Application of life cycle assessment in municipal solid waste management: A worldwide critical review

Application of life cycle assessment in municipal solid waste management: A worldwide critical review

Accepted Manuscript Application of Life Cycle Assessment in Municipal Solid Waste Management: A Worldwide Critical Review Harshit Khandelwal, Hiya Dh...

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Accepted Manuscript Application of Life Cycle Assessment in Municipal Solid Waste Management: A Worldwide Critical Review

Harshit Khandelwal, Hiya Dhar, Arun Kumar Thalla, Sunil Kumar PII:

S0959-6526(18)33259-1

DOI:

10.1016/j.jclepro.2018.10.233

Reference:

JCLP 14635

To appear in:

Journal of Cleaner Production

Received Date:

15 June 2018

Accepted Date:

22 October 2018

Please cite this article as: Harshit Khandelwal, Hiya Dhar, Arun Kumar Thalla, Sunil Kumar, Application of Life Cycle Assessment in Municipal Solid Waste Management: A Worldwide Critical Review, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.10.233

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ACCEPTED MANUSCRIPT

Total word count: 4482

Application of Life Cycle Assessment in Municipal Solid Waste Management: A Worldwide Critical Review Harshit Khandelwala, b, Hiya Dharb, Arun Kumar Thallaa, Sunil Kumarb,* aNational

Institute of Technology Karnataka, Surathkal, Mangluru 575 025, Karnataka, India

bCSIR-National

Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur 440 020, Maharashtra, India *Corresponding author: [email protected]

Abstract The whopping increase in solid waste generation all over the world calls for the development of waste management strategies for a sustainable environment. By the quantification of environmental impacts, life cycle assessment (LCA) tool can help in answering the call. It evaluates the environmental performance of municipal solid waste management (MSWM) system which helps decision-maker in selecting the best management strategy with minimum impacts on the environment. But, up to what extent the LCA methodology can be applied to MSWM systems? To address this question, the present study analyzed the 153 LCA studies published till date since 2013 all over the world. The present study analyzed the time evolution, geographical distribution, and methodology applied in LCA studies. It summarized the use of the functional unit, LCA model, Life Cycle Impact Assessment (LCIA) method, MSWM options, and the critical findings of the selected LCAs, along with MSW composition, income group, and the gaps in the application of the studies. For evaluating the dependence of publication of studies and country’s economic condition, the countries in which LCA studies were conducted are classified into four groups on the basis of income level viz., lower income, lower middle income, upper middle income, and higher income countries. In terms of technological coverage, 1 ton of MSW was the most used functional unit. SimaPro was the majorly used LCA model while 56 of the total studies didn’t mention about the use of LCA 1

ACCEPTED MANUSCRIPT model, only 66 of the total studies included sensitivity analysis in the assessment. Integrated solid waste management was found to be the most preferred waste management option. Also, a very limited number of studies have included life cycle costing and social aspects of MSWM system. The results indicated that the majority of the LCA studies are based in Europe and Asia. Shockingly, 178 out of the total countries in the world have not published a single LCA study on MSWM since 2013. Also, it was found that the effect of increasing Gross Domestic Product (GDP) on the publication of LCA studies is irrelevant, possible reasons being the lack of data, time and economic constraints. Establishment of environment-friendly policies and initiatives by the Government along with the participation of public, non-government and private organizations through training courses and seminars might help in improving the LCA applicability in the field of MSWM. Keywords: Life cycle assessment, municipal solid waste management, waste treatment, waste disposal, LCA, LCI.

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ACCEPTED MANUSCRIPT 1.

Introduction At present, the world’s total urban population is approximately 4.028 billion and is

increasing tremendously at a rate of 2.035% yearly (World Bank, 2016a, 2016b). This whopping increase in population leads to rapid industrialization, urbanization, and economic growth, which are the main factors of increased municipal solid waste (MSW) generation worldwide. The waste generated by urban residents is expected to get almost doubled from 3.5 million metric tons/day in 2002 to 6.1 million metric tons/day in 2025, and a total of $375 billion will be spent for its management in 2025 (World Bank, 2012). The MSW composition and its generation rate solely depend on the social, economic and environmental conditions of a country. The rate of MSW generation is directly proportional to the increase in the income level of the countries, i.e., countries with higher GDP produces more waste with a higher proportion of paper and packaging waste while countries with lower GDP produces more biodegradable waste (Shekdar, 2009). Improper management of MSW through open burning, open dumping and unsanitary landfilling contributes to many environmental problems, such as global warming, ozone depletion, human health hazards, ecosystem damages, abiotic resource depletion, etc. (Laurent et al., 2014). This further leads to a lack of public approval for new waste management sites. Also, the decision-making in MSWM industry requires an assessment to minimize the hazards associated with all the afore-mentioned impacts. Life cycle assessment (LCA) is a computer-based tool used to assess the environmental burden as well as benefits associated with a product or service throughout its life cycle, i.e., back to the raw materials acquisition and down to it disposal (Klopffer, 1997). In waste management, LCA studies the potential impacts of waste’s life cycle (starting from its generation till its disposal) on the environment. The popularity of LCA for analyzing the MSW management (MSWM) systems has been illustrated in numerous published studies. Also, over the past few decades, many organizations, such as the International Organization of Standards (ISO) have contributed to the development of the LCA methodology (Cleary, 2009). ISO 14040 series has defined the LCA methodology into four phases, i.e., goal and scope definition, inventory analysis, impact assessment and interpretations, and have also revised the requirements and guidelines for LCA in 2006 (Cleary, 2009). A large number of review studies have been conducted for the investigation of the application of LCA in the field of MSWM. But, they mainly focus on a specific waste or waste treatment systems. For example, Villanueva 3

ACCEPTED MANUSCRIPT and Wenzel (2007) analyzed nine LCA studies focusing on the management of paper and cardboard wastes; Cleary (2009) reviewed the methodological conducts and outcomes of 20 studies addressing the management of MSW; Finnveden et al., (2009) comprehensively analyzed the methodological development in LCA studies and differences between the attributional and consequential LCA methodology; Lazarevic et al., (2010) reviewed ten studies focusing on the LCA of post-consumer plastic wastes specifically for Europe; Gentil et al., (2010) and Bjorklund et al., (2010) compared the models of LCA applied to MSWM; Michaud et al., (2010) critically reviewed the outcomes of 55 LCA studies focusing on recycling; Bernstad and la Cour Jansen (2012) analyzed 25 LCA studies on the bio-waste management; Morris et al., (2013) conducted a meta-analysis of 82 LCA studies for the assessment of the management of organic wastes; Othman et al., (2013) reviewed various LCA studies conducted in seven Asian countries; Laurent et al., (2014a) and 2014 b, analyzed 222 LCA studies published till 2012, related to various solid waste and also compared the LCA studies with the requirements of ISO standards and International Reference Life Cycle Data System (ILCD); Allesch and Brunner, (2014) provided the guidelines for the selection of proper evaluation methods for assessment of MSWM; Yadav and Samadder (2017), reviewed 30 LCA studies form 26 countries on the basis of income distribution; Yadav and Samadder, (2018) compared 91 LCA studies published in between 2006 and 2017 on MSWM in Asian countries. But, none of the studies has covered all the LCA studies on MSWM for the entire world in the recent years. There isn’t any published comprehensive study covering the entire scope of LCA of MSWM all over the world after Laurent et al., (2014). The present study aims at the distribution of LCA on MSWM all over the world. The study critically examines the methodology and the key findings of the LCA studies published since 2013, considering the gaps and incorporation of social and economic LCA in the selected studies. This critical review will help the LCA practitioners to understand the recently published LCA studies in the field of MSWM all over the world.

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ACCEPTED MANUSCRIPT 2.

Methodological Framework

2.1. Selection Criteria of LCA Studies A total of 153 LCA studies on MSWM system published since 2013 were used for the comparative analysis. The study focused on LCA studies related to MSWM system covering MSW generated from residential and/or commercial (non-residential) areas, excluding LCA studies on the management of construction and demolition waste, hazardous and E-waste, and sewage sludge. The identification and selection of the studies were based on the keyword search of “life cycle assessment of municipal solid waste” on web sources, such as Google Scholar and SCOPUS, and also from the references of the published LCA studies of MSW. 2.2. Review Scheme Numerous elements were reviewed for each of the identified studies: (1) mapping of study area, (2) MSW composition and management options, (3) evolution of LCA studies, (4) functional unit, (5) system boundary, (6) waste management system assessed, (7) use of LCA model, (8) LCIA method, (9) sensitivity analysis; and (10) best MSWM options. 2.3. Classification of LCA Studies on the Basis of the Economy The studies were evaluated and classified into four groups (World Bank, 2016c) i.e., (1) low income (LI) countries (GDP ≤ $1,005), (2) lower middle income (LMI) countries ($1,006 < GDP < $3,955), (3) upper middle income (UMI) countries ($3,956 < GDP < $12,235), and (4) higher income (HI) countries (GDP ≥ $12,236). As shown in Fig. 1, the maximum number of countries those who have conducted LCA belongs to HI group, while in 4 and 11 countries under study belong to LMI and UMI. Only 1 country, i.e., Nepal which belongs to the LI group, had conducted only one LCA since 2013.

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ACCEPTED MANUSCRIPT 25

22

20 15 11 10 6 5 1 0 LI

LMI

UMI

HI

Fig. 1. Distribution of the countries under consideration on the basis of income groups 3.

Results

3.1. Mapping of Study Area and Evaluation of LCA Studies with Time Fig. 2 shows the distribution of the LCA studies selected for review; China shared the maximum number of studies (31). Iran (10), Italy (14), Spain (10), United Kingdom (11), and the United States (9) have a nearly similar number of studies. Whereas, 6 studies have been published in Thailand as well as in Brazil. Out of the studies, Seventy-two case studies focus on Asia, fifty-three studies in Europe, ten studies based in North America, nine studies focus on South America and thee in America. Two LCA studies, Dong et al., 2018 and Gabbar et al., 2017, who addressed generic cities and assumed the waste generation, its characteristics, and environmental emissions, along with the studies, which considered more than one country as study area (Aracil et al., 2017; Jensen et al., 2015; Liikanen et al., 2017; Margallo et al., 2014a) are not shown in Fig. 2. Very fewer LCAs have been found for Africa, may be because of the poor penetration of LCA methodology in the region.

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Fig. 2. Geographical distribution of selected LCA studies (generic studies along with the reviews which considered more than one country as a study area, were excluded from the figure).

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ACCEPTED MANUSCRIPT As shown in Fig. 3, the trend in the publication of the studies in the field of MSW is not consistent. The maximum numbers of studies are published in the year 2017 (41), while in the year 2015 and 2016, the lowest numbers of studies are accounted. There isn’t any specific reason for the fluctuations in the publication of the studies. It depends on the existing MSW related problems, attentiveness of LCA communities and the availability of funds (Yadav and Samadder, 2018a). Also, the increasing trend in the adoption of LCA studies correlated well with the intensification of waste-management policies (for Europe) and the implementation of the ISO 14044: 2006 standards for LCA methodology (worldwide). The use of LCA for the prevention and recycling of waste, in EU Thematic Strategy and establishment of the “obligation to handle waste in a way that does not have a negative impact on the environment or human health,” by EU Waste Framework Directive and the use of life-cycle thinking in waste management. This initiative supported the growth of LCA, but this initiative was limited to Europe only and was the reason why the majority of the studies in the review carried out by Laurent et al., 2014 is based in Europe. Therefore, assuming a time lag of 0 to 3 years between the time when the study was performed and the publication date, Figs. 2 and 3 could indicate that these initiatives have influenced the growth of the application of LCA studies in the field of MSWM in other countries as well, and as a reason, the number of LCA studies started increasing in other regions as well.

Number of the studies

45 40 35 30 25 20 15 10 5 0 2013

2014

2015 Year of the study

2016

2017

Fig. 3. Time evolution of LCA studies (2013-2017)

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ACCEPTED MANUSCRIPT 3.2. Functional Unit The functional unit (FU) provides a common basis for the comparison of results (ISO 14040, 2006). An FU in case of MSWM is generally adopted as the amount of waste getting managed. It can also be linked with the usable products produced during MSWM, including electricity, heat, and fertilizer (Cleary, 2009). The FUs of the reviewed LCA studies has been shown in Fig. 4. The most commonly used FU in the LCA of MSWM is 1 MT of waste, 88 out of total studies used it as an FU, while, 33 and 3 of the total studies used more than 1 MT and less than 1 MT as FU, respectively. 21 of the total studies used FU other than Ton of MSW, such as in Intaniwet and Chaiyat, (2017) and Song et al., (2018) used as 1 kWh of electricity as FU; Malijonyte et al., (2016) who used 1 GJ of fuel input of Incineration plant as FU. 4 out of total studies used more than 1 FU such as in Jeswani and Azapagic, (2016) and Rajaeifar et al., (2017) who included two FUs i.e., disposal of 1 Ton of MSW and generation of 1 kWh of electricity from MSW; Dong et al., (2013) who used 2,502,100 MT (total MSW generated) and 363,800 MT (total FW generated) as FUs; and Ghinea et al., (2014) who included amount of MSW generated 1 MT of tissue paper as FUs. Apart from these, 6 studies didn’t clearly specify the usage of FU in their study. The selection of FU depends on the goal and scope definition of the study. The reliability of the LCA study is dependent on its goal and scope definition, and the FU defined, whereas undefined FU introduces unreliability of the results (Martínez-Blanco et al., 2014). 100

Number of the Studies

90

87

80 70 60 50 40

33

30

21

20 10

3

4

6

2 FUs

Not specified

0 1 MT

> 1 MT

< 1 MT Other Functional Unit

Fig. 4. Distribution of studies concerning functional units used 9

ACCEPTED MANUSCRIPT 3.3. Use of LCA Model These are computer-based tools that help in the collection, organization, and analysis of data, waste management systems’ modeling and evaluation of emissions and their impacts on the environment. All the computer models of LCA are process-based, and supply multiplication factors for all the model parameters using databases (Cleary, 2009). Several sources are available from where LCA databases specific to a region, nation, industry, agriculture or for a consultant, can be obtained, such as Ecoinvent, NEEDS, ELCD, etc. Generally used LCA models in the field of MSWM include SimaPro, GaBi, EASETECH (Environmental Assessment System for Environmental TECHnologies) whose former version was EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies), IWM (Integrated Waste Management), and many others. As presented in Fig. 5, nearly 64% of the studies incorporated LCA models to simplify the MSWM systems and for the calculations of environmental benefits and burdens. From the study, it was observed that 44 studies used Sima Pro, 25 used GaBi, 16 used EASETECH, 7 used IWM, and 4 studies used other LCA models. Whereas in two studies more than one LCA model was used for the calculations viz., Burnley et al., (2015) and Kulczycka et al., (2015). While 56 of the total studies didn’t specify the usage of the LCA model; these studies used equations for the LCA calculations. Use of LCA models is not mandatory, but its usage eases the LCA calculation along with time-saving in lengthy calculations. Model suitability depends on its cost, availability, language, the goal of the study and user’s choice (Yadav and Samadder, 2018a). 56

Numer of studies

60 50

44

40 30

25

20

15 7

10

4

2

0 Sima Pro

GaBi

EASETECH IWM LCA Models

Others

2 Models

NS

Fig. 5. LCA models used in the selected studies expressed in the number of studies 10

ACCEPTED MANUSCRIPT 3.4. Sensitivity Analysis Sensitivity analysis identifies whether any of the assumption made has a considerable influence on the results of the Life Cycle Inventory (LCI) and if so which assumption has the highest influence. Hence, it provides information about the robustness of the LCI results, and about where there is the greatest need for more precise data to improve the inventory. Table 1 presents the utilization of sensitivity analysis in the reviewed studies. 66 out of the total studies included sensitivity analysis, where, the major sensitivity analysis scenarios took up in sensitivity analysis were treatment efficiency, waste composition, sorting efficiency, recycling rate, impact assessment method, electricity recovered, electricity mix, carbon sequestration, and functional unit. Ideally, sensitivity analysis should be carried out for every parameter within LCI study, but in practice, this is often limited to a selected number of parameters. Particularly important parameters for sensitivity analysis are those that are being omitted either on purpose, because of lack of data (McDougall et al., 2001). 3.5. MSWM Systems Assessed and Suitable MSWM Options Fig. 6 illustrates that there is a relatively proportionate share of landfilling (107 studies), biological (104 studies) and thermal (109 studies) treatments, while the share of recycling/Material Recovery Facility (MRF) is relatively less (65 studies).

Fig. 6. Waste Management Techniques Assessed in Selected LCA Studies 11

ACCEPTED MANUSCRIPT Landfilling, being the least environmental-friendly, is still one of the most commonly used MSWM options. Also, almost all the treatment options generate inerts and residual waste, which are either very expensive to recover or are useless and hence are expected to be dumped at the landfill, and as a reason being at the bottom of the pyramid of waste management system hierarchy, landfills can’t be closed. Thinking of system without landfill is in-practical. Unscientific treatment options like open dumping and open burning generated large emissions of methane contributing to various impacts on human health and the environment. Only 11 and 3 studies were focused on open dumping and open burning, respectively, and are mainly based on LMI countries. Studies conducted by Babu et al., 2014; Menikpura et al., 2013c and Yang et al., 2013 found landfilling to be the better treatment option than open dumping and open burning. Waste management techniques in selected LCA studies as shown in Fig. 6 and detailed review of techniques presented in Table 1 indicated about the adoption of waste to energy system in recent years. But, the majority of the studies are still focused on incineration, and only 4 and 9 studies are based on pyrolysis and gasification, might be because of technological and financial constraints. Landfilling and thermal treatment are followed by biological treatment in the total number of studies published. Coincidently both anaerobic digestion and composting share an equal number of studies, i.e., 52 each, both the techniques can be used based on the availability of skilled labor and high-end technologies. From the results of the previously published studies as presented in Table 1 and Fig. 7, 48 of the total studies recommended the use of integrated solid waste management (ISWM), followed by energy generation from waste via thermal treatment viz., incineration, gasification and pyrolysis, total 46 studies. A large number of studies in the field of waste to energy is due to the evolution of waste management from just “treatment of waste to recovering the wealth out of waste.” This is followed by recycling (13), the increasing number of policies to limit the never-ending increase of plastic pollution, has been coming into action which obligates the better recycling and handling of the waste to have a minimum or no effects on the environment. 9 of the total studies concluded biological treatment, such as composting and anaerobic digestion, as the most suitable treatment option in comparison to thermal treatment and landfilling. While 11 studies concluded that the landfilling could be the best management option when Landfill gas to energy (LFG) is considered. However, due to the heterogeneous nature of MSW, no single treatment option can be used for all the waste fractions in different geographical regions. Also, because of the difference in MSWM structure, system boundary, MSWM practices, MSW composition, amount of waste generated, and selection of impact categories, the results LCA study of one region may vary. 12

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Suitable MSWM options

60 48

46

50 40 30 20

13

11

9

10 0 Recycling

Biological Treatment

Thermal Treatment

Landfill

Integrated

MSWM options

Fig. 7. Suitable MSWM options

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Table 1 Details of Reviewed LCA Studies Income Group, Country, and Reference LI Nepal Singh et al., 2014 LMI India Babu et al., 2014 LMI India Mali et al., 2016 LMI India Sharma and Chandel, 2016 LMI India Yadav and Samadder, 2018 LMI Indonesia Raharjo et al., 2016

Functional Unit (FU), Model & Method

FU: NS Model: NS Method: NS

FU: NS Model: NS Method: CML FU: 1 Ton of MSW Model: SimaPro Method: CML FU: 1 Ton of MSW Model: GaBi Method: IPCC FU: 1 Ton of MSW Model: SimaPro Method: CML FU: NS Model: NS Method: NS

Scenarios S1: LF with no LFG collection and 50% leachate collection S2: LF with 100% methane and leachate collection and 60% leachate is getting treated S3: RECY (12.78%), AD (40% of commercial waste), COMP (20% of household waste) and LF (80.91%) S4: RECY (18.68%), AD (80% of commercial waste), COMP (40% of household waste) and LF (68.68%) S1 open dumps S2 LF without gas recovery S3 LF S4 BLF S1: open dumping S2: RECY, COMP, and LF S3: RECY, AD, and LF S4: RECY and PY-GAS S1. 31% BLF + 69% open dumping S2. 3.2% RECY + 96.8% SLF S3. 3.2% RECY + 32% COMP + 64.8% SLF S4. 3.2% RECY + 32% AD + 64.8% SLF S5. 3.2% RECY + 16% COMP + 16% AD + 64.8% SLF S6. 3.2% RECY + 8% COMP + 88.8% INC S7. 3.2% RECY + 96.8% INC S1: COLL, TRANS S2: RECY, open burning, open dumping, UnLF S3: COMP, LF without gas recovery S4: RECY, COMP, disposal of inert waste at LF without gas recovery S1: COMP and LF S2: COMP, Garbage bank, and LF S3: COMP and LF with LFG recovery S4: COMP, garbage bank and LF with LFG recovery

Critical Findings

S4 would be the most environmentally suitable option.

The results found that the bioreactor LF scenario was a better option among the four scenarios. The study was also based on cost-benefit analysis. S2 was found to be the option with minimum environmental impacts.

The scenario with the combination of MRF, COMP, AD, and INC had the least environmental impacts. Also, the study conducted sensitivity analysis for change in recycling rate which has highlighted the accuracy of inventory analysis in the study.

Scenario S4 had the least environmental impacts.

S4 had the least environmental impacts.

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Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: 1 Ton of MSW Model: NS Method: EDIP

S1: informal RECY, open dumping, SLF, UnLF S2: formal RECY, MRF, SLF, UnLF S3-A: formal RECY, MRF, SLF S3-B: formal RECY, MRF, SLF (diff ratio than S3-A) S3-C: formal RECY, MRF, SLF, LF with gas recovery S3-D: same as S3-C but different ratios S4-A: formal RECY, MRF, SLF, COMP S4-B: formal RECY, MRF, SLF, biogas S4-C: formal RECY, MRF, SLF, COMP, biogas S5: formal RECY, MRF, SLF, INC with energy recovery

S3-D was the best scenario regarding environmental impacts and economic cost. The study was also based on cost-benefit analysis.

S1: Open dumping S2: RECY, COMP, and LF S3: RECY and INC S4: RECY and LF

S2 is the best and most favorable alternative.

S1. LF without gas recovery S2. LF with gas recovery S3. AD + INC + LF with gas recovery S4. LF + INC

LFG to energy is the worst in terms AP but best-regarding dioxins/furans emissions. INC/AD has 75.7-93.3%, INC/LFGTE has 75.3-84.8% and GWP has 75% GWP reduction.

COLL and LF

Significant GHG emissions are associated with the collection and landfilling of waste which can be reduced through biogasification

LMI Pakistan Ali et al., 2017

FU: 1 Ton of MSW Model: EASETECH Method: ILCD

S1: Open dumping S2: MRF S3: Incineration S4: AD, INC S5: COMP S6: LF with energy recovery

Incineration showed the best results in GWP, HTcar, HTnon.car, PM, ADP, TA, TE whereas AD had the least FE.

LMI Pakistan Syeda et al., 2017

FU: Amount of MSW generated Model: EASETECH Method: NS

S1: a Baseline scenario S2: MRF, COMP, and Open Dumping S3: MRF, AD, and Open Dumping S4: MRF, AD, and RDF to a cement plant for energy recovery

S4 presented the least impacts among all the scenarios.

LMI Jordan Ikhlayel et al., 2016

LMI Nigeria Ogundipe and Jimoh, 2015 LMI Nigeria Ayodele et al., 2017 LMI Pakistan Munir et al., 2015

FU: 1 Ton of MSW Model: SimaPro Method: Impact 2002+ FU: Amount of MSW generated Model: SimaPro Method: EcoIndicator FU: 5000 Ton of MSW Model: IWM Method: NS

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Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios S1: Open dumping S2: LF S3: LF with LFG to energy S4: COMP + LF S5: INC + LF S6: AD + INC + LF S1: Anaerobic LF S2: Semi- aerobic LF S3: LF with LFG gas recovery S4: COMP S5: Pre-composting before LF S6: AD

Critical Findings

LMI Vietnam Thanh and Matsui, 2013

FU: NS Model: NS Method: IPCC

LMI Vietnam Otoma and Diaz, 2017

FU: 1 ton of MSW Model: NS Method: NS

UMI Bosnia and Herzegovina Bjelic et al., 2015

FU: 1 Ton of MSW Model: EASETECH Method: EDIP and Usetox

S1: Unsanitary LF S2: LF with LFG flare S3: LF with LFG to energy

S3 leads to saved emission and avoided impact potential in several environmental categories. Also, the study conducted sensitivity analysis for bulk density, gas collection period, which has highlighted the accuracy of inventory analysis in the study.

UMI Brazil Leme et al., 2014

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1: INC S2: LF S3: LF with LFG to energy using reciprocating Internal Combustion Engine S4: LF with LFG to energy using gas turbines

The direct combustion of waste as a fuel for electricity generation presented the best performance. Also, the study conducted sensitivity analysis for a selling price of electricity, the cost of CER’s, total COI, and annual O&M, which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis.

FU: Annual MSW generated Model: IWM Method: CML

S1. MC, COMP, RECY, LF S2. MC, COMP, RECY, INC, LF S3. MC, COMP, RECY, AnMBT, LF S4. MC, COMP, RECY, AeMBT, LF S5. SC, COMP, RECY, LF S6. SC, AD, RECY, LF S7. SC, COMP, AD, RECY, INC, LF S8. SC, COMP, AD, RECY, INC, LF (diff ratio than S7)

S8 showed the least environmental burdens while S1 had the most. Also, the study conducted sensitivity analysis for electricity which has highlighted the accuracy of inventory analysis in the study.

UMI Brazil Coelho and Lange, 2016

Incineration with energy recovery seems the most suitable option.

AD had the least environmental impacts. The study was also based on cost-benefit analysis.

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Income Group, Country, and Reference UMI Brazil Angelo et al., 2017 UMI Brazil de Andrade Junior et al., 2017

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: NS Model: EASETECH Method: ILCD FU: 1 MT of waste paper without distinction Model: SimaPro Method: NS

S1. LF S2. 20% SC of organics for AD + LF (residual waste) S3. 50% SC of organics for AD + LF (residual waste) S4. MRF + AD + LF

S3 was the best alternative as it considers more quantity of organic waste diverted from LF whereas S4 appeared as the worst option for the case study having the highest impact on several categories including GWP and ODP.

S1: LF S2: 14% RECY and 86% LF S3: 37.5% RECY, 37.5% to reverse logistic recycling stream and 25% LF

The results showed that the impacts on the climate change due to the waste paper management could be reduced by 41% when the recycling rate is increased, and landfilling is reduced.

UMI Brazil Saraiva et al., 2017

FU: 1 Ton of MSW Model: EASETECH Method: ILCD

S1: LF with LFG to energy S2: 50% SC, AD with biogas to energy and LF with LFG to energy S3: 75% post-separation, MRF, AD with biogas to energy and LF with LFG to energy

S3 proved to be the preferred alternative in relation to both the attributional and consequential approach. Also, the study conducted sensitivity analysis for the use of urea in nitrate fertilizers and calcium ammonia nitrate as marginal nitrogen fertilizer which has highlighted the accuracy of inventory analysis in the study.

UMI Brazil Soares and Martins, 2017

FU: 1 Ton of MSW Model: SimaPro Method: Traci

UMI China Dong et al., 2013

FU1: 2502100 MT MSW generated FU2: 363800 MT FW generated Model: NS Method: NS

UMI China Ning et al., 2013

FU: 1 Ton of MSW Model: NS Method: Recipe & Eco-Indicator

S1. LF with gas recovery S2. LF with and energy generation S3. AD and LF S4. INC and LF S5. AD, INC, and LF S1: Mixed collection, INC, and landfill S2: Mixed collection, source separation, INC and LF S3: Mixed collection, source separation, INC and LF with energy recovery S4: Mixed collection, source separation, INC, COM, and LF S5: mixed collection, source separation, INC, COM, and LF S6: Mixed collection, source separation, INC, AD, and LF with LFG recovery S1: Fluidized-bed incinerator S2: Mechanical-grate incinerator

The electricity generated from S5 is the most attractive option regarding minimizing the environmental impacts.

The GWP can be reduced to 23% by using source segregated waste collection system as compared to the current scenario. The study was also based on cost-benefit analysis.

S1 is more environmentally benign than S2. Also, the study conducted sensitivity analysis for CO2 emission and electricity consumption which has highlighted the accuracy of inventory analysis in the study.

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Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

UMI China Song et al., 2013

FU: Annual MSW generated Model: SimaPro Method: EcoIndicator

S1. SC, INC S2. LF S3. SC, COMP, LF S4. INC, COMP S5. SC, INC S6. SC, INC, COMP

Both S4 and S5 are preferable options, but from a financial point of view, S4 should be the priority as it is primarily an enhancement of the current MSW management system. Also, the study conducted sensitivity analysis for recycling rate which has highlighted the accuracy of inventory analysis in the study.

UMI China Tang et al., 2013

FU: 1 ton of MSW Model: NS Method: NS

S1: MSW oxy-fuel incineration S2: MSW incineration with monoethanolamine (MEA)

UMI China Yang et al., 2013

FU: 1 Ton of MSW Model: NS Method: NS

S1: Open dumping S2: LF without LFG collection S3: LF S4: LF with LFG utilization

UMI China Dong et al., 2014a

FU: 1 Ton of MSW Model: NS Method: NS

S1: LF S2: LF with energy generation S3: INC

UMI China Dong et al., 2014b

FU: 1 Ton of MSW Model: NS Method: NS

S1: LF S2: LF with LFG to energy S3: INC

UMI China Ma et al., 2014 UMI China Woon and Lo, 2014

FU: Annual MSW generated Model: NS Method: NS FU: 1 Ton of MSW Model: SimaPro Method: Ecoindicator

MSW oxy-fuel incineration was better than MSW incineration with MEA from the standpoint of energy consumption and the environmental impacts. Also, the study conducted sensitivity analysis for transport distance, electricity power consumption and electric power consumption of CO2 compressor which has highlighted the accuracy of inventory analysis in the study. S4 has the least environmental burdens among all the scenarios considered. Also, the study conducted sensitivity analysis for variance based global sensitivity analysis which has highlighted the accuracy of inventory analysis in the study. S2 showed the best results if the carbon sequestration is considered. Also, the study conducted sensitivity analysis for electricity mix and increase in valorization due to extra one ton of MSW which has highlighted the accuracy of inventory analysis in the study. Results showed that INC performs best among all the scenarios. Also, the study conducted sensitivity analysis for change in weight factors which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis.

S1: INC with heat recover-dry absorption S2: INC with heat recovery-wet absorption S3: INC with heat recover-semidry absorption

The environmental burden of S1 is lower than the other two.

S1: Landfill extension (LFE) S2: Advanced incineration facility (AIF)

AIF performs better than the LFE for human health but vice versa for ecosystem quality.

18

Income Group, Country, and Reference

UMI China Yang et al., 2014

Functional Unit (FU), Model & Method

FU: 1 Ton of MSW Model: NS Method: EDIP

UMI China Chi et al., 2015

FU: Annual MSW generated Model: NS Method: EDIP

UMI China Huang and Chuieh, 2015

FU: 1 Ton of MSW Model: Method: IMPACT 2002+

UMI China Jin et al., 2015

FU: 1 MT of OFMSW Model: SimaPro Method: CML

Scenarios

Construction and operation of MSW LF

S1. INC and LF S2. INC, RECY, and LF S3. Short term MSWM plan system (more proportion of waste to INC and RECY) S4. Long-term MSWM plan system (BT of food waste instead of LF) S4a. AD S4b. COMP S1: INC and LF of flash after acidification S2: INC and reuse of fly ash as cement after a washing process S3: INC and reuse of fly ash as bricks after the washing process S4: INC and reuse of fly ash as an alkali in the waelz process of steelmaking AD of OFMSW to biogas with biodiesel recycling and treatment of digested food

Critical Findings The environmental impacts induced by LF construction and operation are important compared with integrated landfilling technology and should not be omitted in future LCA studies. Also the research conducted sensitivity analysis for use of geomembrane instead of soil, use of geo-nets instead of the gravel, use of single clay layers below the geo-membranes, single natural component liners as bottom liner system, single composite liner system, and LF without LFG collection system which has highlighted the accuracy of inventory analysis in the study. As compared to short-term plan, more diversion of waste from LF to INC is better. As compared to long-term strategy, biological decomposition by AD is better than COMP as well than the present MSWM plan AD, recycling and INC (strikethrough). Also, the study conducted sensitivity analysis for LFG collection efficiency and source separation efficiency which has highlighted the accuracy of inventory analysis in the study. Reutilization of fly ash as bricks was the most environment friendly. The increase in oil content and biogas rate could reduce the energy consumption and environmental impacts significantly. Also, the study conducted sensitivity analysis for moisture content, oil content and impurity rate, and biogas production rate, which has highlighted the accuracy of inventory analysis in the study.

19

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

UMI China Li et al., 2015

FU: MSW generated in between 2013 and 2015 Model: NS Method: NS

S1: TRANS and treatment S2: TRANS, COMP, INC, and LF S3: TRANS to LF

(i) The capacities of transfer stations should be expanded with the associated routing distance being optimized and operation costs being minimized, (ii) the technology improvements for incinerators should be conducted to reduce production costs and to abate pollutant emissions, and (iii) the trade-off between the economic cost and environmental consequences should be balanced in identifying desired waste management strategies. The study was also based on cost-benefit analysis.

UMI China Lou et al., 2015

FU: NS Model: EASETECH Method: EDIP

S1: INC S2: INC with a waste sorting system S3: INC with the application of dewatering system S4: INC with a reduction in leachate effluent and dioxin concentrations

S2 and S3 might be two promising pre-treatment methods but are not practiced due to high investment. S4 can reduce EP, AP, HT, and ET.

UMI China Xu et al., 2015

FU: 1 Ton of MSW Model: NS Method: ReCiPe

S1: AD of FW and sludge S2: AD of FW S3: LF

S2 was the most suitable environmental scenario. Also, the study conducted sensitivity analysis for a change of LCIA method, variation in electricity mix, transport, infrastructure, calcium oxide and diesel which has highlighted the accuracy of inventory analysis in the study.

UMI China Yang et al., 2015

FU: 1 Ton of MSW Model: NS Method: NS

UMI China Havukainen et al., 2016

FU: MSW generated in 2013 Model: GaBi Method: CML

UMI China Woon et al., 2016

FU: 1 MT of FW Model: SimaPro Method: ReCiPe

S1: TRANS S2: LF S3: COMP S4: INC S1: INC and LF S2: RDF, INC, and LF S3: RDF, INC, and biodrying S4: RDF, INC, and AD S5: RDF, INC, and ethanol S6-S9: Same as S2-S5, the difference being the INC plants are new and have higher electric efficiency. S1: Valorising FW S2: Valorising FW for city GSA S3: Valorising FW for biogas fuel

LF has the highest environmental burdens for PO and GWP, while S3 and S4 have high AP. The study was also based on costbenefit analysis. According to the results, RDF production and INC could improve Hangzhou's MSWM global warming, acidification, and the eutrophication in comparison to the INC of MSW with coal. Also, the study conducted sensitivity analysis for LFG collection efficiency, LFG yield, electricity efficiency, the calorific value of MSW, RDF and dried organic rejects which have highlighted the accuracy of inventory analysis in the study. S3 was better than other scenarios.

20

Income Group, Country, and Reference UMI China Woon and Lo, 2016 UMI China Zhao et al., 2016 UMI Iran NasrollahiSarvaghaji et al., 2016 UMI China G. Liu et al., 2017 UMI China Y. Liu et al., 2017a UMI China Y. Liu et al., 2017b

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: NS Model: NS Method: NS

S1: LFE S2: AIF

S2 was more eco-efficient than S1. The study was also based on cost-benefit analysis.

FU: 1 kg of RDF-5 Model: NS Method: NS

RDF from MSW

RDF produced using a raw material having 8% MC, and 10 MPa molding pressure reduces the environmental impacts considerably.

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1: AD S2: INC S3: LF

S3 had the least environmental impacts among the considered scenarios.

FU: NS Model: NS Method: EcoIndicator

S1: Separate collection and TRANS S2: LF S3: Fluidized bed INC S4: COMP

Results indicated that as the source separation rate increases, the yield of recycled materials and sorted waste increased. High source separation rate and separated transportation rate would help in making recycling more beneficial: however, by the utilization of more than one approach, it is possible to minimize costs and losses.

FU: 1 Ton of MSW Model: EASETECH Method: NS FU: 1 Ton of MSW Model: EASETECH Method: NS

S1: LF with LFG to flare S2: LF with LFG to energy S3: INC S4: AD with biogas to energy and LF with LFG to flare S5: INC, AD with biogas to energy and LF with LFG to flare S1: LF with LFG flaring S2: LF with LFG to energy S3: INC S4: INC, COMP, and LF with LFG to flaring S5: INC, AD with biogas to energy and LF with LFG to energy

S5 would be the optimum MSWM technique for the area under study. Also, the study conducted a sensitivity analysis for the change in every parameter which has highlighted the accuracy of inventory analysis in the study. INC had the best energy recovery rate with a substantial decrease in net GHG emissions.

21

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: 1 Ton of MSW Model: EASETECH Method: NS

S1: LF with LFG flaring S2: LF with LFG to energy S3: INC with the disposal of inert ash at LF S4: INC, COMP, and LF with LFG flaring S5: INC, COMP with compost land application and LF with LFG to flare S6: INC, AD with Biogas to energy and LF with LFG to flare S7: AD with biogas to energy and digestive residue land application, INC, LF with LFG to flare

S7 performed the best among all the scenarios.

UMI China Tang and You, 2017

FU: 1 Ton of MSW Model: NS Method: ReCiPe

S1: MSW power plant without carbon capture and separation (CCS) unit S2: MSW INC with chemical post-combustion capture via monoethanolamine (MEA) S3: MSW INC with physical post-combustion capture via twostage pressure/vacuum swing adsorption (P/VSA) S4: MSW INC with oxy-fuel combustion

UMI China Fei et al., 2018

FU: 1 Ton of MSW Model: GaBi Method: Traci

S1: LF with LFG to energy S2: LF with LFG to natural gas S3: INC and LF with fly ash disposal S4: MBT, AD with biogas to energy, RDF INC and inerts to LF S5: MBT, AD with biogas to natural gas generation, RDF INC and Inerts to LF

FU: 1 kWh of electricity Model: SimaPro Method: CML

S1: Baseline scenario S2: Energy from Oil, Natural gas, and MSW fired power plants, and import from China S3: Energy from Oil, Natural gas, and MSW fired power plants and solar energy S4: Imported energy from China S5: Energy from heavy oil S6: Energy from natural gas S7: Energy from Solar source

UMI China Y. Liu et al., 2017c

UMI China Song et al., 2018

The use of CCS reduced the impacts on ecosystem quality and human health and increased the resources impact value. S4 had the best economic performance followed by S2 and S3. Also, the study conducted sensitivity analysis for change in consumption of CCS technology, investment in CCS system, online electrovalence, unit MSW treatment revenue, and MSW treatment capacity which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis. S1 and S2 had the highest environmental impact followed by S3, while S4 and S5 had the lowest environmental impact. Also, the study conducted sensitivity analysis for leachate treatment, MSW treatment, income tax rate, gate fee, electricity price and bio-natural gas price which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis. Natural gas-fired electricity would be the best for short-term GHG emission reduction while utilization of solar energy would be the best for long-term GHG emission reduction. Also, the study conducted sensitivity analysis for energy transformation, efficiency and carbon capture, and storage which has highlighted the accuracy of inventory analysis in the study.

22

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

UMI China Zhao et al., 2018

FU: 1 Ton of MSW Model: NS Method: NS

S1: LF S2: LF with LFG utilization S3: INC with leachate spray S4: COMP and LF S5: COMP and INC S6: INC with centralized leachate treatment

S5 has the lowest resource impacts except for raw coal extraction. Also, the study conducted sensitivity analysis for the decrease in construction material, decrease in equipment and machinery use, decrease in construction activity, decrease in diesel use and transportation, decrease in diesel use for operation, and increase in the by-product recovery efficiency which has highlighted the accuracy of inventory analysis in the study.

UMI Iran Nouri et al., 2014

FU: 1 Ton of MSW Model: SimaPro Method: EcoIndicator

UMI Iran Mahmoudkhani et al., 2014

FU: NS Model: NS Method: NS

UMI Iran Naderi et al., 2014 UMI Iran Rajaeifar et al., 2015

FU: 8393 Ton of MSW Model: IWM Method: NS FU: 1 Ton of MSW Model: SimaPro Method: Impact 2002+

S1: RECY, COMP, and LF S2: RECY, INC, and COMP S3: RECY and LF S4: RECY and INC S5: RECY, LF, and COMP S6: LF S7: INC S1: Open dumping S2: LF with LFG flaring S3: LF with energy generation S4: COMP, RECY, and LF S5: Source reduction, COMP, RECY, and LF S6: Source reduction, COMP, RECY, INC, and LF with energy generation S7: INC

S5 and S1 have the least environmental impacts and are costeffective.

S6 showed the best results among all the treatment scenarios evaluated. The study was also based on cost-benefit analysis.

S1: LF S2: RECY, COMP, and LF

Composting and recycling reduced the environmental burdens considerably

S0. AD S1. LF, COMP S2. INC S3. INC, COMP S4. AD, INC

The results showed that S4 would be the most eco-friendly scenario to be implemented in the future.

23

Income Group, Country, and Reference UMI Iran NasrollahiSarvaghaji et al., 2016

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1: AD S2: INC S3: LF

S3 had the least environmental impacts among the considered scenarios.

UMI Iran Harijani et al., 2017

FU: NS Model: NS Method: NS

S1: MRF, LF with LFG to energy, INC, and AD S2: scenario without sustainability (MRF, COMP, LF with LFG to energy, INC, and AD) S3: Scenario without budget limitations (MRF, LF with LFG to energy, AD, and INC) S4: scenario without sustainability and budget limitations (INC, COMP, and MRF)

S1 achieved a profit of 43.49 M USD. S2 and S3 in comparison to S1 had -308.60 M and 99.73 M USD, respectively, and implied that the budget constraints might restrict the benefits from the MSW quite severely.

UMI Iran NabaviPelesaraei et al., 2017a

FU: Daily generated MSW Model: SimaPro Method: CML

Recycling of MSW

UMI Iran NabaviPelesaraei et al., 2017b

FU: Daily produced MSW Model: SimaPro Method: CML

S1. LF S2: INC

UMI Iran Omid et al., 2017

FU: Annual MSW generated Model: IWM Method: CML

S1. COLL, TRANS, LF S2. SC, TRANS, COMP, RECY, LF S3. Same as S2, diff ratios S4. SC, TRANS, COMP, RECY, LF with gas recovery

RECY reduces pollution and the amount of MSW compared to the time when MSW is abandoned in nature, buried or burned. Also, the study conducted sensitivity analysis for variation in human labor, transportation, natural gas, electricity, and water, which has highlighted the accuracy of inventory analysis in the study. LCA results indicated that INC leads to the reduction of damaging factors related to toxicity as the results of electricity generation and the production of phosphate fertilizers. Besides, the rates of daily GHG emissions from incineration and landfill are estimated at 4499.07 and 92,170.30 kg CO2 eq., respectively. Also, the study conducted sensitivity analysis for variation in human labour, transportation, electricity and diesel fuel, which has highlighted the accuracy of inventory analysis in the study. S4 was found to be the option with the minimum environmental impacts

24

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

UMI Malaysia Elwan et al., 2013 UMI Malaysia Elwan et al., 2015 UMI Mauritius Rajcoomar and Ramjeawon, 2017

FU1: 1 Ton of MSW FU2: 1 kWh of electricity produced Model: SimaPro Method: NS FU: Annual MSW generated Model: GaBi Method: NS FU: Annual MSW generated Model: GaBi Method: NS FU: MSW generated in 2010 Model: SimaPro Method: CML and ReCiPe

UMI Peru Gilardino et al., 2017

FU: 1 Ton of MSW Model: SimaPro Method: ReCiPe

UMI Iran Rajaeifar et al., 2017

UMI Romania Ghinea et al., 2014

FU1: Amount of MSW generated FU2: 1 MT of tissue paper Model: GaBi Method: CML

Scenarios

Critical Findings

S1: AD and INC S2: AD and GAS-PY

S2 performed the best while considering FU1 and S1 while considering FU2

S1: MSW to energy S2: MSW to compost

S1 is a better option for the study area from the environmental point of view

S1: MSW to energy recovery and LF S2: MSW to compost

S1 has lesser environmental impacts and also has energy generation

S1. LF with gas recovery S2. INC S3. COMP, INC, and LF S4. COMP, RECY, INC, and LF S1: Compactor truck S2: Combination of compactor truck, containers, and nonmotorized load vehicles LCA of MSW S1: RECY, COMP, and LF S2: RECY, COMP, AD, and LF S3: RECY, COMP, LF, and INC S4: RECY, COMP, and INC

S2 and S4 can be considered for strategic planning. Also, the study conducted sensitivity analysis for increasing the recycling rates, recycling in a different country, and assuming more net CV of the wastes, which has highlighted the accuracy of inventory analysis in the study. Implementation of container collection system reduces the number of compactor trucks required by up to 50%. Also, the study conducted a sensitivity analysis for different collection system, container maintenance, and emission standards, which has highlighted the accuracy of inventory analysis in the study.

For LCA of MSW, S3, and LCA of paper waste, S2 is the most environmentally friendly scenario.

LCA of paper waste S1: Virgin fibers S2: Recycled fibers

25

Income Group, Country, and Reference UMI Romania Popita et al., 2017

Functional Unit (FU), Model & Method FU: Annual MSW generated Model: GaBi Method: CML

UMI Russia Kaazke et al., 2013

FU: Annual MSW generated Model: IWM Method: CML

UMI Russia Tulokhonova and Ulanova, 2013

FU: Annual MSW generated Model: IWM Method: NS

UMI Russia Starostina et al., 2014

FU: NS Model: EASETECH Method: NS

UMI Thailand Menikpura et al., 2013a UMI Thailand Menikpura et al., 2013b UMI Thailand Menikpura et al., 2013c

FU: 1 Ton of MSW Model: NS Method: ReCiPe and Eco-Indicator FU: 1 Ton of MSW Model: NS Method: NS FU: 1 Ton of MSW Model: NS Method: NS

Scenarios S1. LF S2. LF, COMP S3. LF, COMP, RECY S4. LF, COMP, RECY, INC S1: LF S2: AeMBT S3: An MBT S4: INC S5: LF and RECY S6: AeMBT and RECY S7: AnMBT and RECY S8: INC and RECY

Critical Findings The results revealed that S4 could be identified as the most environmentally friendly one, due to the good results regarding all environmental impacts and higher energy recovery Scenarios S7 and S8 demonstrated lower environmental impact in both Khanty-Mansiysk and Surgut. Also, the study conducted sensitivity analysis for change in waste composition and change in transportation distance, which has highlighted the accuracy of inventory analysis in the study.

S1: LF S2: RECY + LF S3: COMP + RECY + LF S4: RECY + Aerobic MBT + LF

S4 had the least environmental and social aspects. The study was also based on cost-benefit analysis.

S1: LF S2: LF with LFG recovery

S2 showed the least environmental burdens. Also, the study conducted sensitivity analysis for change in gas generation rate, methane oxidation in the top cover, and gas collection period, which has highlighted the accuracy of inventory analysis in the study.

S1: LF S2: RECY S3: Existing MSWM scenario

The results showed that the increase in recycling rate could reduce GHG emissions up to 92% of the existing MSWM system. The study was also based on cost-benefit analysis.

S1: LF without LFG recovery S2: Open Dumping S3: COLL, MRF, AD, COMP, Animal feed and LF

ISWM system had 60% less GHG emissions than S1

S1: Open dumping S2: LF without gas recovery S3: LF with LFG to energy S4: LF with enhanced LFG recovery

S3 can be adopted as the most suitable option, but the sooner the LFG recovery system is installed, the higher will be the GHG emission reduction.

26

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

UMI Thailand Menikpura et al., 2016

FU: 1 Ton of MSW Model: NS Method: NS

S1: LF S2: LFG to energy S3: INC

Both S2 and S3 show the potential for GHG mitigation and resource savings as compared to S1. However, financial returns from these projects are meager due to the low efficiency of energy recovery and high logistics costs. The study was also based on cost-benefit analysis.

UMI Thailand Sedpho et al., 2016

FU: 1 Ton of MSW Model: SimaPro Method: ReCiPe

S1: LF S2: RDF, LF and Organic Rankine Cycle (ORC) 20KW S3: RDF, LF, and ORC 70 kW

S3 provides more benefits both of energy consumption and resource aspects

UMI Thailand Intaniwet and Chaiyat, 2017

FU: 1 kWh of electricity Model: NS Method: NS

Water hyacinth and MSW (50/50) as a heat source for organic Rankine cycle

Water hyacinth-MSW-ORC showed better results than standard power plant in Thailand regarding energy, economic and environment. The study was also based on cost-benefit analysis.

S1. LF without gas recovery S2. MRF, LF S3. MRF, COMP, LF S4. INC, LF S5. MRF, COMP, INC, LF

The highest environmental impacts arise from S1 and S4, and the most environmentally friendly waste management option is S5, but may not be economically sustainable owing to its high investment and operation costs in the short term. And hence, S 3 can also be a favorable option. Also, the study conducted sensitivity analysis for change in LCIA methodology from CML to ReCiPe and increase in recycling rate, which has highlighted the accuracy of inventory analysis in the study.

UMI Turkey Yay, 2015

FU: 1 Ton of MSW Model: SimaPro Method: CML

27

Income Group, Country, and Reference

UMI Turkey Yıldız-Geyhan et al., 2016

Functional Unit (FU), Model & Method

FU: 1 MT of recyclable packaging waste Model: CML Method: NS

Scenarios S1: Mixed packaging waste from door-to-door and glass waste from drop-off points S2: Mixed packaging waste from door-to-door and curbside, and glass waste from drop-off S3: Mixed packaging waste from door-to-door S4: Paper-Cardboard waste, heavy-lightweight packaging waste and glass waste from door-to-door S5: Paper-cardboard waste, Lightweight packaging waste, heavyweight packaging waste and glass waste from door-todoor S6: Mixed packaging waste and glass waste from curbside S7: Paper and cardboard waste, heavy and lightweight packaging waste, and glass waste from curbside S8: Mixed packaging waste and glass waste from drop-off S9: Paper and cardboard waste, heavy and lightweight packaging waste and glass waste from drop-off S1: 25% COMP and 75% LF S2: 35% AD, 15% INC and 50% LF S3: 25% AD, 25% COMP, 15% INC and 35% LF S4: 70% INC and 30% LF

Critical Findings

S6 and S7 showed more environmental benefits among all scenarios

UMI Turkey Çetinkaya et al., 2018 HI-UMI Finland and China Liikanen et al., 2017

FU: NS Model: SimaPro Method: Impact 2002+ FU: Annual MSW generated Model: NS Method: NS

MSWM in South Karelia (Finland) vs. Hangzhou (China)

Higher LFG collection and INC energy recovery in South Karelia than Hangzhou. Also, the study conducted sensitivity analysis for change in input parameters, which has highlighted the accuracy of inventory analysis in the study.

HI Belgium Belboom et al., 2013

FU: 1 Ton of MSW Model: SimaPro Method: ReCiPe

S1. LF S2. INC, LF S3. INC S4. AD, INC

S4 showed the least environment burdens among all the evaluated scenarios. Also, the study conducted sensitivity analysis for electricity mix and increase in valorization due to extra one ton of MSW, which has highlighted the accuracy of inventory analysis in the study.

Results suggested that S1 is the best solution from health and environmental viewpoints

28

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI Canada Reza et al., 2013

FU: 1 MT of RDF production and use Model: GaBi Method: NS

S1: Target 2015, an adequate amount of MSW available for 80,000 t of RDF production S2: Target 2015, 1,00,000 t of MSW available for RDF production S3: Target 2020, an adequate amount of MSW available for 80,000 t of RDF production S4: Target 2020, 1,00,000 t MSW available for RDF production

Net benefits per ton of RDF production are higher in the year 2020 (S3>S4) as compared to 2015. The study was also based on cost-benefit analysis.

HI Chile Bezama et al., 2013

FU: 1 Ton of MSW Model: NS Method: NS

S1: LFG recovery and flaring S2: LFG recovery and energy generation S3: AD with energy recovery

S2 can be identified as the most environmentally appropriate solution

HI Chile Casas-Ledón et al., 2017 HI Denmark Habib et al., 2013 HI Denmark and Germany Jensen et al., 2015 HI Denmark Bisinella et al., 2017

The results show that the most significant environmental impacts are associated with gasification and are mainly caused by chemical exergy destruction and pollutants formation. Also, the study conducted sensitivity analysis for methane and carbon monoxide are not considered as a pollutant in the gasifier, which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis. S3 was the most environmentally friendly scenario. Also, the study conducted sensitivity analysis for same marginal electricity and MSW composition, which has highlighted the accuracy of inventory analysis in the study.

FU: NS Model: NS Method: NS

Exergy-environmental analysis of a waste based Integrated Combined Cycle

FU: 1 Ton of MSW Model: NS Method: NS

S1: LF S2: INC S3: COMP, INC, and LF

FU: NS Model: EASETECH Method: ILCD

S1: Denmark system: INC S2: German system: MBT, COMP, AD, and INC

Danish system performed well in GWP, AP, MEP and ADP fossil, while the German system performed well only in ADP elements

S1. SC + RECY + INC S2. SC + RECY + INC + AD S3. SC + RECY + INC + LF

S1 had the lowest impacts for all impact categories except FE. S2 showed the highest impacts in the categories HT non. car. and ET, while the largest benefits in the FE impact category. Overall, S3 had considerably lower benefits than S1 and S2. S3 obtained the highest impacts especially for ME, OD, HT car., PO, FE, ET, and TE. Also, the study conducted sensitivity analysis for 10% deviation in each scenario parameter, which has highlighted the accuracy of inventory analysis in the study.

FU: 1 Ton of MSW Model: EASETECH Method: NS

29

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI Finland Hupponen et al., 2015

FU: Annual MSW generated Model: GaBi Method: CML

S1: LF S2: INC in grate furnace, produced district heat replaces thermal energy from natural gas S3: INC in grate furnace, produced steam replaces process steam from natural gas S4: INC in fluidized bed boiler, produced district heat replaces biofuels, plastic waste, heavy fuel oil, and coal.

GWP results show that the combustion of mixed MSW is a better alternative than landfilling the waste. Also, the study conducted sensitivity analysis for combustion cost, MSW composition, choice of fuel for substituted heat production, and RDF production, which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis.

HI France Beylot and Villeneuve, 2013

FU: 1 Ton of MSW Model: NS Method: ReCiPe

INC of MSW

Results highlight the relatively large variability of the impact potentials as a function of the plant technical performances.

HI France Déchaux et al., 2017

FU: 1 Ton of MSW Model: GaBi Method: CML

Analysis of regionalized municipal solid waste incineration model for French MSW incineration

HI Germany Fiorentino et al., 2015

FU: 1 Ton of MSW Model: SimaPro Method: ReCiPe

S1. LF with gas recovery S2. MBT, WTE S3. Material Advanced Recovery Sustainable Systems, LF S4. MARSS, WTE

HI Germany Ripa et al., 2017

FU: NS Model: SimaPro Method: ReCiPe

MSWM using MBT-MARSS

HI Greece Georgiopoulou and Lyberatos, 2017

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1. AD S2. AD energy generation S3. COMP S4. INC

Chemical production for gas treatments was identified as a major contributor to AD, while NH3 and NOx emissions were mainly responsible for AP, EP, GWP and smog creation. Also, the study conducted sensitivity analysis for variation in chemical composition and change in carbon content and NOX emissions, which has highlighted the accuracy of inventory analysis in the study. S4 showed best results in HT and FE, i.e., the ones with the highest impact on all the waste management processes. Also, the study conducted sensitivity analysis for change in FU from 1 ton of MSW to the exergy content of products, which has highlighted the accuracy of inventory analysis in the study. Design and management of the MBT lead to substantial reduction of environmental impacts as well as material and energy resource savings. Also, the study conducted sensitivity analysis for replacement of German electricity production mix by electricity produced in MBT-MARSS, which has highlighted the accuracy of inventory analysis in the study. The results indicated that the most environmentally friendly scenario is the S2. The scenarios S1 and S4 are the most undesirable from an environmental point of view.

30

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI Italy Di Maria et al., 2013

FU: Monthly MSW generated Model: NS Method: CML

HI Italy Nessi et al., 2013

FU: 1 Ton of MSW Model: NS Method: NS

HI Italy Arena et al., 2014

FU: 1 Ton of MSW Model: SimaPro Method: Impact 2002+

Scenarios MBT and LF with partly gas flaring and partly energy generation S1: 0-week gas stabilization and 50% gas collection S2: Same as S1 but 60% gas collection S3: Same as S1 but 70% gas collection S4-S6: same as S1-S3 but four weeks of aerobic stabilization S7-S9: same as S1-S3 but eight weeks of aerobic stabilization S10-S12: Same as S1-S3 but 16 weeks of aerobic stabilization Waste treatment scenario vs. Waste prevention cum treatment scenario, by two approaches S1: conventional waste management techniques S2: domestic/on-site waste management considering the upstream processes and waste generation

S1. Combustion S2. GAS

Critical Findings

Scenarios with o week of pre-treatment have the highest weighted global impact along with highest energy recovery while four weeks of pre-treatment showed rather a negligible variation in the global impact of system emissions

Both the approaches can be used if a comparison between waste prevention techniques has to be made, while approach one can be used if a same type of waste is treated by different treatment technique or vice versa. Combustion with ferrous, non-ferrous and inert material recovery from bottom ash or Gasification with use of renewable biomass coke. Also, the study conducted sensitivity analysis for variation in the energy mix, exported electricity, the percentage of bottom ash diverted to treatment, and type of additive fuel for the high-temperature gasifier, which has highlighted the accuracy of inventory analysis in the study.

31

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI Italy Di Maria and Micale, 2014

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1: LF S2: INC and LF S3: MBT and LF S4: MBT, RDF, and LF S5: MBS, RDF, and LF S6: 25% SS, MPS, and LF S7: 25% SS, MPS, INC and LF S8: 25% SS, MBT, MPS, and LF S9: 25% SS, MBT, MPS, LF and RDF S10: 25% SS, MBS, MPS, LF and RDF S11: 30% SS, MPS, LF and COMP S12: 30% SS, MPS, INC, LF and COMP S13: 30% SS, MBT, MPS, LF and COMP S14: 30% SS, MBT, MPS, LF, RDF and COMP S15: 30% SS, MBS, MBS, LF, RDF, and COMP S16: 30% SS, MPS, LF, COMP and AD S17: 30% SS, MPS, INC, LF, COMP and AD S18: 30% SS, MBT, MPS, LF, COMP and AD S19: 30% SS, MBT, MPS, LF, RDF, COMP and AD S20: 30% SS, MBS, MBS, LF, RDF, COMP, and AD S21-S30: Same as in S11-S20 but SS is 35% S31-S40: Same as in S11-S20 but SS is 52%

The scenarios with LF were the worst followed in order by MBT and INC. The best environmental results were obtained from RDF production preferably adopting the MBS method. Also, the study conducted sensitivity analysis for change in LCIA method to EDIP, which has highlighted the accuracy of inventory analysis in the study.

HI Italy Passarini et al., 2014

FU: 1 Ton of MSW Model: SimaPro Method: Ecoindicator

Up-gradation of an INC plant from 1996 to 2011

The results showed better environmental scores after the implementation of both new procedures and the integration of operations. Also, the study conducted sensitivity analysis for change in impact categories, which has highlighted the accuracy of inventory analysis in the study.

FU: 1 MT of plastic Model: EASEWASTE Method: EDIP

S1: 90% WTE and 10% MBT S2: source separation (SS) 22% plastic collection efficiency, PET flakes and HDPE granules S3: SS 58% plastic collection efficiency, PET flakes, HDPE granules, polyolefin fraction and Plasmix to cement kilns S4: Dry bin scheme, 43.5% overall plastic collection efficiency S5: Mechanical sorting PET and HDPE to recycling, Plasmix to cement kilns

S5 implies the highest quantities of specific polymer types sent to recycling, resulted in the best option in most impact categories. Also, the study conducted sensitivity analysis for change in marginal energy, which has highlighted the accuracy of inventory analysis in the study.

HI Italy Rigamonti et al., 2014

32

Income Group, Country, and Reference HI Italy Schiavon et al., 2014

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: NS Model: NS Method: NS

S1: Bio-drying of MSW, GAS, AD, LF S2: AD, GAS, LF S3: Thermal drying, GAS, AD, LF S4: AD, INC, LF

INC has higher impacts regarding HTP and AD coupled with COMP and GAS preceded by thermal drying would contribute to net emissions corresponding to 6% of the amounts emitted by INC

design phase vs. operation phase of MSW INC

The operative process had a lower impact on all the impact categories except climate change in water depletion than process under design. Also, the study conducted sensitivity analysis by applying cut off approach in place of the system approach, which has highlighted the accuracy of inventory analysis in the study.

HI Italy Toniolo et al., 2014

FU: 1 MT of unrecyclable waste from source collection over one year Model: NS Method: ReCiPe

HI Italy Buratti et al., 2015

FU: 1 Ton of MSW Model: SimaPro Method: Impact 2002+

S1. Mixed organic waste COLL, MBT, LF S2. SC and COMP

HI Italy Di Maria et al., 2015

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1. AD, COMP, LF S2. INC, LF

HI Italy Rigamonti et al., 2016

FU: NS Model: NS Method: NS

ISWM systems in Lombardia, Milano, Bergamo, Pavia, and Mantova

S1 has the least impact on the analyzed impact categories, except on the GW. As regards the S2, the efforts to reduce the impact should be mainly focused on the reduction of air emissions from the bio-stabilization process. Also, the study conducted sensitivity analysis for carbon sequestration, increase in biogas collection efficiency, which has highlighted the accuracy of inventory analysis in the study. S2 showed maximum environmental benefits compared to AD and COMP. Furthermore, AD and COMP have high gaseous emissions with high GHG potential even if the production of organic fertilizer gave some benefits concerning the avoided exploitation of mineral resources. Also, the study conducted a sensitivity analysis for different electricity mix, which has highlighted the accuracy of inventory analysis in the study. Bergamo performed best regarding material recovery indicator, Milano in energy recovery indicator whereas Pavia in cost indicator

33

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI Italy Ripa et al., 2016

FU: MSW generated in 2012 Model: SimaPro Method: ReCiPe

HI Italy Cremiato et al., 2017

FU: 1 Ton of MSW Model: GaBi Method: CML

Scenarios S1, S2, and S3 without regional MSWM facilities S1: 95% COLL efficiency 13% Recyclable Fraction of MSW (RFMSW): 11% RECY, 1% WTE and 1% LF; 19% OFMSW: 16% COMP, 2% AD, 1% Storage and residues to LF; and 63% Mixed MSW (MMSW): 60% MBT (WTE and LF), 1% Private and 2% LF. S2: 95% COLL efficiency, 18% RFMSW: 17% RECY, <1% WTE and <1% LF; 27% OFMSW: 23% COMP, 3% AD, 1% Storage and residues to LF; and 50% MMSW: 49% MBT (WTE and LF) and 1% Private. S3: 95% COLL efficiency, 26% RFMSW: 23% RECY, 1% WTE and 2% LF; 34% OFMSW: 28% COMP, 5% AD, 1% Storage and residues to LF; and 35% Mixed MSW MMSW: 34% MBT (WTE and LF) and 1% Private. S1-b, S2-b, and S3-b with new regional MSWM facilities S1. Diversion rate = 50%. MRF, MBT (unsorted waste), WTE, COMP, LF S2. Same as S1, diversion rate = 60% S3. Diversion rate = 50%. MRF, WTE, AD, LF S4. Same as S3, diversion rate = 60%

HI Italy Lombardi and Carnevale, 2017

FU: 1 Ton of MSW Model: NS Method: ReCiPe

MSWM by WTE

HI Japan Tabata, 2013

FU: 1 MT of combustible waste Model: NS Method: NS

Incineration plant in seven Japanese metropolises: Sapporo, Tokyo, Nagoya, Osaka, Kobe, Takamatsu, and Fukuoka

Critical Findings

S3-b implements the high-quality source separation strongly reduces the need for MBT and WTE plants, resulting in a net reduction of the environmental burdens

The scenario with high diversion rate and AD gave the best results, as the use of integrated AD and digestate composting, and utilization of recovered material reduces the environmental burdens considerably. Cogeneration of heat and power leads to negative values for climate change. Also the inclusion of bottom ash recovery - in place of LF - can reduce the environmental impacts considerably. Also, the study conducted sensitivity analysis for change in electric energy mix and change of MSW transportation distance, which has highlighted the accuracy of inventory analysis in the study. Emissions from INC without power generation were 620 kg CO2 t-1 and with power generation was 571 kg CO2 t-1. Plastic burning and electricity savings were the significant factors or GHG reducers. From the results for Kobe, 16.2% and 25% of electricity demand and hot water demand, respectively, could be satisfied bub a net GHG reducer.

34

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI Japan Takata et al., 2013

FU: 1 Ton of MSW Model: NS Method: NS

S1: Integrated wet AD S2: Integrated dry AD S3: Simple wet AD S4: Simple dry AD S5: Machine-integrated COMP S6: Conventional COMP

S3 was the best scenario with the minimum environmental impact

HI Japan Matsuda et al., 2018

FU: 1 MT of prevented or recycled waste Model: NS Method: NS

S1: Relative change from the baseline year S2: Absolute change from potential waste generation S3: Absolute amount of activities

S1 is most suitable when the focus is on monitoring of a specific policy. S2 and S3 can be used when comparing the total impact of waste prevention to that of recycling.

HI South Korea Yi and Jang, 2018

FU: 1 Ton of MSW Model: TOTAL 4.0 Method: CML

HI Kuwait Aleisa and Aljarallah, 2017

FU: 1 Ton of MSW Model: SimaPro Method: CML

HI Lithuania Malijonyte et al., 2016

FU: 1 GJ of fuel input to INC Model: SimaPro Method: NS FU: Annual MSW generated Model: SimaPro and IWM Method: NS

HI Poland Kulczycka et al., 2015

S1: RDF with biodrying S2: RDF with heated air dry gas drying S3: RDF with hot air drying using fossil fuels S4: same as in S3 with different input and output of MSW composition S5: same as in S3 with different input and output of MSW composition S6: Fluff type RDF with natural air drying S7: INC S1: LF S2: INC S3: COMP, LF S4: COM, INC S5: MRF, INC S6: MRF, COM, INC

S1 was found to be the best performing among all scenarios. Also, the study conducted sensitivity analysis for drying MSW from heated dry gas from RDF combustion, which has highlighted the accuracy of inventory analysis in the study.

Environmentally, S1 scored the worst in almost all impact categories and, thus, was labeled the worst-case scenario environmentally. S6 performed the best from an environmental perspective. Financially, S1 is the most economical scenario, and S2 and S5 were economically unfavorable. The S3 and S4 were scored as economically reasonable.

S1: Tires S2: RDF-MRF S3: RDF-sludge

S1 had the highest energy potential followed by S3 and S1

S1. IWM S2. Simapro

IWM identified a smaller number of substances and did not cover emissions to the soil and consumption of resources. Also, the study conducted sensitivity analysis for change in the data source, which has highlighted the accuracy of inventory analysis in the study.

35

Income Group, Country, and Reference HI Saudi Arabia Ouda et al., 2016 HI Saudi Arabia Shahzad et al., 2017 HI Singapore Ahamed et al., 2016 HI Spain Margallo et al., 2013 HI Spain Montejo et al., 2013 HI Spain Sevigné Itoiz et al., 2013 HI Spain FernandezNava et al., 2014 HI Spain Margallo et al., 2014b

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

FU: NS Model: NS Method: NS

S1: INC S2: RDF and AD

S2 proved to be the most suitable WTE technology for the study area. The study was also based on cost-benefit analysis.

FU: Annual MSW generated Model: NS Method: NS

Biodiesel production from MSW

It reduces the operation and environmental burdens along with saving in the expenditure by the diversion of waste from LF and revenue generation from biodiesel. The study was also based on cost-benefit analysis.

FU: 1 MT of FW Model: SimaPro Method: CML

S1: Inc, LF S2: AD S3: Food waste to bio-diesel

S3 would be the best-suited option when oil content is less than 5%, else S2 when oil content is more than or equal to 5 %. The study was also based on cost-benefit analysis.

S1: INC of MSW and ash solidification and LF S2: INC of MSW and utilization of sludge in portland cement production

S2 leads to environment benefits

S1: MBT 1 (RDF, MBT, AD) MBT 1-1 to 1-4 (COLL, RECY, LF, and INC) S2: MBT 2 (RDF, MBT, COMP) MBT 2-1 to 2-4 (COLL, RECY, LF, and INC)

S1 plants with efficient energy recovery from the biogas performed better.

FU: 1 Ton of MSW Model: GaBi Method: CML FU: 1 Ton of MSW Model: EASETECH Method: EDIP FU: 1 Ton of MSW Model: NS Method: NS FU: Annual MSW generated Model: SimaPro Method: NS FU: 1 Ton of MSW Model: GaBi Method: NS

S1: 45.7% MBT, 9.1% INC and 45.3% LF S2: 90.9% MBT and 9.1% INC S3: 45.7% MBT and 54.4% INC S4: 68.3% MBT and 31.7 % INC S1. INC S2. INC, AD S3. INC, AD, MRF S4. INC, AD, MRF, aerobic stabilization S5. AD, MRF, aerobic stabilization, LF INC of MSW

S4 presented the least impacts on GW. Also, the study conducted sensitivity analysis for waste composition, waste collection and transport, waste treatment, and waste recycling, which has highlighted the accuracy of inventory analysis in the study. The results obtained suggest that S3 has the least impact while the S5 produces the greatest impact in all the categories analyzed. INC plants had 1.1 to 2 times higher consumption of natural resources than Spanish reference consumption

36

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI Spain Quirós et al., 2014

FU: 1 MT of horticulture crop of cauliflower per hectare Model: SimaPro Method: CML

S1: Industrial Compost S2: Home compost S3: Mineral fertilizer

HI Spain Bueno et al., 2015

FU: NS Model: IWM Method: CML

S1: INC S2: Aerobic MBP

HI Spain FernándezGonzález et al., 2017

FU: NS Model: SimaPro Method: Impact 2002+

S1: MBT and LF S2: MBT and LF with LFG to energy S3: MBT, AD, and LF with LFG to energy S4: MBT, RDF, LF with LFG to energy S5: MBT, GAS, and LF S6: MBT, INC, and LF

Scenarios

Critical Findings Mineral fertilizer had the highest yield, while, home compost resulted in larger, heavier cauliflowers. HC had the best environmental performance in all the impact categories except EP. Also, the study conducted sensitivity analysis for change in transportation distance, which has highlighted the accuracy of inventory analysis in the study. When the separation rate is 25%, S1 showed better environmental results, but the situation changes as the separation rate increases to 75%. Also, the study conducted sensitivity analysis for change in the rate of recycling and change in the electricity mix, which has highlighted the accuracy of inventory analysis in the study. Any WTE alternatives including INC present important advantages for the environment when compared to BMT. The study was also based on cost-benefit analysis.

37

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI Spain Pérez et al., 2017a

FU: Annual MSW generated Model: SimaPro Method: ILCD

RL: Rear loading, SL: Side Loading and TP: Top loading F1: Mixed waste, F2: Packaging waste, F3: Paper/cardboard waste and F4: glass M1: HDPE, M2: Rubber, M3: Steel, M4: Glass fiber reinforced plastic S1: RL-120L, F1, F2, M1 S2: RL-240L, F1, F2, F3, M1 S3: RL-330L, F1, F2, M1 S4: RL-360L, F1, F2, M1 S5: RL-800L, F1, F3, M1 S6: RL-800L, F2, M1 S7: SL-2400L, F1, M1 S8: SL-2400L, F2, M1 S9: SL-3200L, F1, M1 S10: SL-3200L, F2, M1 S11: TP-2700L, F2, M1 S12: TP-2700L, F3, M1 S13: TP-2700L, F4, M1 S14: TP-3000L, F3, M3 S15: TP-3000L, F4, M4

for similar containers, environmental impacts decrease with both increase in capacity and lifetime HDPE containers performed better than steel and glass fibre containers in all the impact categories except ME

HI Spain Pérez et al., 2017b HI Spain and Portugal Margallo et al., 2014a HI Spain and Sweden Aracil et al., 2017

FU: 1 Ton of MSW Model: GaBi Method: NS

S1: CNG S2: Diesel S3: Biogas from AD

S3 performed better than S1 followed by S2. Also, the study conducted sensitivity analysis for annual fuel consumption and average service life of the vehicle, which has highlighted the accuracy of inventory analysis in the study.

FU: 1 Ton of MSW Model: NS Method: NS

S1: INC with Thermal and Flue gases treatment S2: INC with Magnetic separation of slag S3: INC with Ash solidification S4: INC with final disposal of slag at LF

For the non-inert waste identical amount of slag and ash is generated as mass allocation was applied, whereas, the entire inert waste is converted to slag and none to ash

FU: 1 MJ of total products from BioRefinery Model: Method:

S1: Biofuels production S2: LF and INC

Production of biofuels from MSW refuse mitigates the climate change. Also, the study conducted sensitivity analysis for change in climate mitigation index, which has highlighted the accuracy of inventory analysis in the study.

38

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI Sweden Zaman, 2013 HI United Arab Emirates Arafat et al., 2015 HI United Kingdom Jeswani et al., 2013

Scenarios

Critical Findings

FU: 1 Ton of MSW Model: SimaPro Method: CML

S1. GAS-PY S2. INC

S1 reflects the low environmental burdens compared to S2. Also, the study conducted sensitivity analysis for 30% fewer emissions in PG process for the next five years with 5% more electricity generation efficiency, which has highlighted the accuracy of inventory analysis in the study.

FU: NS Model: SimaPro Method: CML

S1. GAS S2. INC S3. AD S4. BLF S5. COMP

AD (for paper, food, yard, and wood wastes); GAS (for treatment of plastic waste); and INC (for treatment of textile waste)

FU: 1 Ton of MSW Model: GaBi Method: NS

S1: INC S2: LF with LFG to energy

HI United Kingdom Al-Salem et al., 2014

FU: Amount of MSW generated Model: GaBi Method: NS

MSW Management S1: MRF and INC S2: LF Plastic waste management S1: Low-temperature pyrolysis S2: Hydrogenation reactor

HI United Kingdom Evangelisti et al., 2014

FU: Annual MSW generated Model: GaBi Method: EDIP

S1: LF S2: INC S3: AD

The results indicate that waste incineration offers significant savings of GHG compared to LF. Also, the study conducted sensitivity analysis for change in waste composition, energy credits, and LFG recovery rate and utilization, which has highlighted the accuracy of inventory analysis in the study. S1 would be the best suited for in MSW management scenario.S1 or S2, would be the best suited depending on the MRF rate for Plastic waste management scenario. Also, the study conducted sensitivity analysis for material recovery and substitution, which has highlighted the accuracy of inventory analysis in the study. S3 showed the best treatment option regarding total GW and AP when energy and organic fertilizer obtained from the waste substitute non-renewable electricity, heat and inorganic fertilizer. Also, the study conducted sensitivity analysis for fugitive emissions of CH4 from the AD plant, efficiency of the ICE running with biogas, emissions from digestate use, and sequestration of digestate carbon, which has highlighted the accuracy of inventory analysis in the study.

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Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI United Kingdom Burnley et al., 2015

FU: 1000 MT of residual MSW Model: WRATE and EASETECH Method: CML

HI United Kingdom Evangelisti et al., 2015

FU: 1 Ton of MSW Model: GaBi Method: NS

HI United Kingdom Parkes et al., 2015

FU: 1 Ton of MSW Model: GaBi Method: CML

HI United Kingdom Jeswani and Azapagic, 2016

FU1: 1 Ton of MSW FU2: Generation of 1 kWh of electricity Model: GaBi Method: CML

Scenarios S1: LF of residual MSW (RMSW) S2: INC with energy recovery and metal recovery from bottom ash S3: INC with energy recovery without aluminum recovery from bottom ash S4: INC with energy recovery without ferrous metal recovery from bottom ash S5: INC with energy recovery without any metal or aggregate recovery from bottom ash S1. GAS and plasma gas cleaning S2. Fast PY and COMB S3. GAS with syngas COMB S4. LF with gas recovery S5. Inc with electricity production S1: COMP, RECY, and LF S2: COMP, RECY, and INC S3: AD, RECY, and LF with energy recovery S4: AD, RECY, and INC S5: RECY and LF with energy recovery S6: RECY and INC S7: COMP, RECY, and GAS S8: AD, RECY, and GAS S9: MBT, COMP, RECY, and INC S10: MBT, AD, RECY, and INC S1: INC with electricity generation S2: INC with CHP S3: LF with LFG to electricity S4: LF with CHP

Critical Findings

INC had a significant advantage over LF and to maximize the benefits of energy recovery, metals, particularly aluminum, should be reclaimed from the residual bottom ash. Also, the study conducted sensitivity analysis for an increase in energy efficiency which has highlighted the accuracy of inventory analysis in the study.

The two-stage GAS and plasma process has a significantly better overall performance than the other compared treatment options

The treatment scenarios that include INC with energy recovery provide a best environmental solution. Also, the study conducted sensitivity analysis for change in recycling rate and MSW composition which has highlighted the accuracy of inventory analysis in the study.

INC has much lower impacts that LF across all the impact categories except HTP. Also, the study conducted sensitivity analysis for change in electricity credits, change in heat credits, change for recovered steel, change in average operational data for incinerators, and change in the rate of gas recovery and utilization which has highlighted the accuracy of inventory analysis in the study.

40

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

HI United Kingdom Ng et al., 2016 HI United Kingdom Turner et al., 2016 HI United Kingdom Sadhukhan and Martinezhernandez, 2017

HI United Kingdom Tagliaferri et al., 2018

HI United States Bozorgirad et al., 2013

Scenarios

Critical Findings

FU: 1 MT of zinc Model: Gabi Method: CML

S1: Zinc recovery from steelmaking dust S2: Zinc recovery from MSW

The additional economic margin of 500 pounds can be generated from the recovery of 1 ton of zinc from MSWM. Also, the study conducted sensitivity analysis for transportation and oil content, which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis.

FU: Annual MSW generated Model: EASETECH Method: NS

S1. Base scenario S2. AD S3. Inc S4. Recycling

S2 was the only scenario that resulted in overall GHG savings. Also, the study conducted sensitivity analysis for carbon sequestration which has highlighted the accuracy of inventory analysis in the study.

FU: 1 ton of MSW Model: GaBi Method: Impact 2002+ and CML

The mechanical, biological chemical treatment system of MSW

RDF and non-recyclable other waste, char, and biogas from MRF, chemical conversion, and AD systems are energy recovered in the CHP system, and Levulinic acid gives profitability independent of subsidies.

FU: 1 MWh of electricity Model: GaBi Method: CML and USEtox

S1: Hardwood combustion at Heathrow combustion and ORC plant S2: Softwood combustion in the Heathrow plant S3: energy from hardwood in a biomass boiler followed by a steam turbine S4: INC of MSW S5: Electricity using hard coal S6: electricity using natural gas S7: UK electricity mix S8: 2030 UK electricity mix

Use of biomass reduces GHG emissions, and more benefits can be achieved by collecting and utilizing bottom ash as a soil conditioner for land-farming or forestry.

S1: INC with heat recovery S2: INC with energy recovery S3: Ethanol production

The results indicated that S2 has the highest benefits for resource availability while S3 is the best alternative to avoid human health and ecosystems diversity impacts. Also, the study conducted sensitivity analysis for change in MW composition which has highlighted the accuracy of inventory analysis in the study. The study was also based on cost-benefit analysis.

FU: 1 ton of MSW Model: SimaPro Method: ReCiPe

41

Income Group, Country, and Reference

Functional Unit (FU), Model & Method

Scenarios

Critical Findings

HI United States Levis et al., 2013

FU: Total MSW set out at the curb Model: SWOLF Method: NS

S1(Minimum cost) S1.1: Mixed waste (MW) to LF S2(Diversion) S2.1: MW to INC and Ash to Ash LF (ALF) S2.2: MW to INC and Ash to LF S3(GHG) S3.1: MW to LF, MRF 80% (efficiency), MRF residual waste (MRFRW) to LF S3.2: MW to LF, MRF 80%, MRFRW to INC and Ash to ALF S3.3: MW to LF, MRF 80%, MRFRW to INC and Ash to LF S3.4: MW to LF, MRF 90%, MRFRW to LF S3.5: MW to LF, MRF 90%, MRFRW to INC and Ash to ALF S3.6: MW to LF, MRF 90%, MRFRW to INC and Ash to LF S3.7: MW to INC, MRF 80%, MRFRW to LF and Ash to ALF S3.8: MW to INC, MRF 80%, MRFRW and Ash to LF S3.9: MW to INC, MRF 80%, MRFRW to INC and Ash to ALF S3.10: MW to INC, MRF 80%, MRFRW and Ash to INC S3.11: MW to INC, MRF 90%, MRFRW to LF and Ash to ALF S3.12: MW to INC, MRF 90%, MRFRW and Ash to LF S3.13: MW to INC, MRF 90%, MRFRW to INC and Ash to ALF S3.14: MW to INC, MRF 90%, MRFRW and Ash to INC

S1 has the minimum cost followed by S2 and S3. S3 showed the best result in the diversion of waste from LF and annual GHG emission reduction.

HI United States Nuss et al., 2013 HI United States Pressley et al., 2014

FU: 1 kg of ethylene Model: SimaPro Method: NS

1. MSW to LF 2. MSW to INC 3. MSW to ethylene

S3 performed better than S1, but in comparison to S2, energy demand, material requirement, acidification, and smog potential were more in S3. The study was also based on cost-benefit analysis.

FU: 1 ton of MSW Model: NS Method: NS

MSW to liquid transportation fuels via GAS and FischerTropsch (FT)

The liquid fuel produced by GAS and FT resulted in a reduction in GWP and increased in gas reaction rate.

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Income Group, Country, and Reference HI United States Pressley et al., 2015 HI United States Wang et al., 2015 HI United States Coventry et al., 2016

Functional Unit (FU), Model & Method FU: 1 ton of MSW Model: SWOLF Method: NS FU: 1 ton of MSW Model: GaBi Method: CML FU: 1 ton of MSW Model: NS Method: NS

HI United States Lee et al., 2017

FU: 1 ton of MSW Model: NS Method: NS

HI United States Tucker et al., 2018

FU: 1 kg glass Model: WARM Method: NS

Generic Gabbar et al., 2017 Generic Gabbar et al., 2017

FU: 1 ton of MSW Model: NS Method: NS FU: 1 ton of MSW Model: NS Method: NS

Scenarios S1: MRF with single stream waste S2: MRF with Dual stream waste S3: MRF with pre-sorted waste S4: MRF with mixed waste S1: Fast pyrolysis S2: AD S3: INC S4: LF S1. Dry-Tomb LF S2. LF gas-to-energy S3. Advanced thermal recycling S4. GAS LF of MSW Residential and Commercial glass recycling program, MRF, Recovered glass cullet, then S1: Traditional container recycling S2: LF S3: Use as a pozzolan in concrete manufacturing S1: PY S2: GAs S3: Combined PY-GAS S1: PY S2: GAS S3: Combined PY-GAS

Critical Findings S3 was the cheaper, less energy-intensive and less GHG intensive among all compared scenarios. The study was also based on cost-benefit analysis. Fast pyrolysis for bio-oil causes the least impact on the environment

S4 outperformed all the treatment scenarios in all the matrices Emissions of non-collected CH4 were the highest contributor to GHG emissions. Also, the study conducted sensitivity analysis for variation in DOC methane oxidation climate condition, methane concentration, LFG collection and utilization, which has highlighted the accuracy of inventory analysis in the study. S3 reduces environmental impacts relative to S1, and similar to S2 Combined PY-GAS shows the best-optimized process with low environmental emissions. The study was also based on costbenefit analysis. Combined PY-GAS shows the best-optimized process with low environmental emissions. The study was also based on costbenefit analysis.

S1, S2, S3: stands for scenarios chosen in respective studies. Abbreviations used in Table1: LF (Landfill); COMP (Composting); RDF (Refuse Derived Fuel); GAS (Gasification); PY (Pyrolysis); INC (Incineration); RECY (Recycling); AD (Anaerobic Digestion); MC (Mixed Collection); SC (Separate Collection); MBT (Mechanical and Biological Treatment); AnMBT (Anaerobic Mechanical and Biological Treatment); AeMBT (Aerobic Mechanical and Biological Treatment); WTE (Waste to Energy); TS (Transfer station); UnLF (Unsanitary Landfill); SLF (Sanitary Landfill);

43

TRANS (Transportation); COLL (Collection); AIF (Advanced INC facility); LFG (Landfill gas); LFGTE (LFG to energy); GHG (Greenhouse gas); NS (Not specified); AP (Acidification Potential); GWP (Global Warming Potential); HTcar (Human Toxicity carcinogenic); HT non car (HT non cacinogenic); ADP (Abiotic Depletion Potential); TA (Terrestrial Acidification); TE (Terrestrial Eutrophication); ODP (Ozone Depletion Potential); PO (Photochemical Oxidation) and ET (Ecotoxicity).

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3.6. Gaps and the Critical Findings of the Application of LCA in MSWM As shown in Fig. 8, the maximum numbers of studies are from HI group countries; however, the number of publication of LCA studies per country is lower in HI (71/22) than UIM group countries (66/11). Even the countries with middle annual per capita income, such as, India (4), Brazil (6) and Thailand (6), have higher number of LCA studies than high annual per capita income countries, such as, Kuwait (1), South Korea (1) and UAE (1), which is equal to the condition in Nepal (1), a low per capita income country. Also as shown in Fig. 9, only 22 out of 78 total HI countries in the world have published LCA studies on MSWM in the recent years, similarly in the case of LIM (6/53) and UIM (11/56), and worst in LI (1/31) group countries. The low or high per capita income is not the sole reason behind the less number of studies, but the availability of reliable data, active LCA communities, organizations and life cycle thinking (Yadav and Samadder, 2018a). Consciousness amongst the educational, scientific & industries and political, motivation are the critical factors which increase the research and number of publications (Fullana et al., 2008). 80

71

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60 50 40 30

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11 6

1

1

0 LI

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Fig. 8. Distribution of LCA studies and the counties in which they are conducted

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80 70 60

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50 40

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Fig. 9. Distribution of considered countries and a total number of countries in a particular income group

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Discussion and Conclusion

The review revealed that most of the studies conducted since 2013 have their coverage limited mostly to Europe and Asia. The distribution of LCA studies concerning the assessed MSWM system and waste types reflected the environmental concerns specific to this region. Out of the total countries throughout the world, 178 countries haven’t published a single study on the LCA of MSWM since 2013. Sustainable MSWM system must be environmentally effective, economically affordable and socially acceptable, but very limited numbers of studies have considered cost-benefit analysis or Life Cycle Costing of MSWM system. It should also be noticed that the social LCA was only reviewed by Fernández-González et al., 2017; Harijani et al., 2017; Menikpura et al., 2013a and Tulokhonova and Ulanova, 2013. It’s mostly because of the data unavailability and time and economic constraints. Participation of public, nongovernment and private organizations through training courses and seminars might help in improving the LCA applicability (Fullana et al., 2008) in the field of MSWM. Also, the government consciousness in providing proper incentives along with the implication of more environment-friendly policies and initiatives like UNEP-SETAC Life Cycle Initiative helps in skill improvisation of decision-makers by providing information, materials, and launching forums for dissemination of best practices. The present study critically analyzed the use of the functional unit, LCA model, LCIA method and various MSWM scenarios. It is not necessary that the maximum used LCA methodology components in previously published studies will always be suitable for every study. But the present study attempted to provide an outlook of previous studies that will help in the selection of LCA components. The selection criteria depend on various factors, viz., MSW composition, MSWM practices, system boundary, environmental regulations, geographical scope, etc. Thus, the present study can help in assisting LCA practitioners in identifying suitable LCA components based on the pertaining factors of the study area. The results of the previous studies should be used by considering the regional conditions. To make better-informed decisions, decision-makers should consider LCA studies which contain waste hierarchy modified to local conditions concerning the site-specific waste composition, treatment efficiencies, energy mix, etc. Thus it may not be identical to the basic waste hierarchy of 3R’s (Reduce, Reuse and Recycle). Decision makers in the field of MSWM are therefore recommended to consider LCA as a tool to determine “context-specific waste hierarchies” that are consistent with the local conditions of each MSWM system. The results 47

ACCEPTED MANUSCRIPT from LCA study can support decision making concerning the planning and optimizing ISWM system which could play a significant role in the development of future waste management strategies.

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ACCEPTED MANUSCRIPT Finkbeiner, M., 2014. Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment. J. Clean. Prod. 69, 34–48. https://doi.org/10.1016/j.jclepro.2014.01.044 Matsuda, T., Hirai, Y., Asari, M., Yano, J., Miura, T., Ii, R., Sakai, S. ichi, 2018. Monitoring environmental burden reduction from household waste prevention. Waste Manag. 71, 2– 9. https://doi.org/10.1016/j.wasman.2017.10.014 McDougall, F., White, P., Franke, M., Hindle, P., 2001. Integrated solid waste management: a Life Cycle Inventory, 2nd ed, Blackwell Science Ltd. https://doi.org/10.1007/978-14615-2369-7 Menikpura, S.N.M., Gheewala, S.H., Bonnet, S., Chiemchaisri, C., 2013a. Evaluation of the effect of recycling on sustainability of municipal solid waste management in Thailand. Waste and Biomass Valorization 4, 237–257. https://doi.org/10.1007/s12649-012-91195 Menikpura, S.N.M., Sang-arun, J., Bengtsson, M., 2013b. Integrated Solid Waste Management: An approach for enhancing climate co-benefits through resource recovery. J. Clean. Prod. 58, 34–42. https://doi.org/10.1016/j.jclepro.2013.03.012 Menikpura, S.N.M., Sang-Arun, J., Bengtsson, M., 2016. Assessment of environmental and economic performance of Waste-to-Energy facilities in Thai cities. Renew. Energy 86, 576–584. https://doi.org/10.1016/j.renene.2015.08.054 Menikpura, S.N.M., Sang-Arun, J., Bengtsson, M., 2013c. Climate co-benefits of energy recovery from landfill gas in developing Asian cities: A case study in Bangkok. Waste Manag. Res. 31, 1002–1011. https://doi.org/10.1177/0734242X13492004 Montejo, C., Tonini, D., Márquez, C., Fruergaard, T., Márquez, M. del C., Fruergaard Astrup, T., 2013. Mechanical-biological treatment: Performance and potentials. An LCA of 8 MBT plants including waste characterization. J. Environ. Manage. 128, 661–673. https://doi.org/10.1016/j.jenvman.2013.05.063 Munir, S., Baqar, M., Saeed, N., Zameer, M., Shaikh, I.A., 2015. Modeling Greenhouse Gases Emissions from MSW of Lahore.

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ACCEPTED MANUSCRIPT Nabavi-Pelesaraei, A., Bayat, R., Hosseinzadeh-Bandbafha, H., Afrasyabi, H., Berrada, A., 2017a. Prognostication of energy use and environmental impacts for recycle system of municipal solid waste management. J. Clean. Prod. 154, 602–613. https://doi.org/10.1016/j.jclepro.2017.04.033 Nabavi-Pelesaraei, A., Bayat, R., Hosseinzadeh-Bandbafha, H., Afrasyabi, H., Chau, K. wing, 2017b. Modeling of energy consumption and environmental life cycle assessment for incineration and landfill systems of municipal solid waste management - A case study in Tehran Metropolis of Iran. J. Clean. Prod. 148, 427–440. https://doi.org/10.1016/j.jclepro.2017.01.172 Naderi, M., Baghonabad, M.S., Amiri, M.J., Rezazadeh, M., 2014. Greenhouse Gas Emissions ( CO 2 -CH 4 ) from Municipal Solid Waste Management Using Life Cycle Assessment ( LCA ) in Mahdsht City ( IRAN ) 9, 470–477. Nasrollahi-Sarvaghaji, S., Alimardani, R., Sharifi, M., Taghizadeh-Yazdi, M.R., 2016. ‫ﺩﻣﺎﺝ‬ ‫ ﻧﺎﺗﺴﺮﻫﺶ ﻧﺎﺭﻫﺖ ) ﻫﺴﯿﺎﻗﻢ ﺗﺎﺭﺛﺎ ﺗﺴﯿﺰ ﯾﻄﯿﺤﻢ ﯾﺎﻫﻮﯾﺮﺍﻧﺲ ﻓﻠﺘﺨﻢ ﺷﺰﺍﺩﺭﭖ ﻭ ﻋﻔﺪ ﺩﻧﺎﻣﺴﭗ‬: ‫ ( ﻫﻌﻼﻃﻢ ﯾﺪﺭﻭﻡ‬LCA ‫ ﯾﺮﻫﺶ ﻫﺐ ﮐﻤﮏ ﺷﻮﺭ‬9, 273–288. Nessi, S., Rigamonti, L., Grosso, M., 2013. Discussion on methods to include prevention activities in waste management LCA. Int. J. Life Cycle Assess. 18, 1358–1373. https://doi.org/10.1007/s11367-013-0570-8 Ng, K.S., Head, I., Premier, G.C., Scott, K., Yu, E., Lloyd, J., Sadhukhan, J., 2016. A multilevel sustainability analysis of zinc recovery from wastes. Resour. Conserv. Recycl. 113, 88–105. https://doi.org/10.1016/j.resconrec.2016.05.013 Ning, S.-K., Chang, N.-B., Hung, M.-C., 2013. Comparative streamlined life cycle assessment for two types of municipal solid waste incinerator. J. Clean. Prod. 53, 56–66. https://doi.org/10.1016/j.jclepro.2012.09.007 Nouri, J., Omrani, G.A., Arjmandi, R., Kermani, M., 2014. Comparison of solid waste management scenarios based on life cycle analysis and multi-criteria decision making (Case study: Isfahan city). Iran. J. Sci. Technol. 38, 257–264. Nuss, P., Gardner, K.H., Bringezu, S., 2013. Environmental implications and costs of municipal solid waste-derived ethylene. J. Ind. Ecol. 17, 912–925. 60

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ACCEPTED MANUSCRIPT 2017. Life Cycle Assessment ( Lca ) of Municipal Solid Waste Management Systems in Cluj County , Romania. Environ. Eng. Manag. J. 16, 47–57. Pressley, P.N., Aziz, T.N., Decarolis, J.F., Barlaz, M.A., He, F., Li, F., Damgaard, A., 2014. Municipal solid waste conversion to transportation fuels: A life-cycle estimation of global warming potential and energy consumption. J. Clean. Prod. 70, 145–153. https://doi.org/10.1016/j.jclepro.2014.02.041 Pressley, P.N., Levis, J.W., Damgaard, A., Barlaz, M.A., DeCarolis, J.F., 2015. Analysis of material recovery facilities for use in life-cycle assessment. Waste Manag. 35, 307–317. https://doi.org/10.1016/j.wasman.2014.09.012 Quirós, R., Villalba, G., Muñoz, P., Font, X., Gabarrell, X., 2014. Environmental and agronomical assessment of three fertilization treatments applied in horticultural open field crops. J. Clean. Prod. 67, 147–158. https://doi.org/10.1016/j.jclepro.2013.12.039 Raharjo, S., Junaidi, N.E., Vera, S., 2016. Development of C ommunity- B ased W aste R ecycling ( garbage bank and 3R waste treatment facility ) for M itigating G reenhouse G as E missions in Padang City , INDONESIA 8–12. Rajaeifar, M.A., Ghanavati, H., Dashti, B.B., Heijungs, R., Aghbashlo, M., Tabatabaei, M., 2017. Electricity generation and GHG emission reduction potentials through different municipal solid waste management technologies: A comparative review. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2017.04.109 Rajaeifar, M.A., Tabatabaei, M., Ghanavati, H., Khoshnevisan, B., Rafiee, S., 2015. Comparative life cycle assessment of different municipal solid waste management scenarios in Iran. Renew. Sustain. Energy Rev. 51, 886–898. https://doi.org/10.1016/j.rser.2015.06.037 Rajcoomar, A., Ramjeawon, T., 2017. Life cycle assessment of municipal solid waste management scenarios on the small island of Mauritius. Waste Manag. Res. 35, 313– 324. https://doi.org/10.1177/0734242X16679883 Reza, B., Soltani, A., Ruparathna, R., Sadiq, R., Hewage, K., 2013. Resources , Conservation and Recycling Environmental and economic aspects of production and utilization of RDF as alternative fuel in cement plants : A case study of Metro Vancouver Waste 62

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ACCEPTED MANUSCRIPT Highlights 

Critical review of LCA studies of MSWM all over the world published since 2013



Adoption of LCA methodology evaluated



Comparison of LCA studies on the basis of income level of the respective countries



Gaps identified in application of LCA all over the world