A review on the quantification of life cycle greenhouse gas emissions at urban scale

Journal Pre-proof A review on the quantification of life cycle greenhouse gas emissions at urban scale Zahra Ghaemi, Amanda D. Smith PII:

S0959-6526(19)34504-4

DOI:

https://doi.org/10.1016/j.jclepro.2019.119634

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JCLP 119634

To appear in:

Journal of Cleaner Production

Received Date: 12 June 2019 Revised Date:

5 December 2019

Accepted Date: 8 December 2019

Please cite this article as: Ghaemi Z, Smith AD, A review on the quantification of life cycle greenhouse gas emissions at urban scale, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/ j.jclepro.2019.119634. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

A Review on the Quantification of Life Cycle Greenhouse Gas Emissions at Urban Scale Zahra Ghaemia , Amanda D. Smitha,b, a Department

of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA Northwest National Laboratory, Richland, WA 99352, USA

b Pacific

Abstract Cities are responsible for 75% to 80% of emissions worldwide, and policies that target emissions reductions are often implemented at an urban scale. This paper reviews studies using life cycle assessment (LCA) methods, specifically input-output and hybrid analysis, which used a consumption based accounting system to quantify direct (Scope 1) emissions and indirect (Scope 2 and Scope 3) emissions at the urban scale. Life cycle greenhouse gas emissions for case studies are scaled toward three metrics: nominal Gross Domestic Product, population, and population density; as these factors are known to be influential in life cycle assessment results at these scales. However, this work illustrates that, for the cities and countries considered in these case studies, the similarity between cities within the same country is more pronounced than any effects of Gross Domestic Product (economy) or population/ population density (city size) across cities in different countries. Accounting for greenhouse gas emissions at city scale using LCA methods presents difficulties in three LCA phases: In Phase 1, there is no agreement available on a concisely defined city boundary and the extent to which indirect emissions are included; In Phase 2, there is a lack of publicly available data from governments for a wide range of locations; In Phase 3, there are few studies among those reviewed that provided a thorough analysis of greenhouse gas emissions using life cycle impact assessment at urban scale. Because of these issues with LCA Phases 1–3, Phase 4 (interpretation of results) cannot be performed effectively to assist with emissions reduction policymaking and planning. Keywords: life cycle assessment, Scope 3 emissions, consumption based, greenhouse gas inventories, carbon dioxide emissions, urban sustainability

Nomenclature CBA

Consumption based approach

CO2eq

Carbon dioxide equivalent

CSA

Complex system approach

Email address: [email protected] (Amanda D. Smith) Preprint submitted to Journal of Cleaner Production

December 23, 2019

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EEIOA

Environmentally extended input-output analysis

EIO-LCA

Economic input-output life cycle assessment

GDP

Gross domestic product

GHG

Greenhouse gas

IO

Input-output

LCA

Life cycle assessment

LCI

Life cycle inventory

LCT

Life cycle thinking

MRIOA

Multi-region input-output analysis

MBA

Metabolism based approach

MFA

Material flow analysis

PBA

Production based approach

QMRIO

Quasi multi regional input-output

SPA

Structural path analysis

1. Introduction 20

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Today, 82% of North Americans, 80% of Latin Americans and Caribbean Islanders, 74% of Europeans, 68% of the people of Oceania, and 50% of Asians live in urban areas (United Nations, 2018), which makes cities important centers of consuming goods, using services, and utilizing energy directly and indirectly. Practically all of the activities which occur in cities produce greenhouse gas (GHG) emissions. Cities are responsible for 70% of global energy use (Moriarty & Wang, 2014), they are the main emitter of CO2 emissions throughout the world (Shan et al., 2016), and they are responsible for 75% to 80% of GHG emissions (Satterthwaite, 2008), which makes them an important target for policies aimed at reducing GHG emissions. Having measurable values of GHG emissions for cities would make it possible to manage GHG emissions in a targeted way (Pandey et al., 2011). A systematic approach would help to reduce emissions in cities effectively. The Life Cycle Thinking (LCT) approach and, specifically, Life Cycle Assessment (LCA) may be considered the most comprehensive methodology (Mirabella et al., 2018) to face the challenge of accounting for environmental performances at the urban scale. The International Organization for Standardization (ISO) describes the principles of LCA, and a formal framework for assessment in Standard 14040: Life cycle assessment is a technique for evaluating the environmental impacts associated with a product through its lifespan (ISO, 2006). LCA studies consist of four phases (ISO, 2006): goal and scope definition, life cycle inventory (LCI) analysis, life cycle impact assessment, and interpretation of results (for a detailed description of LCA phases, please refer to ISO (2006)). Performing LCA at an urban scale can be complicated as the system now consists of many infrastructures and material flows within a campus, neighborhood, city, or larger region (Heinonen et al., 2011), and it is crucial to provide assessments for cities and urban regions so that policies can be targeted effectively. Comparative analysis between 2

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cities is necessary to share best practices among different urban centers (Sovacool & Brown, 2010). In large countries with diverse economies, such as the United States (O’Shaughnessy et al., 2016) and China (Shan et al., 2018), cities are uniquely positioned to lead in climate mitigation efforts by implementing policies that are informed by their unique situations. The carbon footprint (CF) is a quantitative statement of GHG emissions that is calculated based on the GHG intensiveness of processes, bodies, and products; it conceptually provides an indicator of global warming potential (Pandey et al., 2011). CF includes a variety of GHG emissions like CO2 , CH4 , and N2 O (Fang et al., 2014) and is defined as the cumulated mass of CO2 emissions of a supply chain or the life cycle of a system (Hertwich & Peters, 2009). The amount of GHG emissions emitted, removed, or embodied in the life cycle of the system should be accounted for (Pandey et al., 2011) to quantify CF. The mass of GHG emissions is expressed in terms of carbon dioxide equivalent (CO2eq ) (Fang et al., 2014). The scope and boundaries of the system range from measuring direct emissions, or those occurring within the facility or area of interest, to a full life cycle GHG emissions accounting. Accounting for GHG emissions at urban scale differs based on the accounting methods, the scope of GHG emissions, the emissions sources and urban definition (Dhakal, 2010), and is a complex task due to the open local economy and the need to establish a boundary (Munksgaard et al., 2005). Defining an exact scope and system boundaries is a crucial step in transforming carbon management principles to practically reduce carbon emissions (Kennedy & Sgouridis, 2011). Therefore, this paper particularly focuses on case studies that have used input-output (IO) and hybrid LCA, which measures direct and indirect GHG emissions at the urban scale. Some of the previous state-of-the-art review literature that has focused on quantifying CF at the urban scale can be grouped into three categories based on common features among these studies: 1. They qualitatively analyzed GHG emissions at the urban scale by considering sustainability approaches or policy implications. Selected review papers show the research gaps and provide the groundwork to apply LCA at urban scale. They focused on defining city boundary, functional unit, effective methodologies, complexities in applying LCA at urban scale, practical approaches to performing LCA, problems associated with lack of data in studies, comparison among case studies, why the results are different among cities, and policies on reducing environmental impacts. They are helpful to define the structure of the LCA method to be used at urban scale and have a structured set of policies that can be applied at urban scale with the use of LCA results. Albert´ı et al. (2019) analyzed LCA with a sustainable perspective at the city scale. The authors proposed the use of the City Prosperity Index to include the social and economic aspects of sustainability using LCA. The authors explained an elaborated definition of the goal of LCA, the function of the system, the functional unit, and the system reference flow. They started each part using definitions provided by ISO (2006), and then, mentioned the challenges that are faced by researchers in literature, compared these challenges when LCA is used at urban scale, and completed the section by providing an example of using LCA at city scale. Chen et al. (2019) reviewed carbon accounting at city scale. They organized the paper using various topics that used CF at city scale, debated on the accounting system (consumption 3

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or production based) and system boundary (direct and indirect emissions). Also, they reviewed the calculation methods and available organization and protocols on CF. They concluded that GHG emissions at urban scale differ in the same city because of differences in protocols, methods, and data sources. Ottelin et al. (2019) focused on the policy implications of consumption-based studies at different spatial scales, including city-scale. They discussed policy recommendations of case studies at the urban scale, and the benefits and challenges of applying the mentioned policies. The authors focused on discussion and conclusion sections of original papers, organized the policy recommendations, and showed the similarities and contradictions among them. Numerical methods to organize the policies are used by showing the proportion of policy levels and policy recommendations for different publication time and spatial scale of the case studies. Further quantitative analysis are missed in this review paper. Mirabella et al. (2018) provided a comprehensive insight on the application of LCA at the city scale, with a focus on various sub-sections (built environment, energy systems, waste, water, consumption patterns, transportation networks, and urban open spaces and green areas). They focused on challenges associated with the use of a proper method, defining the boundary system, and data collection process. Furthermore, challenges associated with using four phases of LCA are discussed, and available solutions in the literature are proposed to ease the application of using LCA method. Fry et al. (2018) discussed the calculation of CF at the city scale under the limited information. IO tables have been used to evaluate CF at the city scale, and this study focused on four selected cities with different levels of IO data to explore the uncertainties and errors associated with the level of input data. They conclude that calculating CF with the sectoral aggregation of data can lead to high errors. This paper focused on lack of data using IO method, one of the most important obstacles of applying a comprehensive LCA method to complex systems, which elucidated areas that differ the most by having various level of data. Albert´ı et al. (2017) performed a review of the background knowledge on LC sustainability at the city scale. The paper focuses on whether available standards and methods can perform LC sustainability at an urban level or not. No sustainability assessment method has been found, in which include all of these aspects: comprehensive point of view, various environmental impact assessment methods, LC perspective, and different cities and regions can be compared. Lombardi et al. (2017) summarized state of the art in research on urban CF, classifying based on the aspects of LCA at urban scale that make a difference on results: economic structure, system boundary (i.e., direct or indirect emissions), accounting systems (consumption based, production based, and hybrid accounting system), and inventory methods (process method, IO method, and hybrid method) for quantifying urban CF. This paper helps future researchers to choose between the mentioned aspects of LCA, and compare the results among the case studies with similar elements. Dhakal (2010) provided an introduction to GHG emissions resulting from urbanization at global, regional, and city scales, and then discussed the opportunities for reducing GHG emissions at the city scale. The writer concluded that GHG emissions differ based on the accounting methods, the scope of GHGs, emission sources, and urban definitions that are used, similar to Lombardi et al. (2017)’s research, Dhakal (2010) focused on the aspects of CF that makes site-specific comparisons difficult. Peters (2010) reviewed the applications of CF at 4

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a variety of levels: individual products, companies, cities, nations, and the global scale. The writer focused on each level, analyzed the challenges in using CF at the specified level, and provided policies that can be applied using CF. 2. They quantitatively scaled GHG emissions within a single country, by using a specific type of input-output method at country level to analyze a large number of cities without a need for site specific data. These two studies focused on the CF relation among cities, how GHG emissions are distributed within a country, and what national policies are more practical to reduce environmental impacts at urban scale. Also, quantitative results were plotted toward the metrics, which revealed a strong correlation among them. Jones & Kammen (2014) quantified the average U.S. household carbon footprints in cities and outlying suburbs. They concluded that dense areas have smaller carbon footprints in the U.S. They released an online software application called the CoolClimate Calculator, which provides a quick CF estimate for any household with commonly available data, based on their work evaluating carbon footprints in several U.S. regions. Ala-Mantila et al. (2014) used an environmentally extended input-output method to analyze the relationship between life cycle GHG emissions and Finnish household types. They quantitatively explored the effects of expenditure, urban models (i.e., metropolitan, urban, semi-urban, and rural), and household size on the total, direct, and indirect emissions. 3. They used different accounting systems, emissions scopes, and methods to conduct case studies. In these two studies, a meta-analysis of a large number of cities are performed to provide global policies to reduce environmental impacts. Moran et al. (2018) calculated CF of 13000 cities in the world using Gridded Global Model of City Footprints. The model uses population, purchasing power, and existing results in the CF at urban scale literature. The results showed that CO2 could be reduced significantly by focusing on a few hundred cities with a high concentration of CF. The authors used one specific method (Gridded Global Model of City Footprints) to calculate the CF for all of the cities using consumption patterns but neglect the LCA method to consider other aspects of consumption at city scale. This approach is rational as the numbers of cases increases, and available data gets limited, but results should be analyzed knowing the system boundary in the study. Hoornweg et al. (2011) reviewed GHG emissions of 100 case studies and presented opportunities to reduce GHG emissions effectively. The authors did not differentiate among case studies that used consumption based/ production based accounting systems, or process/ input-output/ hybrid methods to calculate GHG emissions. These review papers are delineated by the first category focusing on qualitative analysis, the second category on quantitative analysis within a single country, and the third category on quantitative analysis of case studies from various countries, where those case studies have used different methods and accounting systems. A high proportion of these review papers focused on qualitative analysis of LCA at urban scale, which is necessary to declare the goal, the system boundary, the proper 5

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methodology, and the potential policies to reduce GHG emissions. However, a quantitative analysis of life cycle GHG emissions covering different regions of the world that focuses on using the consumption based accounting system and clearly defined GHG inventory methods was not found in the current literature. Such a study is needed to understand the variability of GHG emissions at urban scale, within a consumption based accounting system (life cycle emissions) framework and within a defined methodological approach. A consumption based accounting system considers emissions outside the system boundary. As cities are open systems with many flows across their boundaries, it is preferable to a production based accounting system for capturing a full picture of urban emissions. Input-out and hybrid methods have been shown to be more suitable for assessing life cycle emissions of large systems (Islam et al., 2016). Therefore, studies that used a consumption based accounting system and employed an input-output or hybrid method are considered here. These two features allow this review paper to provide more comparable results for GHG emissions at urban scale, especially when case studies are from different regions of the world, with economic, social, and environmental differences. This paper also provides a meta-analysis of results, where specific features and results from the studies reviewed are taken into consideration: • Do they consider the sensitivity and/or uncertainty analysis?

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• What LCA methods are used? • What are the GHG emissions in each city? • What types of emissions have been considered in calculating CO2eq ? • Do they report sector specific emissions? • Are the GHG emissions scaled toward specific metrics?

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• What types of input-output (IO) tables are used? The overall objectives of this work are to: 1. Describe commonly used methods for evaluating GHG emissions using LCA. 2. Review previously published LCA GHG emission methodologies that are relevant to the quantification of GHG emissions at the urban scale.

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3. Compare and contrast studies that address GHG emissions at the urban scale. 4. Examine the sensitivity of carbon dioxide equivalent (CO2eq ) calculations across studies conducted at different scales. 5. Identify limitations and difficulties that must be addressed for LCA to be more widely used in practice for reducing GHG emissions at the city level.

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Phase 1. Goal and scope

Phase 2: Life cycle inventory analysis

Goal

+ Normalizing GHG emissions toward metrics. + Comparing the GHG emissions between cities. + GHG emissions mitigation policies + Most emission emitter sectors + Evaluating the GHG emissions associated with different sectors. + Comparing methods of evaluating GHG emissions at urban-scale

Scope 1

Scope 2

Process analysis

EEIO

Scope 3

Scope

IO analysis

Direct Emissions

Indirect Emissions of energy sectors

Multi-region IO

Indirect Emissions of goods and services

Accounting model

Quasi multiregion IO

Consumptionbased system

Combined systems

Production-based system

IO-based hybrid analysis

Hybrid analysis

Tiered hybrid analysis

Phase 3: Life cycle impact assessment 

Human health

Natural environment

Integrated hybrid analysis

Natural resources

Phase 4: Interpretation of results The findings from the inventory analysis and the impact assessment are combined together in this phase. GHG emissions reduction policies, strategic plannings, conclusions, and recommendations are set. 

Figure 1: Framework for quantifying GHG emissions at urban scale using LCA in four phases.

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2. Life cycle GHG emissions at urban scale The four phases described in Section 1 should be performed to use the LCA method for quantifying GHG emissions at urban scale. Figure 1 shows the four phases of LCA and highlights the areas covered by the case studies considered for this review that quantitatively measured GHG emissions at the urban scale.

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2.1. Goal and scope definition Three scopes are defined by The World Resources Institute (Fong et al., 2015): • Scope 1 considers the direct GHG emissions that occur within the city boundary.

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• Scope 2 considers the indirect GHG emissions that occur outside the system boundary as a consequence of activities inside the system boundary (encompassing indirect emissions as a result of purchased energy); for example, the purchase of electrical power that was generated outside of the boundary. • Scope 3 considers the embodied emissions in non-energy processes and products (encompassing all indirect emissions as the outcome of activities within the city boundary).

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Larsen & Hertwich (2009) studied four accounting methods in the literature to measure CF at urban scale:

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• Consumption based approaches (CBA): CBA account for the embodied environmental aspects associated with consumption of a product (Baynes & Wiedmann, 2012), which are defined as Scope 3 emissions Larsen & Hertwich (2009). CBA study these impacts inside and outside the city boundary. Input-output analysis is one of the most widely used methods for CBA (Baynes & Wiedmann, 2012), in which an analysis evaluates the direct and indirect energy usage, water usage, GHG emissions, and ecological footprint (Baynes & Wiedmann, 2012). By using multiregional IO tables, CBA can be flexibly used with boundaries from the national scale to the regional and city scales.

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• Production based approaches (PBA): PBA estimate GHG emissions that occur within a defined city boundary(i.e., Scope 1 emissions) (Larsen & Hertwich, 2009). In general, two main methods are available in developing PBA: a top-down approach which uses national GHG emissions at the city boundary, and a bottom-up approach which makes use of local emissions data (Larsen & Hertwich, 2009).

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• Metabolism based approaches (MBA): MBA analyze four flows (material, water, waste and energy) at urban regions with defined boundaries (Baynes & Wiedmann, 2012). MBA can be conducted at various scales such as households, neighborhoods, and countries (e.g., using urban materials and energy flows in a city). 250

• Complex system approaches (CSA): CSA recognizes the dynamic features of systems, such as interacting systems and subsystems, network relationships, and feedback loops. CSA can solve problems related to urban planning, transport, housing, and employment (Baynes & Wiedmann, 2012). 8

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Life cycle assessment is widely used with CBA and MBA methods (Lotteau et al., 2015). For assessing the impacts on local climate or urban microclimate, a purely territorial basis may be appropriate, but indirect contributions from outside a city boundary should be considered to evaluate global impacts such as climate change (Munksgaard et al., 2005). Feng et al. (2014) mentioned that many studies prove the utility of CBA, but national policymakers have neglected CBA in measuring CFs (Feng et al., 2014). CBA is preferable to PBA at urban scale as cities are open systems which depend on the import of goods and services produced outside city boundaries (Vause et al., 2013). Thus, including CBA as a supplemental approach to the current PBA and territorial approaches would help city planners to make appropriate decisions (Barrett et al., 2013). Therefore, this study focuses on the consumption based accounting system, which is compatible with LCA methodologies. 2.2. Life cycle inventory analysis Three methods are used for performing an LCI analysis: process analysis, inputoutput analysis, and hybrid analysis (Suh et al., 2004).

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• Process analysis: The process analysis shows the commodity flows that are interconnected to result in a product (Suh & Huppes, 2005). This method is site and economy specific, but it suffers from the problem of an incomplete boundary, especially for more complex systems (Treloar, 1998). • Input-output analysis: Input-output (IO) analysis shows the interdependencies of different sectors in a complex economic system using monetary data (Lenzen, 2000). The problem related to the incomplete boundary in the process analysis is solved using IO analysis (Treloar, 1997). This method is less labor intensive than process analysis, is based on publicly available data (Munksgaard et al., 2005), and its results have more relevance to managing consumption behavior (Baynes et al., 2011). • Hybrid analysis: The hybrid analysis is the combination of the two previous methods, with different degrees of integration between these approaches (Yang et al., 2017). This method is suitable and effective for quantifying GHG emissions at urban scale (Peters, 2010). Three types of hybrid analysis are used in the literature (Suh et al., 2004): (a) Tiered hybrid analysis calculations can be conducted in two different ways (Suh et al., 2004): First, direct paths for consumption and production of the models are calculated using process analysis, and downstream and upstream paths are analyzed using IO analysis. Second, paths that are available in the IO tables are measured by IO analysis, and the remaining paths are measured by process analysis (Islam et al., 2016). (b) IO-based hybrid analysis was developed by Treloar (1997) to overcome the disadvantages associated with the process-based analysis. The model requires the extraction of energy paths from the direct IO table and substitution of them with process data. With this method, the advantage of using reliable local process data is added to the advantage of a complete system boundary in the IO analysis. 9

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(c) Integrated hybrid analysis was developed by Suh and uses a mathematical framework to combine process analysis, as described in a technology matrix, with the use of IO analysis (Suh & Huppes, 2005). The advantages of the integrated hybrid analysis are the use of a consistent mathematical framework, the prevention of double-counting, and the ease of application (Bilec et al., 2006). The focus of this paper is on using the LCA method for GHG emissions accounting. Case studies were selected which consider Scope 3 emissions in addition to Scopes 1 and 2. Selected case studies use hybrid analysis (Subsection 2.3) or IO analysis (Subsection 2.4). Some of the studies did not specify the use of LCA. As the consumption-based accounting system captures the direct and life cycle GHG emissions (Wiedmann et al., 2016), case studies that did not mention the use of the LCA method but did use the consumption based accounting system were also included in this study. These case studies were found using a combination of these keywords: life cycle, carbon footprint, greenhouse gas inventories, greenhouse gas, consumption based, urban scale, cities; using Google Scholar and ScienceDirect databases. It should be noted that in the International Reference Life Cycle Data (ILCD) system handbook for the LCA method, inventories refers to the amount of all emission types (European Commission et al., 2012). However, this paper is only focused on CO2 and CO2eq GHG emissions, and in the case studies reviewed here, GHG emission footprints and carbon footprints have been measured using the LCA method. 2.3. Case studies which are focused on Scope 3 emissions and use the hybrid analysis method Table 1 provides an overview of the case studies which focused on Scope 3 emissions as defined by the Greenhouse Gas Protocol and which have used the hybrid analysis method. The objectives of the case studies in this subsection can be divided into five categories: • Meta-analysis of multiple cities to make a comparison on the accounting systems, showing how consumption based or production based perspectives can change the results: Larsen & Hertwich (2009) compared consumption based and production based methods. As 93% of the total carbon footprints in cities are indirect emissions, consumption based approaches were found to be less misleading, and more useful for evaluating the effectiveness of emissions mitigation policies. Therefore, they concluded that consumption based methods measure GHG emissions more accurately. • Analysis of the extent of change between the proportion of indirect and direct emissions among different cities: Hillman & Ramaswami (2010) calculated GHG emissions for eight cities in the U.S., and the results show that the cross-boundary emissions are, on average, 47% higher than in-boundary emissions. Qi et al. (2018) explored direct and indirect greenhouse gas emissions for eleven years in Jinan, China, and the results showed that indirect emissions exceeded direct emissions. • Analysis of the extent of change in GHG emissions with population density among different cities with different urban forms: Heinonen et al. (2011) concluded that 10

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2010

2011

2012

2013

Heinonen

Chavez

Lin

2009

Larsen

Hillman

2008

No

No

No

No

No

No

Publication Sensitivity year analysis

Ramaswami

1st author

Hybrid-EIO-LCA

Consumption-based hybrid analysis

Tiered hybrid LCA

Hybrid life cycle-based trans-boundary

Tiered hybrid-LCA

Hybrid-based LCA demand centered

Method

Routt, CO: 31.9, Denver, CO: 29.9, Sarasota, FL: 29.7 (tCO2eq /person) 9.29 tCO2eq /person

14.7, 12, 11 tCO2eq

23.7 tCO2eq /person)

Top 20 sectors: 0.46 tCO2eq /person

25 tCO2eq /person/year

Results

1 case

3 cases

3 case

8 cases

Xiamen, China

Denver, Routt, and Sarasota

Helsinki downtown, Helsinki surrounding suburbs, and Finland

The U.S.

Trondheim, Norway

Denver, Co, USA

1 case

1 case

Location of the study

Number of case studies

Table 1: Case studies which are focused on Scope 3 emissions using hybrid analysis

Lin et al. (2013)

Chavez & Ramaswami (2013)

Heinonen et al. (2011)

Hillman & Ramaswami (2010)

Larsen & Hertwich (2009)

Ramaswami et al. (2008)

Sources

1st author 2014

Publication year

2018

2018

2017

2016

Jones

Heinonen

Fang

Qi

Martire

Method

Hybrid analysis

Tiered hybrid LCA

Hybrid model

Hybrid LCA

LCT a territorial oriented approach with consumption-oriented aspects

Results

Average: urban core cities= 40 tCO2eq , outlying suburbs 50 tCO2eq Town residents: 8.7 tCO2eq /person, Cities: 10.7 tCO2eq /person and Metropolitan: 11.9 tCO2eq /person

4.5, 11, 6.5 tCO2eq /person (in 2002-2007-2012) 4.13 GtCO2eq

1 case

31531 zip codes

Number of case studies

California, USA

The U.S.

Location of the study

Heinonen (2017)

Jones & Kammen (2014)

Sources

Martire et al. (2018)

Qi et al. (2018)

Fang et al. (2017)

Italy

Jinan, China

Guiyang, China

16 cases

1 case

1 case

Table 1: Case studies which are focused on Scope 3 emissions using hybrid analysis (continue) Sensitivity analysis Yes/ scaling

No

No

No

No

Vimercate: 150, Agrate Brianza: 170, Molgora: 30, Bellusco: 45, Mezzago: 25, Sulbiate: 20, Cornate dAdda: 50, Ronco Brianno: 20, Bornago: 50, Bussero: 42, Uboldo: 75, Molteno: 30, Bosisio Parini: 20, Carnate: 40, Annone: 25, Ornago: 40 (MCO2eq )

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an increase in GHG emissions due to higher standards of living outweighs the reduction in GHG emissions due to the denser population in urban areas. Therefore, energy reduction in buildings is more important than increasing the population density for overall reductions in GHG emissions. Heinonen (2017) concluded that increasing population density does not always result in decreasing CO2 emissions. Travel behaviors change the urban structure, which could lower transportation emissions. Also, urban structure alters the consumption patterns and the lifestyles of the inhabitants. Therefore, the increase in emissions due to new lifestyles might outweigh the reduction in emissions due to less travel. • Detailed analysis of indirect (Scope 3) emissions in a single case study: Ramaswami et al. (2008) discussed the system boundary for Scope 3 emissions and extended the system boundary by considering the emissions related to airline travel across cities. Lin et al. (2013) stated that Scope 3 emissions were neglected in various studies, although they contributed to a significant amount of emissions in other studies. They measured the carbon footprints in Xiamen, China in 2009, and concluded that Scope 1 and Scope 2 emissions were responsible for 66.14% of total emissions, and Scope 3 was responsible for 33.84% of total emissions in Xiamen. • Analysis of the effect of policymaking on GHG emissions within a specific time span: Fang et al. (2017) calculated greenhouse gas emissions from 2002 to 2012 in several cities in China, and investigated the effect of the circular economy on GHG emission changes in these cities. They concluded that GHG emissions were reduced in most of the cities because of the circular economy, and increased in a few cities, probably because of their highly advanced industry that out weighted reductions due to the circular economy. Among the cases discussed in this subsection, the following cross-cutting themes were found to be present as follows: • Sensitivity and uncertainty analysis: Ramaswami et al. (2008) considered sensitivity analysis of GHG emissions per capita to a 10% variation in the key modeled parameters: Qi et al. (2018) performed uncertainty analysis using the Monte Carlo method. Lu & Li (2019) used an error transfer formula to measure the uncertainty of the inventory. Other studies neglected sensitivity and uncertainty analysis, or the analysis was limited to the description of uncertain areas. • Emissions accounting: Fang et al. (2017) only considered CO2 emissions in their study: Lu & Li (2019) measured CO2 , CH4 , N2 O, and SF6 to evaluate the GHG emissions. Lin et al. (2013) measured CO2 , CH4 , N2 O, HF Cs, P F Cs and SF6 to evaluate the GHG emissions. Other studies reported final numbers for GHG emissions but did not mention the specific gas species considered in their studies. • Applicability of the method to other studies: Ramaswami et al. (2008) and Hillman & Ramaswami (2010) used the same method and reported that this method, hybrid-based demand centered LCA, applies to eight cities in the U.S., whose GHG emissions were quantified by the authors. Jones & Kammen (2014) used a combination of IO analysis and process analysis to measure GHG emissions in all U.S. zip codes, which shows applicability across the U.S. Heinonen et al. (2011) mentioned 13

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the applicability of their method, consumption based tiered hybrid LCA, toward their future research. Other studies disregarded the broader applicability of their methods.

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• Sector specific results of GHG emissions: All of the studies mentioned the GHG emissions associated with a set of sectors chosen by the authors except for Jones & Kammen (2014), which was a mass study of GHG emissions associated with U.S. cities, counties, and zip codes, so that sector-specific GHG emissions were outside the scope of their study.

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• Scaling GHG emissions toward metrics: Heinonen (2017) considered GHG emissions per capita and per household, Fang et al. (2017) mentioned GHG emissions per capita and GDP of the city, Lu & Li (2019) considered GHG emissions per capita and GDP of the city, and per energy use, Jones & Kammen (2014) considered GHG emissions per household. Other studies reported the GHG emissions per capita. 2.4. Case studies which are focused on Scope 3 emissions and use the IO analysis method

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420

Table 2 provides an overview of the case studies which focused on Scope 3 emissions as defined by the Greenhouse Gas Protocol and which have used the IO method. The objectives of the case studies in this subsection can be divided into five categories: • Meta-analysis between accounting systems, showing how consumption based or production based perspective can change the results in different cities: Feng et al. (2014) used a consumption based method to measure all of the CO2eq emissions along the production chain and at the place of final consumption. They analyzed four megacities in China (Shanghai, Beijing, Tianjin, and Chongqing) and showed that for CO2eq emissions related to goods, more than 48% of emissions in Chongqing and more than 70% of emissions in Beijing, Shanghai, and Tianjin occurred outside of the city boundary. Therefore, using production based methods misleads policymakers and governments by only reporting emissions released within the city boundary. Mi et al. (2019) used a production based approach to measure the emissions from fossil fuel combustion and industrial processes in eleven cities in Hebei province, China, and compared the results to a consumption based approach. They conclude that consumption based emissions are higher in import-dependent cities, and also, how emission-intensive are the products related to import or export. • Comparative analysis based on urban form and lifestyles: Ala-Mantila et al. (2013) used the environmentally extended input-output analysis (EEIOA) method to evaluate all of the emissions associated with high-rise (apartments) and low-rise (semidetached and detached housing) residential lifestyles in Finland. Their results show that low-rise lifestyles cause approximately 14% more emissions than highrise lifestyles. Hermannsson & McIntyre (2014) studied urban form and lifestyle connections with GHG emissions using three different classifications of types of urban form (metropolitan, urban, and semi-urban) in Finland. They concluded that more urbanized areas are associated with more GHG intensive lifestyles.

14

15

2013

2013

2013

2013

Heinonen

Chen

Wang

2011

Larsen

Vause

2010

Lenzen

2013

2010

Larsen

Dias

2006

No

No

No,

No

No

No

No

No

No

IO-SDA

Three-scale IO

IO-LCA

EEIO

EEIO

EEIO

MRIO

EEIO

EIO-LCA

Method

-

9 tCO2eq /person

HMA: 10.9, cities: 9.6, semi-urban: 9.500, rural: 8.9 tCO2eq /person

21.8 tCO2eq

9.49 tCO2eq /person

0.4-0.5 and 0.14-0.56 tCO2eq /person

Sydney: 77.4Melbourne: 91.3 tCO2eq / household

-

0.4 (low population density)- 0.6 (high population density) tCO2eq /person

Results

1 case for 10 years

1 case

3 regions

1 case

1 case

1 case for 6 years

2 cases

429 municipalities areas

2 cases

Number of case studies

Beijing, China

Beijing, China

Finland

Xiaman, china

Aveiro, Portugal

Sogn og Fjordane, Norway

Sydney & Melbourne, Australia

Norway

Toronto, Canada

Location of the study

Table 2: Case studies which are focused on Scope 3 emissions using the IO analysis

Publication Sensitivity year analysis

Norman

1st author

Wang et al. (2013)

Chen et al. (2013)

Heinonen et al. (2013)

Vause et al. (2013)

Dias et al. (2014)

Larsen & Hertwich (2011)

Lenzen & Peters (2010)

Larsen & Hertwich (2010a)

Norman et al. (2006)

Sources

1st author Ala-Mantila

Results

Low-rise: 14.83 high-rise: 12.98 tCO2eq /person Shanghai: 11.3, Beijing: 9.8, Tianjin: 10.0, Chongqing: 3.6 tCO2eq /person

Multi-scale, multi-region EIO

Melbourne: 23.66, Sydeny: 20.57 tCO2eq /person

-

Multi-scale, multi-region EIO

EEIO

Embodied GHG emissions were 13,201.31

8.355 (EEIOA) 8.408 (MFA) tCO2eq /person

ktCO2eq

39.2 tCO2eq /person

Perth: 31, Melbourne: 22, Adelaide: 19, Sydney: 19, Brisbane: 14 tCO2eq /person

3 regions EEIO

MRIO

EEIO

Method

Ala-Mantila et al. (2013)

Sources

Finland

Feng et al. (2014)

Location of the study 1 region

China

Chen et al. (2017)

4 cases

Australia

Wiedmann et al. (2016)

Hermannsson & McIntyre (2014)

5 cases

Melbourne, AUS

Meng et al. (2017)

The UK

1 case

Xiamen, China

Dias et al. (2017)

3 regions

1 case

Aveiro, Portugal

Mi et al. (2019)

Chen et al. (2016)

1 case

China

Australia

11 cases

2 cases

Number of case studies

Table 2: Case studies which are focused on Scope 3 emissions using the IO analysis (continue)

No

Publication Sensitivity year analysis 2013

No

No

2014

2014

Feng

Hermannsson

No

No

No

2015

2016

2016

Chen

Chen

Wiedmann

EEIOA vs. MFA

EIOA-LCA

No

EEIOA and MFA

No

2017

No

2017

Dias

2019

Meng

Mi

8.355 (EEIOA) 8.408 (MFA) tCO2eq /person

16

425

430

435

440

445

450

455

460

465

• Meta-analysis of cities to reveal correlations among GHG emissions and socioconomic metrics: Larsen & Hertwich (2010a) measured CO2eq emissions of 429 Norwegian municipalities and found that the size and wealth of municipalities affect the amount of CO2eq emissions associated with them. They concluded that small and/or wealthy municipalities have higher CO2eq per capita in comparison to dense and/or less wealthy cities. • Development of a new model or method to calculate or visualize GHG emissions at urban scale: Lenzen & Peters (2010) modeled indirect greenhouse gas emissions, water usage, monetary and employment consequences of household consumption in Melbourne and Sydney, Australia. They also visualized how a household in Melbourne or Sydney, directly and indirectly, affects the environment across Australia. Dias et al. (2014) adopted a method to compensate for the lack of data for local household expenditures. This method can also be used for other areas with missing data for local household expenditures. Chen et al. (2013) developed a three-scale input-output model to measure GHG emissions for Beijing, China, in 2007. This model measured GHG emission at three different levels, the urban, domestic and international systems, which have different emission intensities because of different industrial structures and technical levels. Wiedmann et al. (2016) provided an accurate, comparable, comprehensive, and complete conceptual framework to account for emissions within local, regional, national, and global origins. Chen et al. (2016) developed a multi-scale multi-region (i.e., state, country, and rest of the world regions) EIO model to analyze GHG emissions of the Melbourne and Sydney metropolitan areas. As multi-scale multi-region EIO model improves the data availability, this method can be relevant for other cities and regions in Australia. • Comparative analysis of GHG emissions by sector : Larsen & Hertwich (2011) evaluated the GHG emissions associated with activities in the county of Sogn og Fjordane, Norway. Results showed that the purchase of services from private sectors contributed to the most significant fraction of emissions for the services provided. Meng et al. (2017) performed an input-output LCA for direct and indirect emissions and emphasized that the most significant amount of embodied emissions are found in the upstream supply chain. Larsen & Hertwich (2010b) focused on implementing a CF tool in Norway to reduce GHG emissions related to the provision of services by local governments. They emphasized that reducing emissions in energy, transportation, and waste sectors do not require a CF analysis. CF tool is valuable when sectors related to Scope 3 emissions (e.g., provision of services) are potential targets to reduce GHG emissions at urban scale, as conventional tools neglect Scope 3 emissions. Wang et al. (2013) analyzed the driving forces that increased the CO2eq emissions in Beijing were production structure change and population growth. Major drivers that lowered the CO2eq emissions were declining CO2eq emissions intensity and reduced final demand volume per capita. Among the cases discussed in this subsection, the following cross-cutting themes were found to be present as follows: • Sensitivity and uncertainty analysis: Dias et al. (2014) performed three scenarios to evaluate the sensitivity of GHG emissions to evaluate the influence of using 17

different estimates of household expenditure. All other studies in this subsection neglected sensitivity and uncertainty analysis. 470

475

• Emissions accounting: Feng et al. (2014), Vause et al. (2013), Chen et al. (2013), Wang et al. (2013), Hermannsson & McIntyre (2014), and Mi et al. (2019) considered only CO2 emissions and did not evaluate other GHG emissions. Wiedmann et al. (2016) considered three greenhouse gases: CO2 , CH4 , and N2 O for accounting for GHG emissions. All the other studies in this subsection reported the results for GHG emissions and did not mention the specific gas species considered in calculating CO2eq . • Applicability of the method to other studies: Chen et al. (2013) and Larsen & Hertwich (2010a) mentioned that their methods are not site-specific and can be applied to other cities. Other studies disregarded the applicability of their methods.

480

485

490

495

• Sector specific results of GHG emissions: all of the studies discussed GHG emissions associated with a set of sectors chosen by the authors except for Larsen & Hertwich (2011), which was a mass study of GHG emissions associated with Norwegian municipalities, and Lenzen & Peters (2010), which focused on providing maps for GHG emissions distribution across Australia. • Scaling GHG emissions toward metrics: Mi et al. (2019) scaled the results toward the area and GDP of the cities, and Larsen & Hertwich (2010a) focused on the metrics and their correlations toward GHG emissions. They considered per person, wealth (available fund indicator), quality of education (number of pupils per computer), average temperature, and school points. Vause et al. (2013) reported the results per Chinese Yuan. Ala-Mantila et al. (2013) reported the results per person and GDP of the cities. Chen et al. (2013), Wang et al. (2013), and Larsen & Hertwich (2010b) reported the results of GHG emissions without any kind of averaging or scaling factors. Other studies reported the GHG emissions per capita. • Use of IO tables: Among the case studies which used IO analysis, ten cases used national IO tables and seven cases used multi-region IO tables. Using national IO tables means that economic interdependence between different regions within the same country is neglected (Mi et al., 2018) and an average number for the country is considered. 3. Scaling GHG emissions toward metrics

500

505

Quantitative results were shown in Tables 1 and 2 in Section 2. Showing how GHG emissions scale with respect to relevant metrics would help with visualizing the results, seeing how GHG emissions change with the selected metrics (i.e., GDP, population, and population density), and illustrating any correlation between the GHG emissions and the metrics. Also, the relative proportions of direct and indirect emissions are shown in the bar charts in this section. Figure 8 shows the share of embodied emissions in non-energy sectors (Scope 3), and Figure 9 shows the share of direct emissions (Scope 1) in total GHG emissions. These two figures illustrate the range of Scope 1 emissions (i.e., from 8% to 47%) and Scope 3 emissions (27% to 71%). Future researchers may find it helpful 18

510

515

520

525

530

535

540

545

550

555

to know the share of Scope 1 and Scope 3 emissions in total GHG emissions at urban scale in the literature. The GDPs and population sizes of case studies were gathered from The World Bank’s web page, and the GDPs of different states in the U.S. were taken from the U.S. Bureau of Economic Analysis (BEA). Dots of the same color denote case studies that belong to the same country. The correlation of GHG emissions with GDP and with population density for China, Norway, and the U.S. individually are shown in the Appendix. It should be noted that all of the case studies included used consumption-based accounting and considered Scope 3 emissions. The results from the studies mentioned in Table 1, scaling tCO2 -eq/capita to the GDP, population, and population density (where the case studies have considered Scope 3 emissions using hybrid analysis) are shown in Figures 2, 4, and 6. The results from the studies mentioned in Table 2, scaling tCO2 -eq/capita to the GDP and population (where the case studies have considered Scope 3 emissions using input-output analysis) are shown in Figures 3, 5, and 7. The average GHG emissions per capita for cases in China is 18.5 tCO2 -eq/capita, and for cases in Norway is 0.34 tCO2 -eq/capita. Figure 2 shows GDP with a range of 37k to 53k ($/capita) and GHG emissions with a range of 15 to 32 tCO2 -eq/capita in the U.S. There is one case in Helsinki, Finland with a GDP of 51k ($/capita) and GHG emissions of 15 tCO2 -eq/capita. In China, GDP ranges from 1k to 16k ($/capita) and GHG emissions are 4 to 57 tCO2 -eq/capita. The average GHG emissions per capita for U.S. cases is 25.14 tCO2 -eq/capita and for China is 17.93 tCO2 -eq/capita. Figure 3 shows GDP with a range of 58k ($/capita) and GHG emissions with a range of 14 to 31 tCO2 -eq/capita in Australia. In Norway, GDP ranges from 43k to 103k ($/capita), and GHG emissions are 0.14 to 0.53 tCO2 -eq/capita. In China, GDP ranges from 2k to 16k ($/capita), and GHG emissions are 3.6 to 102 tCO2 -eq/capita. Figure 4 shows population with a range of 700 to 900 thousands of people and GHG emissions with a range of 15 to 32 tCO2 -eq/capita in the U.S. There is one case in Helsinki, Finland with a population of 591 thousand people and GHG emissions of 15 tCO2 -eq/capita. In China, population ranges from 700 to 1650 thousands of people and GHG emissions are 4 to 57 tCO2 -eq/capita. Figure 5 shows population with a range of 1.3 to 4.3 millions of people and GHG emissions with a range of 14 to 31 tCO2 -eq/capita in Australia. In Norway, population ranges from 75 to 518 thousands of people and GHG emissions are 0.14 to 0.53 tCO2 eq/capita. In China, population density ranges from 1 to 28 millions of people and GHG emissions are 3.6 to 102 tCO2 -eq/capita. Figure 6 shows population density with a range of 811 to 3400 people/m2 and GHG emissions with a range of 15 to 32 tCO2 -eq/capita in the U.S. There is one case in Helsinki, Finland with a population of 2770 people/m2 and GHG emissions of 15 tCO2 eq/capita. In China, population density ranges from 74 to 2000 person/m2 and GHG emissions are 4 to 57 tCO2 -eq/capita. Figure 7 shows population density with a range of 150 to 430 people/m2 and GHG emissions with a range of 14 to 31 tCO2 -eq/capita in Australia. In Norway, population density ranges from 1 to 333 person/m2 and GHG emissions are 0.14 to 0.53 tCO2 eq/capita. In China, population density ranges from 75 to 3000 person/m2 and GHG emissions are 3.6 to 102 tCO2 -eq/capita. 19

560

565

Figure 2: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to GDP ($) for the hybrid analysis method

Figure 3: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to GDP ($) for the IO analysis method

Figure 4: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to population for the hybrid analysis method

Figure 5: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to population for the IO analysis method

Figure 6: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to population density for the hybrid analysis method

Figure 7: Scaling Scope 3 GHG emissions (tCO2 -eq/capita) to population density for the IO analysis method

These figures illustrate that GHG emissions per capita are unique to a given country rather than changing with GDP, population, and population density similarly across different countries. As mentioned by Kennedy et al. (2009), GHG emissions in cities differ by geophysical factors (climate, access to resources, and gateway status) and technical factors (power generation, urban design, and waste processing), and the similarity between cities within the same country is more influential here than the effects of GDP (economy) or population/ population density (city size), although these factors are known to be influential in LCA results at urban scale within the same country (Jones & Kammen (2014), Larsen & Hertwich (2010a), Minx et al. (2013), and Ramaswami & Chavez (2013)). Figure 8 shows the proportion of GHG emissions made up of the aggregation of Scope 1 and Scope 2 GHG emissions, and the proportion of Scope 3 GHG emissions, for cities analyzed in the studies reviewed here. The Helsinki suburb and Helsinki downtown re20

Helsinki suburbs Helsinki downtown Xiamen City, China Austin, TX Minniapolice, MN Seattle, WA Scope 1 + 2 Scope 3

Portland, OR Arvada, CO

Fort Collins, CO Boulder, CO Denver, CO 0

5

10 15 20 GHG emissions (tCO2-eq/capita)

25

30

Figure 8: Proportion of GHG emissions between Scope 1+2 (direct and energy-related indirect emissions) and Scope 3 (embodied emissions in non-energy sectors) emissions.

570

575

580

585

sults were extracted from Heinonen et al. (2011), in which they measured GHG emissions for all scopes. Their results are used here to report the proportion of GHG emissions based on these scopes as shown in Figures 8 and 9. The percentage of Scope 3 GHG emissions as a part of the total GHG emissions for Helsinki suburbs was 65% and for Helsinki downtown was 71%. The Xiamen, China results were gathered from Lin et al. (2013). They reported the GHG emissions for Scopes 1+2 and for Scope 3 emissions. The percentage of Scope 3 GHG emissions as a part of the total GHG emissions in Xiamen, China was 35%. The results for eight cities in the U.S. were taken from Hillman & Ramaswami (2010), in which they provided GHG emissions for in-boundary (Scopes 1+2) and cross-boundary contributions. The percentage of Scope 3 GHG emissions as a part of the total GHG emissions of these eight cities ranged from 27% (Fort Collins, Colorado, USA) to 47% (Arvada, Colorado, USA). Figure 9 shows the proportion of GHG emissions made up of Scope 1 GHG emissions, and the proportion made up of the aggregation of Scope 2 and Scope 3 GHG emissions. Trondheim (Larsen & Hertwich, 2009), Jinan (Qi et al., 2018), and 3 cases for Guiyang (Fang et al., 2017) were provided with the direct (Scope 1) and indirect (Scopes 2+3) GHG emissions. The percentage of Scope 1 GHG emissions as a part of the total GHG emissions of Trondheim was 8%, Jinan was 46%, and of the 3 cases for Guiyang were 16%, 25%, and 18% in 2012, 2007 and 2002.

21

Helsinki suburbs

Helsinki downtown

Trondheim, Norway

Jinan, China Scope 1 Scope 2+3 Guiyang, China 2012

Guiyang, China 2007

Guiyang, China 2002 0

5

10 15 20 25 30 GHG emissions (tCO2-eq/capita)

35

40

Figure 9: Proportion of GHG emissions between Scope 1 (i.e., direct emissions) and Scope 2+3 (indirect emissions).

4. Limitations, gaps, and related problems

590

595

600

605

Scaling of GHG emissions been presented in several papers (Larsen & Hertwich (2010a), Ramaswami & Chavez (2013), Minx et al. (2013), and Jones & Kammen (2014)). In each of these studies the researchers analyzed a set of case studies and they showed a correlation between GHG emissions and the chosen metrics. In this paper, a specific system boundary (i.e., the scope of emissions) and method (i.e., IO and hybrid analysis) are used to align the results of case studies more effectively. However, when the results include different research studies, the relationships are less clear (as can be seen in Figures 2, 3, 4, 5, 6, and 7). Therefore, there are gaps in comparing case studies which have worked on GHG emissions at the urban scale. Some of these gaps, limitations, and discussion topics are related to quantifying GHG emissions accurately and consistently at the urban scale, and are described further in this section. 4.1. Population density Jones & Kammen (2014) posed the question of how population density affects GHG emissions at the urban scale. They analyzed over 30,000 municipal districts in the U.S. based on postal zip codes. They concluded that there were lower household carbon footprints within downtown areas and higher household carbon footprints in less urbanized areas and suburbs. They used a hybrid life cycle assessment method and estimated household energy, transportation, consumption of goods and services, and total household CF. By using a comprehensive and consumption based LCA approach, Heinonen et al. (2011) concluded that increasing urban density might result in higher carbon footprints 22

610

615

620

625

630

due to higher living standards in dense cities. In another study, Heinonen (2017) stated conclusively that a higher population density did result in higher carbon footprints. Also, they noted that the common belief that increasing population density is more sustainable results from an incomplete system boundary and the exclusion of embodied emissions. Sovacool & Brown (2010) found that when heat islands exist in urban areas with high population densities, the excess heat increases the need for air-conditioning; therefore, the urbanized areas consume more energy and produce more emissions. Conversely, Dodman (2009) found that increased population density results in more compact buildings; therefore, energy consumption decreases with high population density (for instance, in New York). The results in existing literature, and those of this paper (Figures 6 and 7), show different effects of population density and how it may decrease or increase the GHG emissions accounted for and attributed to a city. Because there is disagreement in the literature on how the magnitude of GHG emissions in cities will change with population density, more research is needed to meaningfully connect population density with carbon footprints. It should be noted that the results mentioned in literature were focused on different cities in one country, and this paper collected case studies from various countries. Therefore, GHG emissions versus population density for China, the U.S., and Norway are shown in Figures 13, 14, and 15 in the Appendix. 4.2. System boundary Two limitations here are associated with the system boundary: 1. A researcher who analyzes GHG emissions at the urban scale may state the system boundary of the study in more than way, using:

635

(a) Scope 1, 2, and 3 emissions (i.e., a boundary can be inferred based on the extent to which direct and indirect emissions have been measured in the study). (b) Accounting systems such as those mentioned by Larsen & Hertwich (2009): CBA, PBA, MBA, and CSA (i.e., a boundary can be known according to the details of each of these methods as they are formally defined). 2. Completeness or incompleteness of the system boundary will affect the quantitative results.

640

645

650

Fang et al. (2017) focused on the system boundary issue and calculated the CBA and PBA emissions for three different years in Guiyang, China, and showed that the quantity of emissions changes by < 1% if CBA or PBA is used. Their production/consumption based carbon emission ratios in 2002 for five sectors were as follows: Agriculture and Forestry: 0.85, Industry: 1.76, Construction: 6.59, Transportation, Storage and Postal Service : 1.48, and Wholesale and Retail: 3.03. Caro et al. (2015) compared the GHG emissions in Luxembourg over 14 years by using CBA, and PBA approaches. Results showed that using CBA led to higher tCO2 emissions compared with using PBA. For example, CBA resulted in an estimated 25 tCO2 emissions while PBA resulted in an estimated 10 tCO2 emissions in 2009. Ramaswami & Chavez (2013) made a comparison between Scope 1, 2, and 3 GHG emissions and stated that the change in the scope (system boundary) of the study also affected the choice of suitable metrics for uncovering relationships with the urban composition. 23

4.3. LCA Methods 655

660

Each method suffers from limitations and disadvantages. Suh & Huppes (2005) analyzed the limitations of different LCA methods (process analysis, IO analysis, and hybrid analysis) in three parts: • Data requirements: process-based analysis requires more process-specific information, but the IO analysis requires less data (when the IO inventory is already available). • System boundary: Methods that use the IO analysis show more completeness. • Simplicity/complexity: The IO based and the integrated hybrid analysis are more complex as they both require an IO assessment.

665

Islam et al. (2016) summarized the limitations of three LCA methods, as shown in Table 3. 4.4. Only specific countries have worked on GHG emissions at the urban scale

670

675

An analysis of the figures in Section 3 shows that only a few countries have quantified GHG emissions at urban scale. Specifically, China, Australia, Norway, Finland, and the U.S. have measured GHG emissions at the city scale. Note that these are mostly developed countries, and therefore, the quantifiable results and possible sustainability solutions may be limited to their types of economy, social progress, and wealth distribution. The lack of studies in other parts of the world, and in less developed countries is another limitation of evaluating GHG emissions at the urban scale, particularly when recognizing that much of the urban growth worldwide will occur in Africa and Asia (Burdett & Cities, 2015). 4.5. Limitations in practical sustainable solutions

680

685

690

Cities play an important role in sustainability due to their magnitude and the geographical scope of their impacts (Albert´ı et al., 2019). Although LCA methods and sustainability at the urban scale are analyzed in existing literature (Albert´ı et al., 2017) and conceptual requirements for sustainability at the urban scale are available (Mori & Christodoulou, 2012), none of the case studies discussed in this paper quantitatively analyzed this type of sustainability index. These case studies provided sustainable solutions in five ways: • Evaluating the direct and indirect emissions in one city using one specific method: (Chen et al. (2013), Dias et al. (2014), Fang et al. (2017), Guo et al. (2012), Heinonen (2017), Larsen & Hertwich (2011), Lin et al. (2013), Lu & Li (2019), Meng et al. (2017), Qi et al. (2018), Ramaswami et al. (2008), Vause et al. (2013), Wiedmann et al. (2016), and Zhou et al. (2010)). The outcomes of these studies showed the sectors (e.g. industry, construction, transportation, etc.) found responsible for the most significant proportion of total emissions. Therefore, policy makers should focus on those sectors to reduce urban emissions and increase sustainability. 24

Table 3: Limitations of three basic LCA methods (Islam et al., 2016) Method Process Flow Diagram

Limitations • Not suitable for larger system/multiple input/output/recycling. • Time consuming for huge data • Truncation error

Matrix method

• Time consuming for huge data • Truncation error • Mathematical expertise required

IO LCI

• Not covering entire life cycle; • Lack in necessary level of detail • Data uncertainty • Out of date/coarse data • Not suitable for import/export

Tiered Hybrid

• Double counting • Interaction between the process based and the IO based is not assessed • Lack of dynamic representations

IO based Hybrid

• Disaggregating of the IO table complex • Uncertainty is higher due to not updated the IO data and lack of newer technologies information • Recurring flows between the main system and use and end-of-life phase are not properly described • Misleading results in case of imports

Integrated Hybrid

• Complexity of use • High data requirement • Time consuming • Double counting

25

695

• Measuring the direct and indirect emissions in one city/country over several years: (Bi et al. (2011), Caro et al. (2015), Druckman & Jackson (2009), Larsen & Hertwich (2011), and Wang et al. (2013)).

700

The results of these studies provide valuable analysis on why certain years produced less emissions, and how that year was different. As the method, data, and system boundary were constant for the study, investigating the reasons behind this change could show whether new regulations, restrictions, and policies have worked to increase sustainability and decrease GHG emissions.

705

710

• Measuring the direct and indirect emissions for several cities using one method: (Ala-Mantila et al. (2013), Chavez & Ramaswami (2013), Feng et al. (2014), Hermannsson & McIntyre (2014), Heinonen et al. (2013), Heinonen et al. (2011), Hillman & Ramaswami (2010), Jones & Kammen (2014), Larsen & Hertwich (2010a), Lenzen & Peters (2010), Markolf et al. (2017), Mi et al. (2019), Minx et al. (2013), Paloheimo & Salmi (2013), and Ramaswami & Chavez (2013)). The results of these studies provide valuable comparisons between the emissions of different cities and provide possible reasons for the differences between results. These reasons could illuminate what steps have been taken by cities who have reduced or kept their GHG emissions at a low level, and how similar results might be possible in other cities. • Using different methods for estimating the direct and indirect emissions for one city: (Caro et al. (2015), Chavez & Ramaswami (2013), Feng et al. (2014), and Mi et al. (2019)).

715

720

725

Using different methods, and showing how they resulted in different outcomes, was discussed in Subsection 4.3. Some studies have worked on finding the reasons behind these differences by calculating emissions for one city (i.e., constant urban form, population, income, wealth, education level, and lifestyles) using multiple methods to examine how methods affect results. These studies help other researchers and policymakers come up with structured methodologies for cities while taking specific characteristics of a particular urban area into account. • Using a different method for estimating the direct and indirect emissions for various cities/ regions (i.e. cities with different urban forms, consumption or production behaviors, income, location, level of education, wealth, etc.): (Norman et al. (2006) and Dias et al. (2017)) The results from these studies showed how quantity of emissions changed from city to city. It should be noted that the relevant characteristics of cities will not be the same; conclusions should be viewed with careful attention to the circumstances surrounding any resulting recommendations.

730

4.6. limitations in selected metrics GHG emissions of case studies have been correlated toward three metrics: GDP (representing economic activity), population (representing city size), and population density (representing city density), but no clear connection between any of these three metrics 26

735

with GHG emissions were found when case studies were compared among different countries. That is, the impact of being within a given country was much more pronounced than any effect these three simple metrics had between different countries. Each of these metrics can be revised to correlate the results more effectively: • GDP:

740

745

Nominal GDP at exchange rate is the economic indicator frequently used in the existing literature (e.g., Mi et al. (2019), Ala-Mantila et al. (2013), Fang et al. (2017), and Lu & Li (2019)). For future research, another type of GDP, GDP at purchasing power parity (PPP), is suggested to scale the results. As the case studies considered here measure consumption based GHG emissions, the purchasing power of people within the city (as represented by GDP PPP) is hypothesized to be more closely related to consumption behavior of residents. • Population and population density:

750

755

Population and population density are widely used metrics that are defined based on a system boundary. One problem with comparing results using population and population density from different urban areas is that the city boundary may be defined differently by different agencies that release demographic data for cities, and the city boundary may also be drawn separately by researchers who measure life cycle GHG emissions. Therefore, considering other economic metrics (like GDP PPP) and defining identical system boundaries for the same city when data is gathered from different sources would help to provide consistent results that can be meaningfully combined with other studies to aid decision making in that particular city. 5. Conclusion

760

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In this paper, commonly used methods for evaluating GHG emissions using LCA were defined, methods used for carbon footprint at the urban scale were described, and the results of a literature review consisting of studies that analyzed consumption based approaches to quantifying direct and indirect emissions using either hybrid life cycle assessment or input-output life cycle assessment at urban scale were presented. The case studies considered were compared and contrasted and the quantitative results were scaled based on nominal GDP, urban population, and population density. Gaps and limitations were also identified related to the use of LCA methods for quantifying emissions at the urban scale with the goal of reducing GHG emissions at the city level. The system boundary of the study and the extent to which upstream emissions have been considered in analyzing the results are two important parameters affecting the quantity of GHG emissions in these case studies. As shown in Figure 8, embodied emissions related to non-energy processes and products (Scope 3) are responsible for 27% to 71% of total GHG emissions in selected case studies. These varying proportions illustrate the importance of including the embodied emissions related to non-energy processes and products in making decisions and setting policies for reducing GHG emissions at urban scale. As shown in Figure 9, direct emissions (Scope 1) ranged from 8% to 46% of GHG 27

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emissions, and by limiting considerations to only direct emissions, more than half of a city’s GHG emissions would be neglected. This study shows that policymakers and city planners should pay particular attention to the subject of population density and how it would affect the greenhouse gas emissions of cities, mainly when rapid urbanization is occurring. The quantitative results from case studies in the literature disagree on how increasing population density would change the amount of greenhouse gas emissions, and a number of other factors (e.g., economic, sociocultural, political, lifestyles, consumption behaviors) are likely to mitigate the scaling effects of life cycle GHG emissions for cities. This indicates that there are complexities in the effects of population density on GHG emissions that cannot be captured with simple metrics using the existing body of work. As quantitative results were scaled toward GDP, population density, and population, available data were concentrated in some specific regions, which shows that a few countries have worked more on this issue. More investigations in developing countries and other parts of the world could illuminate how greenhouse gas emissions are changing with respect to these metrics outside of the largely developed countries where these more extensive studies have been conducted. A critical research gap exists due to the need for a definition of an urban boundary, affecting the results of all studies considered here, and possibly slowing research progress in this area going forward. As mentioned by Mirabella et al. (2018), a proper definition of city is not available yet, and defining its boundaries and functions is even more challenging. Thus, an agreed-upon protocol which concisely defines the city boundary is needed (LCA Phase 1). The inclusion of embodied emissions in non-energy processes and products (Scope 3) needs to include a procedure to specifically list the sectors included. After specifying the city boundary and the extent to which indirect emissions are calculated, a proper GHG inventory can be chosen (LCA Phase 2). Governments should publish aggregated data and information for researchers whenever possible to help with the process of calculating GHG inventories for a wider range of locations worldwide. Life cycle impact assessment (LCA Phase 3) at urban scale is used in few studies among those reviewed that provided a thorough analysis of greenhouse gas emissions. It is essential that cities understand how human health, natural resources, and the natural environment would change as GHG emissions are released to the atmosphere in order to set regulations and make policies that benefit the people of the city. Finally, because of these issues with LCA Phases 1–3, Phase 4 (interpretation of results) cannot be performed effectively to assist with emissions reduction policymaking and planning. The issues mentioned above related to Phases 1–3 must be addressed, and ideally these solutions would be formalized in an agreed-upon protocol, so that results between between case studies conducted by different researchers that account for LCA GHG emissions at urban scale would be more readily comparable. Funding

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GHG emissions of case studies for three countries (China, the U.S., and Norway) are compared here with GDP ($) and population density (persons/m2 ). The curve fitting Tool in Matlab R2013a was used and first or second degree polynomial correlation with robust bisquare analysis was performed.

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Figure 10: Correlation of GHG emissions toward GDP for cities in China. R-square= 0.80

Figure 11: Correlation of GHG emissions toward GDP for cities in the U.S. R-square= 0.34

Figure 12: Correlation of GHG emissions toward GDP for cities in Norway. R-square= 0.80

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Figure 13: Correlation of GHG emissions toward population density for cities in China. R-square= 0.81

Figure 14: Correlation of GHG emissions toward population density for cities in the U.S. R-square= 0.21

Figure 15: Correlation of GHG emissions toward population density for cities in Norway. R-square= 0.50

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Highlights • Life cycle greenhouse gas (GHG) emissions of cities have been analyzed using two life cycle methods. • Studies quantifying (Scope 1) and indirect (Scope 2, Scope 3) emissions at urban scale were reviewed. • Direct emissions accounted for 8% to 47% of GHG emissions among case studies reviewed. • GHG emissions of cities were not found to consistently scale with GDP or population. • Gaps and limitations in quantifying GHG emissions at urban scale are discussed.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: