# Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building

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## Abstract

**:**

## 1. Introduction

- Credit 2.1 (one point): recycle and/or salvage at least 50 percent of construction, demolition and land-clearing waste.
- Credit 2.2 (one point): recycle and/or salvage an additional 25 percent (75 percent total) of construction, demolition and land-clearing waste.

#### Novelty and Organization of the Research

## 2. Case Study

## 3. Methodology

#### 3.1. Hybrid LCA Model

^{−1}× f

_{i}= E

_{i}× X = E

_{i}× (I−A)

^{−1}× f

_{i}is the total environmental output vector for the environmental impact category of i and E

_{i}represents a diagonal matrix, which consists primarily of the environmental impacts per dollar of output for each industrial sector. In this research, a hybrid EIO-LCA model is built to consider the environmental impacts associated with different waste management scenarios. This LCA model quantifies the total environmental burdens associated with the waste management system, which is presented in the following equation [20]:

_{i}= E

_{i}× (I−A)

^{−1}× f + Q

_{i}× e

_{i}

_{i}denotes the total environmental impact defined as the summation of environmental burdens associated with the production of resource inputs (by tracing all supply chains) and the direct environmental impacts related to waste treatment processes. Q

_{i}is the total input requirement for a process, and e

_{i}is the unit environmental impact factor associated with the consumption of Q

_{i}. For example, the production of reinforced steel, which is widely used in residential and commercial buildings, has high GHG emissions. During its production process, electricity is consumed as an energy source for steel manufacturing. In our hybrid LCA model, to quantify the direct and indirect GHG emissions considering the whole supply chain of electricity production, we used Equation (2). In addition, Q

_{i}represents the amount of electricity used in the steel production process, and e

_{i}represents the emission factor related to electricity generation. In this way, Equation (3) presents the total carbon emission related to the indirect supply chains of electricity production (E

_{i}× (I−A)

^{−1}× f) and onsite electricity production processes (Q

_{i}× e

_{i}).

#### 3.2. Multi-Criteria Optimization Model

_{a}metric. The L

_{a}metric defines the distance between two points, such as ${Z}_{k}^{*}(X)$ and Z

_{k}(X). As can been seen from Equation (4), compromise programming uses a distance-based function in order to minimize the difference between ideal and compromise solutions. The formulation of the L

_{a}metric is presented as follows:

**Notation:**

_{rf}) (see Equation (13)). Subsequently, the decision variable (X

_{i}) must be less than or equal to the recycled waste (Q

_{i}), as shown in Equation (14). Finally, all decision variables are greater than or equal to zero (see Equation (15)).

_{rf}, is used. Due to being one of the most robust optimization software in the applied optimization field, the LINGO

^{©}software package is used for solving the multi-objective optimization model [26]. Since we have four objective functions (Z

_{1}, Z

_{2}, Z

_{3}and Z

_{4}) and three compromise programming functions (a = 1, a = 2 and a = $\infty $), the LINGO

^{©}program has been run to solve each mathematical model. By using the mathematical optimization model, the optimal recycling amount of each construction material has been calculated for the overall 50% recycling goal for single and multiple objectives.

#### 3.3. Data Collection

## 4. Results

#### 4.1. LCA Results

#### 4.1.1. Energy Savings

Material | Recycling | % Share | Landfilling | % Share | Incineration | % Share |
---|---|---|---|---|---|---|

Cardboard | 0.271 | 3.6% | −0.004 | 5.6% | 0.000 | 27.2% |

Non-ferrous | 5.720 | 75.5% | −0.002 | 2.8% | N/A | N/A |

Ferrous Metal | 0.156 | 2.1% | −0.002 | 2.8% | N/A | N/A |

Concrete | 0.241 | 3.2% | −0.024 | 37.0% | N/A | N/A |

Plastic | 0.978 | 12.9% | −0.002 | 2.7% | 0.000 | 13.2% |

Wood | 0.093 | 1.2% | −0.008 | 12.3% | 0.000 | 59.6% |

Glass | 0.010 | 0.1% | 0.000 | 0.2% | N/A | N/A |

Drywall | 0.084 | 1.1% | −0.004 | 6.1% | N/A | N/A |

Asphalt | 0.027 | 0.4% | −0.020 | 30.3% | N/A | N/A |

#### 4.1.2. GHG Emission Savings

_{2}equivalents related to recycling, landfilling and incineration of per ton C&D waste. Based on the analysis results, recycling of ferrous and non-ferrous metals are found to have significant benefits for reducing total GHG emissions with a total percent share of 79.3% (see Table 2). This is because recycling of these metals reduced the amount of electricity and fuel inputs, which are highly utilized for the production of metal products. Additionally, on-site emissions are decreased when using recycled metals instead of virgin resources. Recycling of other C&D materials, such as paper, glass and cardboard, also contribute to reductions in the net GHG emissions.

Material | Recycling | % Share | Landfilling | % Share | Incineration | % Share |
---|---|---|---|---|---|---|

Cardboard | 9.430 | 7% | −2.714 | 6% | -26.718 | 18% |

Non-ferrous | 38.142 | 30% | −1.370 | 3% | N/A | N/A |

Ferrous Metal | 61.732 | 49% | −1.370 | 3% | N/A | N/A |

Concrete | −23.800 | - | −17.918 | 37% | N/A | N/A |

Plastic | 15.733 | 12% | −1.318 | 3% | −32.250 | 22% |

Wood | −7.910 | - | −5.955 | 12% | −90.400 | 61% |

Glass | 0.436 | 0% | −0.108 | 0% | N/A | N/A |

Drywall | 0.562 | 0% | −2.963 | 6% | N/A | N/A |

Asphalt | −8.930 | - | −14.663 | 30% | N/A | N/A |

#### 4.1.3. Water Savings

Material | Recycling | % Share | Landfilling | % Share | Incineration | % Share |
---|---|---|---|---|---|---|

Cardboard | 0.201 | 0.2% | −1.102 | 5.6% | −0.048 | 27.3% |

Non-ferrous | 5.707 | 5.2% | −0.556 | 2.8% | N/A | N/A |

Ferrous Metal | 0.130 | 0.1% | −0.556 | 2.8% | N/A | N/A |

Concrete | −0.211 | - | −7.276 | 37.0% | N/A | N/A |

Plastic | 0.945 | 0.9% | −0.535 | 2.7% | −0.023 | 12.9% |

Wood | −0.058 | - | −2.418 | 12.3% | −0.106 | 59.8% |

Glass | 0.007 | 0.0% | −0.044 | 0.2% | N/A | N/A |

Drywall | 0.009 | 0.0% | −1.203 | 6.1% | N/A | N/A |

Asphalt | 101.988 | 93.6% | −5.954 | 30.3% | N/A | N/A |

#### 4.2. Optimization Results

## 5. Conclusions, Limitations and Future Work

## Author Contributions

## Conflicts of Interest

## References

- USEPA. Estimating 2003 Building-Related Construction and Demolition Materials Amounts; USEPA: Washington, DC, USA, 2009. [Google Scholar]
- Townsend, T.; Wilson, C.; Beck, B. The Benefits of Construction and Demolition Materials Recycling in the United States; University of Florida: Gainesville, FL, USA, 2014. [Google Scholar]
- USGBC. LEED 2009 for New Construction and Major Renovations Rating System; U.S. Green Building Council: Washington, DC, USA, 2009. [Google Scholar]
- Tatari, O.; Kucukvar, M. Cost premium prediction of certified green buildings: A neural network approach. Build. Environ.
**2010**, 46, 1081–1085. [Google Scholar] [CrossRef] - Lave, L.B.; Hendrickson, C.T.; Conway-Schempf, N.M.; McMichael, F.C. Municipal Solid Waste Recycling Issues. J. Environ. Eng.
**1999**, 125, 1–16. [Google Scholar] [CrossRef] - Gustavsson, L.; Sathre, R. Variability in energy and carbon dioxide balances of wood and concrete building materials. Build. Environ.
**2006**, 41, 940–951. [Google Scholar] [CrossRef] - Petersen, A.K.; Solberg, B. Environmental and economic impacts of substitution between wood products and alternative materials: A review of micro-level analyses from Norway and Sweden. For. Policy Econ.
**2005**, 7, 249–259. [Google Scholar] [CrossRef] - Lasvaux, S.; Habert, G.; Peuportier, B.; Chevalier, J. Comparison of generic and product-specific Life Cycle Assessment databases: Application to construction materials used in building LCA studies. Int. J. Life Cycle Assess.
**2015**, 20, 1473–1490. [Google Scholar] [CrossRef] - Dixit, M.K.; Culp, C.H.; Fernandez-Solis, J.L. Embodied energy of construction materials: Integrating human and capital energy into an IO-based hybrid model. Environ. Sci. Technol.
**2015**, 49, 1936–1945. [Google Scholar] [CrossRef] [PubMed] - Pierucci, A. LCA evaluation methodology for multiple life cycles impact assessment of building materials and components. Tema:Tempo Mater. Arch.
**2015**, 1, 1–6. [Google Scholar] - Cabeza, L.F.; Rincón, L.; Vilariño, V.; Pérez, G.; Castell, A. Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renew. Sustain. Energy Rev.
**2014**, 29, 394–416. [Google Scholar] [CrossRef] - Dixit, M.K.; Fernández-Solís, J.L.; Lavy, S.; Culp, C.H. Need for an embodied energy measurement protocol for buildings: A review paper. Renew. Sustain. Energy Rev.
**2012**, 16, 3730–3743. [Google Scholar] [CrossRef] - Van den Heede, P.; de Belie, N. Environmental impact and life cycle assessment (LCA) of traditional and “green” concretes: Literature review and theoretical calculations. Cem. Concr. Compos.
**2012**, 34, 431–442. [Google Scholar] [CrossRef] - Ortiz, O.; Castells, F.; Sonnemann, G. Sustainability in the construction industry: A review of recent developments based on LCA. Constr. Build. Mater.
**2009**, 23, 28–39. [Google Scholar] [CrossRef] - University of Central Florida Sustainability and Energy Management Department. LEED GOLD Physical Sciences Building: Material and Resources Credits. 2012. Available online: http://www.sustainable.ucf.edu/?q=node/108 (accessed on 5 March 2015).
- Hendrickson, C.; Lave, L.; Matthews, H. Environmental Life Cycle Assessment of Goods and Services: An Input-Output Approach; Routledge: London, UK, 2006. [Google Scholar]
- Suh, S.; Huppes, G. Methods for Life Cycle Inventory of a product. J. Clean. Prod.
**2005**, 13, 687–697. [Google Scholar] [CrossRef] - Egilmez, G.; Kucukvar, M.; Tatari, O. Sustainability assessment of U.S. manufacturing sectors: An economic input output-based frontier approach. J. Clean. Prod.
**2013**, 53, 91–102. [Google Scholar] - Egilmez, G.; Kucukvar, M.; Tatari, O. Supply chain sustainability assessment of the U.S. food manufacturing sectors: A life cycle-based frontier approach. Resour. Conserv. Recycl.
**2014**, 82, 8–20. [Google Scholar] - Kucukvar, M.; Egilmez, G.; Tatari, O. Evaluating environmental impacts of alternative construction waste management approaches using supply-chain-linked life-cycle analysis. Waste Manag. Res.
**2014**, 32, 500–508. [Google Scholar] [CrossRef] [PubMed] - Noori, M.; Kucukvar, M.; Tatari, O. A macro-level decision analysis of wind power as a solution for sustainable energy. Int. J. Sustain. Energy
**2013**, 34, 629–644. [Google Scholar] [CrossRef] - Tatari, O.; Nazzal, M.; Kucukvar, M. Comparative sustainability assessment of warm-mix asphalts: A thermodynamic based hybrid life cycle analysis. Resour. Conserv. Recycl.
**2012**, 58, 18–24. [Google Scholar] [CrossRef] - Kucukvar, M.; Noori, M.; Egilmez, G.; Tatari, O. Stochastic decision modeling for sustainable pavement designs. Int. J. Life Cycle Assess.
**2014**, 19, 1185–1199. [Google Scholar] [CrossRef] - Onat, N.C.; Kucukvar, M.; Tatari, O.; Zheng, Q.P. Combined Application of Multi-Criteria Optimization and Life-Cycle Sustainability Assessment for Optimal Distribution of Alternative Passenger Cars in US. J. Clean. Prod.
**2015**, 30, 1–17. [Google Scholar] - Chang, N.-B. Systems Analysis for Sustainable Engineering; McGraw-Hill: New York, NY, USA, 2011. [Google Scholar]
- Lindo Inc. LINDO Systems—Optimization Software: Integer Programming, Linear Programming, Nonlinear Programming, Stochastic Programming, Global Optimization. 2012. Available online: http://www.lindo.com/ (accessed on 10 May 2012).
- Christensen, T.H.; Bhander, G.; Lindvall, H.; Larsen, A.W.; Fruergaard, T.; Damgaard, A.; Manfredi, S. Experience with the use of LCA-modelling (EASEWASTE) in waste management. Waste Manag. Res.
**2007**, 25, 257–262. [Google Scholar] [CrossRef] [PubMed] - Denison, R.A. Environmental life-cycle comparisons of recycling, landfilling, and incineration: a review of recent studies. Annu. Rev. Energy Environ.
**1996**, 21, 191–237. [Google Scholar] [CrossRef] - USEPA. Waste Reduction Model (WARM). 2010. Available online: http://www.epa.gov/climatechange/wycd/waste/calculators/Warm_Form.html (accessed on 3 March 2015). [Google Scholar]
- Diaz, R.; Warith, M. Life-cycle assessment of municipal solid wastes: Development of the WASTED model. Waste Manag.
**2006**, 26, 886–901. [Google Scholar] [CrossRef] [PubMed] - NREL. U.S. Life Cycle Inventory Database: Transport, Train, Diesel Powered; NREL: Washington, DC, USA, 2010. [Google Scholar]
- Carnegie Mellon University Green Design Institute. Economic Input-Output Life Cycle Assessment (EIO-LCA), US 2002 Industry Benchmark Model. 2012. Available online: http://www.eiolca.net/ (accessed on 5 March 2015).
- Tam, V.W. Economic comparison of concrete recycling: A case study approach. Resour. Conserv. Recycl.
**2008**, 52, 821–828. [Google Scholar] [CrossRef] - Tam, V.W. Comparing the implementation of concrete recycling in the Australian and Japanese construction industries. J. Clean. Prod.
**2009**, 17, 688–702. [Google Scholar] [CrossRef]

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**MDPI and ACS Style**

Kucukvar, M.; Egilmez, G.; Tatari, O.
Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building. *Sustainability* **2016**, *8*, 89.
https://doi.org/10.3390/su8010089

**AMA Style**

Kucukvar M, Egilmez G, Tatari O.
Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building. *Sustainability*. 2016; 8(1):89.
https://doi.org/10.3390/su8010089

**Chicago/Turabian Style**

Kucukvar, Murat, Gokhan Egilmez, and Omer Tatari.
2016. "Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building" *Sustainability* 8, no. 1: 89.
https://doi.org/10.3390/su8010089