Supporting Information. Content

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1 Supporting Information Changing urban carbon metabolism over time: historical trajectory and future pathway Shaoqing Chen and Bin Chen * State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, China Content (14 Pages, 5 Figures, 5 Tables) 1. Details in materials and methods......page S Integration of methods in urban metabolism framework Figure S1 The usefulness of methods integration in a unified urban metabolism framework Table S1 Benefits and new insights of integrating IOA and ENA in urban carbon metabolism study 1.2. Inventory of urban carbon flows Table S2 Heat values and CO 2 emission factors of various energy types 1.3. Analysis of functional shifts in carbon metabolism 1.4. Description of case study and setting of scenarios Table S3 Changing economics and population in Beijing over Table S4 The setting of two extreme scenarios in Data collection 1.6. Uncertainty analysis Table S5 Uncertainty analysis of the integrated model in this study 2. Extended model results......page S10 Figure S2 A summary of sector contribution to DCE (direct carbon emission) and CBF (consumption-based carbon footprint) of Beijing over S1

2 Figure S3 Direct carbon intensity of Beijing by sector over Figure S4 CBF intensity of Beijing by sector over Figure S5 Sectoral carbon throughflow of Beijing over References...Page S12 1. Details in materials and methods 1.1. Integration of methods in urban metabolism framework Figure S1 demonstrates usefulness of methods integration in a unified urban metabolism framework. There have been a lot of studies exploring carbon emissions by using these individual methods, but herein we propose the integrated use of MFA, IOA, LA, ENA and SDA can reveal more details on how and when the carbon transition happens. As shown in Figure S1, MFA and IOA provide quantitative analyses of carbon metabolism, while LA, ENA and SDA examines the carbon transition from sector, process and driver -based perspectives, respectively. These methods can answer distinct but very closely-related research questions, each of which fits in a certain piece of the holistic picture of carbon metabolism. Such integration is very important for a more informed and systemic management of carbon metabolism in cities. Integrating IOA and ENA into modelling the functioning of carbon metabolic system is major idea of this work. IOA is an impact-based method quantifying the amount of carbon flows embodied in urban economy, while ENA is a mechanism-based technique analyzing the relationships between sectors. They are complementary in many senses. Therefore, in Table S1, we elaborate the benefits of methods integration from literature and the new insights of method integration from this study. S2

3 Specific sector -based management Relationship -based management Figure S1 The usefulness of methods integration in a unified urban metabolism framework Table S1 Benefits and new insights of integrating IOA and ENA in urban carbon metabolism study Individual features Benefits of methods integration from literature New insights of method integration from this study S3

4 IOA ENA IOA is widely used in quantifying the carbon footprint of human economy, including both direct and indirect carbon emission. It can be seen as impact-based method. ENA is a strong tool for assessing structure and functioning of a system based on direct and indirect carbon flow pathways. It is a mechanism-based method. Compared to traditional input output models, the combination of IOA and ENA provides a more systemic view of ecological relationships and structure at sector level, as found in the energy and carbon metabolism studies by Zhang et al. 1 and Xia et al. 2 Without using ENA, it is difficult to articulate how one sector control the others in a IO model, as shown in the work of Duan and Chen 3. Introducing ENA indicators like TST to IOA is useful in diagnosing economic systems (how sustainable it can be), as reported by Huang and Ulanowicz 4. We are now able to capture the snapshots of intermediate carbon flows among sectors of different years from a network perspective (ENA), which has not yet been done in former IOA. We identify the carbon transition in sectors and processes through time from both backward and forward views, benefiting from the Combination of LA and ENA. We are able to look into significant details for urban carbon mitigation with the method integration, as shown in the Discussion section Inventory of urban carbon flows Energy flow analysis is used to account for the energy consumption of Beijing in the investigated years. The consumption of different energy types over is provided by Beijing Statistics Bureau. The energy-related carbon emissions of all economic sectors are calculated based on the recommended methods from IPCC (i.e. energy activity carbon emission factor). Heat values and CO 2 emission factors of energy types are shown in Table S2. The emissions from industrial processes (cement and steel/iron production) inside the city are also estimated. Inventory of direct carbon flows of Beijing is the basis of further embodied carbon accounting. Input-output analysis (IOA) has been widely used for modelling carbon emissions embodied in the supply of products and services 5,6. Final consumption- driven emissions are accounted for considering all the direct and indirect processes among sectors. The modelling of embodied carbon emissions considers all the socioeconomic activities associated with urban metabolism, either within or out of city boundary (so-called hinterland). Note that the local inputs, domestic imports and foreign imports are matched with corresponding carbon intensities for a more accurate analysis of embodied emission. S4

5 Energy type Table S2 Heat values and CO 2 emission factors of energy types Emission factor (KgCO 2 /TJ) Heat value Unit of heat value Crude Oil Kcal/L Gasoline Kcal/L Jet Kerosene Kcal/L Other Kerosene Kcal/L Diesel Oil Kcal/L Liquefied Petroleum Gases Kcal/M 3 Bitumen Kcal/Kg Petroleum Coke Kcal/Kg Other Petroleum Products Kcal/L Coking Coal Kcal/Kg Other Bituminous Coal Kcal/Kg Coke Kcal/Kg Coal Tar Kcal/L Natural gas Kcal/M Analysis of functional shifts in carbon metabolism We use linkage analysis to identify the changing roles sectors play (exporter or importer) in transferring carbon flow. Linkage analysis is a metrics developed based on IOA, which has been introduced in details in literature 7,8. On the other hand, as a metrics derived from ENA, network control analysis (NCA) is applied to assess inter-relationships among economic sectors. ENA has been widely used to model the changing structure and function of urban carbon metabolism From a network perspective, the control (dominance of one over the other) is distributed among components of a system Each component has an impact on the overall systemic function through its interactions with the other components within the same system. NCA has been employed in assessing the interactions among components in urban systems. For example, the energy flows controlled by local consumption was quantified to provide insight into the regulation of urban energy consumption 15. The control relationships among various economic sectors and natural components were identified through the modelling of carbon flows in Vienna 16. In NCA, the pair-wise interactions between urban sectors are addressed by considering both S5

6 direct and indirect pathways bridging them. In Equation 9 in the main text, to avoid double-counting on indirect effect, we use embodied intermediate flows to calculate the integral ( control matrices ( t ) ( ) b = f / T, b ' = f t / T ), which defines the directions and magnitudes of ij ij j ji ji i control. Control allocation (CA) and Dependence allocation (DA) are formulated to represent the control strength of one sector over the other, and the reliance of one sector on the other, respectively (Equations 10 and 11). These two metrics are used to reflect the functional changes of urban carbon metabolism over time. More explanation on these metrics can be found in references 14, Description of case study and setting of scenarios Table S3. A summary of the social and economic conditions of Beijing from is provided in Table S3 Changing economics and population in Beijing over Year Agriculture Industry Services per capita Population GDP GDP GDP GDP GDP /million /billion Yuan /billion Yuan /billion Yuan /billion Yuan /Yuan As shown in Table S4, two extreme scenarios are developed for 2030 based on current governmental policies (such as 13th Five Year Plan). In pessimistic scenario (2030-O), we assume higher improvement of efficiency, lower population growth and slower growth of consumption level through In 2030-P scenario, we use a more conservative estimation of efficiency improvement, faster population growth, and higher final consumption than 2030-O scenario. Although these two scenarios will probably not be met in reality, they can provide the range of potential for mitigating the city s carbon emission in the near future. IOA-SDA can be used to project emission for the two scenarios in 2030, based on the setting of all five driving factors of S6

7 CBF [population(p), emission intensity(ɵ), economic production structure(l), consumption structure(y_s), and per capita consumption volume (y_v)]. Table S4 The setting of two extreme scenarios in 2030 Scenario 2030-P Scenario 2030-O Population (p) 30 million 26 million emission intensity (Ɵ) economic production structure (L) consumption structure (y_s) per capita consumption volume (y_v) CO 2 emission intensities of all sector decrease by 40% RAS technique is used to estimate the Leontief technical coefficients matrix for We project the GDP of the city in 2030 as the main constraint for balancing the table, which would be $575 billion in Based on the GDP in 2030, the value-added of agricultural sector, manufacturing sectors, and services sector in Beijing will increase at average of 2%, 5% and 7% over The consumption pattern for Beijing in 2030 reflects a transition to an even more service-based economy. Services would account for 60% (compared to 45% in 2012) overall consumption and the share of agriculture would drop to 1.5% by 2030 (compared to 2.5% in 2012). $30000, 90% higher than the level in This reaches the current average US consumption and expenditures. CO 2 emission intensities of all sector decrease by 60% We project the GDP of the city in 2030 as the main constraint for balancing the table, which would be 430 billion in For a more conservative projection of urban development, the value-added of agricultural sector, manufacturing sectors, and services sector will increase at average of 1.5%, 4% and 6% through The same with Scenario 2030-P. In both scenarios, sector-specific elasticity of final demand and per capita income estimations are used to project urban consumption patterns 17. $22500, 60% higher than the level in This is about 75% of the current average US consumption and expenditures. We use the RAS method to re-balance the updated input output tables in The RAS method, a technique of biproportionally adjusting the rows and columns of a given matrix was developed in 1930s. It has been widely used as an automatic technique in updating IO tables 18. The process in RAS can be seen as an iterative scaling of a non-negative matrix [f ij ] until its column sums and row sums equal given vectors. The multiplication of the initial coefficient matrix by the row and column multipliers gives this method its name (r i A ij s i ). The detailed iterative process and its equations have been described in references 19,20. In this study, we use RAS method to re-balance the carbon networks for two scenarios in We use the GDP of the city in 2030 as the main constraint for balancing the tables. S7

8 1.5. Data collection The historical socioeconomic data (population, sector-level GDP and per capita GDP) is mainly collected from Beijing Statistics Bureau (1985, 1990, 1995, 2000, 2005, 2010 and 2012). They can reflect the urbanization process of Beijing in terms of population and economic scale. Energy use and industrial process data are used to account for direct carbon emissions of the city. They are collected from the Beijing Statistics Bureau from 1985 to Beijing s input-output tables in 1985, 1990, 1995, 2000, 2005, 2010 and 2012 are derived from Beijing statistical books related to these years. Heat values and CO 2 emission factors of fuel types of are derived from IPCC recommended values 21 (Table S1), while emission factor of electricity uses the value from North China grid. The emission factors of industrial processes are based on multiple sources 22, Uncertainty analysis Table S5 Uncertainty analysis of the integrated model in this study No. Source of uncertainty Influenced aspect Variation /(%) Influence category 1 Variation between heat values of local fuel types and heat values derived from IPCC 2 Uncertainty in the industrial techniques used in different companies (cement production and steel/iron production) 3 Variation in price deflators in adjusting IO table to constant prices 4 RAS technique in balancing IO table in 2030 scenarios Results of historical and future carbon footprints 0 ~ ±1 Low Results of historical 0 ~ ±2 Low and future carbon footprints Results of historical ±1 ~ ±3 Low carbon footprints Results of future ±1 ~ ±3 Low carbon footprints 5 Prediction on future carbon intensities Results of future 0 ~ ±4 Low carbon footprints 6 Prediction on future urban population Results of future 0 ~ ±3 Low carbon footprints 7 Prediction on future industrial structure Results of future ±1 ~ ±3 Low carbon footprints 8 Prediction on future consumption structure Results of future 0 ~ ±2 Low carbon footprints 9 Prediction on future consumption level Results of future ±2 ~ ±4 Low carbon footprints Cumulative uncertainty in historical carbon / ±1 ~ ±6 Low or S8

9 footprint (1+2+3) Medium Cumulative uncertainty in future carbon footprint (all items) / ±5 ~ ±25 Medium or High Note: The uncertainty analysis has been run for both historical results and future projection on carbon footprints 24. A low influence indicates insignificant changes in the conclusions; A medium influence denotes a noticeable change in evaluation, but the general conclusions still hold; A high influence has the potential to cause inaccurate conclusions. S9

10 2. Extended model results DCE CBF Figure S2 A summary of sector contribution to DCE (direct carbon emission) and CBF (consumption-based carbon footprint) of Beijing over S10

11 Note: Code numbers of sectors are: S1: Agriculture; S2: Coal Mining, Petroleum and Natural Gas Extraction; S3: Ferrous and Nonferrous Metals Mining and Dressing; S4: Nonmetal Minerals Mining and Dressing; S5: Food Processing and Production; S6: Textile Industry, Garments and Other Fiber Products and Leather, Furs, Down and Related Products; S7: Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products and Furniture Manufacturing; S8: Papermaking and Paper Products and Printing and Record Medium Reproduction; S9:Petroleum Processing and Coking; S10: Chemicals; S11: Nonmetal Mineral Products; S12: Smelting and Pressing of Ferrous and Nonferrous Metals; S13: Metal Products; S14: Ordinary and special machinery and equipment; S15: Transportation Equipment; S16: Electric Equipment and Machinery; S17: Electronic and Telecommunications Equipment, Instruments, Meters, Cultural and Office Machinery; S18: Other Manufacturing Industry; S19: Production and Supply of Electric Power, Gas and Hot Water; S20: Construction; S21: Transportation, Storage, Post and Telecommunication Services; S22: Wholesale, Retail Trade and Catering Services, Restaurant and Renting; S23: Finance, insurance, scientific, environmental and technical services; S24: Public services and others services Direct carbon intensity of economic sectors (t/ 1000 Yuan) S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S Figure S3 Direct carbon intensity of Beijing by sector over CBF intensity of economic sectors (t/1000 Yuan) S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S Figure S4 CBF intensity of Beijing by sector over S11

12 Sectoral carbon throughflow (Mt) 0.0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S Figure S5 Sectoral carbon throughflow of Beijing over References (1). Zhang Y; Zheng H; Yang Z; Li, Y.; Liu, G.; Su, M.; Yin, X. 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. Clean. Prod. 2016, 114, (2). Xia L; Fath B D; Scharler U M; Zhang, Y. Spatial variation in the ecological relationships among the components of Beijing's carbon metabolic system. Sci. Total Environ. 2016, 544, (3). Duan, C.; Chen, B. Energy water nexus of international energy trade of China. Appl. Energ. 2017, 194, (4). Huang, J.; Ulanowicz, R. E. Ecological network analysis for economic systems: Growth and development and implications for sustainable development. PloS One, 2014, 9(6), e (5). (27) Minx, J.; Baiocchi, G.; Wiedmann, T.; Wiedmann, T.; Barrett, J.; Creutzig, F.; Feng, K.; Förster, M.; Pichler, P. P.; Weisz, H.; Hubacek, K. Carbon footprints of cities and other human settlements in the UK. Environ. Res. Lett. 2013, 8, (6). Lenzen, M. Primary energy and greenhouse gases embodied in Australian final consumption: an input-output analysis. Energ. Policy 1998, 26(6), (7). Lenzen, M. Environmentally important paths, linkages and key sectors in the Australian economy. Struct. Change Econ. Dyn. 2003, 14(1), S12

13 (8). Duarte, R; Sanchez-Choliz, J.; Bielsa, J. Water use in the Spanish economy: an input output approach. Ecol. Econ. 2002, 43(1), (9). Gattie D. K.; Schramskic J. R.; Bata S. A. Analysis of microdynamic environ flows in an ecological network. Ecol. Model. 2006, 28, (10). Fath, B. D; Patten, B. C. Review of the foundations of network environ analysis. Ecosystems 1999, 2, (11). Fath, B. D. Quantifying economic and ecological sustainability. Ocean Coast. Manag. 2015, 108, (12). Fath, B.D. Distributed control in ecological networks. Ecol. Model. 2004, 179, (13). Schramski, J. R.; Gattie, D. K.; Patten, B. C.; Borrett, S. R.; Fath, B. D.; Thomas, C. R.; 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-Steady-state analysis. Ecol. Model. 2006, 194, (14). 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. Model. 2007, 206, (15). Chen, S. Q.; Chen, B. Urban energy consumption: different insights from energy flow analysis, input-output analysis and ecological network analysis. Appl. Energ. 2015, 138, (16). 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), (17). Hubacek, K.; Sun, L. A scenario analysis of China s land use and land cover change: incorporating biophysical information into input output modeling. Struct. Change Econ. Dyn. 2001, 12 (4), (18). Miller, R. E., Blair, P. D. Input Output Analysis: Foundations and Extensions. Prentice-Hall, Englewood Cliffs, NJ, (19). Deming, W. E.; Stephan, F. F. On least-squares adjustments of a sampled frequency table when the expected marginal total are known, Ann. Math. Stat. 1940, 11, S13

14 (20). Lenzen, M.; Wood, R.; Gallego, B. RAS matrix balancing under conflicting information. Intermediate Input Output Meetings (21). IPCC (Intergovernmental Panel on Climate Change) IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change, Hayama, Kanagawa, Japan, (1) Worrell, E.; Price, L.; Martin, N.; Hendriks, C.; Meida, L. Carbon dioxide emissions from the global cement industry. Annu. Rev. Energ. Environ. 2001, 26(1), (22). Chen, G. Q.; Zhang, B. Greenhouse gas emissions in China 2007: inventory and input output analysis. Energ. Policy 2010, 38(10), (23). Lenzen, M.; Wood, R.; Wiedmann, T. Uncertainty analysis for multi-region input output models a case study of the UK's carbon footprint. Econ. Sys. Res. 2010, 22(1), S14