Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households

The U.S. household consumption, a key engine for the global economy, has significant carbon footprints across the world. Understanding how the U.S. household consumption on specific goods or services drives global greenhouse gas (GHG) emissions is important to guide consumption-side strategies for climate mitigation. Here we examined global GHG emissions driven by the U.S. household consumption from 1995 to 2014 using an environmentally extended multi-regional input-output model and detailed U.S. consumer expenditure survey data. The results show that the annual carbon footprint of the U.S. households ranged from 17.7 to 20.6 tCO2eq/capita with an expanding proportion occurring overseas. Housing and transportation contributed 53–66% of the domestic carbon footprint. Overseas carbon footprint shows an overall increasing trajectory, from 16.4% of the total carbon footprint in 1995 to the peak of 20.4% in 2006. These findings provide valuable insights on the scale, distribution, and variations of the global GHG emissions driven by the U.S. household consumption for developing consumption-side strategies in the U.S. for climate mitigation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150690/1/Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdfDescription of Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdf : Main articl

A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here, we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149443/1/EEICAV_Taiebat et al (2018)_Environmental Science & Technology.pdfDescription of EEICAV_Taiebat et al (2018)_Environmental Science & Technology.pdf : Main articl

Distinguishing sensor and system faults for diagnostics and monitoring

Automated FDD systems depend entirely on reliability of sensor readings, since they are the monitoring interface of the system. With an unexpected variation in a sensor’s reading from its anticipated values, the challenge is to determine if it is symptom of a fault in the sensor or the monitored system. The ability to identify the source of faults is crucial in the monitoring of a system, as different corrective actions are required in case of sensor or system faults. To address this issue, first, it is clarified that by strict duplication of sensor elements, it is feasible to differentiate between sensor and system faults. However, duplication is not always practical. Hence, by aiming to identify the minimum degree of sensor redundancy, a priori knowledge of physical relationships (functional redundancy) between monitored variables is used to check the credibility of existing sensor observations via Analytical Computational Substitutions (ACS). In the proposed methodology for a certain class of systems, the system variables are modeled with serially connected causal network. Then the concept of Moving Monitoring Window (MMW) is introduced, which covers three nodes at the same time, as it traverses through the nodes of the system in the direction of causality. The Logic Set Unit consists of all system/sensor state possibilities called System Behavioral Modes, which allows decision-making on the health status of sensor or system or a combination. The generalization by deduction reveals that if the number of sensors is greater than 1.5 times of the number of monitored variables, the task of distinguishing between sensor and system faults can be done, as long as serial causality is valid between the monitored variables. Removing any more sensors from this configuration leads to inability to locate the faults, due to the lack of adequate behavioral modes for diagnosis decision. The effectiveness of the approach is verified on a system of interconnected multi reservoirs and control valves.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

Renewable Energy, Infrastructure and GHG Implication of Electrified Transportation: Metro Vancouver Case Study

This study is aimed to assess the fleet composition for the new portion of light and medium duty vehicles (LMDV) in Metro Vancouver forecasted for the year 2020. Accordingly, the analysis evaluates the sensitivity of the regional electricity demand on transportation electrification policies. Considering electricity and hydrogen as transportation infrastructures, sixteen scenarios of zero tailpipe emission Electric Vehicle (EV) penetration in the new fleet are investigated. The study assesses the efficiency of EV technologies, quantifies energy demand for the electric transportation, and summarizes the implications of using renewable electricity to power the transportation sector.The analysis shows that wind energy is the superior resource in terms of life cycle Greenhouse Gases (GHGs). The life cycle GHGs of electricity production via wind turbines ranges from 390-3000 tonnes yr- 1 and for photovoltaic cells from 1300-9900 tonnes yr-1 of CO2eq across the scenarios. Furthermore, it is observed that 92% to 96% of life cycle greenhouse emissions could be reduced by deploying zero emission vehicles, which utilize solar or wind energy as a renewable resource. In this category, battery electric vehicles enable larger energy efficiency. Moreover, the results show that in order to respond to FCEV demand by 2020, the number of on-site hydrogen refueling stations should vary between 3 and 62, across different scenarios. The electricity demand to power these stations ranges from 32 to 248 GWh yr-1 which translates to annual production of 5 to 37 wind turbines with 2.24 MW of rated capacity, or alternatively 0.2 to 1.6 km2 of photovoltaic cell surface

Renewable Energy, Infrastructure and GHG Implication of Electrified Transportation: Metro Vancouver Case Study

This study is aimed to assess the fleet composition for the new portion of light and medium duty vehicles (LMDV) in Metro Vancouver forecasted for the year 2020. Accordingly, the analysis evaluates the sensitivity of the regional electricity demand on transportation electrification policies. Considering electricity and hydrogen as transportation infrastructures, sixteen scenarios of zero tailpipe emission Electric Vehicle (EV) penetration in the new fleet are investigated. The study assesses the efficiency of EV technologies, quantifies energy demand for the electric transportation, and summarizes the implications of using renewable electricity to power the transportation sector.The analysis shows that wind energy is the superior resource in terms of life cycle Greenhouse Gases (GHGs). The life cycle GHGs of electricity production via wind turbines ranges from 390-3000 tonnes yr- 1 and for photovoltaic cells from 1300-9900 tonnes yr-1 of CO2eq across the scenarios. Furthermore, it is observed that 92% to 96% of life cycle greenhouse emissions could be reduced by deploying zero emission vehicles, which utilize solar or wind energy as a renewable resource. In this category, battery electric vehicles enable larger energy efficiency. Moreover, the results show that in order to respond to FCEV demand by 2020, the number of on-site hydrogen refueling stations should vary between 3 and 62, across different scenarios. The electricity demand to power these stations ranges from 32 to 248 GWh yr-1 which translates to annual production of 5 to 37 wind turbines with 2.24 MW of rated capacity, or alternatively 0.2 to 1.6 km2 of photovoltaic cell surface