• Energy Research

While carbon footprint reduction potential and energy security aspects of renewable and non-renewable resources are widely considered in energy policy, their effects on water resources are mostly overlooked. This research aims to develop a framework for water and carbon footprint analysis to estimate the current and future trends of water consumption and withdrawal by electricity production sectors for national energy development plans – alongside carbon emissions from various electricity sources. With this motivation, the Turkish electric power industry is selected as a case study and a decision support tool is developed to determine the water consumption, withdrawal and carbon emissions from energy mixes under three different scenarios, namely Business-As-Usual (BAU), Official Governmental Plan (OGP), and Renewable Energy-Focused Development Plan (REFDP). The results indicate that water is used substantially even by renewable resources, such as hydroelectricity and biomass, which are generally considered to be more environmental friendly than other energy sources. The average water consumption of the OGP energy mix in 2030 is estimated to be about 8.1% and 9.6% less than that of the BAU and REFDP scenarios, respectively. On the other hand, it is found that the water withdrawal of the energy mix in 2030 under the REFDP scenario is about 46.3% and 16.9% less than that of BAU and OGP scenarios. Carbon emissions from BAU are projected to be 24% higher than OGP and 39% higher than REFDP in 2030. Carbon emissions and water usage are strongly correlated in BAU scenario as compared with OGP and REFDP, thus carbon friendly energy sources will result in fewer water consumptions and withdrawals, particularly under REFDP.

With the increasing concerns related to integration of social and economic dimensions of the sustainability into life cycle assessment (LCA), traditional LCA approach has been transformed into a new concept, which is called as life cycle sustainability assessment (LCSA). This study aims to contribute the existing LCSA framework by integrating several social and economic indicators to demonstrate the usefulness of input–output modeling on quantifying sustainability impacts. Additionally, inclusion of all indirect supply chain-related impacts provides an economy-wide analysis and a macro-level LCSA. Current research also aims to identify and outline economic, social, and environmental impacts, termed as triple bottom line (TBL), of the US residential and commercial buildings encompassing building construction, operation, and disposal phases. To achieve this goal, TBL economic input–output based hybrid LCA model is utilized for assessing building sustainability of the US residential and commercial buildings. Residential buildings include single and multi-family structures, while medical buildings, hospitals, special care buildings, office buildings, including financial buildings, multi-merchandise shopping, beverage and food establishments, warehouses, and other commercial structures are classified as commercial buildings according to the US Department of Commerce. In this analysis, 16 macro-level sustainability assessment indicators were chosen and divided into three main categories, namely environmental, social, and economic indicators. Analysis results revealed that construction phase, electricity use, and commuting played a crucial role in much of the sustainability impact categories. The electricity use was the most dominant component of the environmental impacts with more than 50 % of greenhouse gas emissions and energy consumption through all life cycle stages of the US buildings. In addition, construction phase has the largest share in income category with 60 % of the total income generated through residential building’s life cycle. Residential buildings have higher shares in all of the sustainability impact categories due to their relatively higher economic activity and different supply chain characteristics. This paper is an important attempt toward integrating the TBL perspective into LCSA framework. Policymakers can benefit from such approach and quantify macro-level environmental, economic, and social impacts of their policy implications simultaneously. Another important outcome of this study is that focusing only environmental impacts may misguide decision-makers and compromise social and economic benefits while trying to reduce environmental impacts. Hence, instead of focusing on environmental impacts only, this study filled the gap about analyzing sustainability impacts of buildings from a holistic perspective.

Abstract Vehicle to Grid technologies utilize idle EV battery power as a grid storage tool to meet fluctuating electric power demands. Vehicle to Grid systems are promising substitutes for traditional gas turbine generators, which are relatively inefficient and have high emissions impacts. The purpose of this study is to predict the future net revenue and life cycle emissions savings of Vehicle to Grid technologies for use in ancillary (regulation) services on a regional basis in the United States. In this paper, the emissions savings and net revenue calculations are conducted with respect to five different Independent System Operator/Regional Transmission Organization regions, after which future EV market penetration rates are predicted using an Agent-Based Model designed to account for various uncertainties, including regulation service payments, regulation signal features, and battery degradation. Finally, the concept of Exploratory Modeling and Analysis is used to estimate the future net revenue and emissions savings of integrating Vehicle to Grid technology into the grid, considering the inherent uncertainties of the system. The results indicate that, for a single vehicle, the net revenue of Vehicle to Grid services is highest for the New York region, which is approximately $42,000 per vehicle on average. However, the PJM region has an approximately $97 million overall net revenue potential, given the 38,200 Vehicle to Grid-service-available electric vehicles estimated to be on the road in the future in the PJM region, which is the highest among the studied regions.

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.

Abstract Analyzing building related carbon emissions remains as one of the most increasing interests in sustainability research. While majority of carbon footprint studies addressing buildings differ in system boundaries, scopes, GHGs and methodology selected, the increasing number of carbon footprint reporting in response to legal and business demand paved the way for worldwide acceptance and adoption of the Greenhouse Gas Protocol (GHG Protocol) set by the World Resources Institute (WRI) and World Business Council for Sustainable Development (WBCSD). Current research is an important attempt to quantify the carbon footprint of the U.S. residential and commercial buildings in accordance with carbon accounting standards and Scopes set by WRI, in which all possible indirect emissions are also considered. Emissions through the construction, use, and disposal phases were calculated for the benchmark year 2002 by using a comprehensive hybrid economic input–output life cycle analysis. The results indicate that emissions from direct purchases of electricity (Scope 2) with 48% have the highest carbon footprint in the U.S. buildings. Indirect emissions (Scope 3) with 32% are greater than direct emissions (Scope 1) with 20.4%. Commuting is the most influential activity among the Scope 3 emissions with more than 10% of the carbon footprint of the U.S. buildings overall. Construction supply chain is another important contributor to the U.S. building's carbon footprint with 6% share. Use phase emissions are found to be the highest with 91% of the total emissions through all of the life cycle phases of the U.S. buildings.

This research aims to advance the existing sustainability assessment framework for alternative passenger cars with a combination of life-cycle sustainability assessment and multi-criteria decision-making. To this end, sixteen macro-level sustainability impacts are evaluated for seven different vehicle types: internal combustion vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles with all-electric ranges of 16, 32, 48, and 64 km of electric powered drive, and battery electric vehicles. Additionally, two battery charging scenarios are considered in this analysis with respect to plug-in hybrid electric vehicles and battery electric vehicles; Scenario 1 is based on existing electric power infrastructure in the U.S., while Scenario 2 is an extreme scenario in which electricity to power battery electric vehicles and plug-in hybrid electric vehicles is generated entirely via solar charging stations. In this study, optimal vehicle distributions are calculated based on the environmental, social, and economic impacts of all vehicle types for each scenario. Various distributions are presented in accordance with the relative importance assigned to each indicator, with different weighting scenarios applied to account for variability in decision-makers' priorities, such as the assignment of higher weights to socio-economic indicators (e.g. maximizing employment) and lower weights to environmental indicators (e.g. minimizing greenhouse gas emissions). In a balanced weighting case (i.e. when environmental and socio-economic indicators have equal importance) under Scenario 1, hybrid electric vehicles have the largest fleet share, comprising 91% of the optimal U.S. passenger car fleet, while internal combustion vehicles dominate the optimal fleet with 99.5% of the optimal fleet share when only socio-economic indicators are given priority. On the other hand, in a balanced weighting case under Scenario 2, the optimal U.S. passenger car fleet consists entirely (100%) of plug-in hybrid electric vehicles with 16 km of all-electric range. In the majority of the considered weighting scenarios, battery electric vehicles were not given any share of the optimal vehicle fleet. The proposed framework can be used as a practical decision-making platform when deciding which vehicle type to promote given each vehicle type's respective environmental, social, and economic impacts. Considering that decision makers are often highly influenced by the “silo effect”, i.e. a lack of communication among different agencies and departments (national or international), the proposed framework provides a holistic system-based approach to minimize the silo effect and can enhance the efficiency of future inter/cross/trans-disciplinary works. Furthermore, the outcomes of this study can pave the way for advancement in the state-of-the-art and state-of-the-practice of current sustainability research.

Abstract Due to growing production and consumption worldwide, the energy demand is increasing rapidly, which puts additional burdens on the world's scarce natural resources. Therefore, there is a need for efficient use of scarce materials of the earth to meet the increasing energy demand. With this motivation, material footprints of Turkey's and UK's national energy development plans are investigated by applying a global, multiregional input-output (GMRIO) model. A spatial material footprint analysis is conducted for 10 metallic and 9 nonmetallic minerals to reveal the regional and global material dependence of Turkey and UK related to electricity production from 11 different sources. As a high-resolution GMRIO database, the EXIOBASE v.2 is extended by material extraction data that enabled us to calculate the material footprints tracing the complex global supply chains of electric power generation sectors. The Autoregressive Integrated Moving Average (ARIMA) model is also developed to forecast the material footprints of electricity production until 2050. Three energy development plans such as Business-as-Usual (BAU), Official Plan (OP), and Renewable Energy development (RED) plan are investigated to compare the materials dependence of different national energy development policies. Current research concluded that environmental policies applied for national energy development should consider the different levels of complexities of regional and global supply chains for material footprint analysis.

Abstract This study explores suitability of battery electric vehicles in the United States by considering their potential market share and operations costs as well as the state-specific variations in electricity generation profiles, given current government policies and the social acceptability of the technology. A performance assessment is developed to compare each state and identify major policy efforts that are needed to increase the environmental and economic competitiveness of electric vehicles. A novel multi-criteria decision-support framework, integrating Life Cycle Assessment, Data Envelopment Analysis, and Agent Based Modeling, is developed. To this end, the environmental and economic impacts of battery electric vehicles are calculated based on three scenarios: an average electricity generation mix, a marginal electricity generation mix, and a solely renewable energy mix with 100% solar. The states are classified, each requiring different policy strategies, in accordance with their performance scores. The results provide important insights for advancing transportation policies and a novel framework for multi-criteria decision-making in the future analyses.

Abstract Electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) are often considered as better options in terms of greenhouse gas emissions and energy consumption compared to internal combustion vehicles. However, making any decision among these vehicle options is not a straightforward process due to temporal and spatial variations, such as the sources of the electricity used and regional driving patterns. In this study, we compared these vehicle options across 50 states, taking into account state-specific average and marginal electricity generation mixes, regional driving patterns, and vehicle and battery manufacturing impacts. Furthermore, a policy scenario proposing the widespread use of solar energy to charge EVs and PHEVs is evaluated. Based on the average electricity generation mix scenario, EVs are found to be least carbon-intensive vehicle option in 24 states, while HEVs are found to be the most energy-efficient option in 45 states. In the marginal electricity mix scenario, widespread adoption of EVs is found to be an unwise strategy given the existing and near-future marginal electricity generation mix. On the other hand, EVs can be superior to other alternatives in terms of energy-consumption, if the required energy to generate 1 kW h of electricity is below 1.25 kW h.