

(0, 1] (1, 3] (3, 6] (6, 9] (9, 12] (12, 20] (20, 30] (30, 1)

area/(m2) CO2 emission /(kg$HH–1$a–1)

(0, 20] 5229 2126 2369 1821 3537 3548

(20, 50] 3509 3952 4373 5152 4262 4739 7608 4554

(50, 80] 4834 4280 5082 5728 5982 6046 9825 9197

(80, 100] 953 5573 6763 7077 6763 12307 12307 8423

(100, 150] 5515 8643 7894 8270 9410 10026 11386 10005

(150, 1) 3685 12632 11333 11439 12328 13807 16634 11455

Fig. 4 Household income/area and CO2 emission in four types ofneighborhoods: (a) household income; (b) household area

Fig. 5 Relationship between density and household CO2 emis-sion

8 Front. Environ. Sci. Eng.

with and without BRT nearby are calculated respectively.Generally residents living near BRT will prefer publictransit because of its comfortable and convenient transitcondition. As BRT is a transport mode characterized bylow-energy and low-pollution, the household transporta-tion CO2 emission in BRT-oriented neighborhoods willdecrease [28]. In our research, there are 2022 householdsclose to BRT. Compared with the 2147 households withoutBRT, the transportation CO2 emission is 22 t$a–1 lower. Forthe neighborhoods with BRT services around, the averagehousehold transportation CO2 emission is 715 kg$a–1,while the annual average is 825 kg$HH–1$a–1 (Fig. 7) forneighborhoods without BRT. The emission gap is about13%.

4 Conclusions

This empirical study mainly discusses the comparativeanalysis results of household in-home and transportationCO2 emission in four types of neighborhoods in Jinan,China, including traditional, grid, enclave and superblock.Not only the emission rates, but also the neighborhoods’socioeconomic and form characteristics (i.e. householddensity, land use mix and distance to public transit) werecompared. There are several key findings: 1) neighborhooddesign and typology do impact the CO2 emission throughmany aspects; 2) with the higher income and larger housearea, the household CO2 emission increases; 3) neighbor-hood’s household density has a negative effect on

household energy consumption; 4) land use mix is relatedwith household transportation behaviors, and the house-holds in mixed-use neighborhoods have a lower transpor-tation CO2 emission; 5) BRT, a convenient andcomfortable mode of transport, tends to cut the transporta-tion CO2 emission by 13%. It is worth noting that mostconclusions above are drawn from descriptive analysis andimpacts of many factors on emissions could not beseparated at the moment. In the next step, rigorousquantitative modeling (e.g., regression analysis, etc) willbe carried out in order to derive more conclusivearguments.

Acknowledgements This work is supported by the Energy Foundation andNational Natural Science Foundation of China (Grant No. 71101078), thespecial fund of State Key Joint Laboratory of Environment Simulation andPollution Control (No. 11K05ESPCT). We also thank Shandong University,Beijing Normal University and Massachusetts Institute of Technology fortheir great support.

References

1. UNDESA. World Population Prospects: the 2008 Revision. United

Nations: United Nations Publication, 2008

2. Wheeler S. Urban planning and global climate change. In: LeGates

R T, Stout F, eds. City Reader, 5th ed. New York: Taylor & Francis,

2011, 458–467

3. Huo Y, Zheng S Q, Yang Z. Low-carbon lifestyle and its

determinants: an empirical analysis based on survey of “household

energy consumption and community environment in Beijing”.

Journal of Urban and Regional Planning, 2010, (2): 55–72 (in

Chinese)

4. National Bureau of Statistics. China Statistical Yearbook 2011.

Beijing: China Statistics Press, 2012 (in Chinese)

5. Dhakal S. Urban energy use and carbon emissions from cities in

Fig. 6 Relationship between land use mix and household CO2

emission

Fig. 7 Transportation CO2 emission of family with and withoutBRT nearby

Jiaxing GUO et al. Neighborhood form and CO2 emission: evidence from 23 neighborhoods in Jinan 9

China and policy implications. Energy Policy, 2009, 37(11): 4208–

4219

6. He D Q, Jiang Y, Wang Y, Calthorpe P. People-oriented urban

planning and construction is the core of low-carbon development.

In: Chinese Society for Urban Studies, ed. China Low-Carbon Eco-

City Development Report, 2nd ed. Beijing: China Building Industry

Press, 2011, 286–299 (in Chinese)

7. Ewing R, Meakins G, Bjarnson G, Hilton H. Transportation and

land use. In: Dannenberg A L, Frumkin H, Jackson R J, editors.

Making Healthy Places. Washington, D C: Island Press/Center for

Resource Economics, 2011, 149–169

8. Kahn M E. The environmental impact of suburbanization. Journal of

Policy Analysis and Management, 2000, 19(4): 569–586

9. Kitamura R, Mokhtarian P L, Daidet L. A micro-analysis of land use

and travel in five neighborhoods in the San Francisco Bay Area.

Transportation, 1997, 24(2): 125–158

10. Jiang Y, He D Q, Zegras C. Impact of neighborhood land use on

residents travel energy consumption. Urban Transport of China,

2011, (4): 21–29 (in Chinese)

11. Li S H, Kang H. Practical Central Heating Handbook. Beijing:

China Electric Power Press, 2005 (in Chinese)

12. National Bureau of Statistics. China Statistical Yearbook 2008.

Beijing: China Statistics Press, 2009 (in Chinese)

13. Zheng Y P, Chen Q S. Simulation calculation of bus fuel economy

under city traffic environment. Tractor & Farm Transporter, 2008,

(04): 44–45

14. Cherry C R, Weinert J X, Yang X M. Comparative environmental

impacts of electric bikes in China. Transportation Research Part D,

Transport and Environment, 2009, 14(5): 281–290

15. Rajamani J, Bhat C, Handy S, Knaap G, Song Y. Assessing impact

of urban form measures on nonwork trip mode choice after

controlling for demographic and level-of-service effects. Transpor-

tation Research Record, 2003, 1831(1): 158–165

16. Mu R, Jong M. Establishing the conditions for effective transit-

oriented development in China: the case of Dalian. Journal of

Transport Geography, 2012, 24(0): 234–249

17. Qin B, Han S S. Planning parameters and household CO2 emission:

evidence from high- and low-carbon neighborhoods in Beijing.

Habitat International, 2012, 37(0): 52–60

18. Kerkhof A, Benders R M, Moll H. Determinants of variation in

household CO2 emissions between and within countries. Energy

Policy, 2009, 37(4): 1509–1517

19. Lenzen M,Wier M, Cohen C, Hayami H, Pachauri S, Schaeffer R. A

comparative multivariate analysis of household energy requirements

in Australia, Brazil, Denmark, India and Japan. Energy, 2006, 31(2–

3): 181–207

20. Fahmy D E, Thumim J, White V. The Distribution of UK

Household CO2 Emissions: Interim Report. York, UK: Joseph

Rowntree Foundation, 2011

21. Ewing R, Cervero R. Travel and the built environment. Journal of

the American Planning Association, 2010, 76(3): 265–294

22. Liu C, Shen Q. An empirical analysis of the influence of urban form

on household travel and energy consumption. Computers, Environ-

ment and Urban Systems, 2011, 35(5): 347–357

23. Newman P, Kenworthy J R. Sustainability and Cities: Overcoming

Automobile Dependence. Washington: Island Press, 1999

24. Ewing R, Rong F. The impact of urban form on U.S. residential

energy use. Housing Policy Debate, 2008, 19(1): 1–30

25. Walters J, Ewing R. Measuring the benefits of compact development

and vehicle miles and climate change. Environmental Practice,

2009, 11(03): 196–208

26. Holden E, Norland I T. Three challenges for the compact city as a

sustainable urban form: Household consumption of energy and

transport in eight residential areas in the greater Oslo region. Urban

Studies (Edinburgh, Scotland), 2005, 42(12): 2145–2166

27. Mashayekh Y, Jaramillo P, Samaras C, Hendrickson C T, Black-

hurst M, MacLean H L, Matthews H S. Potentials for sustainable

transportation in cities to alleviate climate change impacts.

Environmental Science & Technology, 2012, 46(5): 2529–2537

28. Xu K M, Xie J H, Feng J. The advantages and benefits analysis for

bus rapid transit stations. Urban Transport of China, 2006(6): 29–33

(in Chinese)

10 Front. Environ. Sci. Eng.