Urban macro-level impact factors on Direct CO2 Emissions of urban residents in China

Energy and Buildings 107 (2015) 131–143

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Urban macro-level impact factors on Direct CO2 Emissions of urban residents in China Zhang Jie a,∗ , Xie Yang a , Luan Bo b , Chen Xiao a a b

Department of Urban Planning, Tsinghua University, Beijing, China Department of Land Economy, 19 Silver Street, Cambridge CB3 9EP, UK

a r t i c l e

i n f o

Article history: Received 10 November 2014 Received in revised form 14 July 2015 Accepted 5 August 2015 Available online 7 August 2015 Keywords: Direct CO2 Emissions of urban residents Urban macro-level impact factor Partial correlation analysis Population density Urban scale

a b s t r a c t Global warming conditions and the impending energy crisis have gained attention of the research community worldwide. Urban researchers have been increasingly focused on energy-efficient cities from varying perspectives to gain insight and improve efficiency contributions in overall design components. Micro-level studies with families or individuals as research units have yielded abundant accomplishments, yet top urban macro-level factors with the highest energy consumption impact have not been identified. Direct CO2 Emissions (DCE) of urban residents in 286 cities at the prefectural level and above in China are first estimated in this study. The relationship between DCE and urban macro-level factors including climatic, socio-economic and urban form with partial correlation analyses is then investigated. Findings and suggestions include: (1) Climatic metrics exert significant impact on residential Central Heating DCE and Electricity DCE with cities in different Building Climate Divisions exhibiting DCE variations. (2) Metrics measuring economic development levels exhibit the most significant positive impact on Electricity DCE, Gas DCE, Transportation DCE and total DCE per capita; however, total DCE per GDP decreases as economic development level rises. (3) Urban form metrics significantly impact Electricity DCE with greater effects on Transportation DCE, thus indicating the need for further research on urban form metrics. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Modern scientific consensus concludes that earth climatic change is occurring as a result of fossil fuel consumption [1–3]. Urban areas consume 60–80% of final energy [4] and a growing need has been developed to gather city-level energy consumption data and analyze the related impact factors. Energy consumption data record for China is currently limited to the provincial level. Researchers have been trying to estimate city-level CO2 Emissions [5,6]; however, previous estimating methods seem inaccurate due to lack of comprehensive data. Required data for comprehensive estimation methods includes: varying patterns of vehicle use among different cities, sub-sector data (absent from the Odiac dataset1 [7]), and electricity consuming data rather than producing

∗ Corresponding author at: Department of Urban Planning, Tsinghua University, Beijing 100084, China. E-mail address: [email protected] (J. Zhang). 1 Odiac (Open-source Data Inventory for Anthropogenic CO2 ) dataset is a global high-resolution emission dataset for fossil fuel CO2 emissions, initially developed at Greenhouse Gas Observing Satellite project, National Institute for Environmental Studies, Japan. http://dx.doi.org/10.1016/j.enbuild.2015.08.011 0378-7788/© 2015 Elsevier B.V. All rights reserved.

data (as in the Edgar dataset2 ). Existing statistical data will be utilized in this study to accurately estimate city-level CO2 Emissions of residents in China. Urban macro-level impact factors on urban energy consumption can then be determined. 1.1. Direct CO2 Emissions of urban residents (DCE) CO2 Emissions in residential sectors is the main focus of this article. As stated in the work of Munksgaard et al. [8], Direct CO2 Emissions (DCE) of urban residents are generated by direct household energy use, including electricity use via household appliance, coal and gas use for space heating and cooking, and gasoline/diesel oil use by vehicle. In China, most urban space heating energy in the north are consumed through central heating systems, and it is counted separately in statistical yearbook. For the convenience of estimating, DCE is divided into four parts: residential electricity

2 Edgar (Emissions Database for Global Atmospheric Research project) dataset is a joint project of the European Commission Joint Research Centre and the Netherlands Environmental Assessment Agency, available at: http://edgar.jrc.ec.europa.eu (last access: 2014).

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energy consumption (EDCE), residential gas energy consumption (GDCE), residential transportation energy consumption (TDCE) and residential central heating energy consumption (CDCE). Considering that statistics data at prefecture-level is only available for prefecture-level cities,3 the 286 cities including 282 prefecturelevel cities (the number is counted by the end of 2010, and Lasa is not included for lack of data) and 4 municipalities (Beijing, Shanghai, Tianjin and Chongqing) above the prefectural level are selected as our research samples.

1.2. Urban macro-level impact factor Industrial CO2 Emissions at city level is mainly determined by urban industrial structure (the ratio of energy-intensive industries) [10], while impact Factors from urban macro-level on Direct CO2 Emissions of urban residents are closely related to climatic factors, socio-economic factors and urban form factors. Climatic factors such as temperature and humidity may affect usage of household appliances such as air conditioning and refrigeration. Socio-economic factors such as household income may impact transportation frequency, distance, means, and choices for electronic appliance purchases. Affluence also tends to impact heating energy consumption through its influence on housing size choices. Urban form factors such as urban scale, population density and land use mixture have been correlated with transportation energy consumption. Dense urban form may also affect residential energy consumption by creating an urban heat island effect, the choice of housing type and electric transmission and distribution losses [11].

2. Literature review and conceptual model This chapter will be devoted to interpreting the mainstream views and conceptual models regarding the crucial issues discussed in this article distilled from the extant literature, as well as the original theoretical model developed by the authors.

2.1. Climatic impact factor on DCE We can find in the extant literature many extensive studies on the effect of climatic factors on DCE, especially that related to electricity consumption. Lariviere and Lafrance [12] presented a statistical model establishing a relationship between annual electricity consumption per capita of 45 most populous cities of Quebec and a series of urban variables at city level. Among the variables, six exhibited significant correlation with electricity consumption, including the number of days in a year that are below 18 ◦ C. Wangpattarapong et al. [13] discovered that cooling degree-days and average amount of rainfall were positively correlated with the residential electricity consumption of Bangkok Metropolis in an empirical research. Yan [14] studied the influences of climatic variables on variation in residential electricity consumption (REC) in Hong Kong, implicating that mean temperature was strongly correlated with REC, and that cloud cover was associated with REC in summer only.

3 In China, the administrative divisions of China are classified by five levels in reality including [9]: province (province, autonomous region, municipality and administrative region), prefecture (prefecture-level city, prefecture, autonomous prefecture and league), county (county, district, county-level city, special district, forestry district, autonomous county, banner and autonomous banner), township (town, township, subdistrict, district public office, ethnic township, Sumu, ethnic Sumu) and village (village and community).

2.2. Socio-economic impact factor on DCE The majority of socio-economic factors impact studies on DCE seemed to have been carried out at country scale. Lee and Chang [15] found strong positive correlation between energy consumption and real GDP based on their observation of capital stock and labor input of 16 Asian countries in the time period of 1971–2002. Bi-directional causal relationship between energy consumption and economic growth was revealed by Belke et al. [16] after they have examined the long-term relationship between energy consumption and real GDP for in OECD (Organization for Economic Co-operation and Development) countries from 1981 to 2007. Socio-economic studies for energy-efficient cities at the micro level usually focus on residential buildings. Yun and Steemers [17] investigated the significant influence of behavioral, physical and socio-economic factors on cooling energy in the hope of improving energy efficiency in residential buildings. Socio-economic factors, especially household income and householder age, were found to be capable of exerting strong impact on cooling energy consumption through mediator variables such as type and age of house and type of equipment. 2.3. Urban form impact factor on DCE The impact of urban form on DCE can be seen mainly from two aspects: the influence of urban form on residential energy consumption and that on transportation energy consumption of residents. Researchers typically focused on the influence of urban form on travel demand (measured in vehicle miles traveled) and congestion prior to transport emission data accessibility in recent years. Several early studies [18–22] sought optimal city size to minimize road congestion based on abstract theoretical models and the assumption of a mainly mono-centric city with all employment located in the Central Business District (CBD) and all travel work-related destined for the CBD. Izraeli and McCarthy [23] and Gordon et al. [24] utilized an empirical approach to measure and analyze average commuting distance and commuting time of different city sizes. After introducing the groundbreaking concept of urban gasoline use as the dependent variable, Newman and Kenworthy [25] analyzed 32 major cities in four continents and found a negative correlation between urban density and annual gasoline use per capita. Strong policies promoting the planning and development of more compact cities were then suggested. Following the findings of Newman and Kenworthy, researches have been conducted to investigate the relationship between transportation energy consumption and urban form with a specific focus on urban scale and population density. Larger cities have been proved to produce higher transportation energy consumption per capita in the U.S. [26–28], Europe [27], and Japan [29]. Empirical studies have confirmed the inverse relationship between population density and transportation energy consumption per capita [29–31], yet a more comprehensive understanding of urban density effects still awaits based on some studies [32–34] suggesting that there might be a U curve between density and TDCE. High density beyond a designated level is thusly a theoretical cause of severe congestion increasing the transportation energy consumption per capita. Ewing and Rong [11] presented a conceptual framework breaking down the influence of urban form on residential energy use into three aspects: electric transmission and distribution (T&D) losses, different energy consumption of different types of housing, and the difference between requirements for space heating and cooling caused by the Urban Heat Island (UHI) effect. Electric T&D losses are incurred as cities with sprawling land-use patterns produce greater energy penalty. As for the differentiated housing stocks, singlefamily detached houses are more common in sprawling counties,

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Fig. 1. Conceptual model (solid lines represent direct impact; dashed lines represent indirect impact).

and are also more likely to be larger and hence cause higher residential energy use. UHI effect affecting energy use for space heating and cooling is stronger in compact areas where an increase in annual cooling degree-days as well as a reduction in annual heating degree-days have been observed. Lee and Lee’s empirical study [31] provided more details for how population density affects household residential energy consumption through multilevel Structural Equation Modeling (SEM) analyses. The research revealed that a 10% increase in population-weighted density was associated with a 3.5% reduction in residential CO2 Emissions in the 125 largest urbanized areas of the U.S. On the other hand, Makido et al. [29] conducted an empirical study of 50 Japanese cities and discovered that settlements beyond a density level in mono-centric form were correlated to greater residential energy consumption per capita. Greater densities may compromise flexibility of building design for features such as sky lighting and natural ventilation.

Socio-economic factors including income, family type, energy price and occupational characteristics may impact household usage of electricity & gas energy and the means, distance and frequency of travels. CDCE is indirectly affected by socio-economic factors because housing size choice often correlates with affordability. Urban form factors including population density, urban scale and the mixture level of land use may affect distance and frequency of household travel [35]; to be more specific, density and scale may influence air condition and central heating use as a result of the UHI effect. Infrastructure conditions are also likely to exert impact on means and frequency of household travel. An overview of each macro-level impact factor and their relationships with energy consumption types is presented in Fig. 1. Socio-economic factors exert the most extensive impacts on different DCE types, while climatic factor effects mainly occur in EDCE and CDCE, urban form factors mainly affect EDCE and, more intensely, TDCE.

2.4. Conceptual model

3. Methodology and data

Through reviewing climatic, socio-economic and urban form impact factors above, we can see a clear need for further study determining the dominant DCE influence. A conceptual model is proposed to explain how urban macro-level factors can exert impacts on energy consumption of urban residents (Fig. 1). Climatic factors such as temperature, humidity, wind speed, sunshine and rainfall may influence usage of household appliances and choice of travel means by altering the city climate at micro level. Air pressure determined by city altitude may impact usage of gas and electricity by determining the boiling point of liquid. Temperature conditions affect the duration and set temperature of central heating. City location may determine DCE indirectly through closer proximity to gas or coal production and result in lower energy supply prices.

Conceptual model testing is conducted with the following three steps. 3.1. Estimating the DCE The 2006 IPCC guidelines for national greenhouse gas inventories [36] provide two calculating methods for evaluating energy consumption per capita: the “Bottom-up Method”, also referred to as the Reference Method; and the “Top-down Method” also known as the Sector Analysis Method. CO2 Emissions is calculated in the “Bottom-up Method” based on macro-level energy consumption data from the energy balance table published in urban statistical annuals. CO2 Emissions is computed in the “Top-down Method” with the activity level and relevant Emissions factors by sector, fuel

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Table 1 Estimating formula of DCE, methods from 2006 IPCC guidelines for national greenhouse gas inventories. C1 EDCE

C2 GDCE

C3 TDCE

C4 CDCE

C1 = E1 × EF1i /P

C2 = F2 × NVI1 × EF2 /P + F3 × NVI2 × EF3 × M1 /P + F4 × NVI3 × EF4 × M2 /P F2 /F3 /F4 is the household dosage of liquefied gas/coal gas/natural gas. NVI1 /NVI2 /NVI3 is the calorific value of liquefied gas/coal gas/natural gas. EF2 /EF3 /EF4 is the carbon emission factor of liquefied gas/coal gas/natural gas. M1 /M2 is the density of coal gas/natural gas.

C3 = Q1 × L1 × 1 × EF5 /P + Q2 × L2 × 2 × EF6 /P + E2 × EF6 × Q3 /Q3 × P Q1 /Q2 /Q3 is the amount of urban buses/taxis/private vehicles. Q3 is the amount of private vehicles in the province where the city lies in. L1 /L2 is the annual mileage of buses/taxis. 1 /2 is the 100 km fuel factor of buses/taxis. EF5 /EF6 is the carbon emission factor of diesel oil/gasoline. E2 is the household gasoline consumption of the province where the city lies in.

C4 = S × N × EF7 /P

E1 is the electricity consumption of urban residents. EF1i is the carbon emission factor of the grid where the city is. P is urban population.

and device. The energy balance table data in China is accessible only at the provincial level, thus the “Top-down Method” is adopted. Industrial and commercial information at city level is also inaccessible in China. As a result, Direct CO2 Emissions of urban residents are applied as the estimating object (Table 1). Formula parameters information sources, including electricity consumption of urban residents, household dosage of liquefied gas/coal gas/natural gas, urban buses/taxis/private vehicles counts, and urban population are extracted from the China City Statistical Yearbook (2010). Parameters can also be obtained from a variety of other sources include: (1) heating area data from the China City Construction Statistical Yearbook (2010), (2) calorific value of fuel and the household gasoline consumption at the provincial level from the China Energy Statistical Yearbook (2010), (3) carbon Emissions factor of liquefied gas/coal gas/natural gas/diesel oil/gasoline/coal from the IPCC National Greenhouse Gas Emissions Inventory Guidebook 2006, (4) carbon Emissions factor of the grid from China’s Regional Grid Baseline Emissions Factor Announcement published by the Climate Change Department of National Development and Reform Commission of the People’s Republic of China, (5) coal consumption for heating unit area from the Energy Conservation Design Standard of the Industry Standard of the People’s Republic of China, and (6) annual bus mileage is obtained from the Beijing Public Transport Group website while bus speed is set at 16 km/h and fuel factor is 32 L/100 km according to Zhang’s research [5]. Taxis are assumed to utilize gasoline because there seems to be no records in city statistical yearbooks about taxis that run on natural gas. Annual taxi mileage is set at 12,000 km, and fuel factor at 10 L/100 km according to Zhao’s research [37]. Previous studies on these matters either ignore [6] or roughly estimate TDCE [5]. Due to difficulty in obtaining accurate annual mileage from surveys conducted in each city, these studies have no option but to assume that the annual mileage of vehicles in all cities is the same. Estimations for buses and taxis are considered approximately accurate as regular driving schedules are adhered to throughout the year. Annual mileage of private vehicles, however, varies widely in different cities as researchers have found out that GDP per capita, urbanized area and other factors can create significant impacts on private vehicle annual mileage [38]. The energy balance tables at the provincial level provide a column for “Gasoline Consumption for Urban Residents”, allowing exact gasoline consumption of urban private vehicles for the province to be extracted based on the consideration of the fact that only taxis and private vehicles use gasoline in China, and that taxi gasoline consumption is calculated and singled out in the Transportation Industry section. Every vehicle in a certain province is then assumed as having the same annual mileage, and the total gasoline consumption of urban private vehicles for that province is disaggregated to every city at the prefectural level and above based on the number of private vehicles in each city. Different usage of private vehicles between different provinces is incorporated into this method; but the annual

S is the heating area. N is the coal consumption for heating unit area. EF7 is the carbon emission factor of standard coal

mileage differences of private vehicles among individual cities at different levels in the same province are still disregarded. The previous method is even less accurate (Fig. 2). First, estimation of TDCE levels for most cities is larger with the previous method than the improved method because the value of Beijing boasted the highest annual mileages of private vehicles in China was applied to all cities. Second, the conceptual model infers that affluence affects private vehicle purchase choices which can lead to elevating TDCE levels in richer cities; yet cities in less developed provinces, such as Guizhou, Yunnan and Fujian, have exhibited high TDCE levels with the previous method which conflicts the conceptual assumption. Both defects have been eliminated with the improved method results. Results of DCE estimation in 286 cities at the prefectural level and above in China are presented in Table 2. EDCE production comparatively assumes a larger DCEP portion than GDCE and TDCE, while CDCE only exists in 133 northern cities with central heating system service. Statistical reliability data for estimated energy consumption outcome often contains errors resulting from, for example, leaking, double counting and immixing. First, EDCE and GDCE data should be approximately accurate as it is estimated based on actual electricity and gas consumption of every family with usage data gathered by the electricity supplier and gas supplier. Second, TDCE estimation for private cars’ energy consumption is based on actual gasoline consumption of residents provided by gasoline stations throughout the city, combined with insignificant gasoline consumption from non-local cars. Energy consumption of buses and taxis is calculated by the number of vehicles based on annual mileage of Beijing which has been applied to different cities due to lack of data on accurate annual mileage in each city. Bias is recognized in the estimation methodology. Yet the fact that both buses and taxis adhere to regular annual driving schedules ensures that the distinction of the annual mileage for buses or taxis between different cities can be contained to a minimum level. Third, For CDCE, energy consumption of non-centralized heating is excluded, as it is difficult to gather coal consumption information for individual boilers, stoves and heated brick beds. Additionally, the majority of Chinese cities retain a low percentage of non-centralized heating systems. The distribution of DCE types per year is presented in Fig. 3 including six types, i.e. EDCE, GDCE, TDCE, CDCE, total Direct CO2 Emissions of urban residents per capita (DCEP), and total Direct CO2 Emissions of urban residents per GDP (DCEG). Higher values of EDCE are distributed in cities of southeast coastal regions, especially the Yangtze River Delta4 and the Pearl

4 The Yangtze River Delta, generally comprises the triangle-shaped territory covering Shanghai, southern Jiangsu province and northern Zhejiang province of China.

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Fig. 2. Spatial distribution of TDCE in China with the previous method (left) and the improved method (right), unit: kg/person.

Table 2 Statistical description of different kinds of DCE in China. N EDCE (kg CO2 /person) GDCE (kg CO2 /person) TDCE (kg CO2 /person) CDCE (kg CO2 /person) DCEP (kg CO2 /person)

Min

Max

Average

SD

286

30.237

3213.097

535.309

383.233

286

18.482

3272.762

245.672

323.315

286

9.625

1071.632

139.058

104.605

133

1.072

2132.444

450.840

334.459

286

154.636

4752.942

1129.697

693.186

River Delta5 boasted of top economic developmental levels in the country and relatively higher average temperature throughout the year, creating increased demand for air conditioning. The central heating systems is not very common in the Yangtze River Delta region, thus citizens tend to use more electricity demanding air conditioning units for heating, and electricity consumption has been increased as a result. Higher values for GDCE are distributed in the western part of the country, such as Sichuan Province and Inner Mongolia Autonomous Region, where gas resources are abundant and relatively inexpensive. Higher TDCE values are observed in cities of the northern part of North China Plain6 and southeast coastal regions. Cities in the North China Plain such as Beijing and Shijiazhuang feature larger urban blocks and more widespread urbanized areas, necessitating longer travel distance especially for private vehicles. Citizens in the southeast coastal regions are relatively more affluent and tend to purchase more private vehicles. Higher values for CDCE can be found in cities of Liaoning and Shanxi Provinces, and Inner Mongolia Autonomous Region, where winter is long with lower temperatures, and fossil fuel resources are rich and relatively cheaper.

5 The Pearl River Delta in Guangdong province is the low-lying area surrounding the Pearl River estuary where the Pearl River flows into the South China Sea. 6 The North China Plain is based on the deposits of the Yellow River and is the largest alluvial plain of eastern Asia. The plain is bordered on the north by the Yanshan Mountains and on the west by the Taihang Mountains. To the south, it merges into the Yangtze Plain. From northeast to southeast, it fronts the Bohai Sea and the Yellow Sea.

The overall DCEP values of cities in North China Plain, northeast China and the southeast coastal regions are higher; while cities in Yangtze River Basin, with the exception of the Yangtze River Delta, exhibit much lower DCEP. DCEP is largely affected by great climate differences in China, and the government has made many efforts to address such issues. A Code for Design of Civil Buildings has been published by the Ministry of Construction of China. According to climatic characteristics such as temperature, rainfall and humidity, the Code divides the country into seven major Building Climate Divisions (BCD) to guide specific energy efficiency measures in different regions, in the respects of shading and ventilation in hot places and heat insulation in severe cold northern regions (Fig. 4). Severe Cold Regions are designated as high priorities for maintaining warmth in buildings. Such regions include: BCD I, covering the majority of Northeast China; BCD VII, located in the north portion of Xinjiang Autonomous Region; and BCD VI, spanning over Tibet Autonomous Region and Qinghai Province. BCD II spans over the North China Plain and is defined as the Cold Regions. Although not as cold as the Severe Cold Regions, municipal central heating systems are still built to resist winter cold for cities in this region. BCD III refers to the Hot Summer and Cold Winter Areas and covers the middle and lower reaches of the Yangtze River where most cities do not have municipal central heating systems. BCD IV mainly includes the inshore regions of South China where cooling is a top priority. BCD V is the smallest division and is located in the Yunnan and Guizhou Provinces where the weather remains mild throughout the year. DCE is relatively low for cities in the Yangtze River Basin that mostly belong to BCD III, where summer temperatures are not as hot as in Southern China and winter temperatures are not cold enough to be incorporated into the central heating system. Weather variation and the presence of energy efficiency measures in accordance with the BCDs impact DCE. According to the average value calculation results of DCEP for the seven BCDs (Fig. 4), BCD I and VII exhibit the highest DCEP as winter conditions are more extreme in these two regions, rendering CDCE as a higher percentage of the total. At the same time, BCD III and BCD V show relatively low DCEP due to moderate climate conditions. Energy consumption can also be measured by Direct CO2 Emissions per GDP (DCEG). Cities with high DCEG are located in Northeast China, North China Plain and West China. Economic development level, however, appears to have an opposite influence on DCEG, as affluent cities tend to produce low DCEG and high DCEP.

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Fig. 3. Spatial distribution of different DCE types in China in 2009. Unit: kg/person (kg/10,000 Yuan for DCEG). (1) EDCE, (2) GDCE, (3) TDCE, (4) CDCE, (5) DCEP, (6) DCEG.

3.2. Defining urban macro-level metrics Thirty-eight metrics are compiled to describe the three types of macro-level characteristics of cities: climatic, socio-economic and urban form. The names and sources of the metrics are listed in Table 3 (spatial distribution of some crucial metrics are presented in Fig. 5). To begin with, climatic metrics are relatively stable over long periods of time. Considering that statistical yearbooks in China do not provide reliable climatic metrics, the values of each city were derived from two global climatic map databases: Worldclim Database and University of East Anglia TMC Database of the Climatic Research Unit. Moreover, household income is usually described by four metrics, i.e. GDP per capita, average worker wage, disposable income per capita and revenue per capita. GDP holds the greatest influence as it measures economic output of a whole country or

region; and it represents the total value created in the current process of production that is being transformed into various economic units of income including residential, commercial and governmental sectors. Rise in GDP per capita will result in residential income increases to a certain extent, while residential income range is dependent on the distribution among residential, commercial and governmental sectors [39]. Average worker wage, as calculated by the Chinese government – omits self-employed and private enterprise sectors – may not reflect the real income of workers as a result [40]. Theoretically, disposable income is total personal income minus personal current taxes and is assumed to be able to reflect the real purchasing power of residents. A relatively small statistic sample is utilized to obtain this metric in China, and differences between the actual value and the observed value may exist [41]. Local government revenue is consisted of total monetary income during a certain period, typically a fiscal year, and reflects capability of performing public functions such as implementing public

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137

Fig. 4. Average value of different DCE types in Building Climate Divisions I–VII.

policies and services. Value of this metric is influenced by factors such as economic level (GDP), industrial structure and government income distribution policy [42]. Strong positive correlation can be found between any two of the four metrics with different emphases reflecting economic development level. Finally, because population density value from statistical yearbooks does not accurately reflect the reality, the population density of the urbanized area is recalculated for urban form metrics based on the population density map at 100-m resolution provided by the Worldpop database. The statistical yearbooks calculation method, which divides the urban population by area of the whole administrative district, results in a much smaller value of population density than the reality, because urbanized area where most citizens settle is usually much smaller than the whole administrative district.

urban metrics attains 33 metrics having simple correlation with EDCE (Table 4). Several metrics among the 33 exhibit high correlation such as the Natural Logarithm (LN) of GDP per capita and LN revenue per capita (simple correlation coefficient = 0.683), while the lower correlation with EDCE is excluded. A multiple linear regression model is then built with the remaining metrics as independent variables, and EDCE is used as a dependent variable in SPSS 21.0 utilizing the stepwise regression method.7 Finally, metrics remaining in the regression are applied to calculate the partial correlation coefficient with EDCE for each metric using all other metrics as control variables (Table 5). The six metrics exerting significant net impact on EDCE are listed in order as follows: LN GDP per capita, the weight of tertiary industry in the GDP, population density, annual average temperature, gender ratio (male/female), and percentage of residents under 15 years old.

3.3. Partial correlation analysis

4. Results and discussion

The six sub-models that were built to test the conceptual model can be seen as follows: an EDCE model, GDCE model, TDCE model, CDCE model, DCEP model, and DCEG model. Dependent variables for each DCE type use natural logarithm of the DCE to eliminate the heteroscedasticity and to observe the correlation between energy consumption and urban macro-level metrics with partial correlation analysis. Partial correlation analysis assumes exceptional significance in cases where the phenomena under consideration have multiple factors influencing them. Strength of the linear relationship between two variables is measured in the simple two-variable correlations without considering the possible influence of a third variable. Partial correlation between two variables with the third variable as a constant is considered as a first order coefficient. A second order coefficient may be defined similarly. The partial correlation coefficient varies between −1 and +1 with higher absolute value indicating a more significant correlation. A multiple linear regression model could be built to compare the standardized regression coefficient of each independent variable so that the impact on the dependent variable can be ranked; yet the reliability of this method may not be as strong as comparing the n − 1 (n is the number of independent variables) order partial correlation coefficient of each independent variable [43]. The EDCE model exemplifies the partial correlation analysis process as the two-variable correlation analysis between EDCE and 38

The main results from the observations of the authors and the tests run in this article will be discussed in more details in the following sections. 4.1. EDCE Economic development turns out to have most significant positive impact on EDCE according to the conceptual model used in this article. Cities with higher GDP per capita and higher weight of Tertiary Industry in the GDP tend to have higher EDCE, possibly as a result of the population’s abilities to afford and use more energy-consuming appliances. All four metrics measuring income share a high simple correlation coefficient with EDCE while GDP per capita exhibits the highest coefficient. GDP per capita is the only metric included in the formula, as adding all four metrics into the regression formula would produce collinearity matter. Conversely, this result shows that GDP per capita is the indicator that is most likely to reflect the real urban residential income. Defects arise in

7 It is a semi-automated method of regressing multiple variables while simultaneously removing those that aren’t important. Briefly speaking, stepwise regression generates multiple regressions a number of times, each time removing the weakest correlated variable based on F-tests or t-tests. At the end the variables that explain the distribution best are left.

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Table 3 List, unit, data source and remarks of urban macro-level metrics. Category

Characteristic

Metrics

Unit

Source

Remark

Climatic factor

Temperature and humidity

Average temperature in coldest month

◦

WorldClim database http://www.worldclim.org Climatic Research Unit of the University of East Anglia http://www.cru.uea.ac.uk/cru/ data/hrg/tmc/

Climatic data is based on the average from 1950 to 2000, and we derive indicator value of 286 cities at the prefectural level and above in China from the two databases which provide global climate raster at the resolution of 10 (about 15 KM).

China Regional Economy Statistical Yearbook

Socio-economic and urban form data’s base year is 2009.

The fifth census data Bulletin

There are no family and population data for 2009, we use data from The sixth census data Bulletin whose base year is 2010.

Statistical Yearbook for every province

Some provinces and cities take Time-sharing pricing, which is not considered here. In China, the legal employment age is from 16 to 60.

China City Statistical Yearbook China Urban Construction Statistics Year Book

Population Density data is derived from Worldpop database, http://www.worldpop.org.uk/

Average temperature in hottest month Annual average temperature Number of days with ground-frost per year Annual average relative humanity Annual average wind speed Annual average sunshine percent of maximum possible Average rainfall in driest month Average rainfall in wettest month Annual average rainfall Altitude Longitude Latitude

◦

Income

GDP per capita

Yuan

Yuan Yuan Yuan

Family type

The average wage of workers Disposable income per capita Local government revenue per capita Household size

%

Energy prices

Percentage of one-person households Percentage of one-generation households Percentage of two-generation households Percentage of three-generation households Percentage of four or more-generation households Residential electricity price

Wind speed Sunshine Rainfall

Location

Socioeconomic factor

Occupational characteristic

Urban form factor

C

Density

Scale Land use mixture Infrastructure

Percentage of residents under 15 years old Percentage of residents between 15 and 60 years old The weight of tertiary industry in the GDP Gender ratio (male/female)

C

◦

C % %

m/s % mm mm mm m degree degree

person

% % % % Yuan/kwh

% % %

Population density

Persons/km2

Variation coefficient (CV) of population density Employment density Housing area per capita Urbanized area Proportion of residential land

Persons/km2 M2 km2 %

Proportion of industrial land Road area of the city per capita Green coverage rate Green area per capita

% m2 % m2

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139

Fig. 5. Spatial distribution of different urban macro-level metrics in China in 2009. (1) Annual average temperature, (2) annual rainfall, (3) annual GDP per capita, (4) annual revenue per capita, (5) urbanized area, (6) urban population density.

statistical methods to obtain both the average wage of workers per capita and disposable income per capita; and local government revenue per capita reflects income of local government rather than that of residents. The singular climatic factor affecting EDCE is annual average temperature. This is understandable considering that hotter summer temperatures in southern cities lead to higher frequency air conditioner use. Moreover, northern cities featuring central heating system do not need air conditioner for heating in winter, making total EDCE throughout the year higher in southern cities where annual average temperatures are higher. Population density has a positive net impact on EDCE as an urban form factor. According to Ewing and Rong [11], in American cities, denser cities experience higher UHI effects leading to increase of cooling energy consumption and decrease of heating

energy consumption. Zhou et al. [44] quantified diurnal and seasonal surface UHI intensity in China’s 32 major cities, and analyzed spatial variations and possible underlying mechanisms. UHI intensity was found to be higher in denser cities since more heat is trapped by street canyons, emitted by human activities, and stored by building materials, with less heat lost from the decreased vertical flux change. In China only cooling energy is related to EDCE for most northern cities feature central heating system. Thus cities with high density foster high UHI intensity, and see an increase in EDCE incurred by cooling in summer. Additionally, the proportion of high-rise residential building may be increased in denser cities, as the Design Code for Residential Buildings published by the Ministry of Construction of China requires residential buildings with more than six floors to have elevators installed; and more electricity is consumed accordingly. This study’s conclusion differs from that

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J. Zhang et al. / Energy and Buildings 107 (2015) 131–143

Table 4 Metrics sharing simple two-variable correlations with EDCE.

Pearson correlation Sig. (2-tailed) N

Pearson correlation Sig. (2-tailed) N

Pearson correlation Sig. (2-tailed) N *

LN GDP per capita

LN local government revenue per capita

Percentage of onegeneration households

LN disposable income per capita

Percentage of one-person households

Urbanized area

Household size

Road area of the city per capita

Population density

LN average wage of workers

Percentage of twogeneration households

.621

.593

.565

.526

.508

.450

−.431*

.416

.409

.399

−.338

0

0

0

0

0

0

0

0

0

0

0

286

286

286

286

286

286

286

286

286

286

286

Percentage of residents under 15 years old

Percentage of threegeneration households

Altitude

Green coverage rate

CV of population density

Annual average rainfall

Percentage of fourgeneration households

The weight of tertiary industry in the GDP

Average rainfall in wettest month

Average rainfall in driest month

Proportion of residential land

−0.355

−.326

−.316

.287

.277

.275

−.272

.266

.249

.229

−.219

0

0

0

0

0

0

0

0

0

0

0

286

286

286

285

286

286

286

284

286

286

285

Number of days with groundfrost per year

Annual average temperature

Annual average relative humanity

Average temperature in hottest month

Annual Average wind speed

Percentage of residents between 15 and 60 years old

Average temperature in hottest month

Residential electricity price

Green area per capita

Gender ratio (male/ female)

Proportion of industrial land

−.215

.214

.203

.196

.194

0.193

.190

.179

.148

−.143

.117

0

0

0.001

0.001

0.001

0.001

0.001

0.002

0.012

0.015

0.049

286

286

286

286

286

286

286

286

286

286

286

variables with Sig. ≤ 0.05 are regarded to share simple two-variable correlations with EDEC.

of Lee and Lee’s based on their observations of the American cities [31] (with low density development), but resembles observations of Japanese cities [29] (with high density development). Conclusions can be drawn, thusly, that density effects on CO2 emissions yet requires careful consideration of living conditions for accurate assessment. 4.2. GDCE Two metrics showing net impact on GDCE include average worker salary and longitude (proxy as city location). Cities with higher average worker salary tend to have higher GDCE. Longitude has significant net impact because cities in the west consume more gas due to low gas price as determined by resource location (Fig. 6). 4.3. TDCE Metrics with significant net impact on TDCE include LN revenue per capita, population density, percentage of residents under 15 years old, and urbanized area. The most significant impact results from the economic development level which is determined by local government revenue per capita rather than GDP per capita, and differs from EDCE. Local government revenue per capita directly reflects the local government’s capability of building and maintaining transportation infrastructure and easing severe road congestion in Chinese cities; and this in turn encourages more purchase and frequent use of private vehicles that lead to higher TDCE. Cities with higher densities and larger urbanized areas tend to exhibit higher TDCE. Urbanized area impacts can be explained as larger city populations are more likely to be the host of large-scale movements including commuting and non-commuting travels [26–29]. In conclusion, density effects on TDCE as discussed in this study differs from that of most the previous works all pointing to the common view that high density tends to lower the TDCE per

capita [29–31]. Density impact on TDCE is two-sided with improved amenities accessibility and more efficient public transportation system shortening average travel distance and encouraging low carbon travel modes [26,45], therefore reducing TDCE. High density, however, also creates more severe road congestion, as Li and Wang have discovered in the background of six Chinese megacities [46]. TDCE is raised because of severe congestion implying more travel time and lower speeds, and this leads to higher value of 100 km fuel factor as Liu studied [47]. A U curve may exist between density and TDCE [32–34], and that is identical to the environmental Kuznets curve [48]. Density exerts a net positive correlation with TDCE, as revealed in this study, even if urban economic development levels are controlled. Congestion effects produced by high-density conditions then supersede the benefits of good public transportation service and accessibility of amenities, consequently pushing TDCE higher in denser Chinese cities [49]. 4.4. CDCE Two metrics have significant net impact for CDCE: number of days with ground-frost per year and LN local government revenue per capita, both can be explained by the conceptual model. Climatic metrics have greater impact on CDCE than other DCE types, because cities with colder climates require extended central heating duration and greater energy consumption to maintain stable indoor temperatures. CDCE is obviously higher in cities with greater local government revenue per capita because in these cities, central heating funds are suffice to maintain more steady comfortable indoor heating temperatures. 4.5. DCEP and DCEG A significant difference can be observed in the impact of LN GDP per capita on DCEP and DCEG. DCEP exhibits the most

J. Zhang et al. / Energy and Buildings 107 (2015) 131–143

141

Table 5 Partial correlation coefficient table for different DCE types. EDCE

LN GDP per capita

The weight of tertiary industry in the GDP

Population density

Annual average temperature

Gender ratio (male/female)

Percentage of residents under 15 years old

Partial correlation Sig. (2-tailed) df

0.465

0.299

0.172

0.150

−0.136

−0.108

0 272

0 272

0.004 272

0.013 272

0.024 272

0.075 272

GDCE

LN average wage of workers

Longitude

Partial correlation Sig. (2-tailed) df

0.244 0 281

−0.191 0.001 281

TDCE

LN local government revenue per capita

Population density

Percentage of residents under 15 years old

Urbanized area

Partial correlation Sig. (2-tailed) df

0.650 0 276

0.158 0.008 276

−0.177 0.003 276

0.121 0.044 276

CDCE

Number of days with ground-frost per year

LN local government revenue per capita

Partial correlation Sig. (2-tailed) df

0.622 0 129

0.465 0 129

DCEP

LN GDP per capita

The weight of tertiary industry in the GDP

Number of days with ground-frost per year

Household size

Gender ratio (male/female)

Partial correlation Sig. (2-tailed) df

0.487

0.281

0.262

−0.124

−0.111

0

0

0

0.040

0.065

273

273

273

273

273

DCEG

LN GDP per capita

The weight of tertiary industry in the GDP

Number of days with ground-frost per year

Gender ratio (male/female)

Partial correlation Sig. (2-tailed) df

−0.528

0.368

0.291

−0.146

0 277

0 277

0 277

0.014 277

significant positive net impact of LN GDP, while DCEG exhibits the most significant negative net impact. Affluent cities consume more energy per capita but less per GDP. The weight of tertiary industry in the GDP produces positive impacts on both DCEP and DCEG, while climatic factors have a significant impact on total energy consumption, because the number of days with ground-frost per year has a positive impact on both DCEP and DCEG. Family types affect DCEP because cities with bigger family sizes tend to have higher DCEP. Density effects on DCEP are positive as can be seen from the positive impacts they have on EDCE and TDCE discussed above. 5. Conclusion

Fig. 6. Highest Gas Station Price (including Value Added Tax) for 29 provinces, autonomous regions and municipalities in China in August 2014 units: Yuan/1000 m3 . http://www.ndrc.gov.cn/fzgggz/jggl/zcfg/201408/ in t20140812 622014.html, visiting time: August, 2014.

Compared to micro-level energy-saving studies which take the family or individual as the research unit and are based on questionnaire survey, macro-level energy-saving studies are more difficult to conduct because such studies tend to observe cities as units, but basic city-level energy consumption data is rarely available in China. Direct CO2 Emissions estimates of urban residents for 286 cities at the prefectural level and above in China are given in this study with an improved estimating method of using patterns of private vehicles. Major urban macro-level factors with the greatest effects on urban residential energy consumption are defined through partial correlation analyses, and the findings are as follows. Firstly, climatic metrics exert significant impacts on Central Heating DCE and Electricity DCE while obvious differences can

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be observed among cities in different Building Climate Divisions (BCD). Cities studied in BCD I and VII produce the highest DCEP due to higher CDCE where winter temperatures are especially low. Cities in the BCD II region show high DCEP and DCEG, and additional research is thusly called for. Secondly, metrics measuring the economic development level have the most significant positive impacts on Electricity DCE, Gas DCE, Transportation DCE, and total DCE per capita. However, total DCE per GDP decreases as economic development level rises, indicating that energy efficiency of urban residents is higher in cities with more developed economies. Energy-efficient studies in urbanized areas should then be focused on factors whose correlation with DCE has been confirmed by this study. Thirdly, significant impacts of urban form metrics can be found on Electricity DCE, especially on Transportation DCE. In contrast to the typical view stating that high population density contributes to limiting energy consumption, this study found out that for 286 cities at the prefectural level and above in China, higher density actually promotes Electricity, Transportation and total DCE as a result of both the Urban Heat Island effect and severe traffic congestion in denser cities. In addition to these, it has been proved that land use mixture is less significant for TDCE. Urban macro-level factors with the highest impacts on urban residential energy consumption have been studied thoroughly in this study despite the fact that more specific interpretation of the influence of population density conditions on EDCE is hard to come by due to limited data. Further studies on direct and precise measurements for the Urban Heat Island effect found by this study in 286 cities at the prefectural level and above in China are thusly necessary for a more integrated, comprehensive analysis. Last but not least, more attention should be paid to additional urban form metrics in future studies in consideration of the complex of urban form that requires our understanding from perspectives beyond density and scale. Acknowledgments This work was supported by the following research projects: Form and Design of Energy-efficient Neighborhood in Megacities in Southern Region of North China funded by Independent Research Program of Tsinghua University (No. 20111081052); and Low Carbon Urban Design—From Options Assessment to Policy Implementation funded by the Low Carbon Energy University Alliance (No. 2011LC002). References [1] J. Barnett, W.N. Adger, Climate dangers and atoll countries, Clim. Change 61 (3) (2003) 321–337. [2] X. Zhang, F.W. Zwiers, G.C. Hegerl, F.H. Lambert, N.P. Gillett, S. Solomon, P.A. Stott, T. Nozawa, Detection of human influence on twentieth-century precipitation trends, Nature 448 (7152) (2007) 461–465. [3] S. Solomon, Cambridge University Press, 2007. [4] S. Frolking, T. Milliman, K.C. Seto, M.A. Friedl, A global fingerprint of macro-scale changes in urban structure from 1999 to 2009, Environ. Res. Lett. 8 (2) (2013) 024004. [5] Y. Zhang, Y.C. Qin, W.Y. Yan, J.P. Zhang, L.J. Zhang, F.X. Lu, X. Wang, Urban types and impact factors on carbon Emissions from direct energy consumption of residents in China, Geogr. Res. 31 (2) (2012) 345–356 (in Chinese). [6] J. Fan, The empirical study of the impact of urban density on energy consumption, Econ. Issues China 6 (2011) 345–356 (in Chinese). [7] T. Oda, S. Maksyutov, A very high-resolution (1 km × 1 km) global fossil fuel CO2 Emissions inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem. Phys. 11 (2) (2011) 543–556. [8] J. Munksgaard, K.A. Pedersen, M. Wien, Impact of household consumption on CO2 emissions, Energy Econ. 22 (4) (2000) 423–440. [9] G.C. Qu, H.Y. Chai, On the changes of the administrative divisions in China in recent 20 years, Inshan Acad. J. 22 (1) (2009) 95–99, http://dx.doi.org/ 10.13388/j.cnki.ysaj.2009.01.014 (in Chinese).

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