Tracking urban carbon footprints from production and consumption perspectives

Because urban areas are concentrated spots with fluxes of energy, water, material and wastes into and out of the city, the purely geographic GHG accounting cannot truly represent a city's global impact, which must also consider the carbon embodied in imports and exports [31, 32]. The purely geographic GHG accounting (PGA) includes energy use, industrial processes and product use, agriculture, forestry and other land use, and waste, according to IPCC Guidelines [17], and provides a production perspective when applied to large boundaries, such as entire nations where, for example, power generation and use often occur within the boundary. For cities, essential energy and other infrastructures transcend the small city boundary. Hence, the CIF, also called the geographic-plus approach, better represents production [19]. The CIF reports direct in-boundary GHG emissions, plus indirect GHG emissions from imported electricity and steam and from critical imported materials, mainly including water, fuel, food, and cement used by the whole community/city [13, 18]. From a consumption perspective, the CBF accounting (CBF) measures the emissions associated with goods and services consumed only by the final consumers in the city (i.e., primarily households), taking account of the emissions embodied in import and export [11].

Note that neither CIF nor CBF tracks all GHG emissions. CIF does not track the GHGs embodied in non-infrastructure flows to the city (to all sectors), while CBF does not track either direct energy use or energy embodied in supply chains of industries and businesses in a city that export goods elsewhere.

Supply chains to/from Chinese cities can arise from three geographical areas: local (within the city), Mainland China (MC) and rest-of-world (RW). By geographical separation of production and consumption, a framework that captures the embodied carbon flows related to a city is presented as figure 1. The total urban-related carbon emissions can be divided into ten parts, in which LP refers to emissions of local production, LC refers to emissions of local consumption, MC refers to imported or exported emissions of MC to the City, and RW refers to imported or exported emissions of the RW to the City. From the production perspective, these can be grouped as emissions of total local production (TLP), imported from Mainland China (IMC), and imported from rest-of-world (IRW). And from the consumption perspective, they can be grouped as emissions of fuel combustion in local consumption (FC-LC), indirect local consumption (ILC), exported to Mainland China (EMC), and exported to rest-of-world (ERW). FC-LC is the direct emissions from FC-LC, such as household gas and private car fuel. And ILC refers to the indirect emissions for local consumption, such as goods and services, including electricity. Within the total urban-related carbon emissions, total carbon flow embodied among regions or intra-city sectors includes TLP, IMC, and IRW, or ILC, EMC, and ERW. The relationship between urban-related carbon emissions can be expressed as follows:

$$\begin{eqnarray*}&&\begin{array}{lllllllllllllll} {{{\rm C}}_{{\rm URE}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm IMC}}}+{{{\rm C}}_{{\rm IRW}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}={{{\rm C}}_{{\rm ILC}}} \\ \quad \quad \;\;\;\;+{{{\rm C}}_{{\rm EMC}}}+{{{\rm C}}_{{\rm ERW}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}, \\ {{{\rm C}}_{{\rm TCF}}}={{{\rm C}}_{{\rm URE}}}-{{{\rm C}}_{{\rm FC}-{\rm LC}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm IMC}}}+{{{\rm C}}_{{\rm IRW}}} \\ \quad \quad \;={{{\rm C}}_{{\rm ILC}}}+{{{\rm C}}_{{\rm EMC}}}+{{{\rm C}}_{{\rm ERW}}}, \\ \end{array}\end{eqnarray*}$$

where C_URE refers to urban-related carbon emissions; C_TCF refers to total urban carbon flow; C_TLP, C_IMC, and C_IRW refer to carbon emissions of TLP, IMC, and IRW, respectively; while C_FC-LC, C_ILC, C_EMC, and C_ERW refer to carbon emissions of direct emissions from FC-LC, ILC, EMC, and ERW, respectively.

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According to accepted accounting methods, the PGA includes TLP and direct emissions from FC-LC, while CIF contains TLP, direct emissions from FC-LC and the imported key materials/infrastructure (IKM) within IMC and IRW. The two-part direct emissions from FC-LC and ILC compose the total local consumption (TLC), also called CBF. In addition to current accounting methods, a production-based footprint (PBF) is proposed from a production perspective, including TLP, IMC and IRW. Furthermore, a purely production footprint (PPF) can be also established to specifically measure production emissions and their related supply chains. PPF include only TLP and production-related supply chains (PSC) within IMC and IRW, excluding final consumption within IMC and IRW. The two new production-based CFs can provide special emission information, benefiting industrial interventions like symbiosis. Hence these accounting methods can be expressed as follows:

$$\begin{eqnarray*}&&\begin{array}{lllllllllllllll} {{{\rm C}}_{{\rm PGA}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}, \\ {{{\rm C}}_{{\rm CIF}}}={{{\rm C}}_{{\rm PGA}}}+{{{\rm C}}_{{\rm IKM}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}+{{{\rm C}}_{{\rm IKM}}}, \\ {{{\rm C}}_{{\rm PBF}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm IMC}}}+{{{\rm C}}_{{\rm IRW}}}={{{\rm C}}_{{\rm ILC}}}+{{{\rm C}}_{{\rm EMC}}} \\ \quad \quad \;\;\;\;+{{{\rm C}}_{{\rm ERW}}}={{{\rm C}}_{{\rm TCF}}}, \\ {{{\rm C}}_{{\rm PPF}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm PSC}}}, \\ {{{\rm C}}_{{\rm CBF}}}={{{\rm C}}_{{\rm ILC}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}} \\ \quad \quad \;\;\;\;+\left( {{{\rm C}}_{{\rm IMC}}}+{{{\rm C}}_{{\rm IRW}}}-{{{\rm C}}_{{\rm EMC}}}-{{{\rm C}}_{{\rm ERW}}} \right) \\ \quad \quad \;={{{\rm C}}_{{\rm TLP}}}+{{{\rm C}}_{{\rm FC}-{\rm LC}}}+{{{\rm C}}_{{\rm BEET}}}, \\ \end{array}\end{eqnarray*}$$

where, C_PGA, C_CIF, C_PBF, C_PPF, and C_CBF refer to urban carbon emissions by PGA, CIF, PBF, PPF, and CBF methods, respectively; C_IKM refers to embodied carbon emissions from IKM (i.e., water, cement and food, etc); C_PSC refers to emissions from the supply chain to support local production; and C_BEET presents the net balance of emissions embodied in trade (BEET) by both import and export.

The IO model is a widely used method for calculating the embodied environmental impact of trade, and includes the competitive IO model and the non-competitive IO model. The competitive IO model does not distinguish between local and imported intermediate input, and cannot reflect the relationship between imported goods and local industry. To analyze the domestic and imported impacts separately, the competitive IO model must be converted to a non-competitive one. In this study, import coefficient matrices were developed to estimate the imported intermediate input from MC and RW, while the non-competitive IO model was constructed to analyze the local relationship. For the imported goods and services from MC and RW, IO tables were used to estimate the carbon intensities of each region, and the local production CF was accounted using a local non-competitive IO table. More details on the analysis of the total urban carbon flow are as follows.

In an open economy, the production of region r, for example, needs both domestic and imported input. In order to discriminate between locally produced and imported intermediate input, the technical coefficient matrix should be divided into two parts [33]:

$$\begin{eqnarray}&&{{A}^{r}}={{A}^{rr}}+\sum \limits_{s=1}^{n} {{A}^{sr}},\end{eqnarray} \tag{ 1 }$$

where A^rr and A^sr represent the normalized industrial requirements of locally produced products and the imported products from region s to region r, respectively, and n indicates the quantity of the importing regions (similarly hereinafter). The production of region r provides both local consumption and exports to region s, so the total local output can be written as

$$\begin{eqnarray}&&\begin{array}{lllllllllllllll} {{X}^{r}}={{(I-{{A}^{rr}})}^{-1}}\left( {{y}^{rr}}+\sum \limits_{s=1}^{m} {{e}^{rs}} \right)={{(I-{{A}^{rr}})}^{-1}}{{y}^{rr}} \\ \quad \quad +{{(I-{{A}^{rr}})}^{-1}}\sum \limits_{s=1}^{m} {{e}^{rs}} \\ \end{array}\end{eqnarray} \tag{ 2 }$$

The consumption of region r may be locally produced (the former part, i.e. (I − A)⁻¹y^rr, in equation (2)) or from imports (y^sr), and y^sr can be obtained by splitting the bilateral trade data into intermediate and final consumption

$$\begin{eqnarray}&&{{e}^{sr}}={{A}^{sr}}{{X}^{r}}+{{y}^{sr}}.\end{eqnarray} \tag{ 3 }$$

By substituting the deconstructed X^r (equation (2)) into equation (3), the imports for region r can be expressed as

$$\begin{eqnarray}\begin{array}{lllllllllllllll} {{e}^{sr}} & = & {{A}^{sr}}{{(I-{{A}^{rr}})}^{-1}}\left( {{y}^{rr}}+\sum \limits_{s=1}^{m} {{e}^{rs}} \right)+{{y}^{sr}} \\ {} & = & \left[ {{A}^{sr}}{{(I-{{A}^{rr}})}^{-1}}{{y}^{rr}}+{{y}^{sr}} \right] \\ {} & {} & +{{A}^{sr}}{{(I-{{A}^{rr}})}^{-1}}\sum \limits_{s=1}^{m} {{e}^{rs}}, \\ \end{array}\end{eqnarray} \tag{ 4 }$$

where the former part yields the total imported consumption for region r, and the latter part represents the re-exported products and services after processing.

In adding the environmental impact to extend the model [34], a diagonal matrix F, which represents the regional carbon intensities, should be applied. The basic function of the EIO model is written as:

$$\begin{eqnarray}&&C=\hat{F}{{(I-A)}^{-1}}\hat{y},\end{eqnarray} \tag{ 5 }$$

where C represents the CF caused by final consumption y. The CFs attributable to TLP, export and import can then be calculated. According to the relationship described in 2.1, the CFs of TLP, IMC, IRW, TLC, EMC, ERW and net BEET can be easily obtained.

Xiamen is a major city on the Southeast (Taiwan Strait) coast of the People's Republic of China, and lies in Fujian Province. It covers an area of 1573 km² with a total population of 3.53 million in 2010 [35]. With rapid economic development, Xiamen's regional GDP reached 206.007 billion yuan in 2010, and its ratio of the three industrial structures was 1.1:49.7:49.2. The rapid economic growth and urbanization have resulted in rising energy consumption in Xiamen City, and its total energy consumption reached 10.7696 million tce (tons of coal equivalents) in 2010, whose energy consumption intensity was 0.569 tce/10⁴ yuan. In August 2010, Xiamen was identified as a low-carbon pilot city as part of the NDRC's low-carbon pilot project.

The data used in this study include three types: (a) the IO tables of Xiamen City, Fujian Province, China and other major trading partner countries; (b) GHG emissions from Xiamen City, Fujian Province, China and other main trading partner countries by sector; (c) domestic and international trade relationships of Xiamen City with outside regions.

For the IO data, the IO table of Xiamen City for 2007 was updated to the year 2010 using the bi-proportional scaling method [36, 37], because the local IO data for 2010 is still unavailable. The IO tables for Fujian province and China were obtained from the Fujian Statistical Bureau and National Statistical Bureau, respectively. Data for the major importing nations, including Japan, India, Germany, Australia, USA, Brazil and the Taiwan province of China, were obtained from the World Input–Output Database (WIOD). The industries of all these tables were merged into 25 aggregated industries, according to the International Standard Industrial Classification [38], in order to make a systematic comparison; the data are shown in table 1.

And for GHG emission data, those of Xiamen City, Fujian Province, and China were calculated from the IPCC's emission factors, and from activity data from municipal, provincial, and national statistical yearbooks of 2011, which provides sectoral details. The GHG emissions from the Taiwan province of China, USA, Japan, Australia, India, Germany and Brazil were obtained from the WIOD. The emission intensity of each region then can be gained from dividing GHG emission by corresponding total output of each sector from IO table. Trading relationship data were from the Statistical Yearbook of Xiamen Special Economic Zone (2011). According to trade proportion, the trading countries or regions (more than 60) were grouped into nine regions: East Asia; Western, Southern and Southeast Asia; Europe; Australia; North America; Central and South America; Africa; MC and the Taiwan province of China. These areas were respectively presented by eight major trading countries or continents: Japan, India, Germany, Australia, USA, Brazil, MC and the Taiwan province of China, as shown in table 2. For example, East Asia accounts for 9.41 and 8.20% of the total imports and exports, respectively, of Xiamen City, and Japan accounts for 57 and 66% of imported and exported trade, respectively, of East Asia.

Before using IO analysis (as described in section 2.2) to calculate the CF of Xiamen City, two additional parameters needed to be determined: the relationships with outside trading partners, and an estimation of the imported-from regions' emission factors by sector. In this study, only two trading partners were considered for Xiamen City: MC and the RW, because the IO table for Xiamen City listed only these two partners' column vectors quantifying the trade relationships (both exports and imports) by industry, although more detailed trading relationship data for the RW can be obtained from the Statistical Yearbook of Xiamen Special Economic Zone (2011). For the emission factors of MC, we used the mean coefficients of China and Fujian province, to eliminate the regional diversity attributable to the large differences of carbon intensities and production technologies throughout China. For the emission factors of the RW, we picked the largest trading country from each of the eight trading regions, to represent the region's mean emission standard, because of the ready availability of GHG emissions data by sector for these countries. The exporting emissions, however, were estimated according to the export ratios shown in table 2.

There were some uncertainties in the CF accounting process, mainly including data sources, parameter selection and data preprocessing. Firstly, major uncertainties came from the imperfections of existing energy statistics and other related activity statistics in Xiamen City, Fujian Province, and China, such as transportation fuel statistics, solid waste and wastewater data. Secondly, uncertainties were also caused by carbon emission factor selection. For example, IPCC's emission factors were adapted in all the GHG emissions we calculated, which does not fully reflect the real local conditions in different locations. Thirdly, data preprocessing might cause some uncertainties, especially in converting IO table of Xiamen city to a non-competitive one and merging the sectors of all the IO tables into 25 sectors. To improve the result's accuracy, the key lies to improve the accuracy of statistical activity data and local emission factors in the future.

The urban-related carbon emissions for Xiamen City in 2010 were calculated and the total urban carbon flows further analyzed using the IO model. The total urban-related emissions were 55.2 Mt CO₂e/y, of which FC-LC contributed 1.5 Mt CO₂e/y, and emissions from the total carbon flow (including TLP, IMC, IRW, or ILC, EMC, and ERW) came to 53.7 Mt ${\rm C}{{{\rm O}}_{2}}e/y;$ these components are shown in figures 2(a) and (b). From the production perspective, the GHG emissions from TLP accounted for 42% (22.2 Mt CO₂e/y), of which only 15% was for local consumption (LP-LC) and the remaining 27% was for exports to MC and RW. GHG emissions for IMC accounted for 34% (18.3 Mt CO₂e/y), of which only 12% was for local consumption and 22% was then exported the outsides. And emissions for IRW accounted for 24% (13.2 Mt CO₂e/y), of which only 8% was consumed locally and the remaining 16% was exported. Therefore, the imported emissions from MC and RW aggregated to 31.5 Mt CO₂e/y, accounting for 59%.

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From the consumption perspective, emissions from ILC accumulated to 19.0 Mt CO₂e/y, accounting for 35% of the total related GHG emissions. Emissions for EMC contributed 24% (12.7 Mt CO₂e/y), of which 11, 8 and 5% were from local production (LP), MC and RW respectively. And emissions for ERW accounted for 41% (22.1 Mt CO₂e/y), of which 16, 14 and 11% were from LP, MC and RW respectively. Considering the trade balance, Xiamen City and MC were net carbon export regions, with exports of 3.2 Mt CO₂e/y and 5.6 Mt CO₂e/y embodied emissions, respectively, then RW imports came to 8.8 Mt CO₂e/y.

According to the international trading relationships (shown in table 2), the GHG emissions embodied between Xiamen City and the major trading regions can be further expressed as figure 3. Besides MC, the top four largest carbon trade regions include North America, Europe, Taiwan, and Western, Southern and Southeast Asia. North America and Europe were net carbon import regions, which respectively imported 4.0 Mt CO₂e/y and 4.2 Mt CO₂e/y, together accounting for 92% of the net exports to RW. However, Taiwan was the largest net carbon-export region, exporting 4.3 Mt CO₂e/y. Western, Southern and Southeast Asia was a small net carbon-import region, as were Oceania, Central and South America, and Africa. Therefore, Xiamen City shows the highest import dependence on MC and Taiwan, and the highest export dependence on the United States and Europe—a common pattern for the coastal China special economic zones.

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Considering the GHG intermediate input relationships among urban sectors, the major carbon flows among sectors are illustrated as figures 4, 5 and 6. Figure 4 shows the total urban inter-sector carbon flows, while figures 5 and 6 respectively show the carbon flow from production and consumption perspectives. Figure 4 shows that the largest inter-sector flow was the carbon embodied in the intermediate input from Manufacturing to Manufacturing, followed by that from Utilities to Manufacturing. The above two flows accounted for 72.4% of total production-based emissions (53.7 Mt CO₂e/y), and all of the flows to Manufacturing together accounted for 81.9%, making Manufacturing the largest emission-consuming sector. Manufacturing, Utilities and Transportation were also the three largest emission-producing sectors, respectively accounting for 49.4, 33.7 and 9.8%. The intermediate input of Utilities and Transportation mostly flowed into other sectors. However, the flows among other industries were much smaller than those of these 'Top 3'.

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From the production perspective, figure 5(a) shows the destinations of carbon emissions embodied in local production, which aggregated to 22.2 Mt CO₂e/y, while figure 5(b) shows the destinations of both MC- and RW-import-embodied emissions, which totaled 31.5 Mt CO₂e/y. These illustrate the same flow trends as figure 4, but the imported flows transferred more to Manufacturing than local production flows did. From the consumption perspective, figure 6(a) shows where the embodied emissions of local consumption come from; these amounted to 19.0 Mt CO₂e/y. Figure 6(b) shows the sources of both MC and RW export, which totaled 34.7 Mt CO₂e/y. In figure 6(a), Manufacturing and Utilities were the large emission-producing sectors, and Manufacturing and Construction were the large emission-consuming sectors (accounting for 52.5 and 25.6% respectively). Figure 6(b) shows that most carbon export trades were through industrial products, which took 98% of total carbon exports.

According to the accounting method discussed in section 2.1, the PGA, CIF and CBF, as well as the newly defined production-based (PBF) and PPF, were applied to evaluate the urban CF of Xiamen City in 2010, and the results are illustrated in table 3. The carbon emissions by PGA were 23.7 Mt CO₂e/y according IPCC guidelines, while CIF yielded 29.7 Mt CO₂e/y by adding net imports of the infrastructure services of energy, water, food, steel and cement. The CF of local consumption (CBF) was only 20.5 Mt CO₂e/y, so 3.2 Mt CO₂e/y was net-exported to outside areas compared with the PGA. Both PGA and CIF were 25.2 and 39.2% higher than the CBF, respectively. Furthermore, the newly defined PBF and PPF were 53.7 Mt CO₂e/y and 44.8 Mt CO₂e/y respectively, which were all much larger than PGA, CIF and CBF. The total upstream supply chain emissions were even larger than the PGA, CIF and CBF. These features indicate a high net production city [11], as has been shown to be the case for Xiamen.

Figure 7 further illustrates sectoral details of the five urban CF accountings, including PBF, PPF, CIF, CBF and PGA, as well as the net BEET by import and export. Utilities, Transportation, Basic Metals and Chemicals had larger CFs than other sectors, by most of the accounting methods. And for most sectors, PBF values are larger than PPF ones, followed by those of either CIF, PGA or CBF. Hence, according to the trading balance by sector, Utilities, Transportation and Chemicals were the three highest net carbon exporters, while Mining, Coke and Oil, and Basic Metals were the three top net carbon importers.

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