sy

e O

me

bDepartment of Environmental Science and Management, University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117-1080, United States

Keywords:Low carbonChinaIndicatorsEnergy efciencyCarbon emissions mitigation

City policy and announced the selection of 5 provinces and 8cities to pilot the low carbon development work (NDRC, 2010).The ve provinces are: Guangdong, Liaoning, Hubei, Shaanxi and

indicators to determine if a city or region meets the denition mustbe developed in order to evaluate the current situation andmeasure progress toward more low-carbon activities.

Macro-level indicators of low carbon development, such asenergy use or CO2 emissions per unit of GDP or per capita may betoo aggregated to be meaningful measurements of whether a cityor province is truly low carbon and do not provide any indication ofwhere the inefciencies occur or where action is needed. Instead,indicators based on energy end-use sectors (industry, residential,

* Corresponding author. Tel.: 1 510 486 6519.E-mail addresses: LKPrice@lbl.gov (L. Price), NZhou@lbl.gov (N. Zhou),

DGFridley@lbl.gov (D. Fridley), SBOhshita@lbl.gov (S. Ohshita), HYLu@lbl.gov

Contents lists available at

Habitat Inte

journal homepage: www.else

Habitat International 37 (2013) 4e21(H. Lu), XZheng@lbl.gov (N. Zheng), CFino-Chen@lbl.gov (C. Fino-Chen).Overview and objectives

In 2009, China committed to reducing its carbon dioxideintensity (CO2/unit of gross domestic product, GDP) by 40e45% by2020 from a 2005 baseline. In August 2010, after receivingpermission from the State Council, the National Development andReform Commission (NDRC) of China established a Low Carbon

Yunnan; and the 8 cities are Chongqing, Tianjin, Shenzhen, Xiamen,Hangzhou, Nanchang, Guiyang, and Baoding. InMarch 2011, Chinas12th Five-Year Plan established a carbon intensity reduction goal of17% between 2011 and 2015.

Given these various CO2 intensity reduction goals, it is impor-tant to develop a clear denition of low carbon, which is nowa popular term in China. In addition to dening low carbon,0197-3975/$ e see front matter 2012 Elsevier Ltd.doi:10.1016/j.habitatint.2011.12.009In 2009, China committed to reducing its carbon dioxide intensity (CO2/unit of gross domestic product,GDP) by 40e45% by 2020 from a s2005 baseline and in March 2011, Chinas 12th Five-Year Plan estab-lished a carbon intensity reduction goal of 17% between 2011 and 2015. The National Development andReform Commission (NDRC) of China then established a Low Carbon City policy and announced theselection of 5 provinces and 8 cities to pilot the low carbon development work. How to determine if a cityor province is low carbon has not been dened by the Chinese government.Macro-level indicators of low carbon development, such as energy use or CO2 emissions per unit of

GDP or per capita may be too aggregated to be meaningful measurements of whether a city or province istruly low carbon. Instead, indicators based on energy end-use sectors (industry, residential, commer-cial, transport, electric power) offer a better approach for dening low carbon and for taking action toreduce energy-related carbon emissions.This report presents and tests a methodology for the development of a low carbon indicator system at

the provincial and city level, providing initial results for an end-use low carbon indicator system, basedon data available at the provincial and municipal levels. The report begins with a discussion of macro-level indicators that are typically used for inter-city, regional, or inter-country comparisons. It thenturns to a discussion of the methodology used to develop a more robust low carbon indicator for China.The report presents the results of this indicator with examples for 6 selected provinces and cities inChina (Beijing, Shanghai, Shanxi, Shandong, Guangdong, and Hubei). The report concludes witha discussion of data issues and other problems encountered during the development of the end-use lowcarbon indicator, followed by recommendations for future improvement.

2012 Elsevier Ltd. All rights reserved.a b s t r a c tDevelopment of a low-carbon indicator

Lynn Price a,*, Nan Zhou a, David Fridley a, StephaniCecilia Fino-Chen a

aChina Energy Group, Energy Analysis and Environmental Impacts Department, Environ1 Cyclotron Road, MS 90R4000, Berkeley, CA 94720, United StatesAll rights reserved.stem for China

hshita a,b, Hongyou Lu a, Nina Zheng a,

ntal Energy Technologies Division, Lawrence Berkeley National Laboratory,

SciVerse ScienceDirect

rnational

vier .com/locate/habitat int

es, a

ternTable 1Comparison of macro-level economic indicators for Chinas large municipaliti

L. Price et al. / Habitat Incommercial, transport, electric power) could offer a betterapproach for dening low carbon and for taking action to reduceenergy-related carbon emissions.

The objective of this work is to develop a methodology for a lowcarbon indicator system at the provincial and city level. This reportoutlines a proposed methodology and provides initial results for anend-use low carbon indicator and ranking system based on dataavailable at the provincial and municipal levels. The report begins

Notes: Data are only for mainland China; data are not available for Tibet.Primary energy: Total end-use energy consumption of each province with electricFinal energy: Total end-use energy consumption of each province with electricityConsumption-based carbon emissions: Emissions from electricity are counted whein non-energy use petroleum products such as asphalt and lubricants, which total aNBS, 2010; IPCC, 1996.utonomous regions, and provinces, 2008.

ational 37 (2013) 4e21 5with a discussion of macro-level indicators that are typically usedfor inter-city, regional, or inter-country comparisons. It then turnsto a discussion of the methodology used to develop a more robustlow carbon indicator for China. The report presents the results ofthis indicator with examples for 6 selected provinces and cities inChina (Beijing, Shanghai, Shanxi, Shandong, Guangdong, andHubei). The report concludes with a discussion of data issues andother problems encountered during the development of the end-

ity converted at 0.404 kgce/kWh.converted at 0.1229 kgce/kWh.re the electricity is consumed. Emissions data include the sequestered carbonbout 150million tonnes CO2 (Fridley, Zheng, & Qin, 2011). Sources: NBS, 2009;

aro

ternTable 2Comparison of macro-level economic indicators for selected cities

L. Price et al. / Habitat In6use low carbon indicator, followed by recommendations for futureimprovement.

Macro-level indicators

Macro-level indicators for measuring the carbon intensity ofa city, region, or country are typically based on either CO2 emissionsper unit of GDP or CO2 emissions per capita.

Macro-level economic (GDP) indicators

An economic-based carbon intensity indictor, or CO2 emissions/unit of GDP, is comprised of two elements: (1) energy intensity,dened as the amount of energy consumed per unit of economic

Notes: Data for international cities are for the 2008e2009 period; NYrates used for the international indicators are: 1) 2010 average exchanthe Asian Green City Index. 2) 2008 average exchange rate of 0.09844Green City Index. Both exchange rates were taken from the Bankbankofcanada.ca/rates/exchange/. Sources: NBS, 2009; NBS, 2010Intelligence Unit, 2009; World Bank, 2010.und the world and Chinas four large municipalities, 2008.

ational 37 (2013) 4e21activity; and (2) carbon intensity of energy supply, dened as theamount of carbon emitted per unit of energy (EIA, 2004). As illus-trated by the formula below, the multiplication of these twoelements produces a countrys carbon intensity, dened as theamount of CO2 emitted per dollar of economic activity:

Energy intensity carbon intensity of energy supply carbon intensity of the economy or energy=GDP

carbon emissions=Energy carbon emissions=GDPWith regard to energy intensity, it needs to be noted that the

scope of energy included in the calculation of energy intensity

C data are for 2005; London data are for 2006. The two exchangege rate of 0.147679 2010 US$ per RMB to convert US$ to RMB for3 2008 Euro per RMB to convert Euros to RMB for the Europeanof Canadas historical exchange rates database at: http://www.; IPCC, 1996; Economist Intelligence Unit, 2011; Economist

Table 3Comparison of macro-level per capita indicators for Chinas large municipalities, autonomous regions, and provinces, 2008.

Notes: Data are only for mainland China; data are not available for Tibet.Primary energy: Total end-use energy consumption of each province with electricity converted at 0.404 kgce/kWh.Final energy: Total end-use energy consumption of each province with electricity converted at 0.1229 kgce/kWh.Consumption-based carbon emissions: Emissions from electricity are counted where the electricity is consumed. Emissions data include the sequestered carbonin non-energy use petroleum products such as asphalt and lubricants, which total about 150 million tonnes CO2 (Fridley et al., 2011). Sources: NBS, 2009; NBS,2010; IPCC, 1996.

L. Price et al. / Habitat International 37 (2013) 4e21 7

und the world and Chinas four large municipalities, 2008.

ternational 37 (2013) 4e21Table 4Comparison of macro-level per capita indicators for selected cities aro

L. Price et al. / Habitat In8can render different results. Specically, it is important todistinguish between nal energy and primary energy for thepurposes of both data collection and construction of the indicator.Final energy, or end-use energy, refers to energy delivered at theend-use site and does not account for electricity generation ef-ciency and energy losses during transmission and distribution(T&D). Primary energy includes nal energy as well as energyconsumed during the generation and T&D of electricity. Therelation between primary energy and nal energy is illustrated bythe formulas below:

Final energy Fuel use Electricity usePrimary energy Final energy Electricity generation

and T&D losses

Notes: Data for international cities are for the 2008e2009 period; NYC dataUnit, 2011; Economist Intelligence Unit, 2009; NBS, 2009; NBS, 2010; IPCCIn China, electricity (in kWh) is converted to energy (in kilo-grams coal equivalent, kgce) using 0.404 kgce/kWh for primaryenergy and 0.1229 kgce/kWh for nal energy.1

Table 1 compares Chinas 4 large municipalities (Beijing, Tian-jin, Shanghai, and Chongqing), four of the ve autonomous regions(Xinjiang, Inner Mongolia, Ningxia, and Guangxi; data are notavailable for Tibet), and 22 provinces using 3 macro-leveleconomic indicators: primary energy/GDP, nal energy/GDP, and

are for 2010. Sources: City of New York, 2011; Economist Intelligence, 1996; World Bank, 2010.

1 To accurately convert electricity to primary energy, a conversion factor thatreects the efciency of power generation combined with electricity T&D lossesshould be calculated. For 2008, a conversion factor of 3.11 is equivalent to Chinasnational average efciency of thermal power generation of 32.15%, including T&Dlosses (Anhua & Xingshu, 2006; Kahrl & Roland-Holst, 2006; NBS, 2008).

greg

L. Price et al. / Habitat International 37 (2013) 4e21 9end-use CO2 emissions/GDP. Focusing just on the 4 large munici-palities, Table 1 shows that using these indicators, Beijing, Tianjin,and Shanghai could all be considered low-carbon cities becausetheir energy use and emissions per unit of GDP are lower thanthose of Chongqing and most of Chinas provinces and autono-

Fig. 1. Calculation of the agmous regions.While this comparison is informative from the point of view of

how municipalities, autonomous regions, and provinces compareinternally in China, it does not provide any indication of how theseregions compare globally. Table 2 provides indicators with which tocompare the four large cities in Chinawith other large cities aroundthe world.2 This comparison shows that all four Chinese munici-palities, including the three that appeared to be low-carbonwhencompared with other cities, regions, and provinces in China, havesignicantly higher nal energy and CO2 intensities than the otherselected cities from around the world.

Macro-level population-based indicators

Similar to the economic-based macro-level indicators, indica-tors using population as the denominator instead of GDP can alsobe used to compare cities, regions, and provinces. Table 3 shows thecomparison using primary energy use/capita, nal energy use/capita, and end-use CO2 emissions/capita for the 30 provinces,autonomous regions, and cities in China in 2008.

The results for the four large municipalities in China using theper capita indicators differ from the results using GDP as thedenominator. On a per capita basis, Chongqing has the lowestenergy use and CO2 emissions per capita of the 4 cities, whileShanghai, Beijing, and Tianjin cannot be dened as low-carbon

2 Note that this comparison is complicated by factors that might potentially affectthe accuracy of such comparisons such as exchange rates between differentcurrencies (purchasing power parity could be used instead).cities. When Chinas 4 large municipalities are compared toselected world cities using both the nal energy/capita and CO2emissions/capita indicators, as shown in Table 4, Chongqing has thelowest per capita energy consumption of all of the cities, buta number of world cities have lower per capita CO2 emissions, most

ated low carbon indicator.likely due to a more decarbonized fuel mix. Beijings per capita nalenergy consumption is also relatively low when compared to theselected international cities, but again the per capita CO2 emissionsof Chinas capital city are higher than most other cities in thecomparison due to the heavy reliance on coal in Chinas fuel mix.Nonetheless the Chinese cities are still of a similar magnitude asother international best practice cities, unlike the GDP based indi-cator which shows that Chinese cities are 20 times more carbonintensive than the selected international cities. The comparisondemonstrates that the choice of indicators is crucial in determiningwhether a city or province is low carbon.3

Issues with macro-level indicators

Based on the discussions above, there are many issues andunderlying factors with these twomacro-level indicators that makethem less desirable for use in dening low carbon cities orprovinces. In the case of the 30 Chinese provinces and cities, theissues include:

Macro-level indicators do not accurately reect end-use (e.g.buildings, transport, industry) energyorcarbon intensities sincethey are created based on a top-down approach for the purposeof providing a general, overall picture of a countrys situation.

3 There are a number of efforts to compare CO2 emissions/capita for the worldscities (Carbon Disclosure Project, 2011; City of New York, 2011; KPMG, 2010) whichemphasize the importance of data quality, boundary denitions, conversion factors,etc. All of these issues also apply to the use of a CO2 emissions/capita indicator inChina.

Table 5End-use sectoral indicators for Chinas large municipalities, autonomous regions, and provinces, 2008.

Notes: Data are only for mainland China; data are not available for Tibet.Primary energy: Total end-use energy consumption of each province with electricity converted at 0.404 kgce/kWh.Final energy: Total end-use energy consumption of each province with electricity converted at 0.1229 kgce/kWh.Consumption-based carbon emissions: Emissions from electricity are counted where the electricity is consumed. Emissions data include the sequestered carbonin non-energy use petroleum products such as asphalt and lubricants, which total about 150 million tonnes CO2 (Fridley et al., 2011). Sources: NBS, 2009; NBS,2010; IPCC, 1996.

L. Price et al. / Habitat International 37 (2013) 4e2110

ion

114 189 15898 143 134

ternTable 6Indexed end-use sectoral indicators for Chinas large municipalities, autonomous reg

Region Residential nalenergy/capita(weather corrected)

Commercial nalenergy/tertiary sectoremployee

Index Index

Beijing 182 166Tianjin 151 237Hebei 101 112Shanxi 146 116Inner Mongolia 304 282Liaoning 143 84Jilin 117 114Heilongjiang 173 81Shanghai 132 267Jiangsu 45 59Zhejiang 73 73Anhui 40 29Fuji an 69 72Jiangxi 41 31Shandong 63 150Henan 60 27Hubei 74 64Hunan 59 65Guangdong 108 79Guangxi 30 45Hainan 33 78Chongqing 69 42Sichuan 70 41Guizhou 54 101Yunnan 28 41Shaanxi 80 93Gansu 98 38Qinghai 212 157

L. Price et al. / Habitat In Migrant/transient populations were not included in ofcialpopulation data until the 2010 Census which could result inover-accounting of energy use per capita in large coastal citiesand provinces that have signicant migrant populations, suchas Beijing and Shanghai, and possible under-accounting ofenergy use per capita in other areas.

Cross-country comparisons have additional issues due todiffering data sources, denitions, exchange rates, conversionfactors, etc. which often make it difcult to ensure that theresults are comparable.

The underlying factors include:

Provinces and cities are varied in their economic structure (i.e.primary, secondary, and tertiary industry); a more faircomparison would account for these structural differences.

Income levels vary by location, with generally higher incomesin the cities and provinces in Eastern China, leading to highercar ownership and fuel use, higher residential energyconsumption, etc.

Building energy consumption is highly dependent on theweather conditions of a region, and the macro level indicatorsignore these differences, which could lead to inaccurateresults.

Economic energy intensity (i.e. energy/GDP or CO2/GDP) isa mixed indicator, accounting for both physical energy efciency andeconomic structure that inuences energy consumption. Aseconomic development proceeds, the economic energy intensitytypically declines yet absolute energy and carbon emissions can still

Ningxia 102 125Xinjiang 144 129

National average 100 100105 103 16166 66 13455 310 10757 76 10347 104 8995 35 11455 92 7277 37 11368 97 12574 35 126

100 108 4197 49 7744 111 8589 60 48

124 105 73109 74 10289 49 57

149 50 89135 69 7264 91 109

153 51 82173 94 40s, and provinces, 2008.

Industrial nalenergy/IndustryGDP

Transportation nalenergy/capita

Electric powerC02/kWh

Index Index Index

63 249 8557 166 116

129 52 128144 108 123

ational 37 (2013) 4e21 11increase.Althoughpercapita indicatorsmayprovideamoreequitablebasis for comparison across cities, provinces, and countries, highlyaggregated per capita indicators (i.e. total energy/capita or CO2/cap-ita), should still be used with caution. A city with heavy industry anda small population,which supplies other citieswith cement and steel,would result in high energy consumption per capita even though thepeople of the citymight use relatively little energy in their residences.Similarly, a city in the cold region will always have higher energyconsumption than cities in moderate climate.

It is important to develop an accurate indicator and associatedsub-indicators because there could be signicant implicationsrelated to mislabeling a city or region as low carbon when it is not(or vice versa) such as inappropriate use of funds for development,misguided efforts to inuence development and behavior that arenot conducive to actually reducing energy use or CO2 emissions,and missed opportunities to focus on specic areas that could havethe most impact in actually making a specic location low carbon.

Sectoral end-use low carbon indicator for China

The goal of this study is to develop a methodology for a lowcarbon indicator system for municipalities, autonomous regions,and provinces in China. To address some of the issues with themacro-level indicators described above, a composite sectoral end-use low carbon indicator is developed for this purpose.

The advantages of using this indicator include:

Its development is based on international experience (ICLEI,2009; IEA, 2010; Zhou, Price, Ohshita, Zheng, & Jiang, 2011),

233 108 131135 119 104

100 100 100

Table 7Weighting factors for end-use sectoral indicators for Chinas large municipalities, autono

Region Share of residentialsector energy in totalend-use energy

Share of commercialsector energy in totalend-use energy

Sse

% % % % %

000000000000000.73 0.12 0.170000000000

L. Price et al. / Habitat Intern12Beijing 0.15 0.20Tianjin 0.10 0.09Hebe 0.09 0.04Shanxi 0.11 0.04Inner Mongolia 0.12 0.08Liaoning 0.10 0.04Jilin 0.10 0.06Heilongjiang 0.16 0.05Shanghai 0.08 0.12Jiangsu 0.06 0.04Zhejiang 0.09 0.07Anhui 0.10 0.03Fujian 0.09 0.06Jiangxi 0.09 0.04Shandong 0.07 0.08Henan 0.10 0.02Hubei 0.11 0.05Hunan 0.10 0.06Guangdong 0.11 0.07Guangxi 0.07 0.05Hainan 0.05 0.08Chongqing 0.10 0.04Sichuan 0.15 0.05Guizhou 0.15 0.13Yunnan 0.08 0.03while factoring in data availability in China and applicability tothe Chinese situation.

It is constructed using the underlying contributors to theoverall level of energy use or CO2 emissions of a city or provincee the energy and emissions of the main energy-consumingend-use sectors: residential, commercial, industry, trans-portation and power.4

It indexes the energy consumption and CO2 emissions of the 5major energy end-use sectors so that they can be weighted andcombined.

It applies a weighting factor for the 5 major energy end-usesectors to account for their contribution to the overall energyuse or CO2 emissions within the province or city.

It also applies climate adjustment factor for the buildingssector that is based on the weather data of each province, toensure the comparison among cities is fair and consistent.

It is operation- and goal-oriented, providing measurability andcomparability and can be used to dene low carbon, rank citiesby energy use and CO2 emissions levels, track progress inenergy efciency and emission reductions, and establishbenchmarks.

Shaanxi 0.14 0.08 0Gansu 0.13 0.03 0Qinghai 0.12 0.07 0Ningxia 0.06 0.04 0Xinjiang 0.11 0.05 0

National total 0.10 0.06 0

Note: The shares for the end-use sectors add to 100%, while the share for electricity is thprovince.

4 Residential includes buildings energy use as well as the energy use of appli-ances and equipment in the buildings. Commercial includes wholesale, retail trade,catering, construction, and other commercial services. Agricultural energy use isnot included in the calculations presented in this report..80 0.07 0.21

.66 0.18 0.16

.73 0.11 0.17

.65 0.18 0.28

.71 0.17 0.21

.61 0.26 0.17

.72 0.13 0.14

.68 0.12 0.17

.61 0.12 0.20

.72 0.16 0.19.41 0.24 0.19

.69 0.13 0.17

.81 0.06 0.14

.76 0.10 0.16

.67 0.14 0.18

.73 0.13 0.15

.71 0.14 0.12

.66 0.12 0.16

.59 0.21 0.20

.80 0.10 0.26

.70 0.15 0.31

.77 0.10 0.20

.68 0.16 0.27

.76 0.11 0.17mous regions, and provinces, 2008.

hare of industrialector energy in totalnd-use energy

Share of transportationsector energy in totalend-use energy

Share of provincialelectricity of totalprovincial energy

ational 37 (2013) 4e21This section begins with a description of the methodology fordevelopment of the sectoral end-use low carbon indicator. This isfollowed by a presentation of the results for Chinas 30 selectedprovinces and cities after applying the methodology, includingmore detailed discussion of the results for 6 Chinese provinces andcities (Beijing, Shanghai, Shanxi, Shandong, Guangdong, andHubei). The section ends with a discussion of identied issues andareas for improvement of the new low carbon indicator.

Methodology

There are 4 key steps in the development of the end-use lowcarbon indicator:

Identify end-use sectors Identify indicators for each end-use sector identied (based onavailable data)

Gather indicator data for each province/city Calculate the end-use low carbon indicator value by indexingand weighting end-use indicators

Identify end use sectorsThe rst step in developing the low carbon indicator is to

identify key end-use energy-consuming sectors of the economy forwhich data are available. For China, 5 sectors were identied thatcover virtually every aspect of Chinas modern living and activities:residential buildings, commercial buildings, industry,

.59 0.18 0.17

.74 0.10 0.22

.72 0.09 0.28

.81 0.09 0.28

.70 0.14 0.11

.71 0.13 0.19

e share of electricity versus other energy sources (e.g. coal, natural gas) used in the

palities, autonomous regions, and provinces, 2008.

ternational 37 (2013) 4e21 13Table 8End-use low carbon indicator for Chinas large munici

L. Price et al. / Habitat Intransportation, and power generation. These ve sectors combineto account for all energy use and related CO2 emissions in China.

Identify indicators for each end-use sectorThe second step in developing the low carbon indicator is to

identify indicators for each of the end-use sectors that were denedin the rst step. Again, it is essential that the data required fordevelopment of each indicator are available.

Residential buildings sectorFor China, the end-use low carbon indicator for the residential

buildings sector is dened as weather-corrected residential

Note: The lower end-use low carbon indicator value denotbuildings nal energy5/capita. This indicator should be weather-adjusted to account for the differing demands on energy use inresidential buildings in various climatic zones in order for theindicator to be comparable across cities and provinces. Forexample, non-weather-adjusted residential energy intensity in

es a more low carbon ranking.

5 Final energy was used for the development of these indicators; a comparison ofthe results using primary energy showed little difference in the overall rankingorder. Final energy was chosen as the method to present here since most cities andprovinces cannot inuence the efciency of the generation or T&D of the electricitythey consume.

L. Price et al. / Habitat International 37 (2013) 4e2114a severely cold zone such as Harbin is not directly comparable tothe non-weather-adjusted residential energy intensity of a mild-weathered city such as Kunming since overall lower energyintensity doesnt necessarily imply higher energy efciencywithout taking the weather into consideration. Weather variationcan be accounted for by calculating cooling degree-days (CDD) andheating degree-days (HDD). HDDs and CDDs are measures of howcold/warm a location is over a period of time relative to a basetemperature, most commonly specied as 18 C. Heating degreedays are the summation of the negative differences between themean daily temperature and the 18 C base; cooling degree days arethe summation of the positive differences (Zhou et al., 2011).

Commercial buildings sectorThe end-use low carbon indicator for Chinas commercial

buildings sector is dened as commercial buildings nal energy/tertiary sector employees.6 Data on the number of employees aremore readily available than data on commercial buildings oor area(m2). However, an indicator based on energy use per square meter

Fig. 2. Results of the end us

6 Commercial building sector energy data were not weather-corrected for thisanalysis due to lack of data; such a correction should be done, if possible, for moreaccurate results.would be more comparable for commercial buildings since thenumber of employees per meter can vary signicantly.7 If data areavailable broken out by types of buildings, then more detailedcomparisons could be provided as the energy consumptionpatterns are very different among the different building types suchas retail, ofce, hotel, education, health care, etc.

Industry sectorThe end-use low carbon indicator for the industry sector in

China is dened as industrial nal energy per/industrial share ofregional GDP (NBS, 2010). This indicator is at a highly aggregatedlevel, combining all industrial energy consumption (and carbonemissions) activities and dividing by the industrial share ofregional GDP. It would be ideal to have industrial value added datainstead of the industrial share of regional GDP, but this value isonly available at the national level in China. This indicator can alsobe developed at a sub-sectoral level, for example, to compare theintensity of overall cement production in a city with the intensity

e CO2/Capita indicator.

7 For the commercial building sector, oor space datamay be collected through thelocal taxation ofce through properties taxes. Or, the building construction commis-sion or the planning bureau often has a record of the building construction area. Suchdata could be used instead of commercial building employees for this indicator.

ternational 37 (2013) 4e21 15L. Price et al. / Habitat Inof other industrial sub-sectors such as chemicals and steel,depending upon data availability.

Transportation sectorThe end-use low carbon indicator for Chinas transportation

sector is dened as transportation nal energy/capita. This indicatorprovides a measure of the energy or carbon intensity of movingpeople and goods around a city. This indicator can also be developedfor individual transportation modes, but this is challenging, since itrequires knowing the usage (passenger-kilometers, freight-kilometers) of all public transportation modes (buses, light rail,subway, trucks, etc.), total person-trip-kilometers for all privatetravel in cars and taxis as well as the total energy consumption ofthese travel modes.

Power sectorThe end-use low carbon indicator for power sector is dened

as CO2 per unit of power produced. CO2 emissions per unit ofgenerated electricity is a common indicator for tracking the de-carbonization of electricity supply. Expressed as kg CO2/kWh,this indicator can be used to track the reduction in use ofcarbon-intensive coal and the impact of the use of renewable,natural gas, and nuclear energy sources in the power generationmix. This indicator also serves as an emission factor for

Fig. 3. Results of the end udetermining carbon emissions from electricity use for each of theend-use sectors.

Gather indicator data for each province/cityThenext step in the construction of the sector-based end-use low

carbon indicator is to identify and gather the required data for eachprovince or city. For China, the data for the development of theindicators outlined above was all collected from published dataprovided by Chinese government statistical ofces.When collectingsuch data, it is important to understand the data denitions andboundaries in order to ensure that the indicators are comparable.

For example, it is important to understand if electricity is pre-sented as nal or primary energy when total energy values areprovided. The end-use low carbon indicator uses nal energy sothat an indicator for the electricity sector can be presented alongwith indicators for each end-use sector.8 It is also important toensure that economic data are presented in the same base year. Theenergy data used for the development of the Chinese indicatorspresented in this report are from the China Energy Statistical

se CO2/GDP indicator.

8 A comparison of the LCI calculated using primary energy to the LCI resultspresented in this report using nal energy showed that there was very littledifference in the resulting indicator values.

ternational 37 (2013) 4e21L. Price et al. / Habitat In16Yearbook 2009 of the China National Bureau of Statistics (NBS) (NBS,2009). The economic data are from the 2010 China Statistical Year-book of NBS (NBS, 2010), and the economic data are converted to2005 RMB based on the price indices provided by NBS. The CO2emission factors are from the Intergovernmental Panel on ClimateChange (IPCC) (IPCC, 1996).

Depending upon the quality and comparability of the data,some adjustments to the data may be needed. For example, for theresidential sector in China, adjustments to the data may be neededto ensure that the energy use of all residences is included in thisindicator. Often, residential energy use in industrial units isaccounted for within the industrial energy use category. Like theresidential buildings sector, energy use for transportation withinindustrial units may also need to be removed from industrialsector data and added to transportation sector data in order tomore accurately reect the energy use of this end-use sector. Amethodology for making such adjustments is provided in Zhouet al. (2007).

Adjustments of the usage of oil products were made in theindustrial, residential, commercial, and transport. Gasoline usagethat was reported under the industrial, residential, commercial andagriculture sectors was reallocated to transport sector. Keroseneand fuel oil consumption in the transport sector was reduced totake into account the inter-provincial and international use of jet

Fig. 4. Results of the end usfuel in airplanes and fuel oil in ships, respectively. Due to a lack ofdetailed data, a reduction factor of 50% was applied.9

Calculate the end-use low carbon indicator value by indexing andweighing end-use indicators

Once the end-use sectors and their indicators have been iden-tied and data have been collected and modied, if necessary, thenal steps for the calculation of the end-use local carbon indicatorare to index each end-use sectors low carbon indicator andmultiply it by a weighting factor and add the results of thatcalculation to the indexed and weighted power sector indicator.This indexing and weighting is done for each of the large munici-palities, autonomous zones, and provinces.

e low carbon indicator.

9 The reduction factor of 50% derives from an analysis of Chinas jet fuel andmarine fuel oil usage in 2009. In that year, 94% of Chinas kerosene consumptionwas jet kerosene and 49% of Chinas fuel oil was used for transportation. Thestatistics do not distinguish between domestic and international bunkers for eitherjet fuel or fuel oil. Considering that some air ights are within provinces (thoughmost are not), and that some marine fuel oil is used for ships moving withinprovinces (though most is not), it was estimated that 80% of each was in inter-provincial/international travel. This assumption results in a total of 20.7 Mt oftotal kerosene and fuel oil for inter-provincial/international travel, out of a total of39.7 Mt total consumption, or about 50% of the total.

Table 9Beijing ranking in macro- and sector-level indicators.

Macro-level indicators Beijing ranking End-use sector-level indicators Beijing ranking

Primary energy consumption/GDP 1 Residential nal energy/capita 28Final energy consumption/GDP 1 Commercial nal energy/tertiary sector employees 27End-use CO2/GDP 1 Industrial nal energy/industry GDP 7Primary energy consumption/capita 24 Transportation nal energy/capita 29Final energy consumption/capita 24 CO2 per power produced 11End-use CO2/capita 24 End-use low carbon indicator 27

En

ReCoInTransportation nal energy/capita 30CO2 per power produced 17En

E

RCInTCE

L. Price et al. / Habitat International 37 (2013) 4e21 17Table 10Shanghai ranking in macro- and sector-level indicators.

Macro-level indicators Shanghai ranking

Primary energy consumption/GDP 3Final energy consumption/GDP 6End-use CO2/GDP 10Primary energy consumption/capita 30Final energy consumption/capita 30End-use CO2/capita 29

Table 11Shanxi Province ranking in macro- and sector-level indicators.

Macro-level indicators Shanxi ranking

Primary energy consumption/GDP 28Final energy consumption/GDP 28End-use CO2/GDP 29Primary energy consumption/capita 25Final energy consumption/capita 26End-use CO2/capita 26The weighting factor for the end-use sectors is the share of eachindividual end-use sector in the combined total residential,commercial, industrial, and transportation energy use. In this way,the energy use for each end-use sector reects the signicance ofthat sector in the city or provinces overall energy use. Theweighting factor for power generation is the share of electricity inthe total city or provincial energy use. Equation (1) provides thecalculation details for the provincial level low carbon indicator(LCI). Fig. 1 illustrates this calculation.

LCI

PR=CapNR=Cap

100PRPT

PC=ENC=E

100PCPT

PI=GDPNI=GDP

100 PIPT

PTr=CapNTr=Cap

100

PTrPT

PCO2=PpNCO2=Pp

100PElec

PT

1

Where:

PR Provincial Residential nal energy useNR National Residential nal energy use

Table 12Shandong Province ranking in macro- and sector-level indicators.

Macro-level indicators Shandong ranking En

Primary energy consumption/GDP 11 ReFinal energy consumption/GDP 11 CoEnd-use CO2/GDP 18 IndPrimary energy consumption/capita 19 TraFinal energy consumption/capita 20 COEnd-use CO2/capita 22 End-use low carbon indicator 26

nd-use sector-level indicators Shanxi ranking

esidential nal energy/capita 25ommercial nal energy/tertiary sector employees 22dustrial nal energy/industry GDP 26ransportation nal energy/capita 23O2 per power produced 22nd-use low carbon indicator 25d-use sector-level indicators Shanghai ranking

sidential nal energy/capita 22mmercial nal energy/tertiary sector employees 29dustrial nal energy/industry GDP 3Cap capitaPT Provincial Total end-use energyPC Provincial Commercial nal energy useNC National Commercial nal energy useE employeePI Provincial Industrial nal energy useNI National Industrial nal energy useI GDP Industrial share of gross domestic productPTr Provincial Transport nal energy useNTr National Transport nal energy usePCO2 Provincial CO2 emissionsNCO2 National CO2 emissionsPp Power producedPElec Provincial Electricity use

End-use low-carbon indicator calculation results

This section provides a description of the results of the calcu-lation of the sector-level end-use low carbon indicator for Chinabased on 2008 data. Table 5 shows the 2008 indicator value for eachof the end-use sector-specic indicators for Chinas large munici-palities, autonomous regions, and provinces.

d-use sector-level indicators Shandong ranking

sidential nal energy/capita 10mmercial nal energy/tertiary sector employees 25ustrial nal energy/industry GDP 10nsportation nal energy/capita 172 per power produced 23d-use low carbon indicator 11

Table 13Guangdong Province ranking in macro- and sector-level indicators.

Macro-level indicators Guangdong ranking End-use sector-level indicators Guangdong ranking

Primary energy consumption/GDP 2 Residential nal energy/capita 20Final energy consumption/GDP 2 Commercial nal energy/tertiary sector employees 15End-use CO2/GDP 2 Industrial nal energy/industry GDP 1Primary energy consumption/capita 17 Transportation nal energy/capita 24Final energy consumption/capita 16 CO2 per power produced 10End-use CO2/capita 16 End-use low carbon indicator 5

L. Price et al. / Habitat International 37 (2013) 4e2118According to Equation (1), in order to construct a single end-uselow carbon indicator, the values shown in Table 5 need to beindexed so that they can be compared. Each indicator is indexed toits un-weighted average national value in order to aggregate thedisparate values. Table 6 presents the results of the indexing step.Indexed values below 100 indicate that for that specic indictor, theprovince or city is below the national average. Indexed values above100 indicate that the indicator is higher than the national average.

The values in Table 6 do not fully explain the situation in eachprovince or city, though, because they do not take into account theshare of each end-use sectors energy use in the province. Thus, thenext step is to calculate weighting factors based on the share ofenergy use in each of the four end-use sectors (residential build-ings, commercial buildings, industry and transportation) as well asthe share of provincial/city electricity use of total provincial/cityenergy are calculated (as shown in Table 7). Theseweighting factorsare then multiplied by the indexed values for each end use sector.

Table 8 shows the aggregated end-use low-carbon indicatorresults for each of the 30 Chinese large municipalities, autonomousregions, and provinces. The results are presented in typical order asshown in Chinese publications on the left side of the table as well asin ascending order on the right side of the table. The lower end-uselow carbon indicator value denotes a more low carbon ranking.The large municipalities of Chongqing, Tianjin, Beijing andShanghai are ranked 15, 16, 26, and 29, respectively, among the 30cities, regions, and provinces evaluated. Since the results take intoaccount all energy use sectors as well as the power sector and areenergy consumption based (exported energy is excluded andimported energy is included), carbon leakage that often resultsfrom using nal energy values is avoided.

These results can be used to dene what qualies as a low-carbon municipality, autonomous region, or province. Based onthe results presented in Table 8, one option would be to dene allregions that have a low-carbon indicator value of less than 100 aslow carbon. Another option would be to designate the ve or tenregions with the lowest indicator value as low carbon. Since thismethodology is applied in this analysis to a mix of cities, autono-mous regions, and provinces, a specic value is not recommendedhere. Application of this methodology to a more homogenous set(e.g. only cities) could provide results that can be used to delineatea specic value for qualication as low-carbon.

Fig. 2 provides a graphical representation of the results of the

end-use CO2 emissions per capita indicator showing that CO2emissions per capita are the highest in the northern, industrial

Table 14Hubei province ranking in macro- and sector-level indicators.

Macro-level indicators Hubei ranking E

Primary energy consumption/GDP 20 RFinal energy consumption/GDP 19 CEnd-use CO2/GDP 4 InPrimary energy consumption/capita 14 TFinal energy consumption/capita 15 CEnd-use CO2/capita 6 Eprovinces of China, followed by neighboring provinces in the east aswell as Xinjiang province in the far west. The indicator, however,doesnt provide a fair comparison among provinces with differenteconomic structures or climate conditions.

Fig. 3 shows end-use CO2 emissions per unit of GDP. The highestCO2 emissions per unit of GDP are concentrated in the west andnorthern regions, with relatively low CO2 emissions per unit of GDPin the coastal and southern regions of China. This economic-basedindicator favors the economically more developed regions orenergy-consumption-based regions with low carbon status,rather than energy-producing regions.

Fig. 4 provides the results of the low carbon indicator (LCI). Thisindicator shows that Chinas eastern and southern provinces havethe lowest carbon ranking, with the exception of Shanghai.Shanghai has a very high carbon using the composite indicator, dueto the dominance of some carbon-intensive sectors. This isexplained further below.With few exceptions, the east and south ofChina rank as lower carbon than the north and west of China usingthe low carbon indicator.

To understand how the sectoral end-use low carbon indicatorcompares to the two more commonly used macro-level indicators(economic- and population-based) and the ve individual end-usesector-level indicators, six Chinese provinces and cities (Beijing,Shanghai, Shanxi, Shandong, Guangdong, and Hubei) that representvarious types of economic development were selected for moredetailedevaluation. Foreachprovince/cityselected, abrief explanationof the overall ranking in the end-use sectoral indicators is provided.

BeijingOf the 30 large municipalities, autonomous regions, and prov-

inces evaluated, Beijing ranks very high in terms of being lowcarbon using metrics that are based on GDP: 1st in primary energyconsumption/GDP, 1st in end-use CO2/GDP, and 1st in nal energyconsumption/GDP (see Table 9). As the nations capital, witha highly-developed, economically-productive commercial sector,this is not a surprise. Alternatively, Beijing does not appear to below carbon when indicators based on population are used,ranking 24th in primary energy use/capita, nal energy use/capita,and end-use CO2/capita. Again, as a densely populated urban area,this also is not surprising. Beijings ranking using a per capita basedindicator will most likely improve after 2010 when migrantworkers, who were previously not included in the national census

and are not included in the denominator for the values reportedhere, are included in the citys population.

nd-use sector-level indicators Hubei ranking

esidential nal energy/capita 15ommercial nal energy/tertiary sector employees 10dustrial nal energy/industry GDP 18ransportation nal energy/capita 21O2 per power produced 2nd-use low carbon indicator 12

ternLooking at the end-use sector-level indicators, Beijing againfares well (7th) with the one indicator that is GDP-based (industrialnal energy/industry GDP) and ranks relatively well at 11th inkilograms (kg) CO2 emitted per kilowatt hours (kWhs) of electricityproduced. Overall, when the end-use sector-level indicators arecombined into the end-use low carbon indicator, Beijing ranks 27thof 30 due to high energy use per capita for residential buildings,high energy use per employees for commercial buildings, and highenergy use per capita for transportation, despite the rapid growthin the subway system and the introduction of bus rapid transit.

ShanghaiSimilar to Beijing, Shanghai e Chinas nancial hub e ranks well

(3rd) in terms of low carbon when the indicator is based on GDP(see Table 10). Also similar to Beijing, this densely populated urbanarea does not rank well in terms of energy consumption and CO2emissions per capita (29th and 30th). Shanghais voracious devel-opment into Chinas top transshipment hub has no doubt driven upits ever-increasing energy consumption in transportation, leading itto trail all the other 29 provinces and cities in the transportationsector indicator. Shanghai also ranks poorly in terms of energy useper capita or per employee for residential and commercial build-ings, respectively. However, industry in Shanghai is relatively lowcarbon ranking 3rd lowest in industrial nal energy use per unit ofindustrial GDP produced. Even though CO2 emissions per unit ofpower produced were in the mid-range (17th), Shanghais overallend-use low carbon indicator value was 26th, making it one of theleast low-carbon areas in China.

Shanxi ProvinceShanxi Province, with abundant coal reserves and a huge coal

mining industry, ranks poorly in terms of lowcarbonusing both theGDP and per capita based macro-level indicators (see Table 11). Theend-use sector-level indicators also reveal that Shanxi Province ranksin the lower half of Chinas large municipalities, autonomous areas,and provinces in terms of energy use per capita for residentialbuildings and transportation, energy use per employee for commer-cial buildings, energy use per unit of industrial GDP, and CO2 emis-sions per unit of electricity produced. This indicates that not only ismore effort needed for improving energy efciency, but a shift todeveloping other secondary industries with lower carbon intensitycould reduce energy use, CO2 emissions, and associated environ-mental strain resulting from years of natural resource exploitation.

Shandong ProvinceShandong Province ranks relatively well (11th) in terms of energy

use/GDP and slightly worse than average (18th) in terms of end-useCO2 emissions/GDP (see Table 12). The Provinces ranking dropsfurther (to 19th, 20th, and 22nd) for primary energy use, nal energyuse, and end-use CO2 emissions per capita. When broken down intothe end-use sector-level indicators, it can be seen that ShandongProvinces residential and industrial sectors are relatively lowcarbon (both ranking 10th), while the commercial buildings, trans-portation, and electricity generation sectors rank lower. As one of thelargest heavy industrial provinces in China, Shandong Province ranksas relatively low-carbon in the industrial sector indicator, predomi-nately due to the Provinces focus and vigorous promotion of indus-trial energy efciency. Since the industrial sector accounts for 74% ofthe provincial energy use, high industrial energy efciency clearlycontributes to the overall relatively lowcarbon rankingof 11thamongChinas large municipalities, autonomous zones, and provinces.

Guangdong ProvinceAs with Shandong Province, the industrial sector of Guangdong

L. Price et al. / Habitat InProvince represents the largest share (64%) of overall provincialenergy use. Industrial production in Guangdong Province is focusedon high value-added products, so Guangdong ranks very well (2nd,2nd, and 2nd, for nal energy use, primary energy use, and end-useCO2 emissions, respectively) when measured by the overall energyconsumption and end-use CO2 emissions per unit of GDP. Therankings drop to 16th and 17thwhenmeasured on a per capita basis(see Table 13).

On an end-use sector-level basis, Guangdong Province is not lowcarbon in the residential buildings (20th) or transportation (24th)sectors. The rankings for commercial buildings (15th) and powerproduction (10th) are much better. Interestingly, Guangdong ranksthe best of all large municipalities, autonomous regions, andprovinces in terms of industrial nal energy use/industry GDP.Since industry represents 66% of total energy use in the province,this ranking heavily inuences Guangdongs overall low-carbonranking (5th in China).

Hubei ProvinceHubei Province ranks in the middle in primary and nal energy

use/GDP (20th and 19th, respectively) as well as per capita (14thand 15th, respectively) (see Table 14). However, due to its abundantwater supply from a few great lakes that provides rich sources forhydropower, Hubei Province has signicantly lower CO2 emissionsper kWh for power generation, ranking 2nd lowest of Chinas cities,regions, and provinces. As a result, the CO2/GDP and CO2/capitarankings are 4th and 6th, respectively.

Despite the 2nd lowest ranking of CO2 per kWh for electricityproduction, Hubeis overall end-use low carbon indicator value of12 is due to the higher rankings for the other end-use sectors,especially transport (21st) and industry (18th).

Issues with the sector-level end-use low carbon indicator

Although the sector-level end-use lowcarbon indicatorpresentedhere represents an improvement over themore simplied energy orCO2/GDP and energy or CO2/capita indicators, there are a number ofissues that arose during the development of this indicator for China.

For the commercial buildings sector, the ideal indicator wouldbe weather-adjusted energy use per unit of commercial oor space(m2). However, for China, information on commercial oor space atthe local level does not exist, so the number of tertiary employeeswas used for this calculation. In addition, more detailed indicatorsbased on commercial building types would be more helpful inunderstanding commercial building energy use and trackingprogress. This information, however, is also not readily available atthe provincial and city level for China.

For the industrial sector, the industrial share of regional GDPwas used as the denominator, but a better value would beprovincial or city industrial sector value added. However, for China,industrial sector value added is only available at the national level.

For the transport sector, itwould be helpful to havemore detailedinformation on usage (passenger-kilometers) of all public trans-portationmodes (buses, light rail, subway, etc.), and the total person-trip-kilometers for all private travel in cars and taxis, as well as thetotal energy consumption of these travel modes in order to developmore detailed indicators and metrics. This information, however, isalso not readily available at the provincial and city level for China.

For the power sector, the indicator used is calculated based ontotal power production by province expressed in terms of CO2/kWh. This approach favors large hydropower producers andexporters such as Hubei province (ranked 2nd), which emitsinsignicant CO2 compared with coal-based power-generatingprovinces such as Shandong province (ranked 23rd). A preferredapproach would be to base this indicator on power production by

ational 37 (2013) 4e21 19grid. Strengths and weaknesses of this approach include:

ternConclusion and recommendations

The results presented above for China illustrate that singleindicators based on energy or CO2 emissions per unit of GDP or percapita do not fully explain or reect the end-use energyconsumption and emissions situation in a given city or province.Such macro-level indicators can lead to inaccurate or confusingcomparisons and conclusions about whether certain cities orprovinces are or are not low carbon which could in turn lead toinappropriate use of funds for development, misguided efforts toinclude development and behavior that are not conducive toactually reducing energy use or CO2 emissions, and missedopportunities to focus on specic areas that could have the mostimpact in actually making a specic location low carbon.

The sectoral end-use low carbon indicator developed in thisreport has been constructed using the underlying contributors tothe overall level of energy use or CO2 emissions of a city or provincee the energy and emissions of themain energy-consuming end-usesectors: residential buildings, commercial buildings, industry,transportation and power. As such, it provides a more robustindication of where energy use is inefcient as well as whereactions can be targeted so that a city or province can become morelow carbon. Such an operation- and goal-oriented indicator canprovide a means for measuring and comparing and can be used todene low carbon, rank cities by energy use and CO2 emissionslevels, track progress in energy efciency and emission reductions,and establish benchmarks.

Although the composite sectoral end-use low carbon indicatorcan reect energy use of a province or a city more accurately thanthe macro-level indicators, to increase its recognition and adoptionby the Chinese government, additional efforts are needed in the It accords more closely with supply region for consumption.Nearly every province both imports electricity from andexports electricity to the regional grid. The current province-based calculation, however, does not exclude carbon emis-sions from generation of power that is exported, nor does itinclude the carbon emissions of imported power. Consequentlythe power sector indicator may not accurately reect the actualend-use consumption within a given province, resulting inoverstating of emissions for major exporters of power, andunderstating of emissions for major power importers. Expan-sion of the boundary from province to the regional grid couldthus better reect the emissions prole of actual power usewithin provincial boundaries.

A grid-based calculation would reduce the disparity betweenprovinces with a high proportion of renewable power gener-ation and with those with a high proportion of fossil powergeneration. As evident in the cases of Shanxi and Hubei,provinces using more fossil fuels to generate the same amountof electricity emit much more CO2 than provinces using morerenewable energy. Measurement by power grid thus could helpsmooth out this disparity.

A challenge to this approach is that grid-based calculations aremore difcult than province-based calculations because somegrid boundaries do not accord to provincial boundaries. Asa result, in the cases where provincial and grid boundaries donot coincide, additional sub-provincial data would be neededto effect the grid calculation, or grid-wide calculations wouldneed to be provided from other sources. NDRC does publishgrid emission factors, but these gures are based on thermalgeneration only, so do not reect the contribution of non-fossilgeneration.

L. Price et al. / Habitat In20following areas. Gather more city-level data. From the tables presented in thisreport, it is obvious that energy use data at the city-level arelimited to only a few large Chinese cities. It is recommendedthat the central and provincial governments encourage citygovernments to collect the required data through developinga clear set of policies, edicts and standardized statistics system,and providing necessary funding for these efforts.

Gather data on preferred indicators, such as energy/m2 ofcommercial buildings sector and grid-based power sectoremission factors. As discussed, replacing tertiary sectoremployee with per square meter is more meaningful forcommercial buildings indicator as energy use is generally usedat the level of oor space. For the industrial sector indicator, itis better to replace industrial share of regional GDP withindustrial sector value added.

Once the necessary data has been gathered, calculate indicatorsand rankings for more cities.

Use the sector-level end-use low carbon indicator to rank citiesbyenergyuse andCO2 emissions levels, trackprogress in energyefciency and emission reductions, and establish benchmarks.

Develop policies and programs to promote low carbon devel-opment at the sector and end-use level.

If data can be gathered, more disaggregated indicators withina sector can also be developed to provide the basis for morespecic status assessments and policy recommendations. Forexample, for the power sector, the efciency of coal-powerplants and the share of renewable energy could be used. Forthe buildings sector, the share of more efcient buildings/lowenergy buildings such as LEED-certied or green buildingscould also be used (Zhou et al., 2011).

There are many resources available for government ofcials,urban planners, and researchers to use to help in the developmentof low carbon cities or regions. Many of these resources have beengathered in Zhou et al. (2011) which draws from both internationaland Chinese domestic experience to provide information onsuccessful policies and measures for local governments in China tocreate low carbon plan or climate action plans.

Disclaimer

This document was prepared as an account of work sponsored bythe United States Government. While this document is believed tocontain correct information, neither the United States Governmentnoranyagencythereof, norTheRegentsof theUniversityofCalifornia,nor any of their employees, makes any warranty, express or implied,or assumes any legal responsibility for the accuracy, completeness, orusefulness of any information, apparatus, product, or process dis-closed, or represents that its use would not infringe privately ownedrights. Reference herein to any specic commercial product, process,or service by its trade name, trademark, manufacturer, or otherwise,does not necessarily constitute or imply its endorsement, recom-mendation, or favoring by the United States Government or anyagency thereof, or The Regents of the University of California. Theviews and opinions of authors expressed herein do not necessarilystate or reect those of the United States Government or any agencythereof, or The Regents of the University of California.

Ernest Orlando Lawrence Berkeley National Laboratory is anequal opportunity employer.

Acknowledgments

The authors would like to thank Hu Min at the Energy Foun-dation for her signicant contribution to this report. We also want

ational 37 (2013) 4e21to thank Adam Hinge of Sustainable Energy Partnerships, Liu Feng

of the World Banks Energy Sector Management AssistanceProgram (ESMAP), Xu Huaqing and Hu Xiulian at the EnergyResearch Institute of NDRC, Zhuang Guiyan at China Academy ofSocial Science, Su Mingshan at Tsinghua Univerisity, Song Guojunfrom Renmin University, Julia Currie of Johnson Controls Institutefor Building Efciency, and Bo Shen of Lawrence BerkeleyNational Laboratorys China Energy Group, for their commentsand review. We would also like to extend our thanks to TiffanyTona of the University of San Francisco for the indicator mapgraphics.

This work was supported by the China Sustainable EnergyProgram of the Energy Foundation through the U.S. Department ofEnergy under Contract No. DE-AC02-05CH11231.

This manuscript has been authored by authors at LawrenceBerkeley National Laboratory under Contract No. DE-AC02-05CH11231 with the U.S. Department of Energy. The U.S. Govern-ment retains, and the publisher, by accepting the article for publi-cation, acknowledges, that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish orreproduce the published form of this manuscript, or allow others todo so, for U.S. Government purposes.

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L. Price et al. / Habitat International 37 (2013) 4e21 21

Development of a low-carbon indicator system for ChinaOverview and objectivesMacro-level indicatorsMacro-level economic (GDP) indicatorsMacro-level population-based indicatorsIssues with macro-level indicators

Sectoral end-use low carbon indicator for ChinaMethodologyIdentify end use sectorsIdentify indicators for each end-use sectorResidential buildings sectorCommercial buildings sectorIndustry sectorTransportation sectorPower sectorGather indicator data for each province/cityCalculate the end-use low carbon indicator value by indexing and weighing end-use indicators

End-use low-carbon indicator calculation resultsBeijingShanghaiShanxi ProvinceShandong ProvinceGuangdong ProvinceHubei Province

Issues with the sector-level end-use low carbon indicator

Conclusion and recommendationsDisclaimerAcknowledgmentsReferences