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Modelling Carbon-energy Metabolism of Cities: A Systems Approach
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 88 (2016) 31 – 37
CUE2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems
Modelling carbon-energy metabolism of cities: a systems approach Shaoqing Chen, Bin Chen* School of Environment, Beijing Normal University, Beijing 100875, P R China
Abstract Urban metabolism has been widely used in accounting for cities’ absorption of energy and materials and elimination of wastes. We aim to introduce a systems approach for modelling carbon metabolism and the influence from energy use activities, thus furthering our understanding of carbon profile in cities. Physical input-output table (PIOT) and material flow analysis (MFA) are used to develop a framework for urban carbon accounting that includes both the carbon flows between sectors and transboundary exchanges between urban economy and natural ecosystems. Based on indicators derived from carbon-energy metabolism modelling, the degree of carbonization is evaluated for cities. © 2016 Published by Elsevier Ltd. This © 2015The TheAuthors. Authors. Published by Elsevier Ltd.is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the organizing committee of CUE 2015
Keywords: Carbon flows, Carbon-energy nexus; Urban metabolism; Cities; Systems analysis
1. Introduction Given that one half of the population now resides in urban areas, urban settlements contribute a greater anthropogenic environmental impact than rural colonies at both local and global scales [1]. On one hand, the building of cities have made a lot people’s lives greener, healthier and more convenient with the advanced facilitates and sophisticated servicers [2]; on the other hand, the speed of urbanization has a direct influence on pollution within urban areas (e.g., eutrophication, solid wastes) and environmental change global level (e.g., global warming) [3]. The influence of cities will be even more prominent in the future since the projected urban population and lands in Africa, South America, and part of Asia will experience another major boost in the following 30 years [4].
* Corresponding author. Tel.: +86-10-58807368; fax: +86-10-58807368 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CUE 2015 doi:10.1016/j.egypro.2016.06.009
32
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
The consensus has been reached that more than 70% of the global carbon emissions are caused by activities in urban areas, such as industrial production, transportation and electricity [5]. In comparison, the carbon sinks in cities are quite limited given the dominance of impervious lands and highly degraded natural ecosystems. Cities are a big source of carbon emissions, but also could be a solution to low-carbon economy and sustainable future. The key is how to set up a proper framework to account for the carbon profiles of cities including all the imports, exports and stock changes. The carbon emissions are one of the flows of these profiles that are closely connected to the rest of the urban metabolic system [6,7]. The modelling and assessment of carbon metabolism are not only an important part of urban ecosystem functioning and global carbon cycle, but also meet the real need of achieving low- carbon pattern of development in China as well as rest of the world. 2. Materials and methods 2.1. System Boundary For consistency of the accounting on all cities, the definition of “core city” was used for carbon metabolism analysis [8]. By this definition, a rich economic dataset is generally available, including urban area, population, and GDP. This is also consistent with the sector-level disaggregation of urban resources and materials. In the accounting of carbon flows, both the biophysical and the anthropogenic processes of the city were taken into account. This not only included the transfer of intermediate products and final products within the administrative boundary, but it also covered carbon exchange activities that are metabolically linked to the city but happening outside the urban area. 2.2. Carbon flow modelling Material flow analysis (MFA), input-output analysis (IOA) and ecological network analysis (ENA) have the potential of integration in the analysis of carbon metabolism (see Table 1). Based on these methods, a system-oriented accounting model called the carbon flow network (CFN) is developed as an analytical framework for tracking carbon flows of cities. The CFN consists of five economic sectors (i.e., agriculture, industry and commercial, construction, domestic, and transportation), two natural components (local ecosystem and external ecosystems) and two divisions of environmental distribution (i.e., gaseous emissions and solid and liquid wastes). Carbon flows between all the economic and natural components were identified, including imports from the local natural environment and other areas, exchange of carbon materials and products between sectors, and economic exports and environmental discharges. In addition, the change of carbon stock was valued for each sector. The main inflows to the urban economy include carbon import embodied in products, carbon extraction from urban ecosystems, and recycled materials. They show the supply of carbon to the city from within the urban ecosystem and other regions in the form of products and raw materials. The outflows and stock change are at the other end of the carbon metabolism showing the metabolic fate of carbon used by cities. Table 1 Comparison of methods used in urban metabolism studies Method
Indicators
Problems addressed
Material flow analysis (MFA)
Total material consumption, useful material consumption, water use, and waste generation
How much useful material and energy are consumed in cities and how many wastes are rejected?
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
Input-output analysis (IOA)
Environmental impact of urban consumption, direct and indirect flows of material
How the trades with other economies impact the magnitude of urban consumption?
Ecological network analysis (ENA)
Metabolic intensity, density, and mutual relationships between sectors
How urban sectors are related to each other and how is overall performance of the city?
2.3. Carbon and energy flows analysis Carbon stock loss (CSL) is defined as the change in the carbon stock (SCF) for urban ecosystems and the rest of the world. In order to identify city’s extraction of natural resources, carbon stock loss (CSL) of natural ecosystems and rest of the world was developed based on the UCC balance (Eq.1): CSL =SCNa+SCROW (1) in which SCNa is the stock change of local natural ecosystems; SCRow is the stock change of rest of the world (ROW). Compared to the footprint accounting based on TCI, CSL is a more integrated indicator covering all the anthropogenic impacts during carbonization in urban areas. The amounts of energy consumption and CO2 from cities are accounted for based on the official reports from the selected 16 cities, which consider energy use by both industrial and residential sectors. The correlation of energy consumption and carbon inflow (or CO2) is analyzed for evaluating the coupling of energy use activities and carbonization process for cities. 2.4. Case study The case study for UCM modeling is the16 global cities from 6 different continents on account of their role and importance in the global economy. The years investigated fall within 2005-2010. The data of carbon and energy flows are compiled from local reports and yearbooks of energy and materials consumption. 3. Results and discussion 3.1. Carbon inflows and outflows Figure 1 shows the share of carbon inflow form import, local extraction and recycling. We found that carbon imports across urban boundaries are very significant in the analysis of carbon inflows. In the 16 cities, between 87 and 93% of the carbon is imported from outside the urban system, while only about 27% of them are derived from urban ecosystems, and 3-9% is recovered from recycling. All 16 cities are heavily reliant on outside markets to support their industrial and domestic activities. For cities such as Moscow and Toronto the share of import of embodied carbon in goods and services exceeds 92% of all carbon inflows into the urban economy, and local extraction of biomass in urban ecosystems only contributes a small part to their carbon balance. For London, Vienna and Stockholm the recycling rates are the highest among the investigated cities (with a share of >8%), but still insignificant compared to carbon imports from outside cities’ boundaries.
33
34
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37 7.0 Recycling
Natural extraction
Imports
6.0 5.0 4.0 3.0 2.0 1.0 0.0
Figure 1 The sources of carbon inflow for cities (on per capita level)
From the perspective of urban economy, the carbon balances can be identified though the inflow and outflow profiles. By applying the symmetric UCC modelling, we are able to track the carbon balance from the opposite perspective too, i.e., urban natural ecosystems and rest of the world, which are two main sources of carbon inflow to the cities. This leads to a new indicator for human appropriation of natural productivity: the proportion of carbon. The carbon stock change of nature and rest of the world caused by the city indicates the extraction of natural resources and products (which are also resources from a life-cycle angle) to meet the need of urban growth and development. Therefore, the net carbon inflows from urban ecosystems and rest of the world (i.e., carbon stock lost, or CSL) can be specific indicators for human appropriation of carbon in cities. Our findings suggest that the CSL ranges from 0.5 to 1.1 t C per capita among global cities, in which 10% is the loss in urban ecosystem, while 90% is the loss in rest of the world (Figure 2). The ranking of cities in CSL is not entirely correlated to that in TCI in that CSL considers not only carbon inflows but also other carbon flows leaving the economy (e.g., exports, wastes and emissions). From CSL’s perspective, the biggest loss in natural carbon stock is caused by Moscow (1.1 t C per capita), followed by Bangkok and Los Angeles (0.8 and 0.7 to C per capita, respectively). In comparison, Delhi, Stockholm and Sao Paulo have the smallest anthropogenic impact on nature in terms of carbon extraction (<0.6 t C per capita).
Carbon stock change per capita /t
0
-0.2 -0.4 -0.6 -0.8
-1
Rest of the world Urban ecosystem
-1.2
35
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
Figure 2 Carbon stock loss (CSL) in urban ecosystems and rest of the world caused by human activities (per capita level)
3.2. Carbon-energy metabolism The results of carbon-energy metabolism modelling are further discussed. Figure 3 shows the correlation of CO2 emission and energy consumption for cities. It is clear that the carbon emissions and energy use are positively related. Cities like Beijing have a very high level of energy consumption each year in total (1730 PJ) and produce a large amount of CO 2 (130 Mt). Moscow has a similar carbon-energy profile with Beijing in that it consumed 1670 PJ of energy and emitted 160 Mt of CO 2. We also investigate the relative small cities (in terms of population) like Stockholm, it has a much lower energy consumption and CO2 emission than Moscow and Beijing. Regardless of their various sizes of energy consumption and CO2 emission, the energy use activities and global warming process are shown highly coupled. The energy related activities can explain 58% of CO2 emission from a range of cities investigated in this study. Figure 4 shows the correlation of carbon inflow and energy consumption for cities. The carbon inflow has a positive correlation with energy consumption by cities, which is not as strong as CO2 vs energy consumption. Moscow, Beijing, Tokyo have a very high level of carbon inflow (115 Mt C, 102 Mt C and 95 Mt C), while Stockholm, Vienna have a much lower carbon inflow (7 Mt C and 12 Mt C). The lowenergy consumption cities tend to have bigger variation in carbon inflow. These results suggest the carbon inflow is influenced by more factors such as construction and foods. Some of processes in these sectors did not consume energy directly but is carbon-demanded. 180 Moscow
CO2 emission (Mt)
160 140
Los Angeles
120
Beijng
100 80 60
40 20 0
Singapore Toronto Sydney Hong Kong Bangkok London Cape Town Delhi Sao paulo Stockholm Vienna
0
500
Tokyo New York
R² = 0.5839 1000
1500
Energy consumption (PJ) Figure 3 The correlation of CO2 emission and energy consumption for cities
2000
36
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
140
Carbon inflow (Mt)
Moscow
Singapore
120
Tokyo
Hong Kong Delhi Los Angeles
100
Beijng
80 60
Toronto
40
Cape Town Bangkok Stockholm Vienna
20 0 0
New York
London Sydney
500
Sao paulo
R² = 0.3381 1000
1500
2000
Energy consumption (PJ) Figure 4 The correlation of carbon inflows and energy consumption for cities
4. Conclusions The network framework provides a new possibility of accounting for carbon flows associated with the urban economy and the energy use activities related to these flows. Not just carbon stocks and emissions need to be considered, as has been covered by some of the above studies, but also other human-related carbon flows should be incorporated into the model for a more holistic understanding of carbon-energy metabolism. This is vital not only for accounting total carbon fluxes between urban ecosystems and natural ecosystems, but also for manifesting the relation of carbon flows with energy use in cities. Systematic accounting of carbon-energy metabolism is of great importance in addressing the degrees and patterns of carbonization in cities, and deserves special attention in the sustainability management of an increasingly urbanized world. Acknowledgements This study was supported by China Postdoctoral Science Foundation funded project. References [1] United Nations Population Division, 2007. State Of World Population 2007: Unleashing the Potential of Urban Growth. United Nations Population Fund, New York. [2] Glaeser E. Triumph of the city: How our greatest invention makes US richer, smarter, greener, healthier and happier. Pan Macmillan. 2011. [3] Güneralp B, Seto KC. Environmental impacts of urban growth from an integrated dynamic perspective: A case study of Shenzhen, South China. Global Environmental Change 2008;18:720–735. [4] Seto KC, Fragkias M, Güneralp B, Reilly MK. A meta- analysis of global urban land expansion. PloS one 2011;6(8): e23777. [5] IEA. World Energy Outlook 2012, Paris: International Energy Agency (IEA), 2012. [6] Chen SQ, Chen B. Network environ perspective for urban metabolism and carbon emissions: A case study of Vienna, Austria. Environmental Science & Technology 2012; 46:4498−4506. [7] Chen SQ, Chen B, Fath, BD. Urban ecosystem modeling and global change: Potential for rational urban management and emissions mitigation. Environmental Pollution 2014; 190: 139-149.
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
[8] Eurostat. European Commission. n_cities/city_urban/spatial_units.
2012.
http://epp.eurostat.ec.europa.eu/portal/page/portal/regio
Biography Bin Chen is a professor of energy science at Beijing Normal University. Dr. Chen has published over 200 peerreviewed papers in prestigious international journals. He is also serving as subject editor of Applied Energy and editorial board member of more than ten journals.
37
ScienceDirect Energy Procedia 88 (2016) 31 – 37
CUE2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems
Modelling carbon-energy metabolism of cities: a systems approach Shaoqing Chen, Bin Chen* School of Environment, Beijing Normal University, Beijing 100875, P R China
Abstract Urban metabolism has been widely used in accounting for cities’ absorption of energy and materials and elimination of wastes. We aim to introduce a systems approach for modelling carbon metabolism and the influence from energy use activities, thus furthering our understanding of carbon profile in cities. Physical input-output table (PIOT) and material flow analysis (MFA) are used to develop a framework for urban carbon accounting that includes both the carbon flows between sectors and transboundary exchanges between urban economy and natural ecosystems. Based on indicators derived from carbon-energy metabolism modelling, the degree of carbonization is evaluated for cities. © 2016 Published by Elsevier Ltd. This © 2015The TheAuthors. Authors. Published by Elsevier Ltd.is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the organizing committee of CUE 2015
Keywords: Carbon flows, Carbon-energy nexus; Urban metabolism; Cities; Systems analysis
1. Introduction Given that one half of the population now resides in urban areas, urban settlements contribute a greater anthropogenic environmental impact than rural colonies at both local and global scales [1]. On one hand, the building of cities have made a lot people’s lives greener, healthier and more convenient with the advanced facilitates and sophisticated servicers [2]; on the other hand, the speed of urbanization has a direct influence on pollution within urban areas (e.g., eutrophication, solid wastes) and environmental change global level (e.g., global warming) [3]. The influence of cities will be even more prominent in the future since the projected urban population and lands in Africa, South America, and part of Asia will experience another major boost in the following 30 years [4].
* Corresponding author. Tel.: +86-10-58807368; fax: +86-10-58807368 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CUE 2015 doi:10.1016/j.egypro.2016.06.009
32
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
The consensus has been reached that more than 70% of the global carbon emissions are caused by activities in urban areas, such as industrial production, transportation and electricity [5]. In comparison, the carbon sinks in cities are quite limited given the dominance of impervious lands and highly degraded natural ecosystems. Cities are a big source of carbon emissions, but also could be a solution to low-carbon economy and sustainable future. The key is how to set up a proper framework to account for the carbon profiles of cities including all the imports, exports and stock changes. The carbon emissions are one of the flows of these profiles that are closely connected to the rest of the urban metabolic system [6,7]. The modelling and assessment of carbon metabolism are not only an important part of urban ecosystem functioning and global carbon cycle, but also meet the real need of achieving low- carbon pattern of development in China as well as rest of the world. 2. Materials and methods 2.1. System Boundary For consistency of the accounting on all cities, the definition of “core city” was used for carbon metabolism analysis [8]. By this definition, a rich economic dataset is generally available, including urban area, population, and GDP. This is also consistent with the sector-level disaggregation of urban resources and materials. In the accounting of carbon flows, both the biophysical and the anthropogenic processes of the city were taken into account. This not only included the transfer of intermediate products and final products within the administrative boundary, but it also covered carbon exchange activities that are metabolically linked to the city but happening outside the urban area. 2.2. Carbon flow modelling Material flow analysis (MFA), input-output analysis (IOA) and ecological network analysis (ENA) have the potential of integration in the analysis of carbon metabolism (see Table 1). Based on these methods, a system-oriented accounting model called the carbon flow network (CFN) is developed as an analytical framework for tracking carbon flows of cities. The CFN consists of five economic sectors (i.e., agriculture, industry and commercial, construction, domestic, and transportation), two natural components (local ecosystem and external ecosystems) and two divisions of environmental distribution (i.e., gaseous emissions and solid and liquid wastes). Carbon flows between all the economic and natural components were identified, including imports from the local natural environment and other areas, exchange of carbon materials and products between sectors, and economic exports and environmental discharges. In addition, the change of carbon stock was valued for each sector. The main inflows to the urban economy include carbon import embodied in products, carbon extraction from urban ecosystems, and recycled materials. They show the supply of carbon to the city from within the urban ecosystem and other regions in the form of products and raw materials. The outflows and stock change are at the other end of the carbon metabolism showing the metabolic fate of carbon used by cities. Table 1 Comparison of methods used in urban metabolism studies Method
Indicators
Problems addressed
Material flow analysis (MFA)
Total material consumption, useful material consumption, water use, and waste generation
How much useful material and energy are consumed in cities and how many wastes are rejected?
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
Input-output analysis (IOA)
Environmental impact of urban consumption, direct and indirect flows of material
How the trades with other economies impact the magnitude of urban consumption?
Ecological network analysis (ENA)
Metabolic intensity, density, and mutual relationships between sectors
How urban sectors are related to each other and how is overall performance of the city?
2.3. Carbon and energy flows analysis Carbon stock loss (CSL) is defined as the change in the carbon stock (SCF) for urban ecosystems and the rest of the world. In order to identify city’s extraction of natural resources, carbon stock loss (CSL) of natural ecosystems and rest of the world was developed based on the UCC balance (Eq.1): CSL =SCNa+SCROW (1) in which SCNa is the stock change of local natural ecosystems; SCRow is the stock change of rest of the world (ROW). Compared to the footprint accounting based on TCI, CSL is a more integrated indicator covering all the anthropogenic impacts during carbonization in urban areas. The amounts of energy consumption and CO2 from cities are accounted for based on the official reports from the selected 16 cities, which consider energy use by both industrial and residential sectors. The correlation of energy consumption and carbon inflow (or CO2) is analyzed for evaluating the coupling of energy use activities and carbonization process for cities. 2.4. Case study The case study for UCM modeling is the16 global cities from 6 different continents on account of their role and importance in the global economy. The years investigated fall within 2005-2010. The data of carbon and energy flows are compiled from local reports and yearbooks of energy and materials consumption. 3. Results and discussion 3.1. Carbon inflows and outflows Figure 1 shows the share of carbon inflow form import, local extraction and recycling. We found that carbon imports across urban boundaries are very significant in the analysis of carbon inflows. In the 16 cities, between 87 and 93% of the carbon is imported from outside the urban system, while only about 27% of them are derived from urban ecosystems, and 3-9% is recovered from recycling. All 16 cities are heavily reliant on outside markets to support their industrial and domestic activities. For cities such as Moscow and Toronto the share of import of embodied carbon in goods and services exceeds 92% of all carbon inflows into the urban economy, and local extraction of biomass in urban ecosystems only contributes a small part to their carbon balance. For London, Vienna and Stockholm the recycling rates are the highest among the investigated cities (with a share of >8%), but still insignificant compared to carbon imports from outside cities’ boundaries.
33
34
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37 7.0 Recycling
Natural extraction
Imports
6.0 5.0 4.0 3.0 2.0 1.0 0.0
Figure 1 The sources of carbon inflow for cities (on per capita level)
From the perspective of urban economy, the carbon balances can be identified though the inflow and outflow profiles. By applying the symmetric UCC modelling, we are able to track the carbon balance from the opposite perspective too, i.e., urban natural ecosystems and rest of the world, which are two main sources of carbon inflow to the cities. This leads to a new indicator for human appropriation of natural productivity: the proportion of carbon. The carbon stock change of nature and rest of the world caused by the city indicates the extraction of natural resources and products (which are also resources from a life-cycle angle) to meet the need of urban growth and development. Therefore, the net carbon inflows from urban ecosystems and rest of the world (i.e., carbon stock lost, or CSL) can be specific indicators for human appropriation of carbon in cities. Our findings suggest that the CSL ranges from 0.5 to 1.1 t C per capita among global cities, in which 10% is the loss in urban ecosystem, while 90% is the loss in rest of the world (Figure 2). The ranking of cities in CSL is not entirely correlated to that in TCI in that CSL considers not only carbon inflows but also other carbon flows leaving the economy (e.g., exports, wastes and emissions). From CSL’s perspective, the biggest loss in natural carbon stock is caused by Moscow (1.1 t C per capita), followed by Bangkok and Los Angeles (0.8 and 0.7 to C per capita, respectively). In comparison, Delhi, Stockholm and Sao Paulo have the smallest anthropogenic impact on nature in terms of carbon extraction (<0.6 t C per capita).
Carbon stock change per capita /t
0
-0.2 -0.4 -0.6 -0.8
-1
Rest of the world Urban ecosystem
-1.2
35
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
Figure 2 Carbon stock loss (CSL) in urban ecosystems and rest of the world caused by human activities (per capita level)
3.2. Carbon-energy metabolism The results of carbon-energy metabolism modelling are further discussed. Figure 3 shows the correlation of CO2 emission and energy consumption for cities. It is clear that the carbon emissions and energy use are positively related. Cities like Beijing have a very high level of energy consumption each year in total (1730 PJ) and produce a large amount of CO 2 (130 Mt). Moscow has a similar carbon-energy profile with Beijing in that it consumed 1670 PJ of energy and emitted 160 Mt of CO 2. We also investigate the relative small cities (in terms of population) like Stockholm, it has a much lower energy consumption and CO2 emission than Moscow and Beijing. Regardless of their various sizes of energy consumption and CO2 emission, the energy use activities and global warming process are shown highly coupled. The energy related activities can explain 58% of CO2 emission from a range of cities investigated in this study. Figure 4 shows the correlation of carbon inflow and energy consumption for cities. The carbon inflow has a positive correlation with energy consumption by cities, which is not as strong as CO2 vs energy consumption. Moscow, Beijing, Tokyo have a very high level of carbon inflow (115 Mt C, 102 Mt C and 95 Mt C), while Stockholm, Vienna have a much lower carbon inflow (7 Mt C and 12 Mt C). The lowenergy consumption cities tend to have bigger variation in carbon inflow. These results suggest the carbon inflow is influenced by more factors such as construction and foods. Some of processes in these sectors did not consume energy directly but is carbon-demanded. 180 Moscow
CO2 emission (Mt)
160 140
Los Angeles
120
Beijng
100 80 60
40 20 0
Singapore Toronto Sydney Hong Kong Bangkok London Cape Town Delhi Sao paulo Stockholm Vienna
0
500
Tokyo New York
R² = 0.5839 1000
1500
Energy consumption (PJ) Figure 3 The correlation of CO2 emission and energy consumption for cities
2000
36
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
140
Carbon inflow (Mt)
Moscow
Singapore
120
Tokyo
Hong Kong Delhi Los Angeles
100
Beijng
80 60
Toronto
40
Cape Town Bangkok Stockholm Vienna
20 0 0
New York
London Sydney
500
Sao paulo
R² = 0.3381 1000
1500
2000
Energy consumption (PJ) Figure 4 The correlation of carbon inflows and energy consumption for cities
4. Conclusions The network framework provides a new possibility of accounting for carbon flows associated with the urban economy and the energy use activities related to these flows. Not just carbon stocks and emissions need to be considered, as has been covered by some of the above studies, but also other human-related carbon flows should be incorporated into the model for a more holistic understanding of carbon-energy metabolism. This is vital not only for accounting total carbon fluxes between urban ecosystems and natural ecosystems, but also for manifesting the relation of carbon flows with energy use in cities. Systematic accounting of carbon-energy metabolism is of great importance in addressing the degrees and patterns of carbonization in cities, and deserves special attention in the sustainability management of an increasingly urbanized world. Acknowledgements This study was supported by China Postdoctoral Science Foundation funded project. References [1] United Nations Population Division, 2007. State Of World Population 2007: Unleashing the Potential of Urban Growth. United Nations Population Fund, New York. [2] Glaeser E. Triumph of the city: How our greatest invention makes US richer, smarter, greener, healthier and happier. Pan Macmillan. 2011. [3] Güneralp B, Seto KC. Environmental impacts of urban growth from an integrated dynamic perspective: A case study of Shenzhen, South China. Global Environmental Change 2008;18:720–735. [4] Seto KC, Fragkias M, Güneralp B, Reilly MK. A meta- analysis of global urban land expansion. PloS one 2011;6(8): e23777. [5] IEA. World Energy Outlook 2012, Paris: International Energy Agency (IEA), 2012. [6] Chen SQ, Chen B. Network environ perspective for urban metabolism and carbon emissions: A case study of Vienna, Austria. Environmental Science & Technology 2012; 46:4498−4506. [7] Chen SQ, Chen B, Fath, BD. Urban ecosystem modeling and global change: Potential for rational urban management and emissions mitigation. Environmental Pollution 2014; 190: 139-149.
Shaoqing Chen and Bin Chen / Energy Procedia 88 (2016) 31 – 37
[8] Eurostat. European Commission. n_cities/city_urban/spatial_units.
2012.
http://epp.eurostat.ec.europa.eu/portal/page/portal/regio
Biography Bin Chen is a professor of energy science at Beijing Normal University. Dr. Chen has published over 200 peerreviewed papers in prestigious international journals. He is also serving as subject editor of Applied Energy and editorial board member of more than ten journals.
37