A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations
Abstract
:1. Introduction
2. Spatiotemporal DLCA Model
2.1. Assessment Framework
2.2. Dynamic Foreground Elementary Flows
2.2.1. Dynamic Analysis
2.2.2. Dynamic Quantification Methods
2.3. Dynamic Background Datasets
2.3.1. Dynamic Analysis
2.3.2. Dynamic Quantification Model
2.4. Dynamic Characterization
2.4.1. Dynamic Analysis
2.4.2. Dynamic Quantification Method
2.5. Normalization and Dynamic Weighting
2.5.1. Normalization
2.5.2. Dynamic Analysis of Weighting
2.5.3. Dynamic Weighting Model
3. Application
3.1. The Evaluated Object
3.2. Dynamic Assessment
3.3. Analysis of Results
3.3.1. Spatiotemporal Analysis of Annual Impacts
3.3.2. Comparison of Different Impact Categories
4. Discussion
4.1. Contribution Analysis of Dynamic Element Type
4.2. Sensitivity Analysis of Parameters
4.3. Meanings of Spatiotemporal Dynamic Assessment
5. Conclusions
- (1)
- The proposed spatiotemporal DLCA model is operable and applicable.
- (2)
- There are obvious differences between the temporal dynamic assessment results and static ones in the application case. Involving temporal variations in assessment studies for products with long-life cycles is meaningful, and can provide an evolutionary perspective.
- (3)
- The spatial dynamic assessment results in two cities that are quite different. Considering regional specifics and adopting local data are highly suggested for LCA studies.
- (4)
- The contribution of three dynamic element types to final results are quantified, and the influence directions and magnitudes depend on location and time.
- (5)
- The sensitivities of involved parameters are various.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Ecological Impacts (per m2) Change | |||
---|---|---|---|---|
10.000% | −10.000% | |||
Nanjing | Guangzhou | Nanjing | Guangzhou | |
Electricity consumption per capital | 9.649% | 9.720% | −9.649% | −9.720% |
Liquefied petroleum gas consumption per capital | 0.017% | 0.071% | −0.017% | −0.071% |
Natural gas consumption per capital | 0.142% | 0.041% | −0.142% | −0.041% |
Water consumption per capital | 0.192% | 0.168% | −0.192% | −0.168% |
Proportion of local thermal power generation among total energy | 10.881% | 9.690% | −8.487% | −9.690% |
Local population | −10.152% | −5.658% | 9.615% | 5.359% |
Environmental carrying capacity of CO2 | −5.862% | −3.268% | 7.165% | 3.994% |
Environmental carrying capacity of dust | −2.015% | −1.523% | 2.463% | 1.861% |
Environmental carrying capacity of SO2/ NOx/ COD | ≈0.001% | ≈−0.001% | ≈0.001% | ≈0.001% |
Target levels of pollutants | −22.610% | −8.260% | 10.844% | 10.096% |
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Su, S.; Ju, J.; Ding, Y.; Yuan, J.; Cui, P. A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations. Int. J. Environ. Res. Public Health 2022, 19, 14000. https://doi.org/10.3390/ijerph192114000
Su S, Ju J, Ding Y, Yuan J, Cui P. A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations. International Journal of Environmental Research and Public Health. 2022; 19(21):14000. https://doi.org/10.3390/ijerph192114000
Chicago/Turabian StyleSu, Shu, Jingyi Ju, Yujie Ding, Jingfeng Yuan, and Peng Cui. 2022. "A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations" International Journal of Environmental Research and Public Health 19, no. 21: 14000. https://doi.org/10.3390/ijerph192114000