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Using hybrid method to evaluate carbon footprint of Xiamen City, China
Energy Policy 58 (2013) 220–227
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Using hybrid method to evaluate carbon footprint of Xiamen City, China Jianyi Lin a,b, Yuan Liu a,b, Fanxin Meng a,b, Shenghui Cui a,n, Lilai Xu a,b a b
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China
H I G H L I G H T S
Carbon emissions from Scope 1 þ2 þ3 are calculated for Xiamen City, China. Carbon footprint in Xiamen is industrial carbon-intensive and high embodied emissions. Management for Scope 3 emissions in the developing cities is important.
art ic l e i nf o
a b s t r a c t
Article history: Received 17 May 2012 Accepted 4 March 2013 Available online 4 April 2013
For more holistic inventory estimation, this paper uses a hybrid approach to access the carbon footprint of Xiamen City in 2009. Besides carbon emissions from the end-use sector activities (called Scope 1þ2 by WRI/WBCSD) in normal research, carbon emissions from the cross-boundary traffic and the embodied energy of key urban imported materials (namely Scope 3) were also included. The results are as follow: (1) Carbon emissions within Scope 1 þ2 only take up 66.14% of total carbon footprint, while emissions within Scope 3 which have usually been ignored account for 33.84%. (2) Industry is the most carbonintensive end use sector which contributes 32.74% of the total carbon footprint and 55.13% of energy use emissions in Scope 1 þ 2. (3) The per capita carbon footprint of Xiamen is just about one-third of that in Denver. (4) Comparing with Denver, the proportion of embodied emissions in Xiamen was 10.60% higher than Denver. Overall, Xiamen is relatively a low-carbon city with characters of industrial carbonintensive and high embodied emissions. Further analysis indicates that the urbanization and industrialization in Xiamen might cause more material consumption and industrial emissions. These highlight the importance of management for Scope 3 emissions in the developing cities. & 2013 Elsevier Ltd. All rights reserved.
Keywords: Carbon footprint Hybrid analysis method EIO-LCA
1. Introduction Future carbon dioxide emissions will hence lead to adverse climate changes on both short and long time scales that would be essentially irreversible (Solomon et al., 2009). Today, humanity is experiencing a dramatic shift to urban living, over 50% of the world population living in cities (UN-HABITAT, 2011). Undoubtedly, urban centers, especially those in the developed world, are the primary source of greenhouse-gas emissions and thus are implicated in global climate change (Grimm et al., 2008). China is now undergoing a rapid urbanization with about 12 million peoples moving to cities each year, and some 60% of the population will live in cities by 2020 (Normile, 2008). Within China, cities consume 84% of commercial energy, among which the top 35 large cities with 18% of the total population contribute 40% of China's energy uses and CO2 emissions (Dhakal, 2009). Consequently, n Corresponding author at: Chinese Academy of Sciences, Institute of Urban Environment, Key Lab of Urban Environment and Health, Xiamen 361021, China. Tel.: þ 86 592 6190957; fax: þ86 592 6190977. E-mail addresses: [email protected] (J. Lin), [email protected] (S. Cui).
0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.03.007
there is a crucial need to present a suitable method for accounting Chinese urban GHG emissions so that urban decision makers can make appropriate policies to reduce their total GHG emission. For carbon mitigation in a broad range, carbon footprint calculations which take both direct carbon emission and indirect emissions into account are in strong demand. Wiedmann and Minx (2007) defined carbon footprint as a measure of the total amount of CO2 emissions that is directly and indirectly caused by an activity or is accumulated over the life stages of a product. Carbon footprint derives from ‘ecological footprint’ (Wackernagel and Rees, 1996)which is the total amount of gases related to the climate change, emitted from human production and consumption activities. Then, other of the Kyoto Protocol greenhouse gases (GHGs) is suggested to be included in carbon footprint calculation (Matthews et al., 2008; Wang et al., 2010; Wright et al., 2011). Carbon footprint asserted the importance of all direct (on-site, internal) and indirect (off-site, external, upstream, downstream) emissions to track total GHG emissions of a product or an activity. Carbon footprint is widely used in carbon emission analysis at different scales of the products, individuals/households, organizations, cities, countries scales. The Carbon-Trust (2007) firstly developed a
J. Lin et al. / Energy Policy 58 (2013) 220–227
pilot methodology to measure the carbon footprint of products as well as a label to display the information on individual products . Other carbon footprint of products such as crisps, office chairs, shopping bags are also studied (Brentona et al., 2008; Gamage et al., 2008; Muthu et al., 2011). For individuals and households, numerous websites have been created to help calculate an individual's carbon footprint (Padgett et al., 2008), and some typical models are compared (Kenny and Gray, 2009). Stockholm Environment Institute (SEI) (2006) used hybrid method to calculate the UK schools carbon footprint, and results show that only around 26% of this total carbon footprint are direct emissions, whereas the other three quarters are from indirect emission sources. Ramaswami et al. (2008) develops a demandcentered, hybrid life-cycle-based methodology for conducting cityscale GHG inventories , and then applied it to 8 US cities and found that the cross-boundary activities contributed 47% on average more than the in-boundary GHG contributions traditionally reported for cities (Hillman and Ramaswami, 2010). Larsen and Hertwich (2009) developed a consumption-based hybrid-LCA based carbon footprint model and applied it to analyzed the emission of Trondheim, Norway. As for countries, Hertwich and Peters (2009) also quantified carbon footprint associated with the final consumption of goods and services for 73 nations and 14 aggregate world regions. Carbon footprint has also been used for a range of activities in different sectors including industry, transport, construction, water supply, and so on. POST (2006) compared the life cycle CO2 emissions of different electricity generation systems in the UK and found that none of electricity generation technologies are entirely 'carbon free'. Lee (2011) explored current business practices in carbon footprint and supply chain management with the case of the automobile industry. Argonne National Laboratory developed a vehicle-cycle module for the greenhouse gases, regulated emissions, and energy use in transportation (GREET) model (Burnham et al., 2006). Cui et al. (2010) proposed a carbon footprint model of the BRT system based on a life cycle assessment (LCA) approach, with three components: infrastructures, fuels and vehicles. Giurco and Petrie (2007) developed an approach to designing preferred futures for entire metal cycles that deliver reduced carbon footprints. Lim and Park (2008) compared cooperative water network system with the individual's and founds that the cooperation reduces their carbon footprint and is economically feasible and profitable. Friedrich et al. (2009) employed LCA studies to analyze carbon footprint of urban water system in Durban. On above studies, there are three principal approaches: bottom-up based on Process Analysis (PA), top-down based on Environmental Input–Output Analysis (EIOA), and hybrid-EIO-LCA which is a combination of PA and EIOA (Wiedmann and Minx, 2007; Wright et al., 2011). Each of the approaches has its own merits and drawbacks. Process analysis, which analyzes the emissions associated with specific processes, has a greater level of accuracy, but requires more time and resources. PA has clear advantages for looking at micro systems such as a particular process, individual product or a relatively small group of individual products. However, EIOA is a much smaller requirement of time and manpower, but it has limitation to assess micro systems. It is superior for the macro and meso systems: industrial sectors, individual businesses, larger product groups, government, and so on. To overcome the shortfalls of EIOA and PA, hybrid-EIO-LCA combines the strength of both methods. This approach overcomes the issues of EIOA not being specific or detailed enough to monitor minor changes at an organizational or sub-national scale and omitting use and end-of-life product life cycle phases and the issue of incompleteness and truncation errors in PA (Wiedmann and Minx, 2007; Wright et al., 2011). As for the importance of Chinese urban GHG emissions accounting, there have been many studies on urban carbon emission analysis (Bi et al., 2011; Dhakal, 2009; Gu and Yuan, 2011). Most of these studies focus on direct emission accounting,
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and few explores the carbon footprint tracking all direct and indirect emissions. Since the great significance of cross-boundary activities for U.S. Cities (Hillman and Ramaswami, 2010), interests are arisen on Chinese urban carbon footprint and its characters. Therefore, this paper will use the hybrid-EIO-LCA to evaluate carbon footprint for Xiamen City to help understand the characteristics of Chinese urban GHG emissions and the differences comparing to those cities in developed country. This paper is organized as follow: (1) introducing the calculating methodology; (2) applying hybrid method to evaluate carbon footprint for Xiamen City; (3) analyzing the characters of Xiamen's carbon footprint and comparing with other cities in the word; (4) drawing our conclusions and discussing emission inventories and our policy implications.
2. Methodology According to the WRI/WBCSD (2004)'s Greenhouse Gas Protocol , urban carbon footprint includes three scopes’ emission: Scope 1 emissions, which refer to the direct emissions inside the cities. These includes fossil fuel combustion; waste; industrial processes and product use; and agriculture, forestry and other land use (AFOLU) (Kennedy et al., 2010). Scope 2 emissions include indirect emissions due to electricity and steam use out-of-boundary. Scope 3 emissions include other indirect emissions and embodied emissions that occur outside of the city boundary. In this paper, the hybrid-EIO-LCA is used to calculate all three scope emissions for Xiamen City. Scope 1 and 2 emissions are determined as IPCC guidelines (2006). The emissions from cross-boundary transport, such as long-distance vehicle (passenger and freight), railway, marine, aviation, are distributed by spatial allocation. All the above emissions are evaluated by Process Analysis (PA) method. Besides, the embodied emissions from urban key imported materials (fuel, cement, water and food) are calculated by Environmental Input– Output Analysis (EIOA) method. According to Kyoto protocol, six GHG emissions (CO2, CH4, N2O, HFCs, PFCs, and SF6) are inventoried and reported together as carbon dioxide equivalents (CO2e). The hybrid calculating method and scopes are shown as Fig. 1. 2.1. Carbon emissions by urban end-use sectors The carbon emissions caused by urban end-use sectors refer to Scope 1 and 2, which include energy use and non-energy use. The calculation of GHG emissions from energy use is based on the final end-use energy consumption data and the emission factors of various fuels with different processes. These include direct fossil fuel combustion (Scope 1) and indirect emissions due to electricity and steam use out-of-boundary (Scope 2). And the emissions by non-energy use include waste, industrial processes and product use, agriculture, forestry and other land use, within Scope 1. The calculation of the energy use and non-energy use are as follow: (1) Energy use emissions The GHG emission factors of fossil fuels are directly obtained from IPCC guidelines (2006) and the emission factors of electricity and heat are calculated based on fossil fuels used in power plants and heat generations. Urban sectors’ emissions of end-use energy use are calculated by transport sector and non-transport sectors as follow: GHG ¼ GHGtrans þGHGnon−trans
ð1Þ
GHGnon−trans ¼ ∑i ðGHGf uel,i þ GHGelectricity,i þ GHGheating,i Þ
ð2Þ
GHGtrans ¼ ∑j GHGf uel,j
ð3Þ
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J. Lin et al. / Energy Policy 58 (2013) 220–227
Industrial Process Analysis Household Energy Use
Scope 1+2
Urban Carbon footprint
Commercial
Industrial Processes
In-Boundary Traffic Construction
Agriculture
Others Long-Distance
Forestry and Other Land Use Railway Marine Waste Disposal
Scope 3
Aviation Cross-Boundary Traffic
Fuel Cement
Urban Key Imported Materials EIO-LCA
Water Food
Fig. 1. Hybrid method on the urban carbon footprint analysis.
where, GHG is carbon emission from urban sectors by end-use energy use; GHGtrans is the emission from transports inside cities; GHGnon-trans is the emission from other urban sectors; GHGfuel,i is direct emissions from fuel combustion by end-use sector i; GHGelectricity,i is emissions from electricity use by end-use sector i; GHGheating,i is emissions from heat use by end-use sector i; GHGfuel,j is emissions from in-boundary transport sector j. To avoid double counting, the industrial sector did not include electricity generation and heat production, and the carbon emissions of this part are allocated to the end-use sectors. (2) Non-energy use emissions The estimation of GHG emissions by non-energy use (industrial processes and product use, waste, agriculture, forestry and other land use) is base on IPCC guidelines (2006). Emissions from industrial processes and product use are based on the industrial products data and corresponding emission factors. For Xiamen, only glass production and electronics industry emissions are involved in the industrial processes and product use calculation. Agricultural activities include manure management, enteric fermentation, rice cultivation and planting soils, and specific emission factors of agricultural are adopted to suit the Xiamen's situation. The forestry and other land use are also calculated by IPCC guidelines (2006). Waste treatment includes solid waste and wastewater, which calculated by simplified version of the IPCC recommended approaches.
data, long-distance vehicles use local fuel sales data to estimate the emissions, and the railway are allocated 50% VKT between Xiamen and other cities. There are differing approaches to the quantification of aviation and marine emissions (Carney et al., 2009; Ramaswami et al., 2008), most cities determine GHG emissions from full volumes of fuels loaded at airports and marine ports within their boundaries (Kennedy et al., 2010). Based on availability of data, this paper estimates GHG emissions from air and marine transportation for Xiamen City using fuels loaded onto planes and ships at the airport and harbor. 2.3. Embodied emissions from urban key imported materials Based on the functionality of cities, indirect energy use and associated GHG emissions were computed for the critical urban imported materials mainly including water, fuel, food, and cement (Ramaswami et al., 2008). For Xiamen City, only the materials which produced outside the city area are calculated, so they have no significant overlap with carbon emissions by urban end-use sectors (Section 2.1). The embodied GHG emissions associated with key urban imported materials were computed by Environmental Input–Output Analysis (EIO-LCA) (CMU-GDI, 2008; Matthews et al., 2008). The basic input–output model tracks flows of purchases (i.e., supply chain) between sectors, calculated as fellow: x ¼ ðI þA þ A A þA A A þ …Þy ¼ ðI−AÞ−1 y
ð4Þ
where, x is a vector including all supplier outputs; I is the identity matrix; A is the direct requirements matrix which represents the direct requirements of the intersectoral relationships; y is the vector of final demand. Terms in Eq. (4) represent the production of the desired output itself (I*y), contributions from the direct or first-tier suppliers (A*y), those from the second-tier indirect suppliers (A*A*y), and so on. Then environmental emissions associated with final demand can be calculated by multiplying the output of each sector by its environmental impact per unit of output, as follow: bi ¼ Ri x ¼ Ri ðI−AÞ−1 y
ð5Þ
where bi is the vector of environmental emissions for sector I; and Ri is a matrix with diagonal elements representing external output (environmental emissions) per unit of output each sector, which is calculated from: Ri ¼ total external output=X i
ð6Þ
where Ri is used to denote the impact in sector i, and Xi is the total dollar output for sector i.
3. Case study 3.1. Study areas and data use
2.2. Carbon emission from cross-boundary transports Cross-boundary transports include long-distance vehicles (passenger and freight), railway, marine, aviation, which need allocation procedures for these transportation activities. As for the ground transportation, there have been three different methods to test and verify calculations of fuel consumption in cities, which are using local fuel sales data, estimating from vehicle kilometers travelled (VKT) within cities, and estimating urban gasoline consumption by scaling from state, provincial, or regional data (Kennedy et al., 2010). And it is showed that the three approaches can produce reasonably close estimates, provided that the VKT calculations are conducted appropriately (Kennedy et al., 2010). Considering Xiamen's actual situation and the availability of
Xiamen is a coastal sub-provincial city in southeastern Fujian province, P.R. China, and is one of China's earliest Special Economic Zones (designated in the 1980s). It lies at 118104′04″ east longitude and 24126′46″ north latitude, looking straight to the Taiwan Strait. Xiamen has a monsoonal humid subtropical climate, with an average annual temperature of approximately 21 1C. It covers an area of 1 573 km² with a total population of 2.52 million in 2009 (XMBS, 2010). With rapid economic development, Xiamen's regional GDP reached 173.72 billion Yuan in 2009, up from 1.72 billion Yuan (comparable GDP value) in 1980, with an average annual increase of 17.49%. Its ratio of three industrial structures is 1.08:47.26:51.56. The urbanization ratio also rapidly increased, from 35% in 1980 to 80.2% in 2009. The rapid economic growth
J. Lin et al. / Energy Policy 58 (2013) 220–227
and urbanization have resulted in rising energy consumption in Xiamen City, and its total energy consumption reached 9.7962 million tce (tons of coal equivalents) in 2009, which energy consumption intensity was 0.579 tce/10,000 Yuan. In 2010, the National Development Reform Commission (NDRC) announced the program for five low-carbon pilot provinces and eight low carbon pilot cities, and Xiamen became one of the eight pilot cities. Thus, there is an urgent need for carbon accounting system which can track all direct (on-site, internal) and indirect (off-site, external, upstream, downstream) emissions of the pilot cities, so that cities can be credited for such global carbon emission reductions. The data used in this study are from three sources: yearbook of statistics, literatures and documents, and government departmental survey data. The main statistical yearbooks include Yearbook of Xiamen Special Economic Zone (2010), China Energy Statistical Yearbook (2010), Fujian Statistical Yearbook (2010), China City Statistical Yearbook (2010), etc. The main document referred is Xiamen's Energy Balance Table (2009). Sector energy consumption data (electricity, coal, gasoline, diesel, fuel oil, LPG, natural gas, crude oil, and so on) were obtained by departmental survey: the Xiamen Development and Reform Commission, the Economic Development Bureau of Xiamen, the Fujian Electronic Power Company Limited, the Xiamen Municipal Works and Municipal Gardens Administration Bureau, the Xiamen Statistical Bureau, the Xiamen Transportation Bureau, the Urban Planning Bureau of Xiamen and the Xiamen Vehicles Administration Center.
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Province power grid is calculated according to the China Energy Statistical Yearbook in 2010 (Fujian power grid only transferred out electric power and did not transferred in).
4. Results and analysis In this paper, the hybrid method is applied to calculate the carbon footprint of Xiamen City in 2009, and the total emission is 24355.16 kt CO2e. The emissions of sectors and sub-sectors are as Table 2. The proportions of carbon footprint are as Fig. 2, where: (1) Carbon emissions from urban end-use sectors (Scope 1 þ2) contribute 66.14%, in which energy use takes 59.39%, and nonenergy use in boundary takes 6.75%. Energy use includes direct fuel combustion and electricity generated outside the city, namely Scope 1 þ2, in which industry sector contributes 32.74% of the total carbon footprint. The transferred-in power accounts for 4.46%, which emissions are 1085.69 kt CO2e, belonging to Scope 2. Non-energy use emissions include industrial processes and product use (0.35%), waste (2.92%), agriculture (0.40%), forestry and other land use (3.08%), which are all in boundary. Among these, industrial sector is the largest emitter, contributing 32.74% of the total carbon footprint. (2) Cross-boundary transport accounts for 9.22%, including aviation, navigation, long-distance vesicles, and railway. (3) Embodied emissions from urban key imported materials takes 24.64.49% proportion. 4.1. Details in carbon footprint of Xiamen City
3.2. Computing boundaries The urban computing boundary mainly considers the administrative boundaries, referring to the availability of research data, for most available statistical data are based on the administrative boundary. This paper includes six GHG emissions (CO2, CH4, N2O, HFCs, PFCs, and SF6) according to Kyoto protocol, and reports them together as carbon dioxide equivalents (CO2e) by global-warming potentials (GWPs) over a 100-year period (by IPCC Second Assessment Report). The GHG emissions from fossil fuel combustion; waste; industrial processes and product use; agriculture, forestry and other land use are calculated. For energy use, six end-use sectors are included in this paper: the industrial sector, the household sector, the in- border transport sector, the commerce sector, the construction sector, and other sectors (as shown in Fig. 1). To avoid double counting, the industrial sector does not include electricity generation and heat production industry, for their emissions are allocated to end-use sectors. Electricity emission factors are computed based on power generation structures of Xiamen City and Fujian Province, as shown in Table 1. The electricity emissions factor of the Fujian
Table 1 Electric power structure and emission factor of Xiamen City and Fujian province. Power type
Fujian Province Ratio (%)
Thermal power (including natural gas) Wind power, hydropower, nuclear et al. Disturbance power Total
Emission factor (t CO2e/ 104 kW h)
Xiamen City Ratio (%)
Emission factor (t CO2e/ 104 kW h)
75
8.5851
87
6.8896
25
0.0000
1
0.0000
0
0.0000
12
6.4695
100
6.4695
100
6.6262
(1) Energy use emissions by urban end-use sectors The total energy use emissions by urban end-use sectors of Xiamen are 14463.48 kt CO2e, accounts 59.39% of the carbon footprint. The details of urban end-use sector emissions are as Fig. 3. Industrial sector is largest source of energy use emissions (7973.28 kt CO2e), accounting for 55.13% of whole energy use emissions. Commercial and public sector takes the second place (2192.53 kt CO2e), accounting for 15.16%. The third large source is household energy use emissions, contributes for 14.32%, which is 2070.48 kt CO2e. Construction, in-boundary transport and others sectors, respectively, account for 7.65%, 6.65%, 1.10% of the total energy use carbon emissions. The in-boundary transport sector mainly includes private cars, motorcycles, taxis, Normal bus transit (NRT), Bus rapid transit (BRT), minibus, and ferries. Private cars have the large share, accounting for 46.36%. Taxi and NRT accounts for 19.72% and 15.33%, respectively, taking the second and third place. Motorcycles, minibuses, BRT and ferries, respectively, account for 12.01%, 4.08%, 1.96%, and 0.54%, as shown in Fig. 3. Fig. 4 shows industrial sector emissions of Xiamen City which emissions are more than 100 kt CO2e. In industrial sector, carbon emissions of chemical materials and chemical products manufacturing industry has the largest contribution, which is 1934.77 kt CO2e, accounting for about 25%. The second and third large contributions are by non-metallic mineral products and rubber products industry, all accounting for about 10%. (2) Non-energy emissions by urban sectors in boundary The total emissions of non-energy use inside the city is 1644.19 kt CO2e, only accounting 6.75% of Xiamen carbon footprint. Among these emissions, forest and other land use is has the largest share, accounting for 45.59%, in which the carbon sink of forestry is very small. Waste (including solid waste and waste water) accounts for 43.19%, and solid waste contributes for 36.32%. Agriculture (including Farming and Livestock) and Industrial processes and product use, respectively, accounts for about 5.99% and 5.23%. Details are shown in Fig. 5.
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J. Lin et al. / Energy Policy 58 (2013) 220–227
Table 2 Carbon footprint Inventory of Xiamen City in 2009. Scope
Sectors
Carbon emissions (kt CO2e)
Sub-sectors
Carbon emissions (kt CO2e)
Scope 1þ 2
Energy use
14,463.48
Scope 1
Industrial processes and product use
Industrial sector Commercial and public sector Household sector In-boundary transport sector Construction sector Other sectors Industrial processes and product use Solid waste Waste water Farming Livestock Forestry Other land use Aviation Navigation Long-distance vesicles Railway Fuel Cement Food Water
7973.28 2192.53 2070.48 962.45 1106.03 158.71 86.02 597.12 113.05 34.17 64.26 −59.38 808.95 775.71 532.00 856.00 82.26 3086.72 2442.90 278.30 193.60
86.02
Waste
710.17
Agriculture
98.43
Forestry and other land use Scope 3
749.57
Cross-boundary transport
2,245.97
Embodied emissions
6,001.52
Energy use,
Industrial
59.39%
processes and product use, 0.35% Waste, 2.92%
In-boundary non-energy use,
Agriculture,
6.75%
0.40%
Forestry and
Cross-boundary
other land use,
transport, 9.22%
3.08%
Embodied energy, 24.64%
Food industry Water production and supply industry Educational and sports goods Transportation equipment manufacturing Machinery manufacturing Manufacture of Chemical Fibers Fabricated metal products Non-ferrous metal processing industry Agro-food processing industry Manufacture of Beverages Plastic products industry Textile industry Electronic Equipment Manufacturing Rubber Products Non-metallic mineral products Chemical materials and products
Kt CO2e
0
Fig. 2. Carbon footprint of Xiamen City in 2009.
500
1000
1500
2000
Fig. 4. Carbon emissions by industries of Xiamen City, 2009.
Commercial and public sector 15.16%
Household sector 14.32%
Taxi 19.72%
Construction sector 7.65% Motorcycle 12.01% In-boundary transport 6.65%
5.23% BRT 1.96% Minibus 4.08% Ferry 0.54%
Other sectors 1.10% Industrial sector 55.13%
Industrial processes
NRT 15.33%
Private car 46.36%
Other land use
Solid waste
49.20%
36.32%
Fig. 3. Energy use emissions by urban end-use sectors of Xiamen City, 2009.
(3) Emissions of cross-boundary transport The cross-border transportation of Xiamen City mainly includes long-distance passenger and freight road vehicle, aviation, passenger and cargo ships, and trains. The carbon emissions by cross-border traffic in 2009 are 2245.97 kt CO2e totally, accounting for 9.22% of the whole carbon footprint of Xiamen City. Fig. 6 shows the proportion of cross-border traffic emissions for Xiamen City in 2009. Long-distance road transport accounted for 38.12%, including passenger and freight. Aviation, marine, and railway, respectively, accounts for 34.54%, 23.68%, and 3.66%.
Waste Forestry -3.61%
Livestock Farming 2.08% 3.91%
water 6.88%
Fig. 5. Non-energy emissions by urban sectors in boundary, 2009.
(4) Embodied emissions of urban key imported materials The embodied emissions of urban key materials imported to Xiamen City in 2009 are 6001.52 kt CO2e, accounting for 24.64% of the whole carbon footprint. As shown in Table 2
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Railway 3.66% Passenger vehicle 32.66%
Cargo ship 22.99%
Passenger ship 0.69%
Aviation 34.54%
Freight vehicle 5.46%
Fig. 6. Carbon emissions by cross-boundary transport of Xiamen City, 2009.
Food 4.64%
Water 3.23%
Cement 40.70%
Fuel 51.43%
Fig. 7. Embodied emissions of urban key imported materials of Xiamen City, 2009.
and Fig. 7, imported fuels, cements, foods, and water, respectively, generates 3086.72, 2442.9, 278.3, and 193.6 kt CO2e emissions, accounting for 51.43%, 40.70%, 4.64% and 3.23%.
4.2. Comparison with other cities To be more comparative with other cities, the calculation of the per capita carbon footprint of Xiamen below excludes the non buildup area emissions (agriculture, forest and other land use) and industrial process and product use emissions (HFCs, PFCs, and SF6). Comparing with Denver (Ramaswami et al., 2008), the per capita carbon footprint of Xiamen is much lower than Denver, just counting about one-third of the per capita carbon footprint in Denver. From the tires of per capita carbon footprint (as Table 3), carbon emission by urban sectors belong to Scope 1 and 2 of Xiamen and Denver, respectively, account for 64.79% and 74.70%, and cross-border traffic all cause about 10%. As for the embodied emissions, Xiamen and Denver, respectively, account for 25.62% and 15.02% of the total per capita carbon footprint, and Xiamen is about 10% more than in Denver. These might show that cities in developing countries consume relatively more fuel, cement, and other substances due to the process of industrialization and urbanization. Thus these highlight the importance of embodied carbon emissions management in developing countries in some senses. According to the UNEP (2011) and World Bank's carbon emission summaries of cities in the world, further comparison is made between some typical cities at home and abroad (as shown Fig. 8).
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They include cross-border transport carbon emissions (aviation, marine etc.), but not including embodied emissions of the city's key imputed materials. From the figure, it can be seen that Denver has the highest per capita carbon emissions, accounting for 21.5 t CO2e/person. The relatively high-emission cities include Angeles, Shanghai, Tianjin, Beijing, and Bangkok, with a per capita carbon emission more than 10 t CO2e/person. For this sense, Xiamen is a relatively low-carbon city, with only 6.91 t CO2e/person. From a regional perspective, the U.S. cities have the highest per capita carbon emissions, and China's major cities are relatively high on per capita emissions, while Cities in Europe, Japan and other countries might relatively low emissions.
5. Conclusion and discussion In this paper, hybrid analysis method of carbon footprint was used to access the carbon footprint of Xiamen City in 2009. Besides the carbon emissions from the city-scale end-use sectors in normal research, the carbon emissions from the cross-boundary traffic and the embodied energy of key urban imported materials were also included. The urban end-use sector activities include energy use (direct fuel combustion and electricity) and nonenergy use (waste, industrial processes and product use, agriculture, forestry and other land use), within Scope 1 þ 2. Cross-border transports include surface, sailing and airline travel, while key urban imported materials contains food, water, fuel, and concrete, all namely scope3. Research result showed that: (1) Carbon emissions from the energy use, waste, industrial processes and product use, agriculture, forestry and other land use within Scope 1 and Scope 2, only take up 66.14% of the total city carbon footprint. However, the emissions from the cross-boundary traffic and embodied emissions of key urban imported materials account for 33.86%, which has usually been ignored as Scope 3. This demonstrates the importance of management on Scope 3 emissions. (2) Within energy use emissions by urban end-use sectors, industry contributed the largest share, which counts for 55.13%. And industry sector contributes 43.44% of the whole carbon emissions excluding embodied emissions by key urban imported materials. Comparing with other cities like New York, Toronto and so on (Dickinson and Desai, 2010; ICF-International, 2007; Lemon et al., 2009), the share is relatively high, which may due to industrialization process currently. Developed countries are responsible for large part of emissions for statistical data show that 39.2% of the industry products are exported to other countries (XMBS, 2010). As for carbon reduction, industry will pay a very important role for its largest abatement potential (Lin et al., 2010). (3) Xiamen has a relatively low per capital carbon footprint, excluding from emissions of industrial processes and product use, agriculture, forestry and other land use to be more comparative. Its per capital carbon footprint is just about one-third of that of Denver, counting for 9.29 t CO2e/person. And its carbon emissions further-reducing embodied emissions is only about half of those of Los Angeles and Shanghai, 6.91 t CO2e/person. According to the structure of carbon footprint, the embodied emissions by Xiamen were about 10% higher than Denver, and this might indicate that the urbanization and industrialization consume more materials in Xiamen. And efficient utilization of materials is very important for carbon footprint reduction. (4) Among non-energy use inside the city, AFOLU has the largest share (49.2%). It is because Xiamen has large areas of hinterland and its build-up areas only accounts for 13.48% (XMBS, 2010). Waste the second largest emitter, accounting for 43.19%
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Table 3 Comparison of urban carbon footprint between Xiamen and Denver. Levels
Denver, 2005 t CO2e/person (percent)
Xiamen, 2009 t CO2e/person (percent)
Unban sector's emissions including Scope 1þ 2 Scope1 þ 2 plus cross-boundary transport of Scope 3
18.90 (74.70%) 21.50 (74.70%þ 10.28%)
6.02 (64.79%) 6.91 (64.91% þ9.59%)
Total emissions scope1 þ 2þ 3
25.30 (74.70%þ 10.28% þ15.02%)
9.29 (64.79% þ 9.59% þ 25.62%)
per capita carbon emissions tCO2e/person
25 20 15 10 5
Lo
s
D
en
ve r, An 20 05 ge le s, Sh 2 an 00 gh 0 ai ,2 00 Ti an 6 jin ,2 Be 00 6 ijin g, Ba 20 ng 06 ko k, 20 Lo 05 nd N ew on ,2 Yo 00 rk 3 C C ity ap ,2 e 0 To 09 w n, 20 Xi am 05 en ,2 00 Pa 9 ris ,2 00 To 5 ky o, 20 06
0
Fig. 8. Comparison of per capita carbon emissions of typical cities.
of the total non-energy emissions in-border. These emissions are associated with waste disposal mode in which landfill accounts about 85%. In another perspective of urban metabolism, more materials consumed by the city might cause more waste through flows of water, foods, and other materials (Kennedy et al., 2007). Thus garbage classification and recycling, as well as imported materials control, is need to carbon reduction of this proportion. (5) In terms of transports, both in-border transport and crossborder transport account for 13.17% of the total carbon footprint, respectively, contributing 3.95% and 9.22%. For inborder transport, private cars emit the most, with 46.36% of inborder transport emissions. Then developing public transport is the effective way to reduce these emissions (Cui et al., 2011). As for cross-border transport, Long-distance vehicle has the largest share, while railway has the least share. With the rapid development of high-speed railway, the situation in surface transportation might change in the near future. However, controlling aviation and marine emissions will be a big challenge for the construction of aviation hinge and shipping center of Xiamen.
Acknowledgements This study was supported by National Natural Science Foundation of China (71003090 and 71273252), Public Welfare Project on Environment Protection (201009055), Natural Science Foundation of Fujian Province (2012J01306) and Science and Technology project of Fujian Province (2011R0094). References Bi, J., Zhang, R.R., Wang, H.K., Liu, M.M., Wu, Y., 2011. The benchmarks of carbon emissions and policy implications for China's cities: case of Nanjing. Energy Policy 39, 4785–4794. Brentona, P., Jonesb, G.E., Jensena, M.F., 2008. Carbon Labelling and Low Income Country Exports: An Issues Paper. The World Bank, Washington DC. Burnham, A., Wang, M., Wu, Y., 2006. Development and Applications of GREET 2.7—The Transportation Vehicle-Cycle Model. Argonne National Laaboratory. Carbon-Trust, 2007. Carbon Footprint Measurement Methodology. The Carbon Trust, London.
Carney, S., Green, N., Wood, R., 2009. Greenhouse Gas Emissions Inventories for Eighteen European Regions. University fo Manchester, Manchester. CMU-GDI, 2008. Economic Input–Output Life Cycle Assessment (EIO-LCA), US 1997 Industry Benchmark model. Carnegie Mellon University Green Design Institute. Cui, S., Meng, F., Wang, W., Lin, J., 2011. GHG accounting for pubilc transport in Xiamen City, China. Carbon Management 2, 383–395. Cui, S., Niu, H., Wang, W., Zhang, G., Gao, L., Lin, J., 2010. Carbon footprint analysis of the bus rapid transit (BRT) system: a case study of Xiamen City. International Journal of Sustainable Development and World Ecology 17, 329–337. Dhakal, S., 2009. Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy 37, 4208–4219. Dickinson, J., Desai, R., 2010. Inventory of New York City Greenhouse Gas Emissions. Mayor's Office of Long-Term Planning and Sustainability, New York. 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Hillman, T., Ramaswami, A., 2010. Greenhouse gas emission footprints and energy use benchmarks for eight US cities. Environmental Science & Technology 44, 1902–1910. ICF-International, 2007. Greenhouse Gas and Air Pollutants Inventory in the City of Toronto, Toronto. Kennedy, C., Cuddihy, J., Engel-Yan, J., 2007. The changing metabolism of cities. Journal of Industrial Ecology 11, 43–59. Kennedy, C., Steinberger, J., Gasson, B., Hansen, Y., Hillman, T., Havránek, M., Pataki, D., Phdungsilp, A., Ramaswami, A., Mendez, G.V., 2010. Methodology for inventorying greenhouse gas emissions from global cities. Energy Policy 38, 4828–4837. Kenny, T., Gray, N.F., 2009. Comparative performance of six carbon footprint models for use in Ireland. Environmental Impact Assessment Review 29, 1–6. Larsen, H.N., Hertwich, E.G., 2009. The case for consumption-based accounting of greenhouse gas emissions to promote local climate action. Environmental Science & Policy 12, 791–798. Lee, K.-H., 2011. Integrating carbon footprint into supply chain management: the case of Hyundai Motor Company (HMC) in the automobile industry. Journal of Cleaner Production 19, 1216–1223. Lemon, M., Foley, P., Gary, F., 2009. Greenhouse Gas Inventory for the City of Bloomington. Footprint, Projections, and Recommendations, Indiana. Lim, S.R., Park, J.M., 2008. Cooperative water network system to reduce carbon footprint. Environmental Science & Technology 42, 6230–6236. Lin, J., Cao, B., Cui, S., Wang, W., Bai, X., 2010. Evaluating the effectiveness of urban energy conservation and GHG mitigation measures: the case of Xiamen City, China. Energy Policy 38, 5123–5132. Matthews, H.S., Hendrickson, C.T., Weber, C.L., 2008. The importance of carbon footprint estimation boundaries. Environmental Science & Technology 42, 5839–5842. Muthu, S.S., Li, Y., Hu, J.Y., Mok, P.Y., 2011. Carbon footprint of shopping (grocery) bags in China, Hong Kong and India. Atmospheric Environment 45, 469–475. Normile, D., 2008. China's living laboratory in urbanization. Science 319, 740–743. Padgett, J.P., Steinemann, A.C., Clarke, J.H., Vandenbergh, M.P., 2008. A comparison of carbon calculators. Environmental Impact Assessment Review 28, 106–115. POST, 2006. Carbon Footprint of Electricity Generation. Parliamentary Office of Science and Technology, London. Ramaswami, A., Hillman, T., Janson, B., Reiner, M., Thomas, G., 2008. A demandcentered, hybrid life-cycle methodology for city-scale greenhouse gas inventories. Environmental Science & Technology 42, 6455–6461. Stockholm Environment Institute (SEI), 2006. UK Schools Carbon Footprint Scoping Study for Sustainable Development Commission by Global Action Plan. Stockholm Environment Institute. Solomon, S., Plattner, G.-K., Knutti, R., Friedlingstein, P., 2009. Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences of the United States of America 106, 1704–1709. UN-HABITAT, 2011. 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UNEP, 2011. Representative GHG Baselines for Cities and their Respective Countries. Wackernagel, M., Rees, W.E., 1996. Our Ecological Footprint—Reducing Human Impact on the Earth. New Society Publishers Gabriola Island, B.C.,Canada. Wang, W., Lin, J.Y., Cui, S.H., Lin, T., 2010. An overview of carbon footprint analysis. Environmental Science & Technology (in Chinese) 33, 71–78. Wiedmann, T., Minx, J., 2007. A definition of ‘carbon footprint’. ISAUK Research & Consulting, Durham, 1–11.
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Contents lists available at SciVerse ScienceDirect
Energy Policy journal homepage: www.elsevier.com/locate/enpol
Using hybrid method to evaluate carbon footprint of Xiamen City, China Jianyi Lin a,b, Yuan Liu a,b, Fanxin Meng a,b, Shenghui Cui a,n, Lilai Xu a,b a b
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China
H I G H L I G H T S
Carbon emissions from Scope 1 þ2 þ3 are calculated for Xiamen City, China. Carbon footprint in Xiamen is industrial carbon-intensive and high embodied emissions. Management for Scope 3 emissions in the developing cities is important.
art ic l e i nf o
a b s t r a c t
Article history: Received 17 May 2012 Accepted 4 March 2013 Available online 4 April 2013
For more holistic inventory estimation, this paper uses a hybrid approach to access the carbon footprint of Xiamen City in 2009. Besides carbon emissions from the end-use sector activities (called Scope 1þ2 by WRI/WBCSD) in normal research, carbon emissions from the cross-boundary traffic and the embodied energy of key urban imported materials (namely Scope 3) were also included. The results are as follow: (1) Carbon emissions within Scope 1 þ2 only take up 66.14% of total carbon footprint, while emissions within Scope 3 which have usually been ignored account for 33.84%. (2) Industry is the most carbonintensive end use sector which contributes 32.74% of the total carbon footprint and 55.13% of energy use emissions in Scope 1 þ 2. (3) The per capita carbon footprint of Xiamen is just about one-third of that in Denver. (4) Comparing with Denver, the proportion of embodied emissions in Xiamen was 10.60% higher than Denver. Overall, Xiamen is relatively a low-carbon city with characters of industrial carbonintensive and high embodied emissions. Further analysis indicates that the urbanization and industrialization in Xiamen might cause more material consumption and industrial emissions. These highlight the importance of management for Scope 3 emissions in the developing cities. & 2013 Elsevier Ltd. All rights reserved.
Keywords: Carbon footprint Hybrid analysis method EIO-LCA
1. Introduction Future carbon dioxide emissions will hence lead to adverse climate changes on both short and long time scales that would be essentially irreversible (Solomon et al., 2009). Today, humanity is experiencing a dramatic shift to urban living, over 50% of the world population living in cities (UN-HABITAT, 2011). Undoubtedly, urban centers, especially those in the developed world, are the primary source of greenhouse-gas emissions and thus are implicated in global climate change (Grimm et al., 2008). China is now undergoing a rapid urbanization with about 12 million peoples moving to cities each year, and some 60% of the population will live in cities by 2020 (Normile, 2008). Within China, cities consume 84% of commercial energy, among which the top 35 large cities with 18% of the total population contribute 40% of China's energy uses and CO2 emissions (Dhakal, 2009). Consequently, n Corresponding author at: Chinese Academy of Sciences, Institute of Urban Environment, Key Lab of Urban Environment and Health, Xiamen 361021, China. Tel.: þ 86 592 6190957; fax: þ86 592 6190977. E-mail addresses: [email protected] (J. Lin), [email protected] (S. Cui).
0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.03.007
there is a crucial need to present a suitable method for accounting Chinese urban GHG emissions so that urban decision makers can make appropriate policies to reduce their total GHG emission. For carbon mitigation in a broad range, carbon footprint calculations which take both direct carbon emission and indirect emissions into account are in strong demand. Wiedmann and Minx (2007) defined carbon footprint as a measure of the total amount of CO2 emissions that is directly and indirectly caused by an activity or is accumulated over the life stages of a product. Carbon footprint derives from ‘ecological footprint’ (Wackernagel and Rees, 1996)which is the total amount of gases related to the climate change, emitted from human production and consumption activities. Then, other of the Kyoto Protocol greenhouse gases (GHGs) is suggested to be included in carbon footprint calculation (Matthews et al., 2008; Wang et al., 2010; Wright et al., 2011). Carbon footprint asserted the importance of all direct (on-site, internal) and indirect (off-site, external, upstream, downstream) emissions to track total GHG emissions of a product or an activity. Carbon footprint is widely used in carbon emission analysis at different scales of the products, individuals/households, organizations, cities, countries scales. The Carbon-Trust (2007) firstly developed a
J. Lin et al. / Energy Policy 58 (2013) 220–227
pilot methodology to measure the carbon footprint of products as well as a label to display the information on individual products . Other carbon footprint of products such as crisps, office chairs, shopping bags are also studied (Brentona et al., 2008; Gamage et al., 2008; Muthu et al., 2011). For individuals and households, numerous websites have been created to help calculate an individual's carbon footprint (Padgett et al., 2008), and some typical models are compared (Kenny and Gray, 2009). Stockholm Environment Institute (SEI) (2006) used hybrid method to calculate the UK schools carbon footprint, and results show that only around 26% of this total carbon footprint are direct emissions, whereas the other three quarters are from indirect emission sources. Ramaswami et al. (2008) develops a demandcentered, hybrid life-cycle-based methodology for conducting cityscale GHG inventories , and then applied it to 8 US cities and found that the cross-boundary activities contributed 47% on average more than the in-boundary GHG contributions traditionally reported for cities (Hillman and Ramaswami, 2010). Larsen and Hertwich (2009) developed a consumption-based hybrid-LCA based carbon footprint model and applied it to analyzed the emission of Trondheim, Norway. As for countries, Hertwich and Peters (2009) also quantified carbon footprint associated with the final consumption of goods and services for 73 nations and 14 aggregate world regions. Carbon footprint has also been used for a range of activities in different sectors including industry, transport, construction, water supply, and so on. POST (2006) compared the life cycle CO2 emissions of different electricity generation systems in the UK and found that none of electricity generation technologies are entirely 'carbon free'. Lee (2011) explored current business practices in carbon footprint and supply chain management with the case of the automobile industry. Argonne National Laboratory developed a vehicle-cycle module for the greenhouse gases, regulated emissions, and energy use in transportation (GREET) model (Burnham et al., 2006). Cui et al. (2010) proposed a carbon footprint model of the BRT system based on a life cycle assessment (LCA) approach, with three components: infrastructures, fuels and vehicles. Giurco and Petrie (2007) developed an approach to designing preferred futures for entire metal cycles that deliver reduced carbon footprints. Lim and Park (2008) compared cooperative water network system with the individual's and founds that the cooperation reduces their carbon footprint and is economically feasible and profitable. Friedrich et al. (2009) employed LCA studies to analyze carbon footprint of urban water system in Durban. On above studies, there are three principal approaches: bottom-up based on Process Analysis (PA), top-down based on Environmental Input–Output Analysis (EIOA), and hybrid-EIO-LCA which is a combination of PA and EIOA (Wiedmann and Minx, 2007; Wright et al., 2011). Each of the approaches has its own merits and drawbacks. Process analysis, which analyzes the emissions associated with specific processes, has a greater level of accuracy, but requires more time and resources. PA has clear advantages for looking at micro systems such as a particular process, individual product or a relatively small group of individual products. However, EIOA is a much smaller requirement of time and manpower, but it has limitation to assess micro systems. It is superior for the macro and meso systems: industrial sectors, individual businesses, larger product groups, government, and so on. To overcome the shortfalls of EIOA and PA, hybrid-EIO-LCA combines the strength of both methods. This approach overcomes the issues of EIOA not being specific or detailed enough to monitor minor changes at an organizational or sub-national scale and omitting use and end-of-life product life cycle phases and the issue of incompleteness and truncation errors in PA (Wiedmann and Minx, 2007; Wright et al., 2011). As for the importance of Chinese urban GHG emissions accounting, there have been many studies on urban carbon emission analysis (Bi et al., 2011; Dhakal, 2009; Gu and Yuan, 2011). Most of these studies focus on direct emission accounting,
221
and few explores the carbon footprint tracking all direct and indirect emissions. Since the great significance of cross-boundary activities for U.S. Cities (Hillman and Ramaswami, 2010), interests are arisen on Chinese urban carbon footprint and its characters. Therefore, this paper will use the hybrid-EIO-LCA to evaluate carbon footprint for Xiamen City to help understand the characteristics of Chinese urban GHG emissions and the differences comparing to those cities in developed country. This paper is organized as follow: (1) introducing the calculating methodology; (2) applying hybrid method to evaluate carbon footprint for Xiamen City; (3) analyzing the characters of Xiamen's carbon footprint and comparing with other cities in the word; (4) drawing our conclusions and discussing emission inventories and our policy implications.
2. Methodology According to the WRI/WBCSD (2004)'s Greenhouse Gas Protocol , urban carbon footprint includes three scopes’ emission: Scope 1 emissions, which refer to the direct emissions inside the cities. These includes fossil fuel combustion; waste; industrial processes and product use; and agriculture, forestry and other land use (AFOLU) (Kennedy et al., 2010). Scope 2 emissions include indirect emissions due to electricity and steam use out-of-boundary. Scope 3 emissions include other indirect emissions and embodied emissions that occur outside of the city boundary. In this paper, the hybrid-EIO-LCA is used to calculate all three scope emissions for Xiamen City. Scope 1 and 2 emissions are determined as IPCC guidelines (2006). The emissions from cross-boundary transport, such as long-distance vehicle (passenger and freight), railway, marine, aviation, are distributed by spatial allocation. All the above emissions are evaluated by Process Analysis (PA) method. Besides, the embodied emissions from urban key imported materials (fuel, cement, water and food) are calculated by Environmental Input– Output Analysis (EIOA) method. According to Kyoto protocol, six GHG emissions (CO2, CH4, N2O, HFCs, PFCs, and SF6) are inventoried and reported together as carbon dioxide equivalents (CO2e). The hybrid calculating method and scopes are shown as Fig. 1. 2.1. Carbon emissions by urban end-use sectors The carbon emissions caused by urban end-use sectors refer to Scope 1 and 2, which include energy use and non-energy use. The calculation of GHG emissions from energy use is based on the final end-use energy consumption data and the emission factors of various fuels with different processes. These include direct fossil fuel combustion (Scope 1) and indirect emissions due to electricity and steam use out-of-boundary (Scope 2). And the emissions by non-energy use include waste, industrial processes and product use, agriculture, forestry and other land use, within Scope 1. The calculation of the energy use and non-energy use are as follow: (1) Energy use emissions The GHG emission factors of fossil fuels are directly obtained from IPCC guidelines (2006) and the emission factors of electricity and heat are calculated based on fossil fuels used in power plants and heat generations. Urban sectors’ emissions of end-use energy use are calculated by transport sector and non-transport sectors as follow: GHG ¼ GHGtrans þGHGnon−trans
ð1Þ
GHGnon−trans ¼ ∑i ðGHGf uel,i þ GHGelectricity,i þ GHGheating,i Þ
ð2Þ
GHGtrans ¼ ∑j GHGf uel,j
ð3Þ
222
J. Lin et al. / Energy Policy 58 (2013) 220–227
Industrial Process Analysis Household Energy Use
Scope 1+2
Urban Carbon footprint
Commercial
Industrial Processes
In-Boundary Traffic Construction
Agriculture
Others Long-Distance
Forestry and Other Land Use Railway Marine Waste Disposal
Scope 3
Aviation Cross-Boundary Traffic
Fuel Cement
Urban Key Imported Materials EIO-LCA
Water Food
Fig. 1. Hybrid method on the urban carbon footprint analysis.
where, GHG is carbon emission from urban sectors by end-use energy use; GHGtrans is the emission from transports inside cities; GHGnon-trans is the emission from other urban sectors; GHGfuel,i is direct emissions from fuel combustion by end-use sector i; GHGelectricity,i is emissions from electricity use by end-use sector i; GHGheating,i is emissions from heat use by end-use sector i; GHGfuel,j is emissions from in-boundary transport sector j. To avoid double counting, the industrial sector did not include electricity generation and heat production, and the carbon emissions of this part are allocated to the end-use sectors. (2) Non-energy use emissions The estimation of GHG emissions by non-energy use (industrial processes and product use, waste, agriculture, forestry and other land use) is base on IPCC guidelines (2006). Emissions from industrial processes and product use are based on the industrial products data and corresponding emission factors. For Xiamen, only glass production and electronics industry emissions are involved in the industrial processes and product use calculation. Agricultural activities include manure management, enteric fermentation, rice cultivation and planting soils, and specific emission factors of agricultural are adopted to suit the Xiamen's situation. The forestry and other land use are also calculated by IPCC guidelines (2006). Waste treatment includes solid waste and wastewater, which calculated by simplified version of the IPCC recommended approaches.
data, long-distance vehicles use local fuel sales data to estimate the emissions, and the railway are allocated 50% VKT between Xiamen and other cities. There are differing approaches to the quantification of aviation and marine emissions (Carney et al., 2009; Ramaswami et al., 2008), most cities determine GHG emissions from full volumes of fuels loaded at airports and marine ports within their boundaries (Kennedy et al., 2010). Based on availability of data, this paper estimates GHG emissions from air and marine transportation for Xiamen City using fuels loaded onto planes and ships at the airport and harbor. 2.3. Embodied emissions from urban key imported materials Based on the functionality of cities, indirect energy use and associated GHG emissions were computed for the critical urban imported materials mainly including water, fuel, food, and cement (Ramaswami et al., 2008). For Xiamen City, only the materials which produced outside the city area are calculated, so they have no significant overlap with carbon emissions by urban end-use sectors (Section 2.1). The embodied GHG emissions associated with key urban imported materials were computed by Environmental Input–Output Analysis (EIO-LCA) (CMU-GDI, 2008; Matthews et al., 2008). The basic input–output model tracks flows of purchases (i.e., supply chain) between sectors, calculated as fellow: x ¼ ðI þA þ A A þA A A þ …Þy ¼ ðI−AÞ−1 y
ð4Þ
where, x is a vector including all supplier outputs; I is the identity matrix; A is the direct requirements matrix which represents the direct requirements of the intersectoral relationships; y is the vector of final demand. Terms in Eq. (4) represent the production of the desired output itself (I*y), contributions from the direct or first-tier suppliers (A*y), those from the second-tier indirect suppliers (A*A*y), and so on. Then environmental emissions associated with final demand can be calculated by multiplying the output of each sector by its environmental impact per unit of output, as follow: bi ¼ Ri x ¼ Ri ðI−AÞ−1 y
ð5Þ
where bi is the vector of environmental emissions for sector I; and Ri is a matrix with diagonal elements representing external output (environmental emissions) per unit of output each sector, which is calculated from: Ri ¼ total external output=X i
ð6Þ
where Ri is used to denote the impact in sector i, and Xi is the total dollar output for sector i.
3. Case study 3.1. Study areas and data use
2.2. Carbon emission from cross-boundary transports Cross-boundary transports include long-distance vehicles (passenger and freight), railway, marine, aviation, which need allocation procedures for these transportation activities. As for the ground transportation, there have been three different methods to test and verify calculations of fuel consumption in cities, which are using local fuel sales data, estimating from vehicle kilometers travelled (VKT) within cities, and estimating urban gasoline consumption by scaling from state, provincial, or regional data (Kennedy et al., 2010). And it is showed that the three approaches can produce reasonably close estimates, provided that the VKT calculations are conducted appropriately (Kennedy et al., 2010). Considering Xiamen's actual situation and the availability of
Xiamen is a coastal sub-provincial city in southeastern Fujian province, P.R. China, and is one of China's earliest Special Economic Zones (designated in the 1980s). It lies at 118104′04″ east longitude and 24126′46″ north latitude, looking straight to the Taiwan Strait. Xiamen has a monsoonal humid subtropical climate, with an average annual temperature of approximately 21 1C. It covers an area of 1 573 km² with a total population of 2.52 million in 2009 (XMBS, 2010). With rapid economic development, Xiamen's regional GDP reached 173.72 billion Yuan in 2009, up from 1.72 billion Yuan (comparable GDP value) in 1980, with an average annual increase of 17.49%. Its ratio of three industrial structures is 1.08:47.26:51.56. The urbanization ratio also rapidly increased, from 35% in 1980 to 80.2% in 2009. The rapid economic growth
J. Lin et al. / Energy Policy 58 (2013) 220–227
and urbanization have resulted in rising energy consumption in Xiamen City, and its total energy consumption reached 9.7962 million tce (tons of coal equivalents) in 2009, which energy consumption intensity was 0.579 tce/10,000 Yuan. In 2010, the National Development Reform Commission (NDRC) announced the program for five low-carbon pilot provinces and eight low carbon pilot cities, and Xiamen became one of the eight pilot cities. Thus, there is an urgent need for carbon accounting system which can track all direct (on-site, internal) and indirect (off-site, external, upstream, downstream) emissions of the pilot cities, so that cities can be credited for such global carbon emission reductions. The data used in this study are from three sources: yearbook of statistics, literatures and documents, and government departmental survey data. The main statistical yearbooks include Yearbook of Xiamen Special Economic Zone (2010), China Energy Statistical Yearbook (2010), Fujian Statistical Yearbook (2010), China City Statistical Yearbook (2010), etc. The main document referred is Xiamen's Energy Balance Table (2009). Sector energy consumption data (electricity, coal, gasoline, diesel, fuel oil, LPG, natural gas, crude oil, and so on) were obtained by departmental survey: the Xiamen Development and Reform Commission, the Economic Development Bureau of Xiamen, the Fujian Electronic Power Company Limited, the Xiamen Municipal Works and Municipal Gardens Administration Bureau, the Xiamen Statistical Bureau, the Xiamen Transportation Bureau, the Urban Planning Bureau of Xiamen and the Xiamen Vehicles Administration Center.
223
Province power grid is calculated according to the China Energy Statistical Yearbook in 2010 (Fujian power grid only transferred out electric power and did not transferred in).
4. Results and analysis In this paper, the hybrid method is applied to calculate the carbon footprint of Xiamen City in 2009, and the total emission is 24355.16 kt CO2e. The emissions of sectors and sub-sectors are as Table 2. The proportions of carbon footprint are as Fig. 2, where: (1) Carbon emissions from urban end-use sectors (Scope 1 þ2) contribute 66.14%, in which energy use takes 59.39%, and nonenergy use in boundary takes 6.75%. Energy use includes direct fuel combustion and electricity generated outside the city, namely Scope 1 þ2, in which industry sector contributes 32.74% of the total carbon footprint. The transferred-in power accounts for 4.46%, which emissions are 1085.69 kt CO2e, belonging to Scope 2. Non-energy use emissions include industrial processes and product use (0.35%), waste (2.92%), agriculture (0.40%), forestry and other land use (3.08%), which are all in boundary. Among these, industrial sector is the largest emitter, contributing 32.74% of the total carbon footprint. (2) Cross-boundary transport accounts for 9.22%, including aviation, navigation, long-distance vesicles, and railway. (3) Embodied emissions from urban key imported materials takes 24.64.49% proportion. 4.1. Details in carbon footprint of Xiamen City
3.2. Computing boundaries The urban computing boundary mainly considers the administrative boundaries, referring to the availability of research data, for most available statistical data are based on the administrative boundary. This paper includes six GHG emissions (CO2, CH4, N2O, HFCs, PFCs, and SF6) according to Kyoto protocol, and reports them together as carbon dioxide equivalents (CO2e) by global-warming potentials (GWPs) over a 100-year period (by IPCC Second Assessment Report). The GHG emissions from fossil fuel combustion; waste; industrial processes and product use; agriculture, forestry and other land use are calculated. For energy use, six end-use sectors are included in this paper: the industrial sector, the household sector, the in- border transport sector, the commerce sector, the construction sector, and other sectors (as shown in Fig. 1). To avoid double counting, the industrial sector does not include electricity generation and heat production industry, for their emissions are allocated to end-use sectors. Electricity emission factors are computed based on power generation structures of Xiamen City and Fujian Province, as shown in Table 1. The electricity emissions factor of the Fujian
Table 1 Electric power structure and emission factor of Xiamen City and Fujian province. Power type
Fujian Province Ratio (%)
Thermal power (including natural gas) Wind power, hydropower, nuclear et al. Disturbance power Total
Emission factor (t CO2e/ 104 kW h)
Xiamen City Ratio (%)
Emission factor (t CO2e/ 104 kW h)
75
8.5851
87
6.8896
25
0.0000
1
0.0000
0
0.0000
12
6.4695
100
6.4695
100
6.6262
(1) Energy use emissions by urban end-use sectors The total energy use emissions by urban end-use sectors of Xiamen are 14463.48 kt CO2e, accounts 59.39% of the carbon footprint. The details of urban end-use sector emissions are as Fig. 3. Industrial sector is largest source of energy use emissions (7973.28 kt CO2e), accounting for 55.13% of whole energy use emissions. Commercial and public sector takes the second place (2192.53 kt CO2e), accounting for 15.16%. The third large source is household energy use emissions, contributes for 14.32%, which is 2070.48 kt CO2e. Construction, in-boundary transport and others sectors, respectively, account for 7.65%, 6.65%, 1.10% of the total energy use carbon emissions. The in-boundary transport sector mainly includes private cars, motorcycles, taxis, Normal bus transit (NRT), Bus rapid transit (BRT), minibus, and ferries. Private cars have the large share, accounting for 46.36%. Taxi and NRT accounts for 19.72% and 15.33%, respectively, taking the second and third place. Motorcycles, minibuses, BRT and ferries, respectively, account for 12.01%, 4.08%, 1.96%, and 0.54%, as shown in Fig. 3. Fig. 4 shows industrial sector emissions of Xiamen City which emissions are more than 100 kt CO2e. In industrial sector, carbon emissions of chemical materials and chemical products manufacturing industry has the largest contribution, which is 1934.77 kt CO2e, accounting for about 25%. The second and third large contributions are by non-metallic mineral products and rubber products industry, all accounting for about 10%. (2) Non-energy emissions by urban sectors in boundary The total emissions of non-energy use inside the city is 1644.19 kt CO2e, only accounting 6.75% of Xiamen carbon footprint. Among these emissions, forest and other land use is has the largest share, accounting for 45.59%, in which the carbon sink of forestry is very small. Waste (including solid waste and waste water) accounts for 43.19%, and solid waste contributes for 36.32%. Agriculture (including Farming and Livestock) and Industrial processes and product use, respectively, accounts for about 5.99% and 5.23%. Details are shown in Fig. 5.
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Table 2 Carbon footprint Inventory of Xiamen City in 2009. Scope
Sectors
Carbon emissions (kt CO2e)
Sub-sectors
Carbon emissions (kt CO2e)
Scope 1þ 2
Energy use
14,463.48
Scope 1
Industrial processes and product use
Industrial sector Commercial and public sector Household sector In-boundary transport sector Construction sector Other sectors Industrial processes and product use Solid waste Waste water Farming Livestock Forestry Other land use Aviation Navigation Long-distance vesicles Railway Fuel Cement Food Water
7973.28 2192.53 2070.48 962.45 1106.03 158.71 86.02 597.12 113.05 34.17 64.26 −59.38 808.95 775.71 532.00 856.00 82.26 3086.72 2442.90 278.30 193.60
86.02
Waste
710.17
Agriculture
98.43
Forestry and other land use Scope 3
749.57
Cross-boundary transport
2,245.97
Embodied emissions
6,001.52
Energy use,
Industrial
59.39%
processes and product use, 0.35% Waste, 2.92%
In-boundary non-energy use,
Agriculture,
6.75%
0.40%
Forestry and
Cross-boundary
other land use,
transport, 9.22%
3.08%
Embodied energy, 24.64%
Food industry Water production and supply industry Educational and sports goods Transportation equipment manufacturing Machinery manufacturing Manufacture of Chemical Fibers Fabricated metal products Non-ferrous metal processing industry Agro-food processing industry Manufacture of Beverages Plastic products industry Textile industry Electronic Equipment Manufacturing Rubber Products Non-metallic mineral products Chemical materials and products
Kt CO2e
0
Fig. 2. Carbon footprint of Xiamen City in 2009.
500
1000
1500
2000
Fig. 4. Carbon emissions by industries of Xiamen City, 2009.
Commercial and public sector 15.16%
Household sector 14.32%
Taxi 19.72%
Construction sector 7.65% Motorcycle 12.01% In-boundary transport 6.65%
5.23% BRT 1.96% Minibus 4.08% Ferry 0.54%
Other sectors 1.10% Industrial sector 55.13%
Industrial processes
NRT 15.33%
Private car 46.36%
Other land use
Solid waste
49.20%
36.32%
Fig. 3. Energy use emissions by urban end-use sectors of Xiamen City, 2009.
(3) Emissions of cross-boundary transport The cross-border transportation of Xiamen City mainly includes long-distance passenger and freight road vehicle, aviation, passenger and cargo ships, and trains. The carbon emissions by cross-border traffic in 2009 are 2245.97 kt CO2e totally, accounting for 9.22% of the whole carbon footprint of Xiamen City. Fig. 6 shows the proportion of cross-border traffic emissions for Xiamen City in 2009. Long-distance road transport accounted for 38.12%, including passenger and freight. Aviation, marine, and railway, respectively, accounts for 34.54%, 23.68%, and 3.66%.
Waste Forestry -3.61%
Livestock Farming 2.08% 3.91%
water 6.88%
Fig. 5. Non-energy emissions by urban sectors in boundary, 2009.
(4) Embodied emissions of urban key imported materials The embodied emissions of urban key materials imported to Xiamen City in 2009 are 6001.52 kt CO2e, accounting for 24.64% of the whole carbon footprint. As shown in Table 2
J. Lin et al. / Energy Policy 58 (2013) 220–227
Railway 3.66% Passenger vehicle 32.66%
Cargo ship 22.99%
Passenger ship 0.69%
Aviation 34.54%
Freight vehicle 5.46%
Fig. 6. Carbon emissions by cross-boundary transport of Xiamen City, 2009.
Food 4.64%
Water 3.23%
Cement 40.70%
Fuel 51.43%
Fig. 7. Embodied emissions of urban key imported materials of Xiamen City, 2009.
and Fig. 7, imported fuels, cements, foods, and water, respectively, generates 3086.72, 2442.9, 278.3, and 193.6 kt CO2e emissions, accounting for 51.43%, 40.70%, 4.64% and 3.23%.
4.2. Comparison with other cities To be more comparative with other cities, the calculation of the per capita carbon footprint of Xiamen below excludes the non buildup area emissions (agriculture, forest and other land use) and industrial process and product use emissions (HFCs, PFCs, and SF6). Comparing with Denver (Ramaswami et al., 2008), the per capita carbon footprint of Xiamen is much lower than Denver, just counting about one-third of the per capita carbon footprint in Denver. From the tires of per capita carbon footprint (as Table 3), carbon emission by urban sectors belong to Scope 1 and 2 of Xiamen and Denver, respectively, account for 64.79% and 74.70%, and cross-border traffic all cause about 10%. As for the embodied emissions, Xiamen and Denver, respectively, account for 25.62% and 15.02% of the total per capita carbon footprint, and Xiamen is about 10% more than in Denver. These might show that cities in developing countries consume relatively more fuel, cement, and other substances due to the process of industrialization and urbanization. Thus these highlight the importance of embodied carbon emissions management in developing countries in some senses. According to the UNEP (2011) and World Bank's carbon emission summaries of cities in the world, further comparison is made between some typical cities at home and abroad (as shown Fig. 8).
225
They include cross-border transport carbon emissions (aviation, marine etc.), but not including embodied emissions of the city's key imputed materials. From the figure, it can be seen that Denver has the highest per capita carbon emissions, accounting for 21.5 t CO2e/person. The relatively high-emission cities include Angeles, Shanghai, Tianjin, Beijing, and Bangkok, with a per capita carbon emission more than 10 t CO2e/person. For this sense, Xiamen is a relatively low-carbon city, with only 6.91 t CO2e/person. From a regional perspective, the U.S. cities have the highest per capita carbon emissions, and China's major cities are relatively high on per capita emissions, while Cities in Europe, Japan and other countries might relatively low emissions.
5. Conclusion and discussion In this paper, hybrid analysis method of carbon footprint was used to access the carbon footprint of Xiamen City in 2009. Besides the carbon emissions from the city-scale end-use sectors in normal research, the carbon emissions from the cross-boundary traffic and the embodied energy of key urban imported materials were also included. The urban end-use sector activities include energy use (direct fuel combustion and electricity) and nonenergy use (waste, industrial processes and product use, agriculture, forestry and other land use), within Scope 1 þ 2. Cross-border transports include surface, sailing and airline travel, while key urban imported materials contains food, water, fuel, and concrete, all namely scope3. Research result showed that: (1) Carbon emissions from the energy use, waste, industrial processes and product use, agriculture, forestry and other land use within Scope 1 and Scope 2, only take up 66.14% of the total city carbon footprint. However, the emissions from the cross-boundary traffic and embodied emissions of key urban imported materials account for 33.86%, which has usually been ignored as Scope 3. This demonstrates the importance of management on Scope 3 emissions. (2) Within energy use emissions by urban end-use sectors, industry contributed the largest share, which counts for 55.13%. And industry sector contributes 43.44% of the whole carbon emissions excluding embodied emissions by key urban imported materials. Comparing with other cities like New York, Toronto and so on (Dickinson and Desai, 2010; ICF-International, 2007; Lemon et al., 2009), the share is relatively high, which may due to industrialization process currently. Developed countries are responsible for large part of emissions for statistical data show that 39.2% of the industry products are exported to other countries (XMBS, 2010). As for carbon reduction, industry will pay a very important role for its largest abatement potential (Lin et al., 2010). (3) Xiamen has a relatively low per capital carbon footprint, excluding from emissions of industrial processes and product use, agriculture, forestry and other land use to be more comparative. Its per capital carbon footprint is just about one-third of that of Denver, counting for 9.29 t CO2e/person. And its carbon emissions further-reducing embodied emissions is only about half of those of Los Angeles and Shanghai, 6.91 t CO2e/person. According to the structure of carbon footprint, the embodied emissions by Xiamen were about 10% higher than Denver, and this might indicate that the urbanization and industrialization consume more materials in Xiamen. And efficient utilization of materials is very important for carbon footprint reduction. (4) Among non-energy use inside the city, AFOLU has the largest share (49.2%). It is because Xiamen has large areas of hinterland and its build-up areas only accounts for 13.48% (XMBS, 2010). Waste the second largest emitter, accounting for 43.19%
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Table 3 Comparison of urban carbon footprint between Xiamen and Denver. Levels
Denver, 2005 t CO2e/person (percent)
Xiamen, 2009 t CO2e/person (percent)
Unban sector's emissions including Scope 1þ 2 Scope1 þ 2 plus cross-boundary transport of Scope 3
18.90 (74.70%) 21.50 (74.70%þ 10.28%)
6.02 (64.79%) 6.91 (64.91% þ9.59%)
Total emissions scope1 þ 2þ 3
25.30 (74.70%þ 10.28% þ15.02%)
9.29 (64.79% þ 9.59% þ 25.62%)
per capita carbon emissions tCO2e/person
25 20 15 10 5
Lo
s
D
en
ve r, An 20 05 ge le s, Sh 2 an 00 gh 0 ai ,2 00 Ti an 6 jin ,2 Be 00 6 ijin g, Ba 20 ng 06 ko k, 20 Lo 05 nd N ew on ,2 Yo 00 rk 3 C C ity ap ,2 e 0 To 09 w n, 20 Xi am 05 en ,2 00 Pa 9 ris ,2 00 To 5 ky o, 20 06
0
Fig. 8. Comparison of per capita carbon emissions of typical cities.
of the total non-energy emissions in-border. These emissions are associated with waste disposal mode in which landfill accounts about 85%. In another perspective of urban metabolism, more materials consumed by the city might cause more waste through flows of water, foods, and other materials (Kennedy et al., 2007). Thus garbage classification and recycling, as well as imported materials control, is need to carbon reduction of this proportion. (5) In terms of transports, both in-border transport and crossborder transport account for 13.17% of the total carbon footprint, respectively, contributing 3.95% and 9.22%. For inborder transport, private cars emit the most, with 46.36% of inborder transport emissions. Then developing public transport is the effective way to reduce these emissions (Cui et al., 2011). As for cross-border transport, Long-distance vehicle has the largest share, while railway has the least share. With the rapid development of high-speed railway, the situation in surface transportation might change in the near future. However, controlling aviation and marine emissions will be a big challenge for the construction of aviation hinge and shipping center of Xiamen.
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