Latest revision as of 13:34, 16 February 2021

Abstract

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. 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. 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. 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.

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/11498/32319

• http://link.springer.com/content/pdf/10.1007/s11367-016-1070-4.pdf,

http://link.springer.com/article/10.1007/s11367-016-1070-4/fulltext.html,

http://link.springer.com/content/pdf/10.1007/s11367-016-1070-4,

http://dx.doi.org/10.1007/s11367-016-1070-4 under the license http://www.springer.com/tdm

• https://link.springer.com/article/10.1007/s11367-016-1070-4,

https://link.springer.com/content/pdf/10.1007%2Fs11367-016-1070-4.pdf,

https://core.ac.uk/display/80953034,

https://trid.trb.org/view/1435201,

https://rd.springer.com/article/10.1007/s11367-016-1070-4,

https://academic.microsoft.com/#/detail/2290886486