Abstract
Purpose
Quantitative life cycle sustainable assessment requires a complex and multidimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social, and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for (1) broadening the existing life cycle sustainability assessment (LCSA) framework by considering macrolevel environmental, economic, and social impacts (termed as the triple bottom line), simultaneously, (2) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling, (3) understanding the dynamic complexity of transportation sustainability for the triple bottom line impacts of alternative vehicles, and finally (4) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macrolevel functional unit considering all of the complex interactions in the environmental, social, and economic aspects.
Methods
To alleviate these research objectives, we presented a novel methodology to quantify macrolevel social, economic, and environmental impacts of passenger vehicles from an integrated system analysis perspective. An integrated dynamic LCSA model is utilized to analyze the environmental, economic, and social life cycle impact as well as life cycle cost of alternative vehicles in the USA. System dynamics modeling is developed to simulate the US passenger transportation system and its interactions with economy, the environment, and society. Analysis covers manufacturing and operation phase impacts of internal combustion vehicles (ICVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). In total, seven macrolevel indicators are selected; global warming potential, particulate matter formation, photochemical oxidant formation, vehicle ownership cost, contribution to gross domestic product, employment generation, and human health impacts. Additionally, contribution of vehicle choices to global atmospheric temperature rise and public welfare is investigated.
Results and discussion
BEVs are found to be a better alternative for most of sustainability impact categories. While some of the benefits such as contribution to employment and GDP, CO2 emission reduction potential of BEVs become greater toward 2050, other sustainability indicators including vehicle ownership cost and human health impacts of BEVs are higher than the other vehicle types on 2010s and 2020s. While the impact shares of manufacturing and operation phases are similar in the early years of 2010s, the contribution of manufacturing phase becomes higher as the vehicle performances increase toward 2050. Analysis results revealed that the US transportation sector, alone, cannot reduce the rapidly increasing atmospheric temperature and the negative impacts of the global climate change, even though the entire fleet is replaced with BEVs. Reducing the atmospheric climate change requires much more ambitious targets and international collaborative efforts. The use of different vehicle types has a small impact on public welfare, which is a function of income, education, and life expectancy indexes.
Conclusions
The authors strongly recommend that the dynamic complex and mutual interactions between sustainability indicators should be considered for the future LCSA framework. This approach will be critical to deepen the existing LCSA framework and to go beyond the current LCSA understanding, which provide a snapshot analysis with an isolated view of all pillars of sustainability. Overall, this research is a first empirical study and an important attempt toward developing integrated and dynamic LCSA framework for sustainable transportation research.
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References
Abbas KA, Bell MGH (1994) System dynamics applicability to transportation modeling. Transp Res A Policy Pract 28:373–390
Amekudzi AA, Jotin Khisty C, Khayesi M (2009) Using the sustainability footprint model to assess development impacts of transportation systems. Transp Res A Policy Pract 43:339–348
Argonne National Laboratory (2014) The VISION Model. http://www.transportation.anl.gov/modeling_simulation/VISION/. Accessed 9 Jun 2014
Baldoni F, Falsini D, Taibi E (2010) A system dynamics energy model for a sustainable transportation system. In: Proceedings of the 28th International Conference of the System Dynamics Society
Barlas Y (1996) Formal aspects of model validity and validation in system dynamics. Syst Dyn Rev 12:183–210
Bossel H (2007) System zoo 3 simulation models–economy
Bradley TH, Quinn CW (2010) Analysis of plug-in hybrid electric vehicle utility factors. J Power Sources 195:5399–5408
CALCAS (2009) D17 Final report: options for deepening and broadening LCA. http://www.estis.net/includes/file.asp?site=calcas&file=B501D8D5-ADC1-4DBA-8EFC-71A764FCFE5A. Accessed 3 Feb 2014
Chateau J, Rebolledo C, Dellink R (2011) An economic projection to 2050: the OECD“ ENV-Linkages” Model Baseline
Chester MV, Horvath, A (2009) Environmental assessment of passenger transportation should include infrastructure and supply chains. Environ Res Lett 4(2):024008
Cucurachi S, Suh S (2015) A moonshot for sustainability assessment. Environ Sci Technol 49(16):9497–9498
Dobranskyte-Niskota A, Perujo A, Pregl M (2007) Indicators to assess sustainability of transport activities. Office for Official Publications of the European Communities, ISBN 978-92-79-07802-6, doi:10.2788/54736
Egilmez G, Kucukvar M, Tatari O, Bhutta M (2014) Supply chain sustainability assessment of the US food manufacturing sectors: a life cycle-based frontier approach. Resour Conserv Recycl 82:8–20
Egilmez G, Kucukvar M, Tatari O (2013) Sustainability assessment of US manufacturing sectors: an economic input output-based frontier approach. J Clean Prod 53:91–102
Egilmez G, Tatari O (2012) A dynamic modeling approach to highway sustainability: strategies to reduce overall impact. Transp Res A Policy Pract 46:1086–1096
EPA (2014) Light-duty automotive technology, carbon dioxide emissions, and fuel economy trends. Washington, pp 1975–2014
EPA (2013) Gasoline emission factor-calculations and references. http://www.epa.gov/cleanenergy/energy-resources/refs.html
Ercan T, Kucukvar M, Tatari O, Al-Deek H (2013) Congestion relief based on intelligent transportation systems in Florida: analysis of triple bottom line sustainability impact. Transp Res Rec: J Transp Res Board 2380:81–89
Fiddaman T (2008) Tom Fiddaman’s system dynamics model library. http://www.metasd.com/models/. Accessed 2 Sep 2014
Furuoka F (2009) Looking for a J-shaped development-fertility relationship: do advances in development really reverse fertility declines. Econ Bull 29:3067–3074
Furuoka F (2010) The fertility-development relationship in the United States: new evidence from threshold regression analysis. Econ Bull 30:1808–1822
Guinée J (2016) Life cycle sustainability assessment: what is it and what are its challenges? In Taking Stock of Industrial Ecology. Springer International Publishing, pp 45–68
Guinée JB, Heijungs R, Huppes G et al (2011) Life cycle assessment: past, present, and future. Environ Sci Technol 45:90–96
Halog A, Manik Y (2011) Advancing integrated systems modelling framework for life cycle sustainability assessment. Sustainability 3:469–499
Han J, Hayashi Y (2008) A system dynamics model of CO2 mitigation in China’s inter-city passenger transport. Transp Res Part D: Transp Environ 13:298–305
Hawkins TR, Gausen OM, Strømman AH (2012) Environmental impacts of hybrid and electric vehicles—a review. Int J Life Cycle Assess 17:997–1014
Heijungs R, Huppes G, Guinée JB (2010) Life cycle assessment and sustainability analysis of products, materials and technologies. Toward a scientific framework for sustainability life cycle analysis. Polym Degrad Stab 95:422–428
Heijungs R, Settanni E, Guinée J (2012) Toward a computational structure for life cycle sustainability analysis: unifying LCA and LCC. Int J Life Cycle Assess 18:1722–1733
Hendrickson CT, Lester BL, Matthews HS (2006) Environmental life cycle assessment of goods and services: an input–output approach. Washington
Hiasa S, Noori M, Kelly C, Tatari O (2016) Dynamic techno-ecological modeling of highway systems: a case study of the Shin-Meishin Expressway in Japan. J Clean Prod 115:101–121
Huang YA, Weber CL, Matthews HS (2009) Categorization of Scope 3 emissions for streamlined enterprise carbon footprinting. Environ Sci Technol 43:8509–8515
Jeswani HK, Azapagic A, Schepelmann P, Ritthoff M (2010) Options for broadening and deepening the LCA approaches. J Clean Prod 18:120–127
Jin W, Xu L, Yang Z (2009) Modeling a policy making framework for urban sustainability: incorporating system dynamics into the Ecological Footprint. Ecol Econ 68:2938–2949
Kloepffer W (2008) Life cycle sustainability assessment of products. Int J Life Cycle Assess 13:89–95
Klöpffer W (2007) Life-cycle based sustainability assessment as part of LCM. Proc 3rd Int Conf Life Cycle Manag:27–29
Kucukvar M, Egilmez G, Onat NC, Samadi H (2015) A global, scope-based carbon footprint modeling for effective carbon reduction policies: lessons from the Turkish manufacturing. Sustain Prod Consum 1:47–66
Kucukvar M, Samadi H (2015) Linking national food production to global supply chain impacts for the energy-climate challenge: the cases of the EU-27 and Turkey. J Clean Prod 108:395–408
Kucukvar M, Egilmez G, Tatari O (2014a) Sustainability assessment of U.S. final consumption and investments: triple-bottom-line input–output analysis. J Clean Prod 81:234–243
Kucukvar M, Noori M, Egilmez G, Tatari O (2014b) Stochastic decision modeling for sustainable pavement designs. Int J Life Cycle Assess 19:1185–1199
Kucukvar M, Gumus S, Egilmez G, Tatari O (2014c) Ranking the sustainability performance of pavements: an intuitionistic fuzzy decision making method. Autom Constr 40:33–43
Kucukvar M, Tatari O (2013) Towards a triple bottom-line sustainability assessment of the U.S. construction industry. Int J Life Cycle Assess 18:958–972
Kucukvar M (2013) Life cycle sustainability assessment framework for the U.S. built environment. Doctoral dissertation, University of Central Florida Orlando, Florida
Laurenti R, Lazarevic D, Poulikidou S et al (2014) Group model-building to identify potential sources of environmental impacts outside the scope of LCA studies. J Clean Prod 72:96–109
Lee S, Geum Y, Lee H, Park Y (2012) Dynamic and multidimensional measurement of product-service system (PSS) sustainability: a triple bottom line (TBL)-based system dynamics approach. J Clean Prod 32:173–182
Litman T, Burwell D (2006) Issues in sustainable transportation. Int J Glob Environ Issues 6:331–347
Litman TA (2009) Sustainable transportation indicators: a recommended research program for developing sustainable transportation indicators and data. In: Transportation Research Board 88th Annual Meeting
Marvuglia A, Benetto E, Murgante B (2015) Calling for an integrated computational systems modelling framework for life cycle sustainability analysis. Journal of Environmental Accounting and Management 3(3):213–216
Meadows DH, Randers J, Meadows DL (2004) Limits to growth: the 30-year update. Chelsea Green, White River Junction, VT
Mitchell M (2009) Complexity: a guided tour. Oxford University Press, Oxford, UK
Myrskylä M, Kohler H-P, Billari FC (2009) Advances in development reverse fertility declines. Nature 460:741–743
NASA (2014) Global annual mean surface air temperature change. http://data.giss.nasa.gov/gistemp/graphs_v3/. Accessed 10 Feb 2015
Noori M, Gardner S, Tatari O (2015a) Electric vehicle cost, emissions, and water footprint in the United States: development of a regional optimization model. Energy 89:610–625
Noori M, Kucukvar M, Tatari O (2015b) A macro-level decision analysis of wind power as a solution for sustainable energy in the USA. J Sustain Energy 34(10):629–644
Noori M, Kucukvar M, Tatari O (2015c) Economic input–output based sustainability analysis of onshore and offshore wind energy systems. Int J Green Energy 12(9):939–948
Nordhaus DW (2006) RICE and DICE models of economics of climate change. http://www.econ.yale.edu/~nordhaus/homepage/dicemodels.htm. Accessed 3 Sep 2014
Oak Ridge National Lab. (2013) Transportation energy data book. http://cta.ornl.gov/data/chapter8.shtml
Oak Ridge National Laboratory (2013) Transportation energy data book Edition 32. Oak Ridge, Tennessee
Offer GJ, Howey D, Contestabile M et al (2010) Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy 38:24–29
Onat NC, Egilmez G, Tatari O (2014a) Towards greening the U.S. residential building stock: a system dynamics approach. Build Environ 78:68–80
Onat NC, Gumus S, Kucukvar M, Tatari O (2015a) Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustain Prod Consum 6:12–25
Onat NC, Kucukvar M, Tatari O (2014b) Towards life cycle sustainability assessment of alternative passenger vehicles. Sustainability 6:9305–9342
Onat NC, Kucukvar M, Tatari O (2014c) Integrating triple bottom line input–output analysis into life cycle sustainability assessment framework: the case for US buildings. Int J Life Cycle Assess 19:1488–1505
Onat NC, Kucukvar M, Tatari O (2015b) Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States. Appl Energy 150:36–49
Onat NC, Kucukvar M, Tatari O (2014d) Scope-based carbon footprint analysis of U.S. residential and commercial buildings: an input–output hybrid life cycle assessment approach. Build Environ 72:53–62
Onat NC, Kucukvar M, Tatari O, Zheng QP (2016) Combined application of multi-criteria optimization and life-cycle sustainability assessment for optimal distribution of alternative passenger cars in U.S. J Clean Prod 112:291–307
Park YS, Egilmez G, Kucukvar M (2016) Emergy and end-point impact assessment of agricultural and food production in the United States: a supply chain-linked ecologically-based life cycle assessment. Ecol Ind 62:117–137
Park YS, Egilmez G, Kucukvar M (2015) A novel life cycle-based principal component analysis framework for eco-efficiency analysis: case of the United States Manufacturing and Transportation Nexus. J Clean Prod 92:327–342
Pindyck RS (2011) Modeling the impact of warming in climate change economics. In: The economics of climate change: adaptations past and present. The National Bureau of Economics Research-Libecap and Steckel
ReCiPE (2009) ReCiPe LCIA methodology. http://www.lcia-recipe.net/project-definition
Rouwette EAJA, Vennix JAM, van Mullekom T (2002) Group model building effectiveness: a review of assessment studies. Syst Dyn Rev 18:5–45
Sala S, Farioli F, Zamagni A (2012a) Life cycle sustainability assessment in the context of sustainability science progress (part 2). Int J Life Cycle Assess 18:1686–1697
Sala S, Farioli F, Zamagni A (2012b) Progress in sustainability science: lessons learnt from current methodologies for sustainability assessment: Part 1. Int J Life Cycle Assess 18:1653–1672
Schade B, Schade W (2005) Assessment of environmentally sustainable transport scenarios by a backcasting approach with ESCOT. In: Proceedings of the 23rd International Conference of the System Dynamics Society
Shepherd S, Bonsall P, Harrison G (2012) Factors affecting future demand for electric vehicles: a model based study. Transp Policy 20:62–74
Shepherd SP (2014) A review of system dynamics models applied in transportation. Transp B Transp Dyn 2:83–105
Stefanova M, Tripepi C, Zamagni A, Masoni P (2014) Goal and scope in life cycle sustainability analysis: the case of hydrogen production from biomass. Sustainability 6(8):5463–5475
Stone S, Strutt A, Hertel T (2012) Socio-economic impact of regional transport infrastructure in the Greater Mekong Subregion
Suh S, Lenzen M, Treloar GJ et al (2004) System boundary selection in life-cycle inventories using hybrid approaches. Environ Sci Technol 38:657–664
The National Research Council (2013) Spreadsheets for transitions to alternative vehicles and fuels. http://www.nap.edu/tavf/. Accessed 8 Sept 2015
The U.S. Energy Information Administration (2014) Annual energy outlook with projections to 2040. Washington
The World Bank (2006) Social analysis in transportation projects: guidelines for incorporating social dimensions into bank-supported projects
The U.S. Social Security (2014) Life Tables for the United States Social Security. http://www.ssa.gov/oact/NOTES/as120/images/LD_fig2a.html. Accessed 5 May 2014
United Nations (2014) Human development report 2014 sustaining human progress: reducing vulnerabilities and building resilience
Wang J, Lu H, Peng H (2008) System dynamics model of urban transportation system and its application. J Transp Syst Eng Inf Technol 8:83–89
Weidema B, Ekvall T, Heijungs R (2009) Guidelines for application of deepened and broadened LCA. CALCAS, Project no.037075
Wood R, Hertwich EG (2012) Economic modelling and indicators in life cycle sustainability assessment. Int J Life Cycle Assess 18:1710–1721
Zamagni A (2012) Life cycle sustainability assessment. Int J Life Cycle Assess 17:373–376
Zamagni A, Buttol P, Buonamici R et al (2009) Blue paper on life cycle sustainability analysis. In: CALCAS Proj. Deliv. http://www.calcasproject.net/. Accessed 5 Jan 2015
Zamagni A, Pesonen H-L, Swarr T (2013) From LCA to life cycle sustainability assessment: concept, practice and future directions. Int J Life Cycle Assess 18:1637–1641
Acknowledgments
This work was supported in part by an award to the University of Central Florida, as part of grant number DTRT13-G-UTC51 from the US Department of Transportation’s University Transportation Centers Program.
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Onat, N.C., Kucukvar, M., Tatari, O. et al. Integration of system dynamics approach toward deepening and broadening the life cycle sustainability assessment framework: a case for electric vehicles. Int J Life Cycle Assess 21, 1009–1034 (2016). https://doi.org/10.1007/s11367-016-1070-4
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DOI: https://doi.org/10.1007/s11367-016-1070-4
Keywords
- Deepening and broadening
- Electric vehicles
- Group model building
- Life cycle sustainability assessment
- Sustainable transportation
- System dynamics