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Changing urban carbon metabolism over time: historical trajectory and future pathway Shaoqing Chen, and Bin Chen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01694 • Publication Date (Web): 02 Jun 2017 Downloaded from http://pubs.acs.org on June 10, 2017

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Environmental Science & Technology

Changing urban carbon metabolism over time: historical trajectory and future pathway

Authors: Shaoqing Chen and Bin Chen* Affiliations: State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, 100875 Beijing, China

Corresponding author: Bin Chen Tel/Fax.: +86 10 58807368 E-mail address: [email protected] (B. Chen) Postal address: No. 19, Xinjiekouwai St., Beijing, 100875, P R China

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ACS Paragon Plus Environment


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ABSTRACT

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Cities are expected to play a major role in carbon emissions mitigation. A key step in

3

decoupling carbon emissions from urban economy is to understand the impact of socioeconomic

4

development on urban metabolism over time. Herein, we establish a system-based framework for

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modeling the variation of urban carbon metabolism through time by integrating a metabolic flow

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inventory, input-output model and network analysis. Using Beijing as a case study, we track the

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historical trajectory of carbon flows embodied in urban final consumption over 1985–2012. We

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find that while the tendency of increase in direct carbon emission continues within this time frame,

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consumption-based carbon footprint might have peaked around 2010. A significant transition in

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emission intensity and roles sectors play in transferring carbon over the period are important signs

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of decoupling urban development from carbonization. Further analysis of driving factors reveals a

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strong competition between efficiency gains and consumption level rise, showing a cumulative

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contribution of -584% and 494% to total carbon footprint, respectively. Projection into future

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pathway suggests there is still a great potential of carbon mitigation for the city, but a strong

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mitigation plan is required to achieve such decarbonization before 2030. By bridging temporal

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metabolic model and socioeconomic planning, this framework fills one of the main gaps between

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monitoring of urban metabolism and design of a low-carbon economy.

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1. Introduction

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The global urban population is expected to increase by 2.5 billion in 2050, when 65% of the

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population will reside in cities1. Urbanization in developing nations (most of them in Asia, Africa

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and South America) is predicted to be very rapid2. For example, in China, the urban population

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percentage is expected to reach 80% in 2050, the same level as many industrialized countries

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today. Cities are key locations for designing a cleaner and more sustainable society. Understanding

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the ecology of cities has been highlighted by scientists for solving global environmental problems

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3-5

26

and how the two interact with the human economy over time.

27

. These scientists seek commonalities of ecological structure and properties in urban systems

The concept of “metabolism” was originally used to describe the exchange of commodities6 2


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and energy/material flows in human settlements7. Urban metabolism is not only a biological

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metaphor to depict the “breath” of a city, but also a pragmatic framework for assessing its

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socio-ecological processes and functioning8,9. With the tracking of in- and outflows, the urban

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metabolism framework has great potential for dissecting interactions between the urban economy

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and natural environment over time, which is crucial for managing sustainability in cities in an

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adaptive way10-12. In fact, the temporal dynamics of urban processes within and across geographic

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boundaries have been an emerging focus in the literature of urban metabolism13.

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The structural and quantitative changes of the urban carbon profile over time have been

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tracked through both top-down14-16 and bottom-up17,18 approaches. Urban areas account for more

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than 70% of CO2 emitted from global final energy use19. City-driven emissions can be made

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20%–50% greater by considering all upstream production activities (including electricity

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generated outside the urban area)20,21. A set of system-oriented approaches have been widely

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followed in modeling carbon flows of cities, given their strong relevance to global warming

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mitigation. Among these approaches, material flow analysis (MFA) and life cycle analysis (LCA)

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are used to account for carbon emissions originating from the urban economy, based on

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inventories of energy and materials use in production supply chains22-24. Input-output analysis

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(IOA) is capable of quantifying both direct and indirect impacts of urban carbon flows on the

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global carbon budget25-27. However, most studies have focused on sizes and intensities of urban

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carbon footprints, while how the transition of carbon metabolism over time and how sectors

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contribute to such transition is largely unknown. Some metrics derived from IOA (such as linkage

48

analysis (LA), field of influence and structural path analysis) are proven useful in articulating the

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roles of economic sector and the pathways of flows28,29. Ecological network analysis (ENA) has

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also been widely used to evaluate the mutual relationships between urban sectors and

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environment30,31. This network approach has important implications for how one can increase the

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sustainability of urban ecosystems by regulating relationships between economic sectors and

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environmental components from a systemic perspective32.

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The integration of various approaches is promising for a comprehensive modeling of urban

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carbon metabolism with quantitative and mechanistic aspects, both of which are important for a

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low-carbon roadmap of cities. The combination of IOA and structural decomposition analysis 3



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(SDA) is widely applied in determining driving factors of changing carbon emissions from the

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economy, which is important for projecting future scenarios of socioeconomic development33-35.

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Recent progress on establishing city-based input output tables20,21,36 and linking them to the

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regional economy37-39 allowed a wider application of input-output model to city-level carbon

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footprint analysis.

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In this paper, we track the dynamics of urban carbon metabolism over a relatively long period

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by integrating the metrics from IOA, ENA and SDA. A system-based framework is developed to

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assess the variation of urban carbon metabolism through time and how it is driven by

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socioeconomic factors. This framework aims to search the key to low-carbon pathway for cities by

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capturing direct and embodied carbon flows related to the urban economy. The changing carbon

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metabolism is mainly explored via the following aspects: 1) temporal changes in the direct and

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embodied carbon flow associated with urban economy; 2) temporal changes in the roles urban

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sectors play and the interactions among sectors in carbon metabolic system; 3) the contribution of

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socioeconomic driving factors to the changing carbon footprints. Using Beijing within the period

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1985–2030 as a case study, we exemplify the tracking of carbon metabolism from historical

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trajectory to future pathway and how it is impacted by the socioeconomic development of the city.

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2. Materials and Methods

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2.1 Modeling framework

75 76

A system-based framework integrating different approaches is proposed for tracking the changing urban metabolism over time (Figure 1). The framework consists of four sections:

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I) Urban inventory: The first step of urban metabolism modeling is to define the system

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boundary of an “urban” system according to the purpose of study. The system boundary of such a

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system is not necessarily the city administrative boundary. We assert that the “hinterland” (lands

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beyond the boundary supporting urban metabolism) should also be considered for assessing the

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global impact of urban activities. A time-series inventory of city-related energy/material flows,

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environmental inputs, or carbon emissions within the system boundary is required to quantify the

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variation of direct metabolic process. II) Flow dynamics: A primary step is to build linkages

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between economic sectors and the environment based on the urban inventory. Metabolic networks 4


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of different time are established by articulating the directions and magnitudes of metabolic (in this

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study, carbon) flows. The functional shift of the carbon metabolic networks is assessed at setctor

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and process level. III) Driving factors: The drivers for the variation in carbon footprint through

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time is identified for the urban economy. This will uncover how changes in different

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socioeconomic factors impact the historical trajectory of urban metabolism over time. IV) Further

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management: A set of plausible scenarios can be developed for cities in terms of future demands,

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economic structure, and technology, among others. The risk of unsustainable and high-carbon

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scenarios associated with specific sectors or processes can be avoided, benefited from a

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system-based regulatory practice.

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The core of the framework is the integrated use of MFA, IOA, LA, ENA and SDA. MFA plays

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an important part in quantifying the direct carbon emissions associated with all urban economic

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sectors, while IOA goes on to track indirect carbon flows embedded in upstream supply chains.

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IOA models the impact of final consumption on urban carbon footprint. Based on the established

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input-output model, LA is used to identify the roles sectors play in backward and forward carbon

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exchanges (“importer” or “exporter” of embodied carbon emission), while ENA is able to uncover

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the inter-relationships between sectors underlying direct and indirect carbon flows. The

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combination of LA and ENA reveals the functional shift of carbon metabolism in a more

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comprehensive view, manifesting not only the impact of individual sectors but also the mechanism

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of carbon exchanges between sectors. SDA analyzes the contributions of socioeconomic drivers

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on carbon footprint during urban development, which can also shed lights on what will happen in

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the future. The integration of all these methods into the urban metabolism framework will provide

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significantly more details on how the urban carbon metabolism has been changing and when the

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decoupling of urban development from carbon emissions will occur than what they can show

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individually. We also provide a detailed description of new insights from method integration

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(Figure S1) and a specific justification of the usefulness in combing IOA and ENA (Table S1).

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IV Future managaement III Driving factors II Flow dynamics

A. Projection of future performance

A. change in footprint

I Urban inventory A. Variation of urban flows

Future Technology Future Future population economics

• change in direct emission

A. Defining system boundary

• change in embodied emission

City

B. Scenario analysis

Hinterland B. Socioeconomic drivers B. Primary urban inventory over time

t1, t2, ti

B. Functional shift • Inter-sector relationship

tn

• Energy & Material flow • Environmental inputs • Sectoral emissions

• Metabolic scenario 1 • Metabolic scenario 2 • Metabolic scenario 3

• Economic change • Demographic shift • Infrastructure & technology promotion

• Backward and forward linkage • Dominance of sectors

C. Sustainable urban planning √ × Sustainable & Unsustainable low-carbon & high-carbon pathway pathway

Contribution of drivers

S3

>

S1

t1, t2, ti

tn

System-based modelling framework for tracking the changing urban metabolism Input of direct flows

MFA

Analysis of socioeconomic factors

IOA

Identification of current and future drivers

SDA

Indirect effect on sectors interaction Relationship -based management Indirect effect on sectors role

ENA

Future scenarios Driver-based management

Integrated management strategies

Specific sector -based management

LA

110

Method integration in the system-based modelling framework

111

Figure 1 A system-based framework for modeling the variation of urban metabolism

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MFA: material flow analysis; IOA: input-output analysis; ENA: ecological network analysis; LA: linkage analysis;

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SDA: structural decomposition analysis

114

2.2 System boundary and metabolic flows inventory

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It has been proven important to examine both the city and its hinterland to fully address the

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impact of the urban economy on the planet26,27. In the present study, the carbon metabolic system

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covers not only production supply chains within an urban area but also trans-boundary supply

118

chains associated with local consumption. The embodied carbon flows within the metabolic

119

system have two categories: 1) in-city direct carbon emissions from economic sectors; 2) indirect

120

carbon emissions outside the city (in its hinterland) associated with consumption of the urban

121

population. We accounted for both categories of carbon flow in our study.

122

Energy flow analysis has been frequently used in tracking energy consumption and carbon 6


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cities40-42.

123

emissions

124

Intergovernmental Panel on Climate Change43, the inventory of direct carbon emission of

125

economic sectors from their energy consumption and industrial process is described by Equations

126

1–3.

associated

with

Adopting

the

approach

recommended

by

the

127

Cik (t)=Eik (t) × hik × ωik

(1)

128

Cip (t)=Eip (t) × ωip

(2)

129

C (t) = ∑∑ Cik (t) + Cip (t)

(3)

i =1 k =1

130

where Cik (t) is the direct carbon emission from sector i from using a certain fuel type (k); Eik (t)

131

is the amount of energy consumed by sector i (in physical units) over time t (for example, one

132

year); hik is the heat value of k (Table S2);

133

the direct emission from certain industrial process (p), in this case, including cement (including

134

clinker) production and steel/iron production by sector i; Eip (t) is the physical amount of

135

products produced in p;

136

production of one ton of cement or steel); C (t) is the total direct carbon emissions from the urban

137

economy.

ωik is the CO2 emission factor of k; Cip (t) refers to

ωip is the CO2 emission factor of p (for example, emission from the

138

Input-output analysis (IOA)44,45 is widely used for computing the emissions embodied in

139

products and services used or further processed in cities. Multi-region input output model (MRIO)

140

has been applied in carbon footprint of nations46 and cities38,47, providing a more accurate analysis

141

of import-related emission than single-region input output model (SRIO). Here, a multi-region

142

approach called three-scale IOA39,48,49 is chosen due to the match of Beijing’s input-output tables

143

with the data of import emission intensities in the research period. With the consideration of

144

efficiency differences of production in various regions, three carbon flows originated from urban,

145

domestic and international economy are calculated to simulate the changing carbon metabolism of

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the city. By including both direct and indirect effects, carbon emissions embodied in the final

147

demand, imports and intermediate inputs originated from different regions are accounted for:

148

f y(t ) = yU(t ) ∂U(t ) + yD( t ) ∂ (Dt ) + yF(t ) ∂ (Ft )

(4)

7



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149

f z(t ) = zD( t ) ∂ (Dt ) + zF( t ) ∂ (Ft )

(5)

150

f ij( t ) = xU( t ) ∂U( t ) + xD( t ) ∂ (Dt ) + xF( t ) ∂ (Ft )

(6) ( t ) −1

151

where the local embodied carbon intensity ∂U = θ U ( I - A ) ,

152

direct carbon intensities of urban sectors, and for sector i, θU (i ) = Ci X i , A = [aij ] , aij = xij X i ,

153

xij represents the monetary flow from sectors i to j, Xi is the total economic output of i, I is the

154

identity matrix; ∂U( t ) is the urban (local) embodied carbon intensity for sectors; ∂ (Dt ) is the

155

embodied carbon intensity of domestic imported commodities, and ∂ F

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intensity of foreign imported commodities. See detailed calculation and explanation of domestic

157

and international emission intensities in literature39,48. yU , yL , and y F

158

consumption (household/government consumption, capital formation and exports) from local

159

output, domestic import and foreign import, respectively; xU , xD , and xF

160

domestic imported and foreign imported intermediate inputs; f y

161

embodied in urban final demand. f Z

162

(t )

(t )

θ (t ) is a diagonal matrix of U

(t )

(t )

(t )

(t )

(t )

(t )

(t )

is embodied carbon

(t )

(t )

are the final

are the local,

is the carbon emissions

is the carbon emissions embodied in the import to city;

f ij( t ) is the carbon emissions embodied in intermediate input from sector i to sector j.

163

The sum of carbon flows from a production-based perspective and that from a

164

consumption-based perspective mathematically balance in urban metabolic systems each year. The

165

carbon flow network of the city can be constructed based on the in- and outflows related to urban

166

metabolism. The balance between in- and out- carbon is described as:

167

Ti in ≡ C ( t ) + f z( t ) + ∑ f ij( t ) = ∑ f ij( t ) + f y( t ) ≡ Ti out i =1

(7)

j =1

168

where Ti in is the sum of carbon inflows to sector i, and Ti out is the sum of carbon outflows

169

from i; Ti

170

2.3 Tracking of functional shifts

in

equals Ti

out

when the carbon network is at steady state43.

171

In addition to the inventory of directions and magnitudes of carbon flows over time, the

172

tracking of the changing interactions among economic sectors is important for evaluating 8


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173

functional changes in the urban metabolic system. ENA is useful in identifying functional shift in

174

ecosystems, based on the quantification of interactions among system components. Detailed

175

reviews of ENA indicators and software are in the literature50-54. Here, we assess the changing

176

inter-relationships in the carbon networks over time to articulate the control or dependence of one

177

sector over the other. Using network control analysis55-58, we identify this control-dependence

178

interaction at sectoral level. The control allocation (CA) and dependence allocation (DA) matrices

179

are developed, representing the forward and backward interactions among sectors.

180

181

b -b′ ≥ 0, bîj =bij -b′ji Bˆ = [bîj ] ,  ij ji

(8)

bij -b′ji < 0, bîj =0

CA = [caij ], caij =

bîj

(9)

∑ bˆ

ij

j =1

182

DA = [daij ], daij =

bîj

(10)

∑ bˆ

ij

i =1

183

where bij = f ij / T j ，b ' ji = f ji / Ti ; caij describes the integral control of sector i over sector j

184

in the carbon network. A detailed description of CA and DA can be found in the Supporting

185

Information.

(t )

(t )

186

For uncovering the roles of sectors play in the carbon metabolism, linkage analysis28,29

187

derived from IOA is further employed. Net forward linkage (NFL) and net backward linkage

188

(NBL) of sectors are calculated to appraise the relative roles (e.g. consumers or suppliers) of

189

sectors in the carbon networks:

190

 ∆ s,s ( I - A)−1 =  ∆ − s,s

191

NFL = θ s (U ) ∆ s ,− s (U ) y− s (U ) + θ s ( D ) ∆ s , − s ( D ) y− s ( D ) + θ s ( F ) ∆ s ,− s ( F ) y− s ( F )

(12)

192

NBL = θ − s (U ) ∆ − s ,s (U ) ys (U ) + θ − s ( D ) ∆ − s , s ( D ) ys ( D ) + θ − s ( F ) ∆ − s , s ( F ) ys ( F )

(13)

∆ s ,− s  ∆ − s , − s 

(11)

193

where ∆ refers to the Leontief inverse (I-A)-1 of a block in the economy; s represents a block of

194

sectors in the network matrix, -s represents the rest of the network; Consistent with the 3-scale IO

195

modelling, both NFL and NBL are quantified based on the carbon flows of three parts, urban 9



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196

(local), domestic import and international import for final consumption in the city. If NBL > NFL,

197

this sector is a net resources consumer, while if NFL > NBL, this sector is a net resources supplier.

198

A full description on linkage analysis is available in literature28,29.

199

2.4 Evaluation of driving factors

200

IO-SDA is adopted to evaluate the contribution of various socioeconomic drivers to the

201

variation of carbon metabolism. The basic formula of IO-SDA is that carbon footprint can be

202

decomposed

203

intensity(Ɵ)×economic

204

consumption volume (y_v)33. Using this approach, we assess the driving factors of

205

consumption-based carbon footprint (CBF; i.e., carbon emissions embodied in urban final

206

consumption by subtracting those in exports). The change in CBF from time t to time t+1 can be

207

decomposed into the changes of these five socioeconomic factors (Eq.14). For brevity, the generic

208

process is introduced, but it integrates information from multi-region analysis (urban, domestic

209

and international flows).

as

the

effects

of

production

five

driving

factors:

structure(L)×consumption

CO2=population(p)×emission structure(y_s)×per

capita

∆CBF = ∆CBF( t +1) − ∆CBFt 210

= θ( t +1) L ( t +1) y _ s( t +1) y_ v( t +1) p( t +1) -θt Lt y _ st y_ vt pt =∆θLt y _ st y_ vt pt +θ( t +1) ∆Ly _ st y_ vt pt + θ( t +1) L ( t +1) ∆y _ sy_ vt pt

(14)

+θ( t +1) L ( t +1) y _ s (t +1) ∆y_ vpt + θ( t +1) L ( t +1) y _ s( t +1) y_ v( t +1) ∆p 211

in this equation each of the four terms represents possible variants of how the driving factors

212

contribute to the change in CBF, evaluating the effect of one driving factor while forcing the rest

213

constant. We average all possible first-order decompositions to derive contributing values; see

214

detailed description of SDA process in literature59,60.

215

2.5 Case study, data and scenarios

216

We used Beijing as a case study for modeling urban carbon metabolism. As the capital of

217

China, Beijing has experienced a rapid increase of population and economic scale over recent

218

decades. In 2015, its population was 21.7 million, more than twice that in 1985. The city’s gross

219

domestic product (GDP) rose from 26 billion Yuan in 1985 to 2290 billion Yuan in 2015 (data

220

from Beijing Statistics Bureau). Meanwhile, as a side effect of economic growth, greenhouse gas 10


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221

emissions driven by the consumption in the city increased rapidly. In Beijing, carbon is

222

re-allocated among economic sectors due to recent adjustment of industrial structure, and part of

223

the carbon emissions is outsourced by moving heavy industries outside the city. These are also the

224

strategies now used in big cities in China as well as the western world in order to cope with global

225

warming. Therefore, lots of study employed Beijing as a typical case for assessing the benefits and

226

costs of low-carbon pathways for cities39,42,61,62. The economics and population in Beijing over

227

1985-2012 is provided in Table S3. The data used in our study include sectoral energy use63,

228

industrial products (cement and steel/iron)63, emissions factors for energy types43 and industrial

229

processes64,65, and 1985–2012 input-output tables for Beijing66. The base year of IOA is 2000. All

230

the input-output tables were adjusted to constant prices of 2000 using the double deflation

231

method67. The data compilation procedure is provided in SI.

232

Future scenarios of carbon metabolism in Beijing are developed: in pessimistic scenario

233

(2030-P) and optimistic scenario (2030-O), we assume 40% and 60% reductions of carbon

234

emission intensities by 2030, respectively (compared with the 2012 level). Also, 2030-P scenario

235

is expected to have larger population growth, higher final consumption and lower value added

236

than 2030-O scenario. 2030-P scenario assumes Beijing will be following “western lifestyle”

237

consumption and having a loose population-control measure, while on the opposite, 2030-O

238

scenario assumes Beijing manages to find a best route for low-carbon development. A detailed

239

description of all driving factors in these two scenarios is provided in Table S4. It should be noted

240

that both scenarios are more like “what-if” hypothetical experiment rather than actual forecast.

241

They are not intended to be accurate prediction of what future Beijing’s economy and carbon

242

footprint will be. Nevertheless, they can show the potential of upper and lower bounds of carbon

243

mitigation policy. Given the complexity of the integrated model, an uncertainty analysis has been

244

conducted for both historical results and future scenario analyses on carbon footprints (Table S5).

245

3. Results

246

3.1 Historical trajectory of urban carbon metabolism

247

Figure 2 shows the historical change of direct carbon emission (DCE) and consumption-based

248

carbon footprint (CBF) of Beijing over 1985–2012. Both DCE and CBF increased rapidly over the 11



249

last three decades. DCE increased from 45 Mt in 1985 to 95 Mt in 2012 (+110%), while within the

250

same timeframe CBF increased from 58 Mt to 124 Mt (+120%). While having the similar growth

251

speed in total, DCE and CBF show very different change trajectories. This indicates the increase

252

of direct and embodied emission are not always synchronous. For example, there is no significant

253

change in DCE between 1990 and 1995, but a major increase in CBF (+26%) is found over the

254

same period. While the DCE in 2012 still increased by 3% compared to that of 2010, the CBF

255

decreased to the level prior to 2010. A credible speculation is that from a consumption perspective,

256

carbon emissions might have peaked in that year, although confirmation from followed-up

257

analysis after 2012 is needed. The SDA analysis in the following section will help uncover the

258

drivers underlying such decrease.

259

Compared to the continuous increase in total direct emission, per capita DCE has been

260

declining to 4.7 t/capita in 2012, after the highest historical record in 2010 (5.1 t/capita). The per

261

capita CBF might also have peaked in 2010 (7.2 t/capita), while a major decrease occurred in 2012

262

(-13%). There is no major augment in both DCE and CBF on per capita level over 1985-2012.

263

One plausible explanation is that recent improvement of technology and efficiency in Beijing

264

synchronized with the growth of household consumption and capital formation.

265

A steadily growth in economy and stringent emission controls have resulted in a dramatic

266

decrease of emission intensity in Beijing as a whole. The DCE intensity decreased from 1.76

267

t/1000 Yuan in 1985 to 0.05 t/1000 Yuan in 2012 on average. Similarly, the CBF intensity

268

experienced a dramatic reduction from 2.26 t/1000 Yuan in 1985 all the way to 0.08 t/1000 Yuan

269

in 2012. A most significant improvement in economic efficiency (or reduction in emission

270

intensity) happened between 1990 and 1995, in which period DCE intensity and CBF intensity

271

decreased by 66% and 59%, respectively.

272

Sector-level results reveal a major transition of dominant sector in producing carbon emission

273

(Figure S2). Before 2000, heavy-industry related sectors such as Petroleum processing and coking

274

(S9), Chemicals (S10), Nonmetal mineral products (S11), Smelting and pressing of ferrous and

275

nonferrous metals (S12) produced a major amount of emission, contributing 50%-60% to the total

276

DCE. But this situation changed rapidly after 2000, about 30% of the total DCE is contributed by

277

services sectors, 15% comes from Production and supply of electricity, gas and hot water (S19), 12


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278

and another 15% is produced by Transportation, storage, post and telecommunication services

279

(S21). Similar trend also exists in CBF. The proportion of emissions from heavy-industry related

280

sectors compressed by half from 1985 to 2012, while the proportion of services related CBF in

281

2012 double the value in 1985. (a)

120 110

direct carbon emission (Mt)

100

Direct emission intensity (t/1000Yuan) 0.0 0.00 00 0.2500 0 .7500 1.000 1.700 0.250.5000 0.50 0.75 1.001.250 1.251.500 1.50

Per capita direct emission (t)

90 80

2.5 3.0 3.5 4.0

4.5

5.0

70 60 50 40 30 1985

1990

1995

2000

2005

2010 20 12

2005

2010 2012

Year Consumption-based emission intensity (t/1000Yuan)

(b)

0.01000 0.03000 0.02000 0.04000 0.000 0.05000 0.06000 0.07000 0.08000 0.09000 0.1000 0.1100 0.1200 0.1300 0.1400 0.1600 0.1500 0.1700 0.1800 0.1900 0.2000 0.2100 0.2300 0.2200 0.2400 0.2500 0.2600 0.2700 0.2800 0.3000 0.2900 0.3100 0.3200 0.3300 0.3400 0.3500 0.3700 0.3600 0.3800 0.3900 0.4000 0.4100 0.4200 0.4400 0.4300 0.4500 0.4600 0.4700 0.4800 0.4900 0.5100 0.5000 0.5200 0.5300 0.5400 0.5500 0.5600 0.5800 0.5700 0.5900 0.6000 0.6100 0.6200 0.6300 0.6500 0.6400 0.6600 0.6700 0.6800 0.6900 0.7000 0.7200 0.7100 0.7300 0.7400 0.7500 0.7600 0.7700 0.7900 0.7800 0.8000 0.8100 0.8200 0.8300 0.8400 0.8600 0.8500 0.8700 0.8800 0.8900 0.9000 0.9100 0.9300 0.9200 0.9400 0.9500 0.9600 0.9700 0.9800 0.9900 1.000 1.010 1.020 1.030 1.040 1.060 1.050 1.070 1.080 1.090 1.100 1.110 1.130 1.120 1.140 1.150 1.160 1.170 1.180 1.200 1.190 1.210 1.220 1.230 1.240 1.250 1.270 1.260 1.280 1.290 1.300 1.310 1.320 1.340 1.330 1.350 1.360 1.370 1.380 1.390 1.410 1.400 1.420 1.430 1.440 1.450 1.460 1.480 1.470 1.490 1.500 1.510 1.520 1.530 1.550 1.540 1.560 1.570 1.580 1.590 1.600 1.620 1.610 1.630 1.640 1.650 1.660 1.670 1.690 1.680 1.700 1.710 1.720 1.730 1.740 1.760 1.750 1.770 1.780 1.790 1.800 1.810 1.830 1.820 1.840 1.850 1.860 1.870 1.880 1.900 1.890 1.910 1.920 1.930 1.940 1.950 1.970 1.960 1.980 1.990 2.000 2.010 2.020 2.040 2.030 2.050 2.060 2.070 2.080 2.090 2.110 2.100 2.120 2.130 2.140 2.150 2.160 2.180 2.170 2.190 2.200 2.210 2.220 2.230 2.250 2.240 2.260 2.270 2.280 2.290 2.300 2.320 2.310 2.330 2.340 2.350 2.360 2.370 2.390 2.380 2.400 2.410 2.420 2.430 2.440 2.460 2.450 2.470 2.480 2.490 2.500 0.00 0.50 1.00 1.50 2.00 2.50

embodied carbon emission (Mt)

150

Per capita consumption-based emission (t)

4.0 5.0 6.0

7.0

8.0

100

50 1985

282 283

1990

1995

2000

Year

Figure 2 Variation of (a) Direct carbon emission (DCE) and (b) consumption-based carbon

284

footprint (CBF) of Beijing over 1985–2012. The carbon emission intensities of DCE and CBF at sectoral

285

level are shown in Figure S3 and Figure S4, respectively.

286

Figure 3 shows the variation of carbon flow networks in Beijing from 1985 to 2012. We

287

found that the magnitudes, intensities and structure of carbon flows embodied in intermediate

288

inputs changed significantly. As revealed by total carbon throughflow of sectors, the size of carbon

13



289

metabolism related to many sectors augmented by >5 times from 1985 to 2012, which is much

290

faster than the change in their CBFs. This is mainly due to the increase of complexity in their

291

economic activities, in addition to the growth of emission levels. It is evident the distribution of

292

carbon flows among sectors has been more sophisticated now than it was 30 years ago.

293

All the manufacturing sectors (S2–S18) combined contributed a big throughflow in 1985,

294

which is 4 times of service sectors combined (S22–S24). Although the absolute magnitudes of

295

both flows increased substantially, the proportion of manufacturing-related sectoral carbon flow

296

decreased to 47%, and the proportion of services-related flow increased to 30% recently. In

297

particular, Petroleum Processing and Coking and Chemicals are main contributors among the

298

manufacturing sectors, with a proportion of about 20% from 1985 to 1995. But after 2000, the

299

carbon flows associated with these two sectors decreased to 10%. The contributions of Production

300

and supply of electric power, gas and hot water (S19) and Transportation (S21) increased by 10

301

and 2 percentage points over the period, respectively. The impact of Agriculture sector to the

302

whole carbon balance is small, and its contribution has been cut by half over 1985-2012. These

303

results indicate the changes in the impact of economic sectors on the dynamics of urban carbon

304

metabolism.

305

A transition of embodied emission intensities related to sectors is also found, suggesting an

306

alteration in efficiency and technology used by the city over the timeframe. In most sectors, fewer

307

carbon emissions were produced per unit GDP in 2012 relative to that in 1985. For example, ~1.00

308

Mt CO2 was produced per 1000 Yuan of GDP in the Agriculture (S1) in 1985, while that in 2010

309

decreased to 0.18 Mt/1000 Yuan. The embodied emission intensities of both the manufacturing

310

and service sectors declined substantially, especially after 2000. For example, the embodied

311

emission intensity decreased by more than 10 times in Petroleum processing and coking (S9) and

312

Production and supply of electric power, gas and hot water (S19) from 2000 to 2012, while the

313

intensity of the service sectors (S22–S24) decreased by 3 times within the same period. This

314

transition is not only driven by the urban supply chains in Beijing, but also associated with the

315

change in domestic and international supply chains.

14


Page 14 of 31

Page 15 of 31


1985

1990

1995

2000

2005

2010

2012 Embodied emission intensity (t CO2/1000 Yuan)

0.01 0.51 1.01

Total carbon throughflow of Sector (Mt CO2)

0.1 0.3 0.9 2.7

Inter-sector carbon flow (Mt CO2)

0.10 0.30 0.90 2.70

1.51

8.1

8.10

2.01

24.3

24.30

2.51

72.9

3.01 3.51

100.0

4.01

316 317

Figure 3 Inter-sector carbon flows of Beijing over 1985–2012

318

Note: Total carbon throughflow refers to the sum of in- or outflows of embodied carbon associated with a sector,

319

implying the size of metabolism for this sector. The carbon throughflows of sectors are also collectively shown in

320

Figure S5. Inter-sector carbon flows are calculated based on the emissions embodied in the intermediate economic

321

flows. Sectors are labeled according to year of investigation and sector number. For example, 85-S1 refers to

322

Sector 1 in 1985, and 90-S2 to Sector 2 in 1990. The 24 sectors are: S1: Agriculture; S2: Coal Mining, Petroleum

323

and Natural Gas Extraction; S3: Ferrous and Nonferrous Metals Mining and Dressing; S4: Nonmetal Minerals

324

Mining and Dressing; S5: Food Processing and Production; S6: Textile Industry, Garments and Other Fiber

325

Products and Leather, Furs, Down and Related Products; S7: Timber Processing, Bamboo, Cane, Palm Fiber and

326

Straw Products and Furniture Manufacturing; S8: Papermaking and Paper Products and Printing and Record

327

Medium Reproduction; S9: Petroleum Processing and Coking; S10: Chemicals; S11: Nonmetal Mineral Products;

328

S12: Smelting and Pressing of Ferrous and Nonferrous Metals; S13: Metal Products; S14: Ordinary and special

15



329

machinery and equipment; S15: Transportation Equipment; S16: Electric Equipment and Machinery; S17:

330

Electronic and Telecommunications Equipment, Instruments, Meters, Cultural and Office Machinery; S18: Other

331

Manufacturing Industry; S19: Production and Supply of Electric Power, Gas and Hot Water; S20: Construction;

332

S21: Transportation, Storage, Post and Telecommunication Services; S22: Wholesale, Retail Trade and Catering

333

Services, Restaurant and Renting; S23: Finance, insurance, scientific, environmental and technical services; S24:

334

Public services and others services

335

3.2 Functional shifts of urban carbon networks

336

The inter-sector relationships in the carbon flow networks over 1985–2012 are shown in

337

Figure 4. Some economic sectors have much wider control than others, and this impact has

338

remained unchanged. The petroleum processing, production and supply of electricity, gas and

339

water, and construction controlled carbon emissions from many other sectors the whole time. Due

340

to the adjustment of industrial structure, some sectors gain more control over the urban carbon

341

metabolism, while others tend to lose control in the system. For example, electronic and

342

telecommunications equipment and machinery (S17) nearly had no control over other sectors

343

before 1990, but it gained control over a couple of manufacturing sectors (such as nonmetal

344

minerals mining, electricity, gas and water supply, other manufacturing industry) in 1995

345

according to CA. In 2010 and 2012, it controlled 15 different sectors from agriculture to services.

346

On the other hand, moving heavy industries outside the city have made the production of metal

347

lose control on a lot of sectors during urban development. Only timber processing (S7) and

348

papermaking (S8) were still controlled by this sector in 2012. From a dependence perspective,

349

almost all the sectors relied on manufacturing sectors (such as Petroleum processing and coking,

350

Chemicals, Nonmetal mineral products, Smelting and pressing of ferrous and nonferrous metals)

351

in the carbon networks before 1995. But since 2000, many sectors have also become much more

352

dependent on services sectors such as Wholesale, retail trade and catering services, and Finance,

353

insurance, scientific, environmental and technical services. The dependence of sectors’ carbon

354

metabolism on Construction and Transportation has also been augmented through the years.

355

Although services sectors have been increasingly important in determining urban carbon

356

metabolism, manufacturing sectors should not be overlooked, because of their control over the

357

services sectors from a systemic perspective. 16


Page 16 of 31

Page 17 of 31


1985

CA 1.00

1990

1995

S1

0.80 0.60 0.40 0.20 0.00 S24 S1

S24

2000

S1

0.80 0.60 0.40 0.20 0.00

2010

1990 S3 S19 S2 S9S3 S4 S2 S12S4

1985

DA 1.00

2005

S24 S1

S24

2000

2005

S11 S19 S13 S13 S11

2012

1995

2010

2012

S8 S2 S3

S19

S9 S12

S9 S3

358 359

Figure 4 Changing control and dependence relationships among sectors over 1985–2012.

360

Note: CA: control allocation; DA: dependence allocation. The direction of network control and dependence is from

361

column sectors to row sectors.

362

A transition in the roles sectors play in carbon metabolism is also detected from the linkage

363

analysis (Figure 5). For example, since 2000 Finance, insurance, scientific, environmental and

364

technical services (S23) has become a net carbon importer (NBL>NFL), but before that it has

365

always been a net a net carbon exporter (NBL

Changing Urban Carbon Metabolism over Time ... - ACS Publications

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