dynamic eco-efficiency projections for construction and demolition waste recycling strategies at the...

R E S E A R C H A N D A N A LYS I S

Dynamic Eco-EfficiencyProjections for Constructionand Demolition WasteRecycling Strategies at theCity LevelRolf Andre Bohne, Helge Brattebø, and Havard Bergsdal

Keywords

buildingsconstructiondemolitionindustrial ecologyrecyclingrenovation

Summary

In this article we have elaborated a consistent framework forthe quantification and evaluation of eco-efficiency for scenar-ios for waste treatment of construction and demolition (C&D)waste. Such waste systems will play an increasingly importantrole in the future, as there has been for many years, and stillis, a significant net increase in stock in the built environment.Consequently, there is a need to discuss future waste man-agement strategies, both in terms of growing waste volumes,stricter regulations, and sectorial recycling ambitions, as wellas a trend for higher competition and a need for professionaland optimized operations within the C&D waste industry. Itis within this framework that we develop and analyze mod-els that we believe will be meaningful to the actors in theC&D industry. Here we have outlined a way to quantify fu-ture C&D waste generation and have developed realistic sce-narios for waste handling based on today’s actual practices.We then demonstrate how each scenario is examined withrespect to specific and aggregated cost and environmental im-pact from different end-of-life treatment alternatives for majorC&D waste fractions. From these results, we have been ableto suggest which fractions to prioritize, in order to minimizecost and total environmental impact, as the most eco-efficientway to achieve an objective of overall system performance.

Address correspondence to:Dr. Rolf Andre BohneNorwegian University of Science &TechnologyDepartment of Civil and TransportEngineeringHøgskoleringen 7ANO-7491 Trondheim, [email protected]

c© 2008 by Yale UniversityDOI: 10.1111/j.1530-9290.2008.00013.x

Volume 12, Number 1

52 Journal of Industrial Ecology www.blackwellpublishing.com/jie


Introduction

The architectural, engineering, and construc-tion (AEC) industry is a major contributor tothe overall waste generation in Norway. Muchof this waste (95%) is technically recyclable(GRIP/Økobygg 2001), but is today not recycledfor various reasons. In this article we investigatethe environmental and economic performance ofdifferent waste handling options in a future con-struction and demolition (C&D) waste recyclingsystem of Trondheim, Norway. The baseline forthe system examined is 2003.

There have been several attempts to describe,quantitatively (Bossink and Brouwers 1996;Dantata et al. 2005; Davidson and Wilson 1982;Muller 2006; Touran et al. 2004; Wilson 1975;Yost and Halstead 1996) or qualitatively (Chungand Lo 2003; Eriksson et al. 2005; Reijnders2000), waste handling and recycling systems ona regional level, but to our knowledge, few haveattempted to combine the quantitative aspect ofboth waste projection and the corresponding en-vironmental impact for C&D waste recycling sys-tems (Bohne 2005; Symonds et al. 1999).

Eco-efficiency analysis (BCSD 1993; Kefferet al. 1999; Schmidheiny 2000; Sturm and Schal-tegger 1989; Verfaille and Bidwell 2000) is a tool,primarily developed for production processes andfirms, where value-added and environmental im-pact are reported mostly at the corporate scale.When we are dealing with recycling systems, thepicture gets more complicated (Brattebø 2005;Huppes and Ishikawa 2005a, 2005b), both for theestimation of value-added and for environmentalinfluence, because these systems involve numer-ous companies, products, and material fractions,as well as open loop recycling options where thevariables are not easily determined, and alloca-tion problems will often arise. This article exam-ines the calculation of eco-efficiency for recyclingsystems, and how eco-efficiency analysis can beutilized as a tool in decision making processes forinvestigations of recycling systems dealing withproducts with a long service life and thus slowturnover. Eco-efficiency, as used in this article,differs from traditional cost-benefit analyses, inthe sense that in eco-efficiency analysis, one doesnot use a welfare function in which environmen-tal aspects can be traded with and expressed in

the same monetary terms as non-environmentalones (Huppes and Ishikawa 2005a, 2005b).

To make well considered predictions aboutthe future, it is necessary to know somethingabout the past (Bergsdal et al. 2007; Bohne 2005;Horvath 2004; Torring 2001). Bergsdal and col-leagues (2007) describe a method for the pro-jection of a future generation of C&D waste inTrondheim, Norway, from 1995 to 2018. Wehave made use of these calculations (see figure 1)as input to our calculations in this article. In or-der to have a reference to the scale of figure 1, thepopulation in Trondheim equals 150,000 personsin 2002, and the total building area is estimatedto 46 million square meters (m2).1

Methods

System Borders and Allocation BetweenCo-Products

In systems modeling and modeling of environ-mental impacts (life cycle assessment or LCA) inparticular, co-production (the joint productionof two or more products from the same processor system) has been seen as presenting a prob-lem to the modeling, and the traditional solutionhas been co-product allocation (the partitioningand distribution of the environmental exchangesof the co-producing processes over its multipleproducts according to a chosen allocation key)in parallel to cost allocation (Udo de Haes 2002;Weidema 2000; Weidema and Norris 2002).

Weidema and others (Udo de Haes 2002;Weidema 2000; Weidema and Norris 2002) havedemonstrated how co-product allocation can beavoided by expanding the system to also includeproduct system B: “the co-producing process (andits exchanges) shall be ascribed fully (100%)to the determining co-product for this process(product A)” (Weidema 2000) (figure 2).

In the recycling system for C&D waste, it isthe enterprise owner (of the construction or de-molition project, for example, of the building un-der construction) or the entrepreneur in systemA who in general determines where and whatto do with the waste. However, local and cen-tral governments often seek to influence thesedecisions through regulation and economic in-struments. This reopens the discussion about

Bohne et al., Dynamic Eco-efficiency Projections for C&D Waste Recycling 53


Figure 1 Projections of construction and demolition (C&D) waste in Trondheim during 1995–2018,projected on the basis of history and stock dynamics of existing buildings (from Bergsdal et al. 2007). EPS =Environmental Priority System.

Figure 2 System expansion (from A to A+B) for the allocation of influence among co-products, as input tothe calculation of eco-efficiency in construction and demolition (C&D) waste recycling systems. Boxes andarrows with dark shading denote system boundaries for this study. Arrows denote transport betweenprocesses. ri represents the ratio of a given waste fraction that enters an alternative end-of-life treatment,and γ i is the factor for how much virgin material that is replaced by r i . The underlying layers representfurther system expansions.

allocation among co-products. Here we have cho-sen to follow Weidema’s (2000) recommendationof system expansion, as our interest is in the over-all system, and our target is to maximize the over-all system performance.

Waste Handling and EnvironmentalImpact

The environmental impact of waste manage-ment options is the result of processing and

54 Journal of Industrial Ecology


disposal methods and transportation types anddistances, for all disposal, recycling, and reuseoptions in the system. Moreover, recycling andreuse should generate positive downstream en-vironmental benefits when recycling and reuseis applied, due to avoided emissions, and someof these benefits should also be allocated to theenvironmental performance of the initial C&Dwaste system. Different end-of-life treatments canbe ranked in a general hierarchy according totheir environmental impact (Bohne and Brat-tebø 2003; Reijnders 2000), in which direct reuseis ranked higher than recycling, which in turn isbetter than energy recovery.

In practice, some of these alternatives are ei-ther not preferred, due to lack of market demand,or because they are not possible or too expensiveto follow (Reijnders 2000; Symonds et al. 1999;Wilson 1975). Some of these processes demandfacilities that are expensive to build and main-tain. It is therefore of public interest to know asmuch as possible about the future waste genera-tion and its possible corresponding environmen-tal impact (Bohne and Brattebø 2003; Chung andLo 2003; Horvath 2004).

Environmental impacts for the different alter-natives for end-of-life treatment are calculatedon the basis of data from many different sourcesusing LCA methodology (Kotaji et al. 2003; PReConsultants 2002). A problem with these kindsof calculations for recycling systems is that wedeal with a wide range of products, of differingages, and from many different producers. Avail-able data do cover products within systems withlong service life, but only to a limited extent.Thus, there is a clear need for the developmentof knowledge and methods, which is our moti-vation for this research. Hence, it is a challengeto make use of appropriate system borders, cut-off rules, and allocation rules when doing theanalysis. It is the aggregated environmental sys-tem impact over time that is of importance andshould be used as a design criteria, rather thanaggregated volume or weight parameters, whichoften are used in industry’s evaluations of systemperformance in the AEC sector.

Figure 1 and 3a shows that the brick and con-crete by far will be the largest fraction and alsothe fastest growing fraction in the forthcoming15 years (Bergsdal et al. 2007). This does not nec-

essarily mean, however, that this is the most im-portant fraction to deal with to reduce environ-mental impact (figure 3b). In order to examinethis question, we need to determine the aggre-gated total environmental impact (�∗

j ) for eachwaste fraction (j) during the whole period weare studying and then for the different waste frac-tions, including all transportation and end-of-lifetreatment activities, over the time period in ques-tion.

The basis for the calculation of environmen-tal impact in this article is life cycle inventorydata from the EcoInvent database, using theSimaPro software package and the EcoIndica-tor 99 method for impact assessment (Huijbregtset al. 2003). Thus, the life cycle inventory foreach of the process or subprocess steps is foundin the EcoInvent database (with the exceptionof reuse of wood), and environmental impact pertonne,2 ψ , for the different processes and/or sub-processes is then calculated using the SimaProsoftware package. The environmental impact foreach process is shown in table 1. The values forenvironmental impact are given in eco-points(Pt.). We have chosen EcoIndicator 99 becausethis is a single value indicator, which makes iteasy to communicate the results to decision mak-ers. Other weighting methods may be used, butthe principle remains the same.

The total environmental impact (� j) of agiven waste fraction (j) is the product of the envi-ronmental impact per tonne (transport included)(ψ j) and the corresponding weight of the wastefraction in tonnes (wj):

� j = ψ j w j (1)where j is the waste fraction in question, wj is

the weight of the waste fraction in tonnes, andψ j is the environmental impact per tonne fromthe given mixture of end-of-life treatment op-tions that are applied to the given waste fraction.(Notations are summarized in a glossary at theend of the article.)

For most waste fractions, there are several end-of-life alternatives to consider. The total environ-mental impact of a given waste fraction is the sumof all environmental impacts from all end-of-lifetreatment alternatives for this waste fraction, seeequation (2):

� j =∑

i

ψi , j r i , j w j (2)



Table 1 Environmental impact and cost data used in the calculations, 2003. All costs with negative values areexpenses while positive cost values are used when the process generates income.

Database-keyWaste fraction Process Unit Database values (Pt.) EI 99 Costs (€)

Transport Lorry 28ta tkm EcoInvent EIN SYSX06573801771 3.24E−02 −0.385Lorry 40tb tkm EcoInvent EINSYSX06573801773 2.26E−02 −0.257

Heat Fuel oilc MJ EcoInvent EIN SYSX06573801431 0.00326 −0.027Brick Landfill kg EcoInvent EIN SYSX06573801832 0.00389 −0.143

Recycling kg EcoInvent EIN SYSX06573801966 0.00234 −0.010Reuse kg EcoInvent EIN SYSX06573801967 0.00419 0.257Virgin gravel kg EcoInvent EIN SYSX06573800460 0.0014 −0.016Virgin brick kg EcoInvent EIN SYSX06573800487 0.0143 −0.128

Concrete Landfill kg EcoInvent EIN SYSX06573801837 0.00402 −0.143Recycling kg EcoInvent EIN SYSX06573801970 0.00247 −0.010Gravel kg EcoInvent EIN SYSX06573800460 0.0014 −0.016

Wood Landfill kg EcoInvent EIN SYSX06573801879 0.222 −0.143Incineration kg EcoInvent EIN SYSX06573801955 0.0219 −0.061Reused kg OE N.A. 0.0002 0.128Virgin wood kge EcoInvent EIN SYSX06573802312 0.115 −0.283Heat 2.74 MJf OC N.A. 0.0053855 −0.059

Gypsum Landfill kg EcoInvent EIN SYSX06573801859 0.00389 −0.143Recycling kg EcoInvent EIN SYSX06573801975 0.00234 −0.103Virgin gypsum kg EcoInvent EIN SYSX06579203037 0.037 −0.385

Cardboard Landfill kg EcoInvent EIN SYSX06573802036 0.0241 −0.143Incineration kg EcoInvent EIN SYSX06573801930 0.0257 −0.039Recycling kg EcoInvent Eco SysX10984500028 −0.0271 0.019Virgin cardboard kg EcoInvent EIN SYSX06573801536 0.105 −0.385Heat 3.23 MJg OC N.A. 0.0063487 −0.069

Glass Landfill kg EcoInvent EIN SYS06573801896 0.00107 −0.143Recyclingh kg EcoInvent Eco SysX10984500030 −0.082 −0.039Virgin (brown glass) kg EcoInvent EIN SYSX06573800781 0.0541 −0.128

Plastic Landfill kg EcoInvent EIN SYSX06573802041 0.0348 −0.143Incineration kg EcoInvent EIN SYSX06573801937 0.0386 −0.039Recycling kg EcoInvent Eco sysX10984500033 −0.128 −0.039Virgin plastic film kg EcoInvent EIN SYSX06573801692 0.138 −1.285Heat 7.03 MJi OC N.A. 0.013818 −0.151

Metals (steel) Landfill kg EcoInvent EIN SYSX06573801907 0.00107 −0.143Recycling kg EcoInvent Eco SysX10984500038 −0.258 0.050Virgin steel kg EcoInvent EIN SYSX06573801072 0.464 −1.541

Units: tkm = tonne-kilometers ≈ 0.7 ton-mile; MJ = megajoule = 106 joules (J, SI) ≈ 239 kilocalories (kcal) ≈948 British Thermal Units (BTU); kg = kilogram ≈ 2.204 pounds (lb). 1 Euro (€) = 7.785NOK; N.A. = not appli-cable.aLocal transport.bLong distance transport.cAlthough Norwegian building stock uses a mixture of 30.5% fuel oil and 69.5% electricity for heating purposes, we haveused oil in our calculation, because we are interested in the residential facilities capable of utilizing waterborn districtheating for substitution. Thus the results are only valid where district heating is available.dDifficult in practice.eConverted from cubic meters (m3) to kilograms (kg) using a factor of 500 kg/m3.f Wood: 2.74 MJ/kg wood incinerated.gCardboard: 3.23 MJ/kg cardboard incinerated.iPlastic: 7.03 MJ/kg plastic incinerated.hPCB containing windows are entering a special waste stream at a gate fee of €186.- (NOK 1450.-).



where j is the waste fraction in question and ri , j

is the share of this waste fraction that is sent to anend-of-life treatment alternative, i (see figure 2).The aggregated total environmental impact ofa given waste fraction owner of given end-of-life treatment options, �∗

j , is then calculated bysummarizing the total environmental impactover the years of interest (t), equation (3):

�∗j =

∑

t

∑

i

ψi , j r i , j w j ,t (3)

where wj,t is the annual waste generation of agiven waste fraction, t is the year of interest, and∗ denotes that it is an aggregated number.

Calculating �∗j

for all waste fractions will thusidentify which fractions should be of greatest con-cern to minimize environmental impact from theC&D waste system (figure 3b).

Economic Data

Decisions cannot be made from environmen-tal data alone. A system owner would prefer tooptimize his system for the best performance pos-sible in order to maximize the return from his in-vestments. However, systems that include reuseor recycling, which are in fact reproduction sys-tems, are complex systems composed of numerousproducts and stakeholders. Due to this complex-ity, economic efficiency is often hard to measure.

For C&D waste, a typical recycling chain in-volves several stakeholders with diverging inter-ests, each of whom seeks to maximize their ownprofit and thus is less driven by a wish to reduceenvironmental impact. The system owner is (of-ten) a municipality, who also is (in part) respon-sible for the policies affecting the system. Giventhese constraints, we can categorize the economicdata in three categories: (1) the source, (2) thetype, and (3) the availability of the data (table 2).

It is most often the values for processing ex-penses that are unavailable due to market com-

Table 2 Availability and source of economic data

Category 1 2 3

Source Public taxes Price lists Personal communication and/or best estimatesAvailability Very good Very good/good Good to unavailableType of data Static Dynamic Rapidly changingData quality Very poor Very poor/good Good to fair

petition (category 3), but actual prices on trans-portation and waste delivery for large deliverieshave also been found to be lower than the offi-cial prices (and therefore shift from category 1 tocategory 2). Hence, for the same reason, accu-rate data can be hard to get. It is also a problemthat some economic values have a considerabledynamic variation, such as the price on trans-portation, which shifts with fuel prices. We havetherefore used historically observed data and cor-rected those data according to the correspondingstatistical index (Statistics Norway 2004). By us-ing actual costs as seen by the stakeholders (trans-fer payments and taxes) as the economic indica-tor (table 1), we have managed to limit our datasources to category 1 and 2.

The economic data is calculated the same way,by summarizing the actual transfer costs and taxesfor each process step or subprocess using the samesystem borders and allocation rules as the envi-ronmental data (figure 3c).

Transport Distances

Most of the C&D waste (by weight) is han-dled by different companies within Trondheim,but some of the fractions have to be shipped longdistances outside the city if they are to be recy-cled. Table 3 takes into account the nearest recy-cling facilities for these fractions and the trans-port methods and distances for each fraction. Wehave used these transport distances in our cal-culations even though waste fractions may fromtime to time be sent to other more far distantplaces.

Sensitivity Analysis

In the calculations, we use weight estimatescombined with either environmental impact dataor economic values. For these calculations to beuseful in the actual decision making processes,knowledge of the uncertainty and sensitivity of



Table 3 Distances to the nearest recycling facilities for waste fractions that are not being recycled inTrondheim

Recycling of Where Distance Recipient Transport by

Gypsum Drammen 539 km Gyproch TruckCardboard Ranheim 15 km Peterson AS TruckGlass Stjørdal 33 km Glava AS TruckPlastics Folldal 197 km Folldal Gjenvinning TruckMetals Mo i Rana 482 km Fundia Armeringsstal AS Truck

the data is important. There are three sourcesof uncertainty in the calculations: (1) weight,(2) life cycle inventory (LCI) data, and (3) costdata, which thus need investigation.

The calculations of waste generation have pre-viously been published by Bergsdal and colleagues(2007). Here Monte Carlo simulations were runto find a probable standard deviation for our ex-pression, which returned an uncertainty of ±5%–10%.

For the environmental impact data, we baseour data on life cycle inventory data from EcoIn-vent (Ecoinvent 2007; PRe Consultants 2002),and it should be stated that the level of inaccu-racy is not widely published with LCI data.

In the scientific literature (Kotaij et al. 2003;PRe Consultants 2002), it is acknowledged thatCO2 emissions are well studied and thereforehave lower uncertainties, while emissions, suchas dust and noise, are less studied and thereforepossess greater uncertainties. We have thereforechosen a single standard error range of ±5% forthe LCI data used in our calculations, which isan accepted approach to uncertainty of LCI data(Huijbregts et al. 2003).

The economic data (i.e., costs) are well knownin our system and only need to be correctedfor dynamic variations over time. In our case,we found that the transportation and fuel priceswere the most relevant factor with respect tovariations. We have shown graphically how a±50% variation in transport distances will af-fect the dynamic eco-efficiency calculations (fig-ure 3).

Eco-Efficiency in Recycling Systems

Eco-efficiency (BCSD 1993; Keffer et al. 1999;Schmidheiny 2000; Sturm and Schaltegger 1989;

Verfaille and Bidwell 2000) was first mentionedby Sturm and Schaltegger in 1989, “The aim ofenvironmentally sound management is increasedeco-efficiency by reducing the environmental im-pact while increasing the value of an enterprise.”Later the Business Council (now the World Busi-ness Council) for Sustainable Development de-scribed how to achieve eco-efficiency in a reportreleased just prior to the 1992 Earth Summit inRio de Janeiro.

The term can be expressed mathematically as(Keffer et al. 1999):

Eco-efficiency = Value addedEnvironmental impact

(4)

The World Business Council for Sustainable De-velopment (WBCSD) (BCSD 1993; Keffer et al.1999; Schmidheiny 2000; Verfaille and Bidwell2000) and the United Nations Conference onTrade and Development (UNCTAD) (Sturmand Upasena 2003) advocates for using interna-tionally standardized economic indicators whencalculating eco-efficiency. Value-added is pro-posed as the indicator of choice for product or ser-vice value. Because eco-efficiency was designedprimarily for measuring efficiency improvementsin production systems within a company, bothvalue-added and environmental influence shouldbe known, at least for internal purposes. Whenwe are looking at recycling systems, however, wecannot use the term value-added in the same wayas at the firm level. With a system of many stake-holders who seek to make profit along the way,this picture gets more complicated.

Even so, some of this profit does not necessar-ily increase the value of the material in question,but arises from the stakeholders’ performanceof services such as collection, transportation,sorting, and processing. Processing activities in



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recycling systems, in fact, despite an increase invalue for the material, normally lead to a consid-erable downcycling3 of the material at the sametime as the stakeholder makes profit. However,the alternative of no processing would of coursebe worse, because this leads to even less valuein the market. We have therefore rewritten theformulae (equation 5) to include all economictransactions (for the extended system):

Eco-efficiency =∑

i costs∑i Environmentalimpact

(5)

We use the term costs to denote all economictransactions when the material is transferred fromone process to another. Equation 5 can be ex-pressed mathematically as:

ε j = κ j

ψ j=

∑i κi , j∑i ψi , j

(6)

where ε j is the eco-efficiency of a given wastefraction within the system, κ i,j is the process costsof a given waste fraction and end-of-life pro-cess alternative on a per tonne basis, ψ i,j is theenvironmental impact of a given waste fractionand end-of-life process alternative on a per tonnebasis, i is the different end-of-life processing al-ternatives, and j is the different waste fractions.Figure 2 shows that we include all processes inthe overall system when calculating κ j and ψ j inequation 6. Hence we will avoid the difficultiesof allocation.

However, for eco-efficiency to have any mean-ing as a tool for decision making, we need tomeasure the change in eco-efficiency betweendifferent end-of-life treatment options, or wastehandling scenarios, for each of the different wastefractions (Bohne and Brattebø 2003; Huisman2003; Saling et al. 2002). Thus, what we want tomeasure is the relative change in eco-efficiency(ε′) of a proposed alternative end-of-life treat-ment option or set of options (b) compared tothe current practice (a):

ε′j =

∑b κb, j − ∑

a κa , j∑b ψb, j − ∑

a ψa , j(7)

where a is a given mixture of end-of-life pro-cess alternatives that are made use of in the cur-rent (reference) system and b is a given mixture



of end-of-life process alternatives that are madeuse of in the proposed alternative system.

However, eco-efficiency is a one-dimensionalnumber (Euro/Pt.) that conceals valuable infor-mation from decision makers, especially whenmore than one alternative process is to be con-sidered. Another issue is that the value of eco-efficiency will increase if the cost increases.Hence we will have to rearrange this parame-ter in order to better communicate informationthe way we prefer.

We will, therefore, follow Huisman (2003)in his attempt to visualize the change in eco-efficiency similar to the BASF method (Salinget al. 2002). As with the BASF method, we vi-sualized eco-efficiency by plotting the numerator(κ j

′) and denominator (φ j′) for a given waste frac-

tion (j) in an xy-plot as shown in figure 4, wherea positive value for the numerator expresses in-creased economic value (the y-axis), and a neg-ative value for the denominator expresses lessenvironmental impact (the x-axis). But unlikethe BASF plot, we do not rescale the values toa normalized value, thus the values in our plotrepresent the net gain or losses in cost or envi-ronmental impacts.

By comparing several end-of-life process alter-natives this way, that is, by plotting the results inthe same graph (keeping the reference process, a,constant), decision makers can make better de-cisions as to what solution to follow. Anotherinteresting feature of this two-dimensional figureis that policy makers here can test how differentpolicies will affect the eco-efficiency of the differ-ent end-of-life treatments within the system.4

Scenarios

The next step is to set up alternative scenariosfor the distribution of C&D waste fractions be-tween various end-of-life treatment options. Sce-nario 0: TP5 (TP = today’s practice) assumesthe continuation of the current end-of-life prac-tice during the whole period. Scenario 1: NAP isthe recommendations for waste handling as pro-posed in the Norwegian National Action Plan(NAP) 2005 (GRIP/Økobygg 2001). Scenario 2:Maximum Recycling is the result if as much aspossible of the C&D waste is directed towardsrecycling, and Scenario 3: Maximum Energy Re-

covery is the result if as much as possible of theC&D waste is directed towards energy recov-ery. Table 4 shows the distribution factors amongthe different end-of-life alternatives used in thesescenarios.

The distribution of waste fractions demon-strates that an ambitious shift away from land-fill towards recycling for concrete/brick, gypsum,cardboard, and plastics must be realized, if thecurrent end-of-life practice (Scenario 0) is to bereplaced by Scenario NAP. Likewise, wood wastewill have to be redirected from landfilling towardsenergy recovery and direct reuse.

Results

In order to examine the eco-efficiency of C&Dwaste systems at the local level in a city, we haveestimated future waste projections for the city ofTrondheim. Trondheim is the third largest cityin Norway, with a population of 150,000 inhabi-tants and a building structure that is characterizedby many detached family houses made of woodcovering a large area, together with larger resi-dential and office buildings made of concrete inthe center of the city and in some clusters aroundthe center of the city.

The projections of local C&D waste fractionsfor Trondheim, as given in figure 1, are accumu-lated for the whole period 2003–2018, and theaggregated amounts are shown in figure 3A. Onecan clearly see the dominant role of the concreteand brick fraction, in addition to wood wastes,even for a city with a majority of the buildingsmade of wood.

On the basis of the data in figure 3A andtable 3, it is now possible to calculate the NetPresent Value (Euro) and environmental impacts(Pt.) for each waste fraction during the 2003–2018 period. Cost data are obtained directly fromthe actors in the system, and environmental im-pact data are obtained from LCA software (PReConsultants 2002) by using the Eco-Indicator 99valuation method (see table 1).

For the calculations of net present value, wehave used the interest rate from the Norwe-gian State Obligations (4% for obligations with10 years running time) to estimate the presentvalue of future costs. This is in order to avoid



. . . . . . . . .

. . . . . . . . .

. . . . . . . .

Figure 3 Aggregated: (A) Waste generation, (B) environmental impacts, and (C) costs of construction anddemolition (C&D) waste handling for selected waste fractions in the city of Trondheim from 2003 to 2018.



Figure 4 Relative change in eco-efficiency for the different end-of-life treatments for selected fractions ofconstruction and demolition (C&D) waste in Trondheim, Norway. Be aware of the large variations of scalebetween the different waste fractions in the figure. The bars indicate the sensitivity to transportation work inthe analysis (±50%), both on the environmental impact and the economic benefit.



problems due to depreciated values of costs overtime in the calculations.

The results given in figure 3B and 3C showthe estimated environmental impact (Pt.) andnet present value (Euro) for each waste frac-tion6 in Trondheim for the next 157 years, whenScenario 0: TP, Scenario 1: NAP, Scenario 2:Maximum Recycling, and Scenario 3: Maxi-mum Energy Recovery are applied for the wholeperiod.8

It can be seen that the concrete and brickfraction by far dominates the material composi-tion of the C&D waste (figure 3A). This trend isto some extent also reflected in the system costs(figure 3C), while its corresponding environmen-tal impact is less obvious (figure 3B). If we look atfigure 3B alone, we would suggest that the woodis the fraction worth focusing on from an envi-ronmental point of view. As can be seen fromfigure 3B, the picture is somewhat different fromthe picture in figure 3A. In figure 3B, wood andto some extent concrete and brick are the twomost important fractions from an environmentalpoint of view. This also corresponds well withhow developed, or mature, the recycling systemsfor these fractions are today.

To make sound decisions on what to do withthe different waste fractions, we need to comparethe eco-efficiency for the different end-of-life op-tions for each of the fractions against each other.This will then be a basis for further sound de-cisions in the overall C&D waste managementsystem.

Figure 4 shows two-dimensional relative plotsof the eco-efficiency for the different end-of-lifealternatives studied. We have provided plots foreach of the waste fractions, and the data showhow different end-of-life treatment options po-sition themselves relative to the current treat-ment practice (as in Scenario 0), which is al-ways represented by the origin. The differencein environmental impact, between a given treat-ment option or scenario and the current treat-ment scheme, is given along the x-axis, wherereduced impact gives a position to the right oforigin. The difference in cost is expressed on they-axis, as the net economic benefit. Thus a re-duced cost—these are relative numbers—is thesum of end-of-life options that are part of boththe current situation and the suggested alterna-

tive. In order to compare on a straightforwardbasis, the results are presented on a per tonne ba-sis (i.e., Euro/tonne and Pt./tonne, by using theEco-indicator 99 method). The better treatmentoptions will always position themselves in theupper right corner of the plot.

Discussion

The results shown in figures 3 and 4 revealsome interesting issues. Let us first take a look atthe results from figure 3.

Although the concrete and brick fraction isby far dominating the waste generation and thecost of waste handling, it does not have a corre-spondingly high impact on the environment. Asecond interesting finding is that the figures showthat wood has the largest environmental impactof all fractions, and it is also the fraction that hasthe largest possibilities for further environmentalsavings (actually, the possible reduction in envi-ronmental impact from wood is larger than forthe other fractions together).

One of the reasons for this is that the con-crete fraction is unique in the sense that there isno real recycling, but a downcycling to crushedaggregates, which then is used as a substitute forgravel. This in turn has implications for our calcu-lations, as concrete production is left outside oursystem borders, while virgin gravel (which hasmuch lower environmental impact) is included.The concrete system as modeled in this study,therefore, systematically underestimates the en-vironmental impact and costs, because only wastehandling (and not production) is included. Reuseof concrete is not a realistic alternative withinthe next few decades and is therefore omitted,as a consequence of the construction techniquesused 30–60 years ago.

Wood, on the other hand, is incinerated withheat recovery, and the heat is substituting foroil through district heating. Wood, in contrast toconcrete, is also an organic fraction, which causesconsiderable environmental impact if landfilled.And lastly, there is the density; in volume—onetonne of wood is almost five times more volumi-nous than concrete. Thus from an environmentalpoint of view, one needs to focus on better col-lection and handling practices of wood debris,so that a larger portion is delivered to energy



recovery. From an economic approach, concrete,wood, brick, and gypsum, in that order, are wherethe potential for increased return is possible.

It is worth mentioning here that, in theory,it is possible to increase environmental savingseven more for the wood fractions if the wood isreused instead of being sent to recycling or energyrecovering. Such a shift can also be more prof-itable for stakeholders. However, this is difficultdue to the need for altered construction practicesand the required handling of materials for reuse.If such a system should be implemented at a largescale, and not as is done today, be a work train-ing facility, the costs would also increase beyondwhat we have used in our calculations, and asa result, the corresponding eco-efficiency woulddecrease.

Figure 4 shows how the different end-of-lifealternatives and scenarios will perform on a pertonne basis in a two-dimensional plot. For allbut the metal fraction, there is still an openingfor both environmental gains and profits (on anoverall systems level).

For the brick fraction, the figure reflects thecurrent situation of 30% recycling, in which mostof the brick is delivered for recycling as a gravelsubstitute (together with concrete) and not forreuse. There is still an unexploited niche in thereuse of bricks, but it is questionable to what ex-tent it is possible to industrialize this in a wood-based city such as Trondheim. Today this part ofthe system is handled by work at a training facilityand by scrap dealers in Norway. A ±50% changein transport has negligible effects on systemperformance.

For the concrete fraction, there is a clear eco-nomic and environmental benefit from recycling,and the figure reflects the current situation of 30%recycling. Because much of the recycling poten-tial is still not realized, other issues are presumedto be of higher economic importance to the deci-sion maker. Due to the environmental gains fromrecycling, a ±50% change in transport has someimpacts on environmental performance.

Wood gives some of the more interesting re-sults, due to the fact that this is one of the frac-tions composed of renewable material and thatit can involve all end-of-life solutions. As pre-viously mentioned, there is a great potential forbetter environmental performance, if more wood

is reused, recycled, or incinerated with energy re-covery. As with concrete and bricks, other factorsare presumed to dominate the decision processesfor stakeholders, because more wood is not ac-tually being delivered, for instance, for energyrecovery. A ±50% change in transport has neg-ligible effects on system performance.

Figure 4 shows that the recycling of gypsum isthe most environmentally friendly and economicsolution for stakeholders located in Trondheim.Recycling however is only an option for freshwastes such as stubs and cutoffs from construc-tion and renovation activities with current tech-nologies. Thus, most waste gypsum, which arisesfrom demolition, will still end up in the landfillin Trondheim.

The gypsum fraction is also relatively sensitiveto a ±50% change in transport. This is due to thefact that there are only two recycling facilitiesfor gypsum in Norway, located in Drammen andFredrikstad, and that the long distant transportis both costly and contributes significantly to theoverall environmental performance.

Cardboard faces a classic situation in whichenergy recovery competes with recycling. Herecontamination and convenience determineswhat end-of-life options to follow. Figure 4 re-flects a functioning recycling system not yet op-timized. Recycling is the favorable environmen-tal and economic choice, however, other factorspresumably dominate the decision processes forstakeholders, because more cardboard is not ac-tually being delivered for recycling. A ±50%change in transport has no effect on systemperformance.

Glass is an example of a recycling system inits early stage, with a great deal of unrealizedpotential (figure 4). A ±50% change in trans-port has negligible effects on system performance.As with cardboard, plastics are in a situation inwhich energy recovery competes with recycling,but with one important difference. Here we aredealing with a nonrenewable resource, and al-though the economic potential is on the sameorder, there is a significant difference in environ-mental potential. The environmental impact forthe system indicates clearly that recycling shouldbe favored, the opposite conclusion of the eco-nomic indicator. Policymakers should, therefore,restructure their use of policy incentives, such



that recycling becomes more favored than energyrecovery with regard to overall system costs. A±50% change in transport has no effect on systemperformance.

Metals from C&D debris have an image of amature recycling system driven by the economicvalue of a material. Almost all the environmentalpotential is therefore realized. Metals are also theonly waste fraction in which one gets paid whendelivering waste. A ±50% change in transporthas no effect on system performance.

Conclusion

There is a need for municipalities and gov-ernments to make more considered decisions onenvironmental issues, with regard to the long-term management of waste handling systems andnatural resources. We have shown that long-termwaste generation models combined with environ-mental and economic information can be a pow-erful tool in such regard.

Total environmental impact and eco-efficiency calculations can be used by system own-ers and stakeholders to evaluate their options andtheir system performance, as well as to identifywhich waste fractions to focus on and which end-of-life alternative to give priority.

Of special interest to waste handling systemsis the possibility of generalizing potentially ag-gregated environmental and economic effects ofdifferent policies regarding end-of-life treatmentalternatives. Important to decision makers willbe how different system alternatives meet givenpolicy targets. Our model is able to simulate suchissues.

However, even though we have demonstratedthat recycling and/or reuse often are the most eco-efficient choices in C&D waste systems, we knowthat they are many times not followed in practicefor a variety of reasons. We assume that this is of-ten due to the fact that other economic processesoutweigh the benefits of sound waste handling de-cisions. Time penalties for delays in constructionor demolition projects are an obvious example,and these issues need more investigation.

Our reflection on this research method is thatthe approach looks very promising. Our way ofpresenting specific and aggregated results on eco-efficiency in C&D waste systems seems intuitive

and attractive with respect to communicationwith stakeholders as a basis for decision making.However, there are two aspects that need im-provement. First, one needs to refine the dynamicmodel estimating future waste generation. Thismodel should be based on more detailed examina-tion and data of the building stock, including itsmaterial composition, lifetime distribution, anddynamic aging phenomena. This would give morerobust projections for waste generation.

Second, we need more precise data on im-portant processes in the C&D waste system, in-cluding environmental and economic indicators.Only then will it be possible to offer models thatare really meaningful to the industry in decisionmaking. As for now, one can, to some extent,change the outcome of the analysis by using adifferent LCI database.9 However, there is reasonto believe that improved data (on a standardizedform) on a national level will solve some of theseissues.

The C&D sector is so important in terms ofits waste amount, that such research work shouldbe given high priority.

Notations

Euro = Euro (currency).Pt.: = Eco-point, environmental per-

formance indicator based onthe EcoIndicator 99 method.

NAP(2005) = The National Action Plan forrecycling of C&D waste. Over-all target of 70% recyclingwithin 2005.

ε′′ = eco-efficiency (Euro/Pt.).ε′ = Relative eco-efficiency

(Euro/Pt.).�∗ = Aggregated-total environmen-

tal impact (Pt.).� = Total environmental impact

(Pt.).ψ = Environmental impact

(Pt./tonne).ψ ′ = Relative environmental impact

(Pt./tonne).κ = Costs (Euro/tonne).κ ′ = Relative Costs (Euro/tonne).w = Waste generation (tonnes).



r = Recycling ratio.t = Time (years).j = Waste fraction.i = End-of-life treatment alterna-

tive.a = Processes of product system A.b = Processes of product system B.

Acknowledgments

The authors want to thank Øyvind Spjøtvoldand Aage Heie, Norsas, for valuable discussionsand help in data collection during this work.We also want to thank Anders Strømman, In-gve Simonsen, Bard Skaflestad, and Glen Peters,all from Norwegian University of Science andTechnology (NTNU), for guidance and discus-sions about calculations and MATLAB program-ming.

Notes

1. One square meter (m2, SI) ≈ 10.76 square feet (ft2).2. All tons are metric and thus spelled tonne; one

tonne (t) = 1000 kilograms (kg, SI) ≈ 1.103 shorttons.

3. Downcycling is the recycling of a material into amaterial of a lesser quality (Wikepedia contributors2008).

4. Be aware that the origin of the plot also changes(relatively) by introducing new policies.

5. Scenario 0 is the reference point for our exami-nation, because it is equal to the current practice.Hence this scenario will be represented by κ a andψ a in equation 7, and the origin location in theeco-efficiency plots (figure 4). Likewise, Scenario 1:NAP, Scenario 2: Maximum Recycling, and Sce-nario 3: Maximum Energy Recovery will be rep-resented by parallel sets of κ b and ψ b values inequation 7 and located away from origin in theplots.

6. In figure 3, the concrete and brick fractions are com-bined to one fraction because of the lack of data onwaste generation. It is, however, believed that thebrick and concrete fraction is dominated by con-crete, thus numbers on environmental impact andnet present values for concrete are used.

7. This work was done in 2005.8. We want to remind the reader that these are cal-

culations for the extended system and are not rep-resentative for the individual stakeholders, but forthe system as a whole, see figure 2.

9. The EcoInvent database is developed for continen-tal Europe and, as such, does not necessarily repre-sent activities performed in Norway.

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About the Authors

Rolf Andre Bohne is a Postdoctoral fellow atNTNU in the Department of Civil and Trans-port Engineering/Industrial Ecology Programme.



Havard Bergsdal is a Ph.D. student at NTNU inthe Department of Hydraulic and Environmen-tal Engineering/Industrial Ecology Programme.Helge Brattebø is a professor at NTNU and cur-

rently head of the Department of Hydraulic andEnvironmental Engineering and Master of Sci-ence program director in the Industrial EcologyProgramme.


dynamic eco-efficiency projections for construction and demolition waste recycling strategies at the...

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