Journal of Environmental Management 135 (2014) 11e18

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Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment Xiaodong Zhang a, *, Gordon Huang b, c a

Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78713, USA Institute of Energy, Environment & Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2, Canada c MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 June 2013 Received in revised form 10 October 2013 Accepted 9 January 2014 Available online 4 February 2014

Waste management activities can release greenhouse gases (GHGs) to the atmosphere, intensifying global climate change. Mitigation of the associated GHG emissions is vital and should be considered within integrated municipal solid waste (MSW) management planning. In this study, a fuzzy possibilistic integer programming (FPIM) model has been developed for waste management facility expansion and waste flow allocation planning with consideration of GHG emission trading in an MSW management system. It can address the interrelationships between MSW management planning and GHG emission control. The scenario of total system GHG emission control is analyzed for reflecting the feature that GHG emission credits may be tradable. An interactive solution algorithm is used to solve the FPIM model based on the uncertainty-averse preferences of decision makers in terms of p-necessity level, which represents the certainty degree of the imprecise objective. The FPIM model has been applied to a hypothetical MSW planning problem, where optimal decision schemes for facility expansion and waste flow allocation have been achieved with consideration of GHG emission control. The results indicate that GHG emission credit trading can decrease total system cost through re-allocation of GHG emission credits within the entire MSW management system. This will be helpful for decision makers to effectively determine the allowable GHG emission permits in practices. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Municipal solid waste Fuzzy possibilistic Integer Greenhouse gases Emission trading Uncertainty

1. Introduction Municipal solid waste (MSW) management is an important environmental problem, and causing more and more attentions. Waste management activities can release greenhouse gases (GHGs) to the atmosphere, including CO2 emissions associated with composting, non-biogenic CO2 and N2O emissions from combustion, and CH4 emissions from landfills (IPCC, 1997; USEPA, 2006; Mohareb et al., 2008; Figueroa et al., 2009). In 2008, waste activities contributed to 2.3% of total U.S. GHG emissions; landfills were the second largest source for total U.S. anthropogenic CH4 emissions (USEPA, 2010). The increased anthropogenic GHG concentrations in the atmosphere may pose serious negative impacts and risks to the humans, the society and eco-environment in the earth (USEPA, 2002). Mitigation of the GHG emissions from waste management activities such as waste collection, transport and disposal * Corresponding author. Tel.: þ1 512 232 2336; fax: þ1 512 471 0140. E-mail addresses: [email protected], [email protected] (X. Zhang). 0301-4797/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2014.01.014

is vital and should be integrated into decision schemes for MSW management planning. With population increase and economic growth, the existing waste management facilities would encounter the difficulties in meeting the requirements of handling rapidly increased waste amounts. Facility expansion planning is becoming a critical issue for waste managers and decision makers. Mixed-integer linear programming (MILP) is a powerful tool to support planning facility expansions in MSW management through integrated consideration of binary and continuous decision variables (Huang et al., 1995; Chang and Wei, 2000; Badran and El-Haggar, 2006; Erkut et al., 2008; He et al., 2009). In MSW management systems, many system parameters are of imprecise nature, which are often expressed as fuzzy possibilistic distributions; replacing them using deterministic values can lead to loss of a lot of useful information for decision making. Fuzzy possibilistic programming (FPP) is a useful method for handling the imprecise coefficients of the objective function and/or the constraints (Lai and Hwang, 1992; Hsu and Wang, 2001; Inuiguchi and Ramik, 2000; Tang et al., 2001; Wang and Liang, 2005; Liang, 2007; Özgen et al., 2008; Zhang et al., 2009, 2010a,

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X. Zhang, G. Huang / Journal of Environmental Management 135 (2014) 11e18

2011; Zhang and Huang, 2013). Previously, a variety of uncertain optimization models have been developed and applied for waste management facility expansion planning and/or waste flow allocation (Huang et al., 1992, 1997; Yeomans and Huang, 2003; Maqsood et al., 2004; Liu et al., 2009; Zhang et al., 2010b; Zhang and Huang, 2013). Recently, some studies on development and application of solid waste management models with consideration of GHG emission issues have been reported. For example, Lu et al. (2009) proposed an interval-parameter optimization model for municipal solid waste management, where GHG emission control was considered. He et al. (2011) developed two mixed-integer bilevel decision making models for MSW management and GHG emission control. Chang et al. (2012) designed five managerial scenarios to investigate global warming potential impacts on optimal planning of municipal solid waste management. However, few of the previous studies could simultaneously deal with fuzzy possibilistic information in waste management facility expansion planning, and consider GHG mitigation and emission trading in MSW management systems. Therefore, one possible potential approach for accounting for the above problems would be to integrate fuzzy possibilistic programming within a mixed-integer linear programming framework, leading to a fuzzy possibilistic integer programming (FPIM) model. The FPIM model would be applied to support planning of facility expansions and operations and associated greenhouse gas mitigation control in a municipal solid waste management system, where optimal facility development and waste flow allocation schemes could be generated with consideration of GHG emission trading. 2. Statement of the problems A hypothetical but representative problem has been used to illustrate the FPIM model for planning waste management facility expansion and waste flow allocation with consideration of GHG emission mitigation within an MSW management system. The system includes three cities which generate solid wastes daily. The planning horizon is divided into three time periods, each of which is five years. Three waste management facilities including one landfill and two waste-to-energy (WTE) facilities are used for treatment and disposal of wastes from three cities. The existing capacity of the landfill is 0.75  106 tonnes, and those of WTE facilities 1 and 2 are 115 and 215 tonne/day, respectively. With the increase of waste generation rate from three cities, capacities of three waste management facilities should be expanded over the planning horizon in order to meet the requirements for treatment and disposal of more wastes. Due to its high GHG emissions compared to WTE facilities, the landfill can be expanded only once in three time periods with an incremental capacity of 1.7  106 tonnes according to relevant environmental policies; each of WTE facilities 1 and 2 can have only one capacity expansion by any of three options (Options 1, 2, and 3 shown in Table 1) in each of three time periods. Table 1 presents the capacity expansion options for three waste management facilities and associated capital costs in present values. Revenues can be obtained through energy sales in the WTE facilities. The residual wastes from the two WTE facilities are transported to the landfill for final disposal; approximately 25% of the incoming waste streams on a mass basis from the two WTE facilities are generated and directly shipped to the landfill. Table 2 shows waste generation rates of three cities, transportation costs of waste delivery, operational costs of three facilities, and revenues from energy sales in the WTE facilities. Due to vagueness and impreciseness in cost estimation and limitation of data collection, these costs coefficients are recognized to be uncertain and expressed as fuzzy possibilistic parameters.

Table 1 Capacity expansion options for facilities and associated capital costs. Time period

Capacity expansion option for the landfill (106 tonne) Capital cost for landfill expansion ($106 present value)

k¼1

k¼2

k¼3

1.7

1.7

1.7

14

Capacity expansion option for WTE facilities (tonne/day) Option 1 100 Option 2 150 Option 3 200 Capital cost for WTE facility expansion ($106 present value) Option 1 10.5 Option 2 15.2 Option 3 19.8

14

14

100 150 200

100 150 200

8.3 11.9 15.5

6.5 9.3 12.2

In the processes of landfilling and waste-to-energy treatment, significant amounts of greenhouse gases including CO2, CH4 and N2O are emitted. For the sake of simplicity and comparison, GHG emissions are considered as CO2 equivalent (CO2e) emissions based on their own global warming potentials (GWPs), an important index for measuring their heat-trapping potential (USEPA, 2006; Papageorgiou et al., 2009; Chang et al., 2012; Zhang and Huang, 2013). In this study, no specific analyses are conducted for each kind of GHGs although the contributions of each GHG to climate change are different; emission rate of each type of GHG from the facilities is converted into that in terms of CO2e and their total contributions to GHG emissions are considered by summation and Table 2 Waste generation rates, transportation costs, operational costs and revenues of facilities. Time period k¼2

k¼3

Waste generation rate (tonne/day) City 1 225 City 2 375 City 3 300

k¼1

250 400 325

275 425 350

Costs of transportation to landfill ($/tonne) City 1 (14.1, 2.0) City 2 (12.3, 1.7) City 3 (14.8, 2.1)

(15.5, 2.2) (13.5, 1.9) (16.3, 2.3)

(17.1, 2.4) (14.9, 2.0) (18, 2.6)

Cost of transportation to WTE facility 1 ($/tonne) City 1 (11.2, 1.6) (12.4, 1.7) City 2 (11.8, 1.6) (12.9, 1.8) City 3 (10.2, 1.4) (11.2, 1.5)

(13.6, 1.9) (14.2, 2.0) (12.3, 1.7)

Cost of transportation to WTE facility 2 ($/tonne) City 1 (14.1, 2.0) (15.5, 2.2) City 2 (15.0, 2.1) (16.4, 2.3) City 3 (5.1, 0.5) (5.4, 0.8)

(17.1, 2.4) (18.1, 2.6) (5.9, 0.8)

Cost of residue transportation from the WTE facilities to the landfill ($/tonne) WTE facility 1 (5.5, 0.8) (6.0, 0.8) (6.6, 0.9) WTE facility 2 (15.6, 2.2) (17.2, 2.5) (18.9, 2.7) Operation costs ($/tonne) Landfill WTE facility 1 WTE facility 2 Revenue from WTE facilities ($/tonne)

(38, 7) (65, 10) (60, 10)

(50, 10) (72, 12) (70, 10)

(65, 15) (80, 15) (75, 10)

(20, 5)

(20, 5)

(20, 5)

X. Zhang, G. Huang / Journal of Environmental Management 135 (2014) 11e18 3 P

incorporated into the proposed FPIM model. Local environmental regulations require that the waste management facilities to purchase GHG emission credits before release. The GHG emissions from transportation of wastes are not considered in this study since the targets of GHG emission credit management are mainly the facilities. Table 3 lists the data related to GHG emissions and mitigation control in the facilities. The problems under consideration are how to simultaneously generate preferred capacity expansion schemes for waste management facilities and allocate the waste flows through integrated consideration of GHG emission trading during the planning periods. Due to the existence of imprecise coefficients in the objective function, the objective function is also of imprecise nature. The proposed FPIM model provides a feasible approach for tackling such planning issues to generate the desired decision schemes for waste flow allocation and facility development under imprecise uncertainties.

xijk0  CFi þ

j¼1

k0 P

3 P

m¼1 k¼1

13

DCFimk zimk ;

(1c) 3 P

xijk  WGjk

i¼1

c j ¼ 1; 2; 3;

k ¼ 1; 2; 3

3 X

Lk $ELk x1jk þ

j¼1 3 P

3 X

! xijk $RF

 EP1k

Lk $EWTk $xijk  EPik

c i ¼ 2; 3;

3.1. FPIM municipal solid waste management model

xijk  0

The management objective is to minimize the total cost of an MSW management system which includes transportation costs, operation costs of facilities, facility expansion costs, purchase cost for GHG emission credits, and revenues from the WTE facilities. The decision variables include continuous variables for waste flow allocation from city j to facility i in period k, and binary variables for facility expansion options. The constraints are related to waste generation, disposal and management, facility capacity limitation, and GHG emission control. Thus, an FPIM municipal solid waste management model considering GHG emission mitigation can be formulated as follows:

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Municipal solid waste management planning considering greenhouse gas emission trading under fuzzy environment.

Waste management activities can release greenhouse gases (GHGs) to the atmosphere, intensifying global climate change. Mitigation of the associated GH...
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