Characterisation factors for life cycle impact assessment of sound emissions

Environmental trade-offs for using low-noise pavements: Life cycle assessment with noise considerations

Noise mitigation is the main advantage of semi-dense asphalt (SDA) pavements compared to traditional pavements such as stone-mastic asphalt (SMA), but noise is not quantitatively considered in traditional life cycle assessment (LCA). This article performs a comprehensive LCA for SMA and SDA including noise considerations. State-of-the-art sound emission and acoustical ageing models were used to determine the road traffic noise. The latest Swiss dose-response curves and current noise exposure data were used to evaluate health impacts due to noise. Additionally, traditional LCA is also included for assessing the greenhouse gas emissions, non-renewable cumulative energy demand and health impacts of non-noise processes. The results show that SDA causes around 70 % higher greenhouse gases and energy demand than SMA, primarily due to its shorter service life. However, the noise impacts in disability adjusted life years (DALYs) are higher by two to three orders of magnitude than non-noise processes, and the use of SDA can reduce 40 % of the total DALYs. It is shown that road traffic noise plays a significant role in the LCA of pavements. The trade-off between greenhouse gas and energy related impacts, on the one hand, and health effects, on the other hand, requires critical consideration by decision makers when promoting low-noise pavements.

Noise footprint from personal land-based mobility

A large part of the world population is exposed to noise levels that are unhealthy. Yet noise is often neglected when impact assessment studies are conducted and when policy interventions are designed. In this study, we provide a way to calculate the noise footprint of citizens directly determined by their use of private and public transport on land. The study combines the results of the large transport simulation model MATSim applied to Switzerland, with a noise characterization model, N-LCA, developed in the context of life cycle assessment. MATSim results allow tracking the use of private and public transportation by agents in the model. The results after characterization provide a consumption-based noise footprint, thus the total noise and impacts that are caused by the private mobility demand of the citizens of Switzerland. Our results confirm that road transportation is the largest contributor to the total noise footprint of land-based mobility. We also included a scenario with a full transition to an electrified car fleet, which showed the potential for the reduction of impacts, particularly in urban areas, by about 55% as compared to the modeled regime with combustion engines.

Assessing the environmental impacts of freshwater thermal pollution from global power generation in LCA

Freshwater heat emissions from power plants with once-through cooling systems constitute one of many environmental pressures related to the thermoelectric power industry. The objective of this work was to obtain high resolution, operational characterization factors (CF) for the impact of heat emissions on ecosystem quality, and carry out a comprehensive, spatially, temporally and technologically differentiated damage-based environmental assessment of global freshwater thermal pollution. The aggregation of CFs on a watershed level results in 12.5% lower annual impacts globally and even smaller differences for the most crucial watersheds and months, so watershed level CFs are recommended when the exact emission site within the basin is unknown. Long-range impacts account for almost 90% of the total global impacts. The Great Lakes, several Mississippi subbasins, the Danube, and the Yangtze are among the most thermally impacted watersheds globally, receiving heat emissions from predominantly coal-fuelled and nuclear power plants. Globally, over 80% of the global annual impacts come from power plants constructed during or before the 1980s. While the impact-weighted mean age of the power plants in the Mississippi ranges from 38 to 51years, in Chinese watersheds including the Yangtze, the equivalent range is only 15 to 22years, reflecting a stark contrast in thermal pollution mitigation approaches. With relatively high shares of total capacity from power plants with once-through freshwater cooling, and tracing a large part of the Danube, 1kWh of net electricity mix is the most impactful in Hungary, Bulgaria and Serbia. Monthly CFs are provided on a grid cell level and on a watershed level for use in Life Cycle Assessment. The impacts per generating unit are also provided, as part of our effort to make available a global dataset of thermoelectric power plant emissions and impacts.

LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis

In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

Assessment of Life Cycle Impacts on Ecosystem Services: Promise, Problems, and Prospects

The analysis of ecosystem services (ES) is becoming a key-factor to implement policies on sustainable technologies. Accordingly, life cycle impact assessment (LCIA) methods are more and more oriented toward the development of harmonized characterization models to address impacts on ES. However, such efforts are relatively recent and have not reached full consensus yet. We investigate here on the transdisciplinary pillars related to the modeling of LCIA on ES by conducting a critical review and comparison of the state-of-the-art in both LCIA and ES domains. We observe that current LCIA practices to assess impacts on "ES provision" suffer from incompleteness in modeling the cause-effect chains; the multifunctionality of ecosystems is omitted; and the "flow" nature of ES is not considered. Furthermore, ES modeling in LCIA is limited by its static calculation framework, and the valuation of ES also experiences some limitations. The conceptualization of land use (changes) as the main impact driver on ES, and the corresponding approaches to retrieve characterization factors, eventually embody several methodological shortcomings, such as the lack of time-dependency and interrelationships between elements in the cause-effect chains. We conclude that future LCIA modeling of ES could benefit from the harmonization with existing integrated multiscale dynamic integrated approaches.

A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment

The life cycle assessment (LCA) framework has established itself as the leading tool for the assessment of the environmental impact of products. Several works have established the need of integrating the LCA and risk analysis methodologies, due to the several common aspects. One of the ways to reach such integration is through guaranteeing that uncertainties in LCA modeling are carefully treated. It has been claimed that more attention should be paid to quantifying the uncertainties present in the various phases of LCA. Though the topic has been attracting increasing attention of practitioners and experts in LCA, there is still a lack of understanding and a limited use of the available statistical tools. In this work, we introduce a protocol to conduct global sensitivity analysis in LCA. The article focuses on the life cycle impact assessment (LCIA), and particularly on the relevance of global techniques for the development of trustable impact assessment models. We use a novel characterization model developed for the quantification of the impacts of noise on humans as a test case. We show that global SA is fundamental to guarantee that the modeler has a complete understanding of: (i) the structure of the model and (ii) the importance of uncertain model inputs and the interaction among them.