Benchmarking GHG Emissions from California Concrete and Readily Implementable Mitigation Methods
- Author(s): Cunningham, Patrick R.;
- Miller, Sabbie A.
- et al.
Published Web Locationhttps://doi.org/10.7922/G24T6GND
The demand for concrete, which is conventionally composed of granular rocks (aggregates), water, and Portland cement (as well as other additives depending on desired performance) continues to grow. The manufacturing of Portland cement leads to notable greenhouse gas (GHG)emissions, which has driven interest in alternative concrete mixture designs, cement production processes, and other emissions mitigation strategies. To demonstrate the efficacy of such mitigation strategies, environmental impact assessments are commonly performed. However, examination of the probability that a reduction in GHG emissions will occur given known limitations on data quality and variability in data remains poorly studied. Additionally, the common practice of focusing primarily on GHG emissions can lead to selection of emissions mitigation methods with unintended consequences, such as increases in other environmental impacts. This work models 12 potential concrete mixtures capable of achieving the same concrete strength and three potential GHG emissions mitigation strategies: changing kiln fuel mix, changing electricity mix, and using a carbon capture and storage (CCS) system. Focusing on GHG and air pollutant emissions, both deterministic comparisons of mean emissions as well as the probability that the alternative mixtures and mitigation strategies can reduce emissions is examined. This work shows that, even when mitigation strategies are employed, GHG emissions are correlated to the cement content of the mixture. Additionally, as modeled, CCS leads to mean reduction in GHG emissions of over 80% for all mixtures, but also led to increases in other emissions (i.e., NOX, SOX, VOC, CO, PM10, and PM2.5). The probability of a reduction in emissions were greatest for GHGs due to the tighter distribution in emissions modeled. Probabilities for reducing other impacts, such as PM10 and PM2.5 emissions, could be improved with better data quality. This work demonstrates how concurrent environmental impact assessment across several impact categories with consideration for uncertainty and variability can be a robust tool for evaluating various mixture designs and environmental impact mitigation strategies.