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Global warming factor of municipal solid waste management in Europe Emmanuel Gentil, Julie Clavreul and Thomas H. Christensen Waste Manag Res 2009 27: 850 originally published online 6 October 2009 DOI: 10.1177/0734242X09350659 The online version of this article can be found at: http://wmr.sagepub.com/content/27/9/850

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ISSN 0734–242X Waste Management & Research 2009: 27: 850–860

DOI: 10.1177/0734242X09350659

Global warming factor of municipal solid waste management in Europe Emmanuel Gentil, Julie Clavreul, Thomas H. Christensen Department of Environmental Engineering, Technical University of Denmark, Kongens Lyngby, Denmark

The global warming factor (GWF; CO2-eq. tonne–1 waste) performance of municipal waste management has been investigated for six representative European Member States: Denmark, France, Germany, Greece, Poland and the United Kingdom. The study integrated European waste statistical data for 2007 in a life-cycle assessment modelling perspective. It is shown that significant GWF benefit was achieved due to the high level of energy and material recovery substituting fossil energy and raw materials production, especially in Denmark and Germany. The study showed that, despite strong regulation of waste management at European level, there are major differences in GWF performance among the member states, due to the relative differences of waste composition, type of waste management technologies available nationally, and the average performance of these technologies. It has been demonstrated through a number of sensitivity analyses that, within the national framework, key waste management technology parameters can influence drastically the national GWF performance of waste management. Keywords: Greenhouse gas emission, global warming contribution, global warming factors, Europe, waste management, environmental performance, municipal solid waste

Introduction The management of waste has evolved significantly over the years in Europe. In most Member States (MS), a steady decline of landfilling practice and an increase of incineration, recycling and composting has been observed between 1995 and 2007 (Eurostat, 2009). However, the different MS have evolved at different pace, with different national priorities. For instance, in Scandinavia, incineration of waste with electricity and heat recovery has been prioritized. Denmark has relied on the incineration of waste for about 100 years and is now considered to have one of the most advanced technologies in terms of energy efficiency and pollution abatement (Kleis & Dalager, 2005). On the other hand, the UK has relied historically mainly on landfilling but is facing drastic changes in its waste management infrastructure. All these waste management activities have well documented environmental impacts that are different depending on the quantity and composition of waste produced, as well as the type of waste management technology used, and their associated technical specifications. It has also been demonstrated that waste management activities generate potential environmental benefits if managed properly (e.g. energy produced substituting fossil

energy sources and production of material substituting raw product elsewhere in the supply chain). Many studies assessed the detrimental and beneficial environmental aspects of waste management using life-cycle assessment (LCA) approach (Clift et al. 2000, Ekvall & Finnveden 2000, McDougall et al. 2001). A number of studies have specifically focused on greenhouse gas (GHG) emissions and their associated global warming potentials for waste management activities on a national level (Fisher et al. 2006) or a European level (Smith et al. 2001; Skovgaard et al. 2007, 2008, 2009). The overall objective of this paper was to investigate the environmental performance of waste management activities in selected MS with a focus on greenhouse gas (GHG) emissions and savings. It should be noted that a comprehensive LCA study should include other environmental impacts. The first part of this paper presents our methodology including the choice of MS and their respective waste management practices, in 2007, applied to life cycle modelling. The second part presents the results of the climate change impact performance of municipal solid waste (MSW) management. Finally, this study discusses how existing waste management

Corresponding author: Thomas H. Christensen, Department of Environmental Engineering, Building 115, Technical University of Denmark, DK2800 Kongens Lyngby, Denmark. E-mail: [email protected] Received 17 August 2009; accepted in revised form 9 September 2009 Figures 1, 3 appear in color online: http://wmr.sagepub.com

850 Downloaded from wmr.sagepub.com at Imperial College London - on July 23, 2010

Global warming factor of MSW management in Europe

practices undertaken in Europe can be potentially improved to reduce GHG emissions and increase savings through material and energy substitution.

Methodology Scope and functional unit The scope of this study is to quantify the impacts and benefits of municipal waste management practice in Europe in 2007, using the EASEWASTE model (Kirkeby et al. 2006). The functional unit used is the management of one metric tonne of wet waste collected by municipal and private waste management organizations in 2007 for selected MS. For specific illustrative examples, the functional unit has been extended to include the total MSW produced in 2007.

System boundaries The system boundaries chosen in the study are MSW from the point of collection by the waste management organization to final disposal for selected MS. The boundaries have been expanded to include exchanges with forestry (paper and card recycling), industry (glass, plastic, aluminium, ferrous materials recycling) and energy systems (heat and electricity substitution). The study includes only the system exchanges that are directly connected to the waste industry. For instance excess wood production, generated from increased recycling of paper and cardboard, is assumed currently not to be used as biomass by the energy sector (substituting fossil energy) and therefore is not credited in the study. Secondary materials exported outside the selected MS are considered to be subject to the same technologies as if managed within the MS. The time horizon for delayed emissions is considered to be 100 years from the moment of disposal, since most of the GHG emitted by a landfill are considered to be released within this time frame (Barlaz, 1998, 2006). Carbon binding in a landfill is considered to be the carbon remaining in the landfill mass within 100 years after disposal. It is assumed that the remaining biogenic carbon not released within 100 years is likely to be stable and therefore is effectively not available in the atmosphere for a long time horizon (>> 100 years). The Intergovernmental Panel on Climate Change (IPCC) 2006 guidelines suggest a default value of 50% of the degradable organic matter that remain in the landfill over long time (IPCC 2006).

Key assumptions, limitations and uncertainties In the course of this study, the term global warming factor (GWF) is employed to express the overall load (positive) or saving (negative) in kg CO2-equivalents for 1 tonne of MSW managed. The GWF accounts for all upstream and downstream loads and savings and ascribes the following global warming potentials: CO2-fossil = 1 kg CO2-eq. kg–1 CO2-fossil, CO2-biogenic = 0 kg CO2-eq. kg CO2-biogenic, Cbiogenic bound = 3.67 kg CO2-eq. kg Cbiogenic bound, CH4 = 25 CO2-eq. kg CH4, N2O = 298 CO2-eq. kg N2O. Other gases have been included for completeness but are insignificant.

Only MSW modelling is included in the assessment because relatively good statistical data is available. This includes source-separated recyclables, residual waste and garden waste but excludes bulky waste. Construction, maintenance and decommission of waste management infrastructure are excluded due to lack of consistent data across the different MS. Newer waste management technologies, such as gasification and pyrolysis, have been excluded because they are not yet representative waste management technologies in Europe and data is very sparse. The collection and intermodal transport of waste has been modelled in a very simplified manner, based on relatively crude assumptions. This was done for two reasons, first it has been demonstrated that collection and short distance (< 1000 km) transport of waste represent a very small contribution to the environmental impacts of the overall waste management system (Smith et al. 2001, Beigl & Salhofer 2004, Salhofer et al. 2007) and second, statistical data necessary for this waste management activity are lacking at national level. Home composting, waste prevention and reuse have not been modelled, because of the lack of statistical data. However, the waste collected by municipalities or deposited in recycling centres has been modelled. Finally, the transported waste of householders, using private vehicles to central facilities, has not been modelled. It should be noted that the methodology used in this paper differs from the IPCC methodology (IPCC 2006). The IPCC guidelines suggest the use of first-order decay equations to calculate the yearly cumulative CH4 emissions from landfills for the reporting year, using historical baseline year, 1950. In contrast, LCA methodology calculates the whole amount of landfill gas likely to be released after the disposal of waste to landfill on a given year. LCA methodology estimates potential emissions and savings from the management of waste in a given year, considering no emission from historical disposal, but paying attention of future emissions caused by the waste management that year.

Representative EU member states A selection of MS considered to be representative of the different European regions was performed to reduce the modelling complexity. Denmark (DK), France (FR), Germany (DE) Greece (GR), Poland (PL) and the United Kingdom (UK) were selected because of the significant differences of their respective waste management systems. The profile of each waste management system is summarized in the Annex of this paper which precedes the reference list (after Eurostat 2009). DK relies heavily on waste incineration with energy recovery (53% of the MSW is incinerated), substantial recycling and low levels of landfilling. FR relies on an equal share between incineration, landfilling and material recovery, including composting (respectively 36%, 34% and 30%). DE was selected for its high level of recycling and incineration, a high level of mechanical–biological treatment and minimal landfilling. Both GR and PL remain highly depend-


E. Gentil, J. Clavreul, T.H. Christensen

Fig. 1: MSW composition (% ww) used in this study (vertical bars) with vertical error bars showing minimal and maximal values found in literature.

ent on landfilling (83% and 90%, respectively) but could be differentiated by external factors such as the type of energy source used, and their waste composition. Finally, the UK was included for its high (but rapidly decreasing) reliance on landfilling and relatively high recycling and biotreatment (composting).

Waste composition The composition of the waste is correlated to the impact and saving potentials of the waste management industry, especially where emissions are input-related (Riber et al. 2008). It is therefore important to define a robust waste composition for each MS. However, no standard methodology exists for defining waste composition as shown by Beigl et al. (2008). A review of waste composition includes studies from den Boer et al. (2005), Eggleston et al. (2006), European Commission Joint Research Centre (2005), Eurostat (2003), Fisher et al. (2006), Koneczny et al. (2007), OECD (2007), Riber et al. (2009), Sander (2008) and Smith et al. (2001). Figure 1 provides the waste compositions with the minimum and maximum values found in the literature. Note that waste composition could change from year to year and that values represent different years of study. There is a dominance of the organic fraction and paper and a small quantity of metals. The ‘other’ fraction is relatively high, indicating a high level of uncertainty and lack of standardized methodology for characterizing the waste composition more precisely. The composition of this unspecified fraction was assumed to be 15% textiles, 1% batteries, 67% miscellaneous combustibles and 17% miscellaneous non-combustibles. Further, the

waste fraction ‘paper/cardboard’ was divided equally, and it was assumed that the metal fraction includes 33% aluminium and 67% ferrous metals (Skovgaard et al, 2008). The organic fraction was subdivided between kitchen and garden waste, based on the collected amounts of garden waste declared by each MS (Danish Ministry of the Environment (2008) for DK, ADEME (2007) for FR, BMU (2007) for DE, Fisher et al. (2006) for the UK and the European Composting Network for other MS). The chosen waste compositions are presented in the summary table in the Annex and the waste properties (lower heating value, methane potential, total solids, ash, biogenic and fossil carbon) are presented in Table 1. The properties of each waste fraction are assumed to be identical between the MS.

Energy system Life-cycle assessment studies related to waste management have indicated that the energy system has a major influence on the LCA results (Finnveden et al. 2000). This is specifically true for the assessment of GWF, since the energy systems in Europe mainly rely on the use of fossil fuels and, therefore, the GWF is strongly affected by the substitutional value of energy from waste. Three energy scenarios have been used to evaluate the importance of energy assumptions. They include the substitution of average energy mix in each MS for 2004– 2005, 100% marginal hard coal substitution and 100% gas substitution, for both electricity and heat. In reality, the marginal electricity is a combination of different marginal energies likely to range between gas and coal. The emission factor of electricity and heat production of each MS was



Table 1: Properties of each waste fraction (EASEWASTE 2008).

Cardboard Glass Metals, aluminium

LHV (GJ t–1 ww)

CH4 pot. (m3 t–1 vs)

TS (% ww)

13.6

154.8

0

0

Ash (% TS)

C bio. (% TS)

C fossil (% TS)

80.6

9.8

42.4

2.1

93.1

100

0

0

8.0

0

86.5

72.7

6.4

13.2

Metals, ferrous

0

0

89.4

100

0

0

Garden waste

7.5

114.6

52.2

23.2

43.1

0.8

Kitchen waste

5.8

435.7

29.6

5.8

49.0

0.5

Other

17.3

32.9

81.5

21.0

23.4

26.9

Paper

12.9

158.1

90.5

19.2

38.7

0.2

Plastics

34.1

0

89.1

4.7

0.4

79.3

LHV, lower heating value; ww, wet weight; vs, volatile solid (dry weight); TS, total solid (dry weight).

determined, using the national energy mix (IEA 2009) and lifecycle inventory data for each energy source, using EcoInvent database (V2.0). The emission factor for electricity generation ranged from FR, with 25 g CO2-eq. MJ–1, to 318 g CO2-eq. MJ–1 for PL. For heat production, the emission factor varied from 63 g CO2-eq. MJ–1 for DK to 130 g CO2-eq. MJ–1 for GR.

own use. Finally, it was assumed that the energy produced by the incinerators substitutes electricity and heat production with no transmission loss because incineration plants are generally located in the vicinity of the energy consumer (cities). Landfill

Waste management technologies in Europe This study is limited by the fact that a choice of generic technologies was made to model the environmental performance of waste management in Europe. However, particular attention was taken within each MS for each technology to estimate key parameters important for modelling the GWF performance of waste management. Incineration

The GWF performance of an incinerator is mostly based on the energy production efficiency (ratio between energy sold, waste input and the substitution with other energy sources) and to a lesser extent on its direct emissions (Ragossnig et al. 2008, Riber et al. 2008). This indicates that it is possible to determine national differences of incinerators, based on their energy recovery efficiency, direct emissions calculated from the composition of the waste, and use generic data on their own energy consumption. The electricity and heat recoveries of waste incineration were compiled and calculated after ISWA (2006). All available data on sold energies in 2004 from every incinerator were aggregated and compared to the average lower heating values of waste inputs in order to obtain average energy recoveries for each MS (see Annex). The method used presented some degree of uncertainty as the data were incomplete, sometimes included co-incineration with industrial and commercial waste and could be considered outdated, but provided the best level of methodological consistency of publicly available data. It should be noted that sold energy was considered, rather than produced energy. It was considered that if energy was produced but not sold, no energy from the grid was substituted. Thus, no energy inputs to the incinerators were modelled as they were assumed to provide electricity for their

When considering GWF, the key parameters to take into account are the landfill gas produced, collected, used for electricity production or flared, and emitted fugitively to atmosphere. As no official average statistics were available, gas collection efficiency of 50% (over 100 years) and utilization of collected gas for electricity of 60% were assumed. However, for the UK, it was assumed 70% for the collection rate, over a 100-year period, and 80% for the gas utilization (Smith et al. 2001). A 25% methane oxidation rate was assumed for all MS, except for the UK, for which 30% was assumed, based on the principle that a higher collection rate tends to induce a slower gas migration through the top cover and thus a higher oxidation rate (Manfredi & Christensen 2009). Due to the ban on landfilling of untreated waste, Danish and German landfills are not expected to produce landfill gas and therefore GHG modelling has not been performed. GHG emissions from the leachate treatment modelled but a sensitivity analysis on this parameter did not show significant influence on the overall results for GWF. Sequestration of biogenic carbon (Cbio bound), or the amount of carbon left in the landfill after 100 years, has been assumed to have a GWF benefit (GWF = –(44/12) * Cbio bound tonnes CO2-eq.), as carbon is effectively removed from the atmosphere when bound into a landfill site (Christensen et al. 2009). The quantity of carbon bound in the landfill is calculated using a simple mass balance, where biogenic carbon deposited in a landfill and not emitted to air as CO2 or CH4 within 100 years, is considered ‘locked’ in the landfill mass. The calculation of the Cbio bound : Cbio ratio indicates 45% (FR), 25% (GR), 38% (PL) and 41% (UK). This is directly linked to the methane potential and type of biogenic carbon in the incoming waste, if landfills are assumed to have the same engineering properties over time.



Table 2: Summary of recycling parameters used in the modelling. Recycling rate* (%)

Substituted amount (%)

Avoided production (%)

Emission factor (kg CO2-eq. t–1 material)

Aluminium

4

79

100

–8225

Cardboard

29.5

100

90

–104

Glass

17

99

100

–253

Paper

29.5

84

100

–1256

Plastic

12

90

90

–759

Steel Source

8

100

100

–1681

Modified from Skovgaard et al. (2008)

EASEWASTE (2008)

EASEWASTE (2008)

EASEWASTE (2008)

*Relative proportion of material recycling compared to the total MSW recycling rate. Wood and textile were removed and reattributed proportionally to the other materials.

It should be noted that the landfill performance possibly is overestimated. For instance, Smith et al. (2001) have assumed that 80% of all the landfills in the EU have a gas collection system. We have assumed that 100% of the sites currently receiving MSW are landfill directive compliant with gas collection system. Recycling

The same recycling processes were used for all MS as it was assumed that the market for recycling was European. Emissions factors include both environmental loads of the recycling process and benefits of the substitution of virgin material production. All materials were considered to be sorted at source with the same relative material recovery contributions for all the MS. Similarly, the material-specific substitution ratio were considered identical across all the MS. Sensitivity analyses were performed on these ratios to evaluate the importance of this parameter on the overall GWF performance of each MS. Finally, the energy system used for calculating the substitutional value of recyclable materials (difference between the energy required for manufacturing a unit of virgin product and the energy used for recovering recyclables) was assumed to be independent from the country where the recycling takes place and therefore it was assumed that the same energy intensity had been used across all the MS for each respective recycling process. This assumption was based partly on the lack of country-specific information, and partly on the fact that recyclables often are traded between MS in Europe. Table 2 summarizes the parameters used for the recycling modelling. Biotreatment

Eurostat (2009) provides the composting contribution in the management of MSW with no distinction between different biotreatment technologies. One hundred percent of the material for composting is assumed to be routed to an in-vessel composting plant, with the same performance for all the MS. Other composting technologies were not modelled to simplify the modelling. The composted material is substituted for fertilizer use (100% for P and K and 20% for N), according to Hansen et al. (2006). The carbon binding is assumed to

be 14% over a 100-year period, according to Bruun et al. (2006). Anaerobic digestion was not modelled as it was considered to be an insignificant (but growing) MSW management activity in the MS. The use of mechanical–biological treatment (MBT) has been increasing in Europe and is already a significant option for residual waste in DE and PL (Steiner 2005, BMU 2009). The purpose of this treatment is to sort recyclables, to produce refuse-derived fuels (RDF) and biostabilized material, which will be landfilled. Two technologies were modelled: a mechanical–biological pre-treatment (MBP) and a mechanical–biological stabilization (MBS), representing 77 and 23%, respectively (BMU 2009). The main difference between these two treatments is the higher proportion of RDF produced in the MBS. The RDF is assumed to be sent to a power plant for co-combustion and thus directly substituting hard coal. All the waste management technologies described above have been combined based on the statistical data from Eurostat and illustrated in a generic mass flow diagram (Figure 2). The specific parameters and labels are presented in the Annex.

Results The modelling of the waste management, based on a generic mass flow diagram (Figure 2), for each MS indicated significant differences in the GWF performance due to a range of country-specific parameters (summarized in the Annex). All MS with a relatively developed and diverse waste management system tend to generate GWF savings (as much as –400 kg CO2-eq. tonne–1 assuming substitution of an average energy mix), whereas MS depending on simple landfills show a GWF load as high as 100 kg CO2-eq. tonne–1 assuming substitution of an average energy mix (Figure 3). This range indicates the potential role of the waste management industry in GHG abatement. The graph indicates that a higher recycling rate, as it is the case in DE, tends to generate a higher GWF saving in comparison with a country relying more on incineration, such as DK. However, if one assumes that the energy produced by the waste management industry displaces a marginal heat and electricity of 100% coal, the net GWF is more



Fig. 2: Simplified mass balance of waste flows in European member states: C, MSW composition (% wet waste); S, sorting efficiencies (% of the material fraction); M, management of residual waste (%); F, quantity of material treated (kg); P, post-treatment of fractions managed by the MBT (%). Transport and collecting are modelled but not shown. Fly ash and air pollution control residues are not modelled because of its insignificant contribution in relation to the GWF. (All numerical values are in the Annex).

Fig. 3: GWF performance for the management of 1 tonne of MSW in six different European member states in 2007. For each MS, three energy substitution assumptions are presented (Av, national average energy mix in 2007; Gas, 100% energy from gas substituted; Coal, 100% energy from coal substituted). The performance of each technology type is shown in net GWF load or GWF saving. All GWF savings from recycling and composting have been modelled separately by material types and have been merged as a single dataset. Due to amalgamation of life-cycle inventory data, recycling has been modelled with the same energy assumption, regardless of the type of substitution or the member state considered.

beneficial to DK. Conversely, if the assumption on marginal energy is based on 100% gas, DE generates more savings than DK. The GWF performance of the French municipal waste management has a small GWF impact (environmental load and environmental benefits are almost outbalancing each other), if the substituted energy is based on the average mix (FR relies mainly on nuclear energy). If it is assumed that the energy produced displaces the marginal energy, based on 100% coal, a relatively small benefit of –123 kg CO2-eq. tonne–1 is observed. This benefit originates from the energy produc-

tion from incinerators and landfill gas utilization, in addition to the benefits of recycling. Greece relies mostly on landfilling for managing its MSW with relatively low environmental controls (significant fugitive CH4 emissions) and relatively low production of energy from the waste. The only net GWF benefit originates from its recycling performance (almost similar to FR on a tonne basis). As the country does not produce a significant quantity of energy from its waste management system, the choice of the energy system (mix, marginal coal or marginal gas) has little influence on the GWF performance of the country. Although



GR, PL and the UK have the three highest landfilling contributions, their performance, on a tonne basis, is extremely different. This is due to better engineered landfills in the UK (83, 80 and 50% for gas collection rate, gas utilization rate and gas oxidation rate, respectively, over 45 years of active landfilling). The difference of landfill performance between GR and PL is mainly due to the higher quantity of carbon bound in the landfill mass and lower CH4 potential of the waste (leading to less fugitive CH4 emissions) in a Polish landfill in comparison with a Greek landfill. The waste composition, specific in each MS, plays an important role in determining CH4 potential. Member States with a high level of recycling (DE) tend to have the highest environmental benefits in comparison with other MS with lower recycling rates. This is explained by the GWF savings from the substitution of different virgin materials production. Paper (high quantity) and aluminium (high substitutional value) generate the highest GWF benefits. MS with a high level of incineration and high energy recovery will also have significant GWF benefits due to the substitution of energy (DK). When considering MS with a high dependence on landfills (GR, PL, UK), the engineering performance of the landfill (ability to collect, flare, utilize and oxidize landfill gases) plays a critical role on the GWF performance of the whole country. For instance GR and PL, with lower performance, indicated a detrimental GWF, whereas a GWF benefit is observed in the UK, indicating the very strong influence of landfill engineering on the performance of waste management, when a MS depends largely on landfills. The GWF performance of a tonne of MSW provides valuable information, however a different picture is observed when considering the total amount of MSW generated by each MS. For instance, in 2007, the production of MSW in

DE was more than 46 Mt (Eurostat 2009). Based on our modelling, it is estimated that the waste management of DE generated savings of about –18 Mt CO2-eq. from the substitution of materials and energy, assuming an average energy mix substitution (–21.5 Mt with coal substitution). For DK, producing 10 times less waste than DE (but 30% more per inhabitant), the overall benefits are considerably lower (–1.6 Mt CO2-eq. or –3.2 Mt with coal substitution). Similarly, GR and PL have a relatively low GWF contribution in comparison with DE, due to the lower quantity of MSW produced. In the UK, waste management generates more than –7.9 Mt CO2-eq. (–10 Mt with coal substitution) of savings, based on almost 35 Mt MSW produced in 2007.

Sensitivity analyses It is essential to undertake sensitivity analyses with key parameters to evaluate the robustness of assumptions and get a better understanding of the influence of these parameters on the system. The parameters were selected, based on the contribution of one particular waste management activity in a specific Member State. For instance, applying a sensitivity analysis on landfill parameters in GR will have a stronger influence on the results than applying this sensitivity in a MS relying less on landfill. Table 3 shows the baseline parameters used as well as extreme values simulated in the study. The influence of the parameters value is tested against the relevant process (technology specific in terms of GWF of the actual technology considering the fraction handled by the actual technology of the 1 tonne of waste being managed in the integrated system) and against the waste management system in the specific country (MS specific in terms of GWF per tonne managed in the integrated system) MS. The analysis was performed assuming a marginal electricity mix of 100% coal for all the MS (Table 3).

Table 3: Influence of key parameters on the GWF performance of technology and whole waste management system in the Member States. Results (kg CO2-eq. tonne–1 ww) Technology

Sensitivity parameter

MS

Baseline (min; max)

Technology specific

MS specific

Incinerator

Electricity/Heat eff. (LHV)

DK

13/69 (5/24; 13/69)

–563 (–62; –563)

–738 (–237; –738)

–1

Incinerator

LHV of MSW (GJ t )

DK

11.4 (10.3; 13.6)

–563 (–486; –713)

–738 (–660; –888)

Incinerator

% Fossil C (kg C kg–1 ww)

DK

11.4 (10.6; 18.3)

–563 (–579; –429)

–738 (–604; –754)

Landfill

Gas collection rate (%)

GR

60 (0; 83)

192 (649; 17)

89 (546; –87)

Landfill

Gas utilization rate (%)

GR

60 (0; 80)

192 (276; 164)

89 (173; 61)

Landfill

Gas oxidation rate (%)

GR

25 (0; 50)

192 (288; 96)

89 (184; –7)

MBT*

Energy recovery of RDF

DE

(0%; 30/70)

–103 (84; –210)

–463 (–276; –570)

Recycling

Paper substitution ratio (%)

DE

84 (50;100)

–122 (–51; –155)

–463 (–392; –496)

Recycling

Aluminium substitution ratio (%)

DE

79 (50;100)

–136 (–81; –175)

–463 (–409; –503)

Recycling

Steel substitution ratio (%)

DE

99 (50;100)

–56 (–6; –56)

–463 (–413; –463)

Recycling

Plastic substitution ratio (%)

DE

81 (50;100)

–42 (–10; –61)

–463 (–432; –482)

Recycling

Glass substitution ratio (%)

DE

99 (50;100)

–20 (8; –20)

–463 (–435; –464)

Recycling

Cardboard substitution ratio (%)

DE

90 (50;100)

–14 (28; –25)

–463 (–421; –474)

*MBT sensitivity was carried out on the GWF performance of the RDF, ranging from no recovery to a best available energy from waste system (30% electricity and 70% heat efficiency)



Transport parameters A sensitivity analysis was performed for the long distance road transport of combined recyclable materials (within Europe). The analysis indicated that transport distance had little influence on the GWF performance of the whole waste management of a given country. These results should not be confused with the work by Gentil et al. (2008), studying the GWF performance of recycling 1 tonne of secondary material when transported over long distances (long distance transport could outweigh the benefits of recycling for specific recyclables). In the present study, the overall transport of MSW (collection, waste transport, recyclable transport) is relatively insignificant, compared to the other waste management activities.

Incineration parameters DK relies mostly on incineration with energy recovery and has developed high electricity and heat recoveries, actually the highest average found in Europe. The sensitivity shows that incinerators with lower recoveries (electricity 5% and heat 24% similar to the average French incinerators) have a much lower GWF benefits than highly efficient incinerators found in DK. The sensitivity analysis also showed the influence of the lower heating value (LHV) and percentage of fossil C but to a smaller extent than energy recovery.

Landfill parameters This sensitivity analysis demonstrates that the landfill parameters have a very strong influence on the GWF performance of GR, either for the landfill only or, more importantly, when considering the overall waste management. For instance, if GR only improves its gas collection rate to 83% (similar to the UK level), the overall GWF performance of the country waste management would change to savings. Interestingly, this dramatic performance improvement could be performed without changing other parameters or other waste management activities. On the other hand, a 50% gas oxidation rate, keeping other parameters constant would bear a small GWF benefit on the whole waste management system (–7 kg CO2eq. tonne–1). Another set of analyses indicated that the CH4 potential of the waste has a strong influence on the GWF performance of the landfill. The higher the CH4 potential, the higher the CH4 emissions and the lower the level of Cbio bound in the landfill, indicating the importance of the waste composition on the GWF performance of landfills. For instance, for a Greek landfill, the calculated CH4 potential of 80 N m3 tonne–1 (PL: 61 N m3 tonne–1) generates CH4 emissions of 454 kg CO2-eq. tonne–1 (PL: 346 kg CO2-eq. tonne–1) and Cbio bound of –147 kg CO2-eq. tonne–1 (PL: –209 kg CO2eq. tonne–1).

MBT parameters The sensitivity analysis of the German MBT indicates that its GWF performance is mainly associated with the management of the RDF downstream from the MBT plant. RDF substituting hard coal is more beneficial as opposed to substituting

electricity produced from coal. However, if the RDF is combusted in a highly efficient incineration plant where the heat is also recovered (30% electricity and 70% of heat recovery of the LHV), GWF benefits for the whole waste management system are the highest (–570 kg CO2-eq. tonne–1).

Recycling parameters The sensitivity analysis of the recycling process was performed for each material for a range of substitution ranging from 50 to 100%. The higher the substitution ratio, unsurprisingly, the higher the potential GWF savings; however the influence of this parameter does not affect results drastically in comparison with other technology parameters. Although, this demonstrates that a poor substitution performance will reduce the overall GWF of a country relying heavily on material recovery.

Conclusions As indicated by many authors, the energy system plays a very strong role on the GWF performance of waste management systems relying strongly on energy production. This has also been confirmed in our study. This can, however, be considered as an ‘external factor’ which the waste management industry does not control, since it depends on the energy policy of a country. The study also demonstrated that the modelling choice (energy substituting marginal energy or average energy mix data), has a significant influence on waste LCA results. Our study showed that when substituting marginal energy, a MS relying mainly on incineration would outperform a MS relying more on material recycling. The opposite is true when the energy substituted is the average mix of the respective MS (recycling outweighs incineration). This could be considered as a ‘modelling external factor’, over which the waste management industry and policy makers have little control. Generally speaking, waste management policy has proved to be beneficial for GWF in DK, DE and the UK but has not yet been implemented successfully in GR and PL. The management of MSW in FR has not created any significant GWF load or benefits. From a GWF stand point, it would probably make sense to focus policy towards recycling optimization rather than energy recovery due to its high dependence on nuclear energy. It could be inferred that other MS with low GWF performance could optimize their practice relatively easily and promptly, by implementing proven waste management policies available in high performance MS. Although the modelling in this study has demonstrated the overall GWF benefits of MSW management in selected MS, it is important to realize that MSW is only one type of all the waste generated in society. Further research is necessary to extend the modelling to all types of waste in order to quantify loads and benefits of all waste generated by society, and demonstrate how waste management can actually contribute towards climate change mitigation by substituting raw materials and fossil energy. This study also demonstrates



that poor environmental control of waste management (or no waste management at all) generates significant GWF load (globally, 90% of anthropogenic CH4 emissions originates from ‘dumpsites’), whereas good waste management practices offer, not only direct reduction of GHG emissions, but, most importantly, provide significant GWF savings. This paper might provide the false impression that waste production is a ‘good thing for the environment’. It is evident that preventing waste production by reducing manufacturing

and improving material re-use, will generate much larger GHG savings. Nonetheless, waste is produced by society now and appropriate management does contribute to climate change mitigation to a small but noticeable extent, at a low technological cost, if applied globally. It is important to repeat that GWF is only one environmental impact category among many others and that any decision-making process on waste management policy should include other environmental indicators, as well as social and economic considerations.

Annex DK

FR

DE

GR

PL

UK

Label

Sources

Waste composition (% of MSW) Others

20

18

14

14

22

21

C0

Kitchen waste

19

19

13

41

28

15

C1

Garden, separated

18

10

10

2

4

12

C2

Garden, non-separated

2

2

1

4

3

2

C2

Glass

6

12

14

5

10

8

C3

Metal

5

5

5

4

4

6

C4

Paper

23

25

33

21

18

26

C5

Plastics

7

9

10

9

11

10

C6

53

36

35

0

0

9

–

Own estimations based on several sources

Waste management (%) Incineration Landfilling

5

34

1

83

90

57

–

Recycling

24

16

46

15

6

22

–

Composting

18

14

18

2

4

12

–

Glass

68

23

56

51

10

47

S3

Metal

58

38

100

45

18

44

S4

Paper

62

38

82

42

20

50

S5

Plastics

41

21

55

20

7

26

S6

Garden waste

90

83

91

33

57

86

S2

Kitchen waste

–

21

62

–

–

–

S1

Landfill

9

49

–

100

89

86

M1

Incineration

91

51

62

–

–

14

M2

MBP

–

–

29

–

8

–

M3

MBS

–

–

9

–

3

–

M4

Eurostat (2009)

Sorting efficiencies (%)

Own estimations based on Eurostat data, waste compositions and waste management

Management of residues (%)

Eurostat (2009)

Quantities in final treatment (for 1000 kg entering the system) Landfill

52

343

–

831

800

568

F1

Incineration

526

356

227

–

–

92

F2

–

–

107

–

76

–

F3

MBP MBS

–

–

32

–

23

–

F4

Composting

180

140

180

20

40

120

F5

Recycling (glass)

41

28

78

25

10

38

F6

Recycling (metal)

29

19

50

18

7

26

F7

Recycling (paper/card)

143

95

271

88

36

130

F8

Recycling (plastic)

29

19

55

18

8

26

F9


Calculated


Annex (Continued) DK

FR

DE

GR

PL

UK

Label

Sources

Incineration: energy recovery (%) Electricity

13.1

5.4

9.6

–

–

20.0

–

Heat

69.0

23.6

20.4

–

–

2.4

–

MBS → inert landfill

–

–

15

–

15

–

P1

MBP → inert landfill

–

–

9

–

9

–

P2

MBP → MBT landfill

–

–

26

–

26

–

P3

MBP → power plant

–

–

60

–

60

–

P4

MBS → power plant

–

–

70

–

70

–

P5

0

50

0

50

50

70

–

Calculations based on ISWA (2006)

MBT management (%)

Grundmann (2009) and Oros (2009)

Landfill Gas collection (% of generated gas, over 100 years) Gas utilization (% of collected gas)

–

60

–

60

60

80

–

Oxidation rate (% of uncollected methane)

–

25

–

25

25

30

–

Various sources

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