Tracking Inter-Regional Carbon Flows: A Hybrid Network Model

Article pubs.acs.org/est

Tracking Inter-Regional Carbon Flows: A Hybrid Network Model Shaoqing Chen and Bin Chen* State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China S Supporting Information *

ABSTRACT: The mitigation of anthropogenic carbon emissions has moved beyond the local scale because they diffuse across boundaries, and the consumption that triggers emissions has become regional and global. A precondition of effective mitigation is to explicitly assess interregional transfer of emissions. This study presents a hybrid network model to track inter-regional carbon flows by combining network analysis and input−output analysis. The direct, embodied, and controlled emissions associated with regions are quantified for assessing various types of carbon flow. The network-oriented metrics called “controlled emissions” is proposed to cover the amount of carbon emissions that can be mitigated within a region by adjusting its consumption. The case study of the Jing−Jin−Ji Area suggests that CO2 emissions embodied in products are only partially controlled by a region from a network perspective. Controlled carbon accounted for about 70% of the total embodied carbon flows, while household consumption only controlled about 25% of Beijing’s emissions, much lower than its proportion of total embodied carbon. In addition to quantifying emissions, the model can pinpoint the dominant processes and sectors of emissions transfer across regions. This technique is promising for searching efficient pathways of coordinated emissions control across various regions connected by trade. regions,18,19 and nations.20−22 To evaluate the carbon exchanged between producer and consumer regions, multiregional input−output models (MRIO) have been constructed and successfully located the destination of emissions bidirectionally, i.e., downstream regions of local production in which emissions occur and upstream regions of local consumption where emissions are triggered.23,24 MRIO is recognized as a strong instrument for assessing the potential to mitigate carbon leakage through optimization of consumption activities in a multidirectional trade network.25,26 One of the most pressing questions for modeling regional carbon emissions is how carbon trade among regions is triggered by final demand, as has been addressed in recent MRIO studies.11,27 Another is how economic sectors interplay with one another and affect their contributions to systemic carbon emissions. A systems approach known as ecological network analysis (ENA) has been adopted to assess the impact of indirect effects among economic sectors on carbon flows in human-dominated systems such as urban systems.28,29 Although ENA has its roots in the research stream of food web networks30,31 and social networks,32,33 it is originally reformed from an input−output model by a group of ecologists successively tracking flows in various ecosystems.34−36 ENA has

1. INTRODUCTION Modern climate change is a complex phenomenon dominated by human influences, in which the impact of local CO2 emissions can go far beyond territorial boundaries.1−3 The major drivers of CO2 emissions include energy use, urbanization, and land use changes on both the local and regional scales.4,5 Carbon emissions not only diffuse across boundaries in physical form (i.e., CO2 molecules) but also transfer among regions in the virtual form while trading products (i.e., CO2 embodied in upstream production processes of products). Because of economic and other concerns, human demand for energy and products is increasingly being met by regional and global markets. It has been reported that 5−6 Gt of CO2 (over 20% of global CO2 emissions) were associated with trade worldwide in the 2000s.6,7 Accordingly, a system wide perspective is imperative to deal with the carbon emissions embodied in activities of multiple connected regions.8,9 There will be much greater progress in mitigation of the impact of human consumption if regions make binding commitments and take compatible actions against unintended carbon emissions, instead of acting as individual regions. To address the carbon emissions embodied in trade, models have been established based on input−output tables designated for production−consumption interplay in human economies.10,11 The framework of streamlined input−output analysis (IOA) was developed in the early 1970s12,13 and has since been adjusted to quantify carbon emissions embodied in trade at different spatial scales, such as buildings,14,15 cities,16,17 © 2016 American Chemical Society

Received: Revised: Accepted: Published: 4731

December 24, 2015 March 21, 2016 April 10, 2016 April 11, 2016 DOI: 10.1021/acs.est.5b06299 Environ. Sci. Technol. 2016, 50, 4731−4741

Article

Environmental Science & Technology

Figure 1. Framework and network structure of hybrid carbon flow model.

flows among multiple regions frames the model structure of inter-regional carbon exchanges. By looking into the flows of a single region, we account for the upstream processes of products and services consumed by the region. The controlled flows from one region to another can be modeled based on the relationships between these regions. We model the embodied carbon flows related to the region by including direct carbon emissions (diCarbon) from the fuel combustion and other activities within the regional boundary and indirect emissions from consuming goods imported from external markets (imCarbon), while excluding the carbon leaving the region through exports of goods (exCarbon). The carbon emissions that are controlled by consumption of the region are assessed. The carbon inflows to a region, to some extent, are controlled by upstream regions from which products and services are imported (i.e., in the magnitude of imControl), while the carbon controlled by the consumption of downstream regions leaves the region via export (in the magnitude of exControl), resembling the bottom-up control in food-web chains of natural ecosystems.48,49 On the basis of the multiregional input−output table among the three hypothetical regions (R1, R2, and R3), embodied carbon flows among regions triggered by the final consumption in each region can be quantified. This will show how the carbon footprint of one region can be produced (controlled) by other regions, or on the other way, how the carbon emitted in one region can be allocated to (depended on) other regions due to the product supply chain. The following sections describe the detailed processes and formulations regarding the inventory of direct emissions, and the accounting of embodied and controlled carbon flows. First (in Section 2.2), a production-based inventory is conducted to quantify carbon emissions associated with energy use and industrial activities of all economic sectors in a region. This is a routine process of accounting for territorial carbon emission. Second (in Section 2.3), one can compute how much carbon is embodied in trade among various regions (at both the economy and sectoral levels) based on emissions intensities and multiregional input−output data. This is used as a major approach of estimating carbon footprint from cities to nations. Following that (in Section 2.4), combing IOA and ENA, one

a lot in common with IOA in that it dissects the system into components connected by imports, exports, and intermediate flow. However, ENA has developed a handful of systemic tools quantifying both direct and indirect effects within ecological networks (structural analysis, utility analysis, control analysis, etc.),37−41 which have been reintroduced to appraise socioeconomic systems.42,43 A few attempts have been made to combine the merits of IOA and ENA in tracking the metabolic processes (both production and consumption) of single regions.44,45 The combination of multiregional input−output tables and ENA has also been shown to be useful for addressing energy flow structure among multiple regions.46,47 In this study, we developed a hybrid network model to determine inter-regional carbon flows by integrating carbon emissions inventory, MRIO and ENA, in which both the directions and magnitudes of flows can be explicitly tracked. A new term called “controlled emissions” was proposed to target the emissions that were actually controllable by different regions and their economic sectors. Taking the Beijing− Tianjin−Hebei Area (also known as the Jing−Jin−Ji Area) as a case study, we quantified the embodied emissions of the regions and economic sectors for their sources and destinations. China is facing serious challenges of balancing economic growth and carbon emissions mitigation. As one of the largest joint economic regions in China, the Jing−Jin−Ji Area needs to filter controlling pathways of regional carbon emissions for an efficient and synergetic mitigation action. By joint analysis of carbon embodied in and controlled by regions, the hybrid model elicits the possibility of more rational consumption and better regional planning in a low-carbon future.

2. MATERIALS AND METHODS 2.1. Framework for Hybrid Carbon Flow Model. A hybrid carbon network model was developed to examine the carbon flows among various regions. Figure 1 shows the technical process of fusing ENA and IOA in the framework of carbon flows analysis regarding both single-region and multiregion scenarios. We develop an inter-regional carbon flow model that is driven by socioeconomic factors (such as household consumption by the regions). A holistic view of 4732

DOI: 10.1021/acs.est.5b06299 Environ. Sci. Technol. 2016, 50, 4731−4741

Article


determined in eqs 4 and 5. The technology coefficient matrix A (also called the direct requirement coefficient) is derived from eq 6, describing the amount of direct intermediate demand of one regional sector from the other regional sector per unit of economic outputs, which is used to calculate the total requirement coefficient (Leontief Inverse) for both direct and indirect forces between sectors. The embodied carbon emissions for sectors of different regions triggered by the final consumption of all the regions (from R1 to Rm) are modeled via eq 7. The embodied carbon flows are the combination of direct and indirect carbon flows.

can assess how much of this carbon is actually controlled by a region due to its consumption of products in the trade network. This is a new metrics that has not yet been covered in literature, which could provide information on emissions mitigation pathways hidden in the supply chains. Lastly (Section 2.5), the dominant flows and sectors in the carbon model are searched for each region based on their relationships form in the multiregional carbon network. This step is important for applying the hybrid model to formulating carbon mitigation policy targeting certain activities or sectors. 2.2. Inventory of Direct Carbon Emissions. Different practices of carbon emissions accounting are dependent on the selection of system boundaries of cities or regions, with50 or without51 considering upstream processes. The difference in boundary definition leads to diverse procedures of regional carbon emission inventories. Herein, we conduct an inventory of direct CO2 emissions (terrestrial emissions) from all economic sectors as a basis of modeling regional carbon flows, for which we apply the Intergovernmental Panel on Climate Change (IPCC) recommended approach.52 Compiling the total direct CO2 emissions from a region (vi) consists of two parts: emissions from energy combustion (vei ) and from industrial processes (vci ) (eq 1). These two sources of terrestrial emissions are accounted for from a production-based perspective (eqs 2 and 3). vi = vie + vic

(1)

vie = uie × hie × ωie × oie

(2)

vic = uic × ωic

(3)

θ1 × n = [θi], V1 × n = [vi],

θi =

X1 × n = [Xi]

vi Xi

A n × n = [aij],

(4)

(5)

aij =

xij Xj

(6)

−1 R1 − Rm E1R×1n− Rm = θndiag × n (I − A) F1 × n

(7)

where θ1×n is the vector of carbon intensities for all regional sectors (n is the number of sectors) (carbon emissions per unit of economic output) with elements θi; V1×n is the vector of direct carbon emissions from the sectors (n is the number of sectors); X1×n is the vector of total outputs of the sectors; xij is the monetary flow between sector i and j, aij is the outputoriented dimensionless flow from sector i to j; An×n is the n × n technology coefficient matrix among sectors; I is an n × n identity matrix; E1R1−Rm is the embodied flow of carbon ×n (footprint) among various regions (from R1 to Rm), triggering by all final demand categories (household consumption, government expenditure, capital formation, and change in stock) in these regions (FR1−Rm 1 × n ). 2.4. Modeling of Inter-Regional Controlled Carbon Flows. Most inter-regional carbon footprint models focused on how much in- and off-boundary emission is triggered by local consumption of regions, while the control mechanism of emissions in the whole trade system has not yet covered. The concept of “control” is often used to describe the dominance of one component over the other in the same natural or mechanic system.59 A tool termed network control analysis (NCA) derived from ENA has been employed to determine the control intensities between system components. For natural ecosystems, NCA assess the control of one species or functional group over another in the same ecosystem based on their predator−prey relations in the food-web network.60,61 By resembling the practices in natural world, NCA has recently been extended to analysis of the control of material and energy flows in human-dominated systems such as urban systems28,45 or industrial parks.62,63 NCA is suitable and important in quantifying the control occurs in socioeconomic sectors constituting the regional economy for two reasons: (1) NCA is universally applicable in analyzing system components that are connected through physical flows, either the connection is established during competition for resources or emissions transfer; (2) the production-consumption relations in economic systems are analogous with the predator−prey relationships in many ways,29,46 making the interpretation of system control is transplantable from natural ecosystems to artificial systems. A detailed description of the technical process and software for NCA can be found in the literature.64−67 We provide a 3-

vei

where is the emissions from energy combustion in a region (such as city or province); uei is the total amount of energy combustion from a certain fuel type (in physical units); uei is the combustion of various types of fuel + energy used for thermal power + energy used for heating; hei is the specific calorific value of different fuel types; ωei is the CO2 emission factor for certain types of fuel; oei is the specific oxidization rate of a certain fuel type and final usage of fuel. vei is the emissions from cement production in a region (Cement production, a main source of industrial emissions53 is calculated in this case); uci is the amount of cement production; and ωci is the emission factor for cement production.52 2.3. Modeling of Inter-Regional Embodied Carbon Flows. Almost all local economies inevitably cause offboundary emissions by drawing on resources from places elsewhere.54 Efficient carbon footprint mitigation entails consumption-based analysis that allocates emissions to the regions where products are consumed.9,55 Input−output model has been widely used in appraising carbon footprint of a region16,26,56 as well as embodied carbon emissions transfer across regions18,19,57 from a consumption perspective. On the basis of the direct carbon emission inventory for regions, we calculated flows of carbon emission embodied in trade among regions (counties, cities, provinces, etc.) using MRIO model with an environmentally extended module. MRIO tables describe the inter-regional monetary flows from donor sectors to receiving sectors triggered by final consumption categories.23 Theory and methods of MRIO model have been well established and depicted in the literature.18,19,58 Here, we focused on the accounting process of inter-regional carbon flows driven by regions’ final consumption. Direct carbon intensity for all regions is 4733


Article


Figure 2. Total direct, controlled, and embodied emissions of Jing−Jin−Ji Area in 2012 (Beijing, Tianjin, and Hebei).

carbon matrix triggered by the final consumption of regions (from R1 to Rm). 2.5. Tracking of Dominant Flows and Sectors. Traditional IOA uses influence and response coefficients to identify key sectors during the transfer of energy or carbon along the production chain.68,69 They only reflect the proportion of sectors in total economic output from supplier and consumer angles, without examining the interactions between sectors and the impact of each flow. Herein, by introducing the control metrics from ENA, we identify the dominant sectors and flows regarding the embodied carbon and how they are interacted with each other. The domination within the region is formulated from a controller’s and observer’s perspective based on a control allocation coefficient (CA) and dependence allocation coefficient (DA) based on the regional control matrix (biĵ ).28 At the process level, caij refers to the relative integral control (both direct and indirect forces) of sector i at the production side over sector j at the consumption side, and daij refers to the integral dependence of sector i at the consumption side on sector j at the production side (eqs 13 and 14). At the sectoral level, the control index (CI) or dependence index (DI) sums the integral control or dependence intensities associated with a regional sector (eqs 15 and 16) and is then used to identify sectors that dominate the accumulating carbon flows for the entire region.

compartment model to exemplify how to define and calculate control relation using NCA in the Supporting Information (SI). Herein, NCA is applied to modeling carbon flows controlled by final consumption. We use it to determine (1) how much of the CO2 emission embodied in the consumption of regions are controlled by economic sectors within the boundary, and (2) how much of the direct CO2 emitted in one region is controlled by other regions in the trade network. In both situations, the amount of emissions that are controlled by a sector or a region from a consumption perspective is called “controlled emissions”. In comparison to “embodied carbon emission” (in the last section), the controlled carbon emissions consider the CO2 directly emitted within the region and that emitted elsewhere but indirectly controlled by the region owing to supply chains. From a network point of view, control is distributed in the economic network and determined by pairwise interactions among sectors.64 The integral control directions and intensities are defined by the ratios of pairwise dimensionless flows between economic sectors in the Leontief inverse B and its opposite matrix (B′) (eqs 8−10). A metric called the control coefficient (B̂ ) was used to indicate the absolute control intensity among sectors; i.e., the control of sector i over sector j (biĵ , eq 11) is determined by the difference in pairwise integral flows between sector i and j. Similar to the modeling of embodied carbon flows, the inter-regional controlled CO2 emissions are calculated from the production−consumption relationship among regions, with the total requirement coefficient (B) replaced by the control coefficient (B̂ ) (eq 12). xji A n′× n = [a′ji], a′ji = Xi (8) −1

Bn × n = [bij] = (I − A)

Bn′× n = [b′ji] = (I − A′)−1

Bn̂ × n = [biĵ ],

⎧b − b′ ≥ 0, b ̂ = b − b′ ji ij ij ji ⎪ ij ⎨ ⎪bij − b′ji < 0, biĵ = 0 ⎩

R1 − Rm ̂ C1R×1n− Rm = θndiag × n Bn × n F1 × n

CA n × n = [ca ij],

DA n × n = [da ij],

ca ij =

da ij =

(9)

CI1 × n = [cii],

cii =

(10)

DI1 × n = [di j], (11)

di j =

biĵ ∑j = 1 biĵ

(13)

biĵ ∑i = 1 biĵ

(14)

∑j = 1 biĵ ∑i = 1 ∑j = 1 biĵ

(15)

∑i = 1 biĵ ∑i = 1 ∑j = 1 biĵ

(16)

2.6. Case Study and Data Acquisition. The Jing−Jin−Ji Area in China was selected as a case study for inter-regional carbon flow modeling. This area is the most economically active and developed area of China, as well as home to a population with one of the highest carbon emissions rates in the country.9 Addressing the cross-boundary transfer of emissions has been an urgent need for planning inter-regional coordinated strategies of emission control. A primary step to achieve this

(12)

where A′n × n is the transpose of An×n; B′n × n is the integral matrix of A′n × n; Bn̂ × n is the regional control matrix, in which bij represents the difference in two dimensionless flows [i.e., the integral output flow (Bn×n) and the dimensionless integral input flow matrix (Bn′ × n)], CR1−Rm 1 × n is the cross-boundary controlled 4734


Article


Figure 3. Fifteen highest emitting sectors in Jing−Jin−Ji Area in all three categories of CO2 emissions. Note: The 25 sectors of Beijing (B), Tianjin (T) and Hebei (H) are (1) farming, forestry, animal husbandry, fishery and water conservancy; (2) coal mining and dressing; (3) petroleum and natural gas extraction; (4) metals mining and dressing; (5) nonmetal minerals mining and dressing; (6) food processing and production; (7) textile industry, garments, other fiber products and related products; (8) timber processing, palm fiber & straw products and furniture manufacturing; (9) papermaking and paper products and printing and record medium reproduction; (10) petroleum processing and coking; (11) chemicals and medicinal products; (12) nonmetal mineral products; (13) smelting and pressing of metals; (14) metal products; (15) ordinary and special machinery and equipment; (16) transportation equipment; (17) electric equipment and machinery; (18) electronic and telecommunications equipment, meters, cultural and office machinery; (19) other manufacturing industry; (20) production and supply of electric power, steam and hot water; (21) production and supply of gas and tap water; (22) construction; (23) transportation, storage, post and telecommunication services; (24) wholesale, retail trade and catering services, restaurant, renting and business services; and (25) other services.

Figure 4. Embodied and controlled carbon flows for the entire regional economy and its dominant industrial sectors. Note: The values are based on the net amounts of bidirectional regional flows (the difference in consumption-triggered emissions between two regions). Units of carbon flow: Mt CO2. GP: gross product for the city/province or their selected sectors (in billion RMB). Metal and nonmetal mining is aggregated from (4) metals mining and dressing and (5) nonmetal minerals mining and dressing; metal and non-metal manufacture is aggregated from (12) nonmetal mineral products, (13) smelting and pressing of metals and (14) metal products, electricity, gas and water supply is aggregated from (20) production and supply of electric power, steam and hot water, and (21) production and supply of gas and tap water.

provincial level (2007) was used for tracking inter-regional flows, but was readjusted from a 30-sector structure in the literature75 to a 25-sector structure for this study.

would be appraising the inter-regional trade connections and associated carbon emissions for the area. The data describing energy consumption was acquired from yearbooks from Beijing, Tianjin, and Hebei in 2012,70−72 while the specific low-calorific value of different fuel types of China73 and CO2 emission factors for different types of fuel,52 Chinese specific oxidization rates of certain fuel types and final usage of fuel74 were collected for the energy-related CO2 emissions inventory. The cement production of the region was derived from the National Bureau of Statistics of China.73 The most recent multiregional input−output table for China at the

3. RESULTS 3.1. Direct, Controlled, And Embodied Carbon Emissions. Figure 2 shows that embodied carbon emissions associated with the regions are double the direct emissions on average, which means that in this area, half of the emissions happen outside the boundaries (indirect emissions) but are triggered by the regions via their consumption of products and 4735


Article


Figure 5. Inter-regional CO2 emissions controlled by and embodied in final demands of Jing−Jin−Ji Area. BJ: Beijing; TJ: Tianjin; and HB: Hebei.

Figure 6. Dominance of economic sectors in the whole area based on control index (CI) and dependence index (DI). Note: The figure is divided into four quadrants (I, II, III, and IV) by the point of average X-axis value (CI = 1.3%) and Y-axis value (DI = 1.3%) of all sectors.

services. Hebei has a much larger amount of embodied CO2 than Beijing and Tianjin, which is also much higher than its own direct emissions. The carbon emissions intensity of Hebei is highest among all three regions, which is 3-fold that of Beijing. On average, adding upstream emissions triples the emission intensity for a region. The amount of controlled carbon emissions associated with the regions fell between the other two categories, accounting for 67−70% of the embodied emissions, while the proportion of indirectly controlled emissions is about 30%. Figure 3 shows the emissions from the top 15 sectors (accounting for 80% of the total emissions in the area), while the emissions results for all 75 sectors are provided in Figures S2−S4. We found that the six sectors having the greatest effects on direct, embodied and controlled emissions were the five sectors of Hebei (H13, H4, H20, H11, and H12) and one

sector of Tianjin (T13). The sectors with the greatest emissions for the entire Jing−Jin−Ji Area are smelting and pressing of metals in Hebei (H13) (up to 45% of the total emissions). However, there is notable evidence that these three categories of emissions represent different aspects of carbon profile. For instance, H10 (petroleum processing and coking) of Hebei is identified as one of the highest emitting sectors among embodied and controlled emissions but is not in the top-15 list of direct emissions. In contrast, H25 (other services) of Hebei is one of the 15 dominant sectors in controlled emissions category but is not included in the embodied emissions category. 3.2. Inter-Regional Carbon Flows Across Boundaries. Figure 4 illustrates the directions and magnitudes of carbon flowing among Beijing, Tianjin, and Hebei in both embodied and controlled accounting categories. The results of the entire 4736


Article


Figure 7. Control allocation matrix (CA) and dependence allocation matrix (DA) among the 75 × 75 sectors of Beijing, Tianjin, and Hebei (Jing− Jin−Ji Area).

consumption-based carbon emissions in the Jing−Jin−Ji Area, while the sectors of Beijing and Tianjin share the other half. In contrast, 33−34% of the carbon emissions of each region in the whole area are dependent on the other two. Most of the sectors are within Quadrant II (strong dependence and weak control), Quadrant III (weak dependence and weak control), and Quadrant IV (weak dependence and strong control), while only a few others are located in Quadrant I (strong dependence and strong control). The sectors in Quadrant I are mostly from Hebei, including H13 (Smelting and Pressing of Metals), H12 (Nonmetal Mineral Products), H14 (Metal Products) and H1 (Farming, Forestry, etc.), which all together control 16% of the total emissions of the area. Observation of Quadrant IV clearly demonstrates that most controlling sectors are from Hebei (e.g., H24 and H20), except for B20 and B23 from Beijing. In comparison, around half of the sectors of Beijing and Tianjin (e.g., T22, T17, and B22) are in Quadrant II, indicating that emissions are highly dependent on the rest of the economy. The control and dependence of each carbon flow were also identified to determine which process contributes most to the sectoral domination for emissions (Figure 6). Take one of the dominated sector H12 as an example, from CA metrics, we find that it has the highest controls over Sector 22 of all regional sectors. On the basis of the DA metrics, both B22 and T22 are highly dependent on H12, H13, and H20 from Hebei, suggesting that metals and nonmetal production and electricity has a strong impact on construction emissions (Figure 7). Both metrics indicate that cleaner manufacturing and electricity generation can be an efficient pathway to mitigation of emissions from construction. A coordinated action performed by all three regions is required to enable such inter-regional emissions control.

regional economy (Figure 4a) indicate that the biggest interregional carbon flows in the whole area are the embodied emissions transferred from Hebei to Beijing and Tianjin, while Beijing and Tianjin controlled the major amount of emission via their large consumption of products from Hebei. Among all three regions, Beijing controls most of the carbon flow in the area (65% of the total) and that only a very small portion of this carbon (0.3%) is influenced by activities in Tianjin. In addition, three high-emitting industrial sectors were selected for carbon flow modeling, including metal and nonmetal mining (Figure 4b), metal and nonmetal manufacturing (Figure 4c), and electricity, gas, and water supply (Figure 4d). Similarly, the largest amount of carbon is transferred from Hebei to Beijing, followed by Hebei to Tianjin, while the directions of flows between Beijing and Tianjin vary among sectors. The directions and magnitudes of carbon flows are not correlated with economic scales in a simple way (i.e., from a high-GP region to low-GP region). For example, in terms of electricity, gas, and water sector, carbon is transferred from low-GP region (Hebei) to high-GP region (Beijing) to meet Beijing’s high demand. The regional embodied and controlled carbon flows triggered by final demands are further examined (Figure 5). Intraregional carbon flows (consumption-based emissions of sectors within the region, triggered by its own final demand) account for about 70% of the total carbon flows of the area, with Hebei’s intraregional flow accounting for 50%. Different aspects of carbon flow are revealed by IOA (embodied carbon accounting) and ENA metrics (controlled carbon accounting). For example, the most significant sectors (highlighted bars) in inter-regional embodied flow triggered by the consumption in Beijing are slightly different from those in terms of controlled flow. About 43% of the embodied carbon flows are triggered by household consumption (the sum of urban and rural consumption), while almost the same proportion is associated with capital formation (Figure S3). Regarding the controlled carbon category, only 25% of Beijing’s emissions are triggered by household consumption, and the proportion of capital formation expands to 63%. This demonstrates that capital formation has greater control over the entire regional economy for driving the emissions, which indicates the need for careful attention when planning low-carbon infrastructure. 3.3. Dominance of Sectors and Processes in Carbon Leakage. The dominance of economic sectors in the regional carbon profile is quantified by the control index and dependence index from a network perspective (Figure 6). Overall, the activities of sectors in Hebei control half of the

4. DISCUSSION 4.1. Inherent Model Logics and Results Interpretation. There will be great pressure to prevent the world from becoming warmer in this century.4,76,77 Many countries and regions have already agreed to limit global warming to less than 2 °C relative to preindustrial levels (before 1850). However, much work still needs to be done, such as defining the responsibility and fairness of carbon mitigation across regions that are willing to cooperate. The allocation of mitigation targets should clearly be assessed based on the productionbased inventory of where emissions are generated, as well as the consumption-based inventory of where products and services are consumed by regions.78 The responsibility of controlling 4737


Article

Environmental Science & Technology carbon emissions should be distributed among regions that drive the growth of emissions (historically or in the future).9,27 Moreover, it is essential to determine which sectors and processes have the greatest potential for carbon mitigation and how they are triggered by consumption of cities or provinces, whereby local authorities can make robust strategies when planning low-carbon industries and construction that will eventually have a global impact. In this study, we proposed a hybrid network model that integrated input−output based metrics (MRIO) and a networkoriented metrics (ENA) into the assessment of inter-regional carbon flows. In particular, ENA is introduced into the tracking of emissions controlled by various sectors and regions as a new technique. The logic and implication of applying this network approach to regional carbon modeling can be explained in three parts: (1) Network theory for describing socioeconomic networks. Socioeconomic systems resemble natural ecosystems in terms of synergy and competition relations between system components, although the concrete forms of interaction vary. The macroeconomic activities in the economy have been analyzed based on economic input− output model since Leontief’s pioneering work.79 It was later adapted to assessing the structure of energy flows in natural ecosystems because of the basic assumption of idealizing a system as a network of interacting parts in input−output model can be transplanted to other disciplines.34,80 In this sense, ENA is not so much a special approach within biological field as a set of toolboxes capable of assessing structure and function of generic networks. Because of its power in handling complex networks, one the most important applications of ENA is as a platform for addressing sustainability issues of human−natural systems.81 Over the past decade, scientists have already successfully applied ENA to appraise various human-natural integrated systems such as urban systems,44,82 resource flow systems,83 energy trading systems,84 and virtual water systems.85 (2) Network metrics for tracking regional carbon flows. The tools from ENA such as control analysis have been proven useful in depicting the pairwise relations between network components (e.g., sectors) in human-natural integrated systems.45,86,87 The “control” distributed in the network is established based on the relative difference of biomass or energy flows between two system components (as controller or observer in this pairwise relationship).64 A significant merit of using the analogy of “controller-observer” relation in economic system analysis is the quantification of dominant-being dominant relations between sectors formed in the supply chain, which has not yet been covered in traditional input− output analysis. The directions and magnitudes of controlled carbon flows are determined by final demand triggered emissions from cities or provinces. For components (or sectors), the controlled carbon accounts for the flows that come into a sector through supply chain and are controlled by the sector from the network perspective. For regions, this controlled carbon specifies the flows into a region from outside the geographical boundary (imports) and are controlled by regional economic activities (which therefore can be adjusted within the region).

(3) Interpretation of network model results. Modeling economic systems via ENA can produce new findings of carbon flows among regions. For example, the case study results suggest a considerable amount of carbon is embodied in the consumption of products and services, and this carbon is transferred among regions via trade. Embodied carbon emissions associated with the consumption of regions double the direct emissions on average, while controlled carbon emissions account for 67−70% of the embodied emissions. These findings indicate that emissions embodied in products are only partially controlled by the region from a network perspective. The directions of carbon flows reflect the great reliance of Beijing and Tianjin on Hebei for economic development and indicate a large portion of their emissions have been outsourced to Hebei. In addition to tracking controllable emissions, ENA can also be used to quantify the dominance of specific processes and sectors over the rest of the economy. For example, the metals and nonmetal production sector and electricity sector were found to have high control over the construction sector, as shown by CI and DI analysis. 4.2. External Models Comparison. Current modeling practices of carbon flows are split into two distinct streams. The first stream inventory the direct carbon embodied in products intake and stored in human settlements and relate that to the land-use pattern.88 The second group is tracking upstream carbon emissions associated with local consumption based on life cycle assessment (LCA) or IOA.22,89 The latter stream of models is often good at analyzing the contribution of economic sectors in the changing carbon footprints of cities or nations. Herein, in addition to the inventory of direct carbon emission, two other consumption-based categories, embodied carbon (from IOA) and controlled carbon (from ENA), accounted for various regions. IOA quantifies the total amount of carbon emissions embodied in consumption, while ENA can pinpoint to what extent these emissions are actually controlled by regional sectors. It has been reported that 78% of a city’s embodied energy is controlled by the activities of its urban sectors.45 The case study of Jing−Jin−Ji Area showed that Beijing and Tianjin control about 70% of CO2 emitted outside of their geographical boundary via the consumption of products from Hebei. In addition to distinguishing controlled emissions from embodied emissions, ENA is also promising in determining which sectors control others in the supply chain and therefore should be better managed for emissions mitigation. This technique can be applied to screening efficient pathways of coordinated emissions controls targeting economic sectors across regions. On the algorithmic level, the major difference between input−output based metrics and network control metrics lies in treatment of the requirement coefficient matrix. In IOA, a flowbased parameter (B) representing the intermediate production−consumption relationships among sectors52 is used to estimate the inter-regional carbon flows triggered by final demand, deem to be full embodied emissions in “magnitude”. Through the ENA-based metrics proposed here, the difference in the two pairwise flow-based parameters (B−B′) is utilized to quantify the integrated interactions among sectors of different regions and the embodied emissions associated with these interactions in a certain order. Unlike pure IOA, the hybrid network model not only cares how much of the emissions are 4738


Article


20130003110027), and China Postdoctoral Science Foundation funded project.

transferred between regions via trade, but also how much are controllable and adjustable within regions given their network relationships. Another important tool from the hybrid model is the control and dependence index, which can be used to identify sectors and processes that dominate the emissions from regions in a systemic way. When compared with the influence and response coefficient, the hybrid model focuses on the abilities of sectors (or processes) to control the economy, rather than its importance in intermediate flows. This can be an essential supplement of instruments used to select determinants defining regional carbon footprints. 4.3. Uncertainties and Limitations. An uncertainty analysis of the parameters involved in the modeling process is provided in the SI (Table S3). Various factors in the modeling process, such as emissions factors, energy flows, and downstream activities, could cause uncertainties for the results to different extents. The impacts from these uncertainty factors have been treated to avoid invalid conclusions. A major limitation of the proposed framework is that it only covers the analyses of sectoral activities and do not include indicators that are directly linked to urban land-use and regional planning. A more rational management of human systems should be based on the combination of top-down (such as economic models) and bottom-up (such as land-use models) metrics.42 One way to accomplish that is to add the heterogeneous data for products processing and consumption to the database, whereby the regional carbon networks can be broken down to cities, districts, communities, and so forth. Another problem is the lack of energy consumption data for some sectors in Hebei province, for which we use the sum of energy consumed by industrial enterprises above a designated size as an approximation. Also, because of the slow update of multiregional input−output tables in China, we had to use the 2007 table for the three regions to calculate the embodied and controlled emissions for 2012. These problems can be fixed in the future by a more accurate and up-to-date compilation of emissions and economic input−output data.

■

■

(1) Wilbanks, T. J.; Kates, R. W. Global change in local places: how scale matters. Clim. Change 1999, 43 (3), 601−628. (2) Falkowski, P.; Scholes, R. J.; Boyle, E.; et al. The global carbon cycle: a test of our knowledge of earth as a system. Science 2000, 290 (5490), 291−296. (3) Schramski, J. R.; Gattie, D. K.; Brown, J. H. Human domination of the biosphere: Rapid discharge of the earth-space battery foretells the future of humankind. Proc. Natl. Acad. Sci. U. S. A. 2015, 112 (31), 9511−9517. (4) Karl, T. R.; Trenberth, K. E. Modern global climate change. Science 2003, 302 (5651), 1719−1723. (5) Qin, D; Plattner, G K; Tignor, M; et al. Climate Change 2013: The Physical Science Basis; Cambridge University Press, Cambridge/ New York, 2014. (6) Peters, G. P.; Hertwich, E. G. CO2 embodied in international trade with implications for global climate policy. Environ. Sci. Technol. 2008, 42 (5), 1401−1407. (7) Davis, S. J.; Caldeira, K. Consumption-based accounting of CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (12), 5687−5692. (8) Yu, Y.; Feng, K.; Hubacek, K. Tele-connecting local consumption to global land use. Global Environmental Change 2013, 23 (5), 1178− 1186. (9) Feng, K.; Davis, S. J.; Sun, L.; et al. Outsourcing CO2 within China. Proc. Natl. Acad. Sci. U. S. A. 2013, 110 (28), 11654−11659. (10) Hubacek, K.; Giljum, S. Applying physical input−output analysis to estimate land appropriation (ecological footprints) of international trade activities. Ecological Economics 2003, 44 (1), 137−151. (11) Minx, J. C.; Wiedmann, T.; Wood, R.; et al. Input−output analysis and carbon footprinting: an overview of applications. Economic Systems Research 2009, 21 (3), 187−216. (12) Leontief, W. Environmental Repercussions and the Economic Structure: An Input−Output Approach. Review of Economics and Statistics 1970, 52, 262−271. (13) Leontief, W.; Ford, D. Air pollution and the economic structure: empirical results of input−output calculations. Paper presented at the Fifth International Conference on Input−Output Techniques; North Holland Pub. Co.: Geneva, Switzerland, 1971. (14) Chen, G. Q.; Chen, H.; Chen, Z. M.; et al. Low-carbon building assessment and multi-scale input−output analysis. Communications in Nonlinear Science and Numerical Simulation 2011, 16 (1), 583−595. (15) Praseeda, K. I.; Reddy, B. V. V.; Mani, M. Embodied energy assessment of building materials in India using process and input− output analysis. Energy and Buildings 2015, 86, 677−686. (16) Chen, G. Q.; Guo, S.; Shao, L.; et al. Three-scale input−output modeling for urban economy: carbon emission by Beijing 2007. Communications in Nonlinear Science and Numerical Simulation 2013, 18 (9), 2493−2506. (17) Lenzen, M.; Peters, G. M. How city dwellers affect their resource hinterland. J. Ind. Ecol. 2010, 14 (1), 73−90. (18) McGregor, P. G.; Swales, J. K.; Turner, K. The CO2 ‘trade balance’ between Scotland and the rest of the UK: performing a multiregion environmental input−output analysis with limited data. Ecological Economics 2008, 66 (4), 662−673. (19) Su, B.; Ang, B. W. Input−output analysis of CO2 emissions embodied in trade: a multi-region model for China. Appl. Energy 2014, 114, 377−384. (20) Machado, G.; Schaeffer, R.; Worrell, E. Energy and carbon embodied in the international trade of Brazil: an input−output approach. Ecological Economics 2001, 39 (3), 409−424. (21) Mäenpäa,̈ I.; Siikavirta, H. Greenhouse gases embodied in the international trade and final consumption of Finland: an input−output analysis. Energy Policy 2007, 35 (1), 128−143. (22) Singh, S.; Bakshi, B. R. Accounting for emissions and sinks from the biogeochemical cycle of carbon in the US economic input-output model. J. Ind. Ecol. 2014, 18 (6), 818−828.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b06299. Details in materials and methods; extended model results; and additional references (Figures S1−S6 and Tables S1−S3 (PDF)

■

REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Tel/Fax.: +86 10 58807368; e-mail: [email protected] (B.C.). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was supported by the Fund for Innovative Research Group of the National Natural Science Foundation of China (No. 51421065), National Natural Science Foundation of China (Nos. 71573021, 41271543), Major Research Plan of the National Natural Science Foundation of China (No. 91325302), Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 4739


Article

Environmental Science & Technology (23) Peters, G. P.; Andrew, R.; Lennox, J. Constructing an environmentally-extended multi-regional input−output table using the GTAP database. Economic Systems Research 2011, 23 (2), 131− 152. (24) Lenzen, M.; Pade, L. L.; Munksgaard, J. CO2 multipliers in multi-region input-output models. Economic Systems Research 2004, 16 (4), 391−412. (25) Feng, K.; Hubacek, K.; Sun, L.; Liu, Z. Consumption-based CO2 accounting of China’s megacities: the case of Beijing, Tianjin, Shanghai and Chongqing. Ecol. Indic. 2014, 47, 26−31. (26) Minx, J.; Baiocchi, G.; Wiedmann, T.; et al. Carbon footprints of cities and other human settlements in the UK. Environ. Res. Lett. 2013, 8, 035039. (27) Hubacek, K.; Feng, K.; Minx, J. C.; et al. Teleconnecting consumption to environmental impacts at multiple spatial scales. J. Ind. Ecol. 2014, 18 (1), 7−9. (28) Chen, S. Q.; Chen, B. Network environ perspective for urban metabolism and carbon emissions: A case study of Vienna, Austria. Environ. Sci. Technol. 2012, 46 (8), 4498−4506. (29) Chen, S. Q.; Chen, B.; Su, M. R. Non-zero-sum relationships in mitigating urban carbon emissions: a dynamic network simulation. Environ. Sci. Technol. 2015, 49 (19), 11594−11603. (30) Richey, J. E.; Wissmar, R. C.; Devol, A. H.; et al. Carbon flow in four lake ecosystems: A structural approach. Science 1978, 202 (4373), 1183−1186. (31) Rydelek, P. A.; Sacks, I. S.; Pacheco, J. F.; et al. Food-web analysis through field measurement of per capita interaction strength. Nature 1992, 355, 73. (32) Wasserman, S; Faust, K. Social Network Analysis: Methods and Applications; Cambridge University Press: Cambridge, U.K., 1994. (33) Gil-Mendieta, J.; Schmidt, S. The political network in Mexico. Social Networks 1996, 18 (4), 355−381. (34) Hannon, B. The structure of ecosystems. J. Theor. Biol. 1973, 41 (3), 535−546. (35) Patten, B. C. Systems approach to the concept of environment. Ohio J. Sci. 1978, 78, 206−22. (36) Ulanowicz, R. E. An hypothesis on the development of natural communities. J. Theor. Biol. 1980, 85, 223−45. (37) Fath, B. D.; Patten, B. C. Review of the foundations of network environ analysis. Ecosystems 1999, 2 (2), 167−179. (38) Kazanci, C. EcoNet, a new software for ecological model simulation and network analysis. Ecol. Modell. 2007, 208 (1), 3−8. (39) Schramski, J. R.; Kazanci, C.; Tollner, E. W. Network environ theory, simulation, and EcoNet® 2.0. Environmental Modelling & Software 2011, 26 (4), 419−428. (40) Borrett, S. R.; Lau, M. K. enaR: An R package for ecosystem network analysis. Methods in Ecology and Evolution 2014, 5 (11), 1206−1213. (41) Fath, B. D.; Borrett, S. R. A Matlab® function for network environ analysis. Environmental Modelling & Software 2006, 21 (3), 375−405. (42) Chen, S. Q.; Chen, B.; Fath, B. D. Urban ecosystem modeling and global change: Potential for rational urban management and emissions mitigation. Environ. Pollut. 2014, 190, 139−149. (43) Li, Y.; Chen, B.; Yang, Z. F. Ecological network analysis for water use systemsA case study of the Yellow River Basin. Ecol. Modell. 2009, 220 (22), 3163−3173. (44) Huang, J.; Ulanowicz, R. E. Ecological network analysis for economic systems: Growth and development and implications for sustainable development. PLoS One 2014, 9 (6), e100923. (45) Chen, S. Q.; Chen, B. Urban energy consumption: different insights from energy flow analysis, input-output analysis and ecological network analysis. Appl. Energy 2015, 138, 99−107. (46) Zhang, Y.; Zheng, H.; Yang, Z. F.; et al. Urban energy flow processes in the Beijing−Tianjin−Hebei (Jing−Jin−Ji) urban agglomeration: combining multi-regional input−output tables with ecological network analysis. J. Cleaner Prod. 2015, 114, 243.

(47) Zhang, Y.; Zheng, H.; Yang, Z.; et al. Multi-regional input− output model and ecological network analysis for regional embodied energy accounting in China. Energy Policy 2015, 86, 651−663. (48) Schramski, J. R.; Gattie, D. K.; Patten, B. C.; Borrett, S. R.; Fath, B. D.; Whipple, S. J. Indirect effects and distributed control in ecosystems; distributed control in the environ networks of a seven− compartment model of nitrogen flow in the Neuse River estuary USA−Time series analysis. Ecol. Modell. 2007, 206, 18−30. (49) Schramski, J. R.; Gattie, D. K.; Patten, B. C.; Borrett, S. R.; Fath, B. D.; Whipple, S. J. Indirect effects and distributed control in ecosystems; distributed control in the environ networks of a seven− compartment model of nitrogen flow in the Neuse River estuary USA−Time series analysis. Ecol. Modell. 2007, 206, 18−30. (50) Liu, Z.; Feng, K.; Hubacek, K.; et al. Four system boundaries for carbon accounts. Ecol. Modell. 2015, 318, 118−125. (51) Sovacool, B. K.; Brown, M. A. Twelve metropolitan carbon footprints: a preliminary global comparative assessment. Energy Policy 2010, 38 (9), 4856−4869. (52) IPCC (Intergovernmental Panel on Climate Change). 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Intergovernmental Panel on Climate Change, Hayama, Kanagawa, Japan), 2006. (53) Gregg, J. S.; Andres, R. J.; Marland, G. China: Emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophys. Res. Lett. 2008, 35 (8), L08806. (54) Hertwich, E. G.; Peters, G. P. Carbon footprint of nations: a global, trade-linked analysis. Environ. Sci. Technol. 2009, 43, 6414− 6420. (55) Lindner, S.; Liu, Z.; Guan, D.; Geng, Y.; Li, X. CO2 emissions from China’s power sector at the provincial level: Consumption versus production perspectives. Renewable Sustainable Energy Rev. 2013, 19, 164−172. (56) Wiedmann, T.; Wood, R.; Minx, J.; Lenzen, M.; Guan, D.; Harris, R. A Carbon Footprint Time Series of the UK - Results from a Multi-Region Input-Output Model. Economic Systems Research 2010, 22, 19−42. (57) Wiedmann, T. O.; Chen, G.; Barrett, J. The Concept of City Carbon Maps: A Case Study of Melbourne, Australia. J. Ind. Ecol. 2015, DOI: 10.1111/jiec.12346. (58) Miller, R E; Blair, P D. Input−Output Analysis: Foundations and Extensions; Cambridge University Press: Cambridge, U.K., 2009. (59) Åström, K J. Introduction to Stochastic Control Theory; Courier Corporation, 2012. (60) Patten, B. C.; Auble, G. T. System theory of the ecological niche. Am. Nat. 1981, 117, 893−922. (61) Patten, B. C. Network perspectives on ecological indicators and actuators: enfolding, observability, and controllability. Ecol. Indic. 2006, 6 (1), 6−23. (62) Lu, Y.; Chen, B.; Feng, K. S.; Hubacek, K. Ecological network analysis for carbon metabolism of eco-industrial parks: A case study of a typical eco-industrial park in Beijing. Environ. Sci. Technol. 2015, 49 (12), 7254−7264. (63) Lu, Y.; et al. Ecological network analysis for a low-carbon and high-tech industrial park. Sci. World J. 2012, 3054749.10.1100/2012/ 305474 (64) Fath, B. D. Distributed control in ecological networks. Ecol. Modell. 2004, 179, 235−246. (65) Chen, S. Q.; Fath, B. D.; Chen, B. Information-based Network Environ Analysis: A system perspective for ecological risk assessment. Ecol. Indic. 2011, 11 (6), 1664−1672. (66) Chen, S. Q.; Chen, B.; Fath, B. D. Assessing the cumulative environmental impact of hydropower construction on river systems based on energy network model. Renewable Sustainable Energy Rev. 2015, 42, 78−92. (67) Schramski, J. R.; Kazanci, C.; Tollner, E. W. Network environ theory, simulation, and EcoNet® 2.0. Environmental Modelling & Software 2011, 26 (4), 419−428. 4740


Article

Environmental Science & Technology (68) Yabe, N. An analysis of CO2 emissions of Japanese industries during the period between 1985 and 1995. Energy Policy 2004, 32 (5), 595−610. (69) Zhang, L.; Hu, Q.; Zhang, F. Input-output modeling for urban energy consumption in Beijing: dynamics and Comparison. PLoS One 2014, 9 (3), e89850. (70) Beijing Statistics Bureau. Beijing Statistical Yearbook; China Statistical Publishing House: Beijing, China, 2013. (71) Tianjin Statistics Bureau. Beijing Statistical Yearbook; China Statistical Publishing House: Beijing, China, 2013. (72) Hebei Statistics Bureau. Beijing Statistical Yearbook; China Statistical Publishing House: Beijing, China, 2013. (73) National Bureau of Statistics of China (2008). Chinese Energy Statistics Yearbook; China Statistics Press: Beijing, China, 2008. (74) Gao, G; Xu, H; Sun, C. The People’s Republic of China National Greenhouse Gas Inventory; Chinese Environmental Science Press: Beijing. (75) Liu, W.; Chen, J.; Tang, Z.; Liu, H.; Han, D.; Li, F. Theories and Practice of Constructing China’s Interregional Input−Output Tables between 30 Provinces in 2007; China Statistics Press: Beijing, 2012. (76) Burke, M.; Hsiang, S. M.; Miguel, E. Global non-linear effect of temperature on economic production. Nature 2015, 527, 235−239. (77) Su, M. R.; Chen, L.; Chen, B.; Chen, S. Q.; Yang, Z. F. Lowcarbon development patterns: observations of typical Chinese cities. Energies 2012, 5, 291−304. (78) Peters, G. P. From production-based to consumption-based national emission inventories. Ecological Economics 2008, 65 (1), 13− 23. (79) Leontief, W. W. The Structure of American Economy, 1919−1939; An Empirical Application of Equilibrium Analysis; Oxford University Press: New York, NY, 1951. (80) Finn, J. T. Measures of ecosystem structure and function derived from analysis of flows. J. Theor. Biol. 1976, 56, 363−380. (81) Fath, B. D. Network analysis in perspective: comments on “WAND: an ecological network analysis user-friendly tool. Environmental Modelling & Software 2004, 19 (4), 341−343. (82) Liu, G. Y.; Yang, Z. F.; Chen, B.; et al. Ecological network determination of sectoral linkages, utility relations and structural characteristics on urban ecological economic system. Ecol. Modell. 2011, 222 (15), 2825−2834. (83) Kharrazi, A.; Rovenskaya, E.; Fath, B. D.; et al. Quantifying the sustainability of economic resource networks: An ecological information-based approach. Ecological Economics 2013, 90, 177−186. (84) Lu, W.; Su, M.; Zhang, Y.; et al. Assessment of energy security in China based on ecological network analysis: A perspective from the security of crude oil supply. Energy Policy 2014, 74, 406−413. (85) Fang, D.; Chen, B. Ecological network analysis for a virtual water network. Environ. Sci. Technol. 2015, 49 (11), 6722−6730. (86) Fath, B. D. Quantifying economic and ecological sustainability. Ocean & Coastal Management 2015, 108, 13−19. (87) Jørgensen, S. E.; Nielsen, S. N. A carbon cycling model developed for the renewable Energy Danish Island, Samsø. Ecol. Modell. 2015, 306, 106−120. (88) Churkina, G.; Brown, D. G.; Keoleian, G. Carbon stored in human settlements: the conterminous United States. Global Change Biology 2010, 16, 135−143. (89) Müller-Wenk, R.; Brandão, M. Climatic impact of land use in LCAcarbon transfers between vegetation/soil and air. Int. J. Life Cycle Assess. 2010, 15 (2), 172−182.

4741


Tracking Inter-Regional Carbon Flows: A Hybrid Network Model

Recommend Documents