Global warming impact assessment of asphalt pavement by integrating temporal aspects: A dynamic life cycle assessment perspective
Introduction
Global warming caused by excessive greenhouse gas (GHG) emissions has become an important environmental issue worldwide. The Paris Agreement reached in 2015 calls for countries to pursue efforts to limit the global mean temperature rise to or even when compared to the pre-industrial level (IPCC, 2018, Unfccc, 2015). In this context, China has proposed carbon peaking and carbon neutrality targets by 2030 and 2060, respectively (Xinhuanet, 2020), which has also motivated the exploration of carbon reduction pathways in various industries.
According to statistical data from the International Energy Agency (IEA, 2019) and Intergovernmental Panel on Climate Change (IPCC (2014), the global transportation industry accounts for approximately 24.6% of the total carbon emissions, of which approximately 83.4% is attributed to road transportation. With regards to the current carbon emission reduction targets, progressive developments have achieved green and low-carbon pavements. The premise of these developments is the accurate understanding and identification of carbon emission levels and influential factors during the whole life cycle of the pavement. It is important to perform a global warming impact (GWI) assessment of pavements with scientific guidance. The GWI regarding to pavement infrastructures defined in this paper refers to greenhouse effect generated at different stages of pavement infrastructures in the unit of CO2 equivalent (CO2-eq.).
In recent years, the environmental impact assessment method based on life cycle assessment (LCA) theory has been widely applied to industrialized products, buildings, and pavements, with the aim of quantifying the environmental issues from “cradle to grave” (Santero et al., 2011). In the pavement field, suggestions related to material selection, structural design, and maintenance management have been provided according to the assessment results. The life cycle of pavements is relatively long, therefore, ignoring the temporal dynamic factors in LCA studies may lead to inaccurate and unrealistic assessment results, thereby risking consequent decisions. Temporally dynamic impact assessment methods have indeed gained particular attention for global warming indictors (Harvey et al., 2016), there are also some obstacles to undertaking temporally dynamic modelling. Therefore, the incorporation of dynamic thinking into pavement environmental impact assessment is important and can provide a new way for exploring carbon pathways. In addition, the dynamic LCA (DLCA) framework for pavements can help reflect the dynamic development process of the transport sector in the context of the carbon reduction policy of China, thereby providing effective practical guidance for achieving the goal of “emission peak and carbon neutrality.”.
This study established a model to assess the dynamic GWIs (DGWIs) of asphalt pavements. Several dynamic aspects over time are analyzed in Section 3, and a DLCA model for asphalt pavements is established. The application of this methodology is discussed in Section 4. Section 5 presents analyses of certain scenarios and a comparative analysis of static and dynamic results. Finally, the conclusions are presented in Section 6.
Section snippets
Life cycle assessment (LCA) studies on pavements
Considerable efforts have been expended to examine the environmental impact of pavements based on the LCA approach. According to statistics, the first LCA study on pavements was conducted in the 1990s (Häkkinen and Mäkelä, 1996). In the last two decades, LCA has attracted increasing interest and has been applied to pavements based on two primary themes: modeling development, and application (Jiang and Wu, 2019). In the modeling development theme, researchers aimed to develop new assessment
Assessment framework
In this study, a DLCA model for asphalt pavements, considering various temporal aspects, was established to quantify the temporal GWIs, as shown in Fig. 1. The dynamic model followed the basic paradigm of LCA and included four steps: goal and scope definition, inventory analysis, impact assessment, and interpretation. Steps and guidelines for performing the analysis are consistent with the ISO standards for LCA (ISO 14040:2006)., which provide the indispensable framework for LCA. The goal and
Basic information
To identify the rationality and effectiveness of the proposed dynamic model, this study applied the model to a sample case. A one-kilometer length asphalt pavement with four lanes in both directions of the Jinliwen expressway located in Zhejiang, China, was used to implement the proposed DLCA model. The structure is composed of a 4 cm AC-13 upper layer, a 6 cm AC-20 middle layer, an 8 cm AC-25 lower layer, and 34 cm 5% cement-stabilized macadam base layer, and 20 cm 4% cement-stabilized macadam
Comparison of dynamic and static results
This study incorporated three types of dynamic elements: dynamic consumption (considering traffic volume, vehicle energy structure, pavement roughness, maintenance timing, and material recycling rate), dynamic inventory data of electricity, and dynamic characterization factors. This study compares the dynamic assessment results with traditional static ones. In the static assessment, all the time-varying aspects discussed in Section 3.3.2 were ignored, and the GWP was adopted for the
Conclusion
This paper presents a DLCA model for asphalt pavements. Five dynamic factors (i.e., traffic volume, vehicle types, pavement roughness, maintenance timing, and material recycling rate) and their influences were considered during the dynamic consumption assessment of the entire assessment. The evolution in the electricity mixes over time was applied to generate temporal inventory datasets of electricity, and dynamic characterization factors were used in the assessment. Finally, the DLCA model was
CRediT authorship contribution statement
Dan Chong: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition. Na Wang: Conceptualization, Investigation, Data curation, Formal analysis, Writing – original draft. Shu Su: Conceptualization, Methodology, Formal analysis, Validation, Writing – original draft, Writing – review & editing. Li Li: Conceptualization, Methodology, Data curation, Validation, Writing – review &
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors are grateful to National Natural Science Foundation of China (No. 71901139 and 71901062), and Shanghai Science and Technology Commission (No. 21692195100 and 19DZ1204203) for supporting this study.
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