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Large energy infrastructure is often socially and environmentally disruptive, even as it provides services that people have come to depend on. Residents of areas affected by energy development often note both negative and positive impacts. This reflects the multicategory nature of socioenvironmental outcomes and emphasizes the importance of careful, community-oriented decision making about major infrastructural transitions for processes like decarbonization. Quantitative tools like life cycle assessment (LCA) seek to collect and report comprehensive impact data, but even when successful, their value for decision support is limited by a lack of mechanisms to systematically engage with values-driven tradeoffs across noncommensurable categories. Sensitivity analyses designed to help decision makers and interested parties make sense of data are common in LCA and similar tools, but values are rarely explicitly addressed. This lack of attention to values—arguably the most meaningful set of decision inputs in such tools—can lead to overreliance on single issue (e.g. climate change impact) or proxy (e.g. monetized cost) outputs that reduce the value of holistic evaluations. This research presents results from preregistered hypotheses for a survey of residents of energy-producing communities in the United States (US) and Australia, with the goal of with the goal of uncovering energy transition-relevant priorities by collecting empirical, quantitative data on people’s priorities for outcomes aligned with LCA. The survey was designed to identify diverse value systems, with the goal of making it easier for users to identify and consider value conflicts, potentially highlighting needs for further data collection, system redesign, or additional engagement. Notably, results reveal remarkably consistent priority patterns across communities and subgroups, suggesting that the common LCA practice of equal prioritization might be masking decision-relevant information. Although this effort was designed specifically to support research on energy transitions, future work could easily be extended more broadly.

 

Electric vehicle (EV) charging infrastructure buildout is a major greenhouse gas (GHG) mitigation strategy among governments and municipalities. In the United States, where petroleum-based transportation is the largest single source of GHG emissions, the Infrastructure Investment and Jobs Act of 2021 will support building a national network of 500 000 EV charging units. While the climate benefits of driving electric are well established, the potential embodied climate impacts of building out the charging infrastructure are relatively unexplored. Furthermore, ‘charging infrastructure’ tends to be conceptualized in terms of plugs and stations, leaving out the electrical and communications systems that will be required to support decarbonized and efficient charging. In this study, we present an EV charging system (EVCS) model that describes the material and operational components required for charging and forecasts the scale-up of these components based on EV market share scenarios out to 2050. We develop a methodology for measuring GHG emissions embodied in the buildout of EVCS and incurred during operation of the EVCS, including vehicle recharging, and we demonstrate this model using a case study of Georgia (USA). We find that cumulative GHG emissions from EVCS buildout and use are negligible, at less than 1% of cumulative emissions from personal light duty vehicle travel (including EV recharging and conventional combustion vehicle driving). If an accelerated EVCS buildout were to stimulate a faster transition of the vehicle fleet, the emissions reduction of electrification will far outweigh emissions embodied in EVCS components, even assuming relatively high carbon inputs prior to decarbonization.

 

Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO₂(−11.83%) and PM_2.5(−2.46%) with concentration reductions of up to −5.1 ppb and −0.98µg m⁻³in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O₃) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO₂concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain.

 

Digitally enabled technologies are increasingly cyber-physical systems (CPSs). They are networked in nature and made up of geographically dispersed components that manage and control data received from humans, equipment, and the environment. Researchers evaluating such technologies are thus challenged to include CPS subsystems and dynamics that might not be obvious components of a product system. Although analysts might assume CPS have negligible or purely beneficial impact on environmental outcomes, such assumptions require justification. As the physical environmental impacts of digital processes (e.g. cryptocurrency mining) gain attention, the need for explicit attention to CPS in environmental assessment becomes more salient. This review investigates how the peer-reviewed environmental assessment literature treats environmental implications of CPS, with a focus on journal articles published in English between 2010 and 2020. We identify nine CPS subsystems and dynamics addressed in this literature: energy system, digital equipment, non-digital equipment, automation and management, network infrastructure, direct costs, social and health effects, feedbacks, and cybersecurity. Based on these categories, we develop a ‘cyber-consciousness score’ reflecting the extent to which the 115 studies that met our evaluation criteria address CPS, then summarize analytical methods and modeling techniques drawn from reviewed literature to facilitate routine inclusion of CPS in environmental assessment. We find that, given challenges in establishing system boundaries, limited standardization of how to evaluate CPS dynamics, and failure to recognize the role of CPS in a product system under evaluation, the extant environmental assessment literature in peer-reviewed journals largely ignores CPS subsystems and dynamics when evaluating digital or digitally-enabled technologies.

 

Abstract Decarbonization is an urgent global policy priority, with increasing movement towards zero-carbon targets in the United States and elsewhere. Given the joint decarbonization strategies of electrifying fossil fuel-based energy uses and decarbonizing the electricity supply, understanding how electricity emissions might change over time is of particular value in evaluating policy sequencing strategies. For example, is the electricity system likely to decarbonize quickly enough to motivate electrification even on relatively carbon-intensive systems? Although electricity sector decarbonization has been widely studied, limited research has focused on evaluating emissions factors at the utility level, which is where the impact of electrification strategies is operationalized. Given the existing fleet of electricity generators, ownership structures, and generator lifespans, committed emissions can be modeled at the utility level. Generator lifespans are modeled using capacity-weighted mean age-on-retirement for similar units over the last two decades, a simple empirical outcome variable reflecting the length of time the unit might reasonably be expected to operate. By also evaluating generators in wholesale power markets and designing scenarios for new-build generation, first-order annual average emissions factors can be projected forward on a multidecadal time scale at the utility level. This letter presents a new model of utility-specific annual average emissions projections (greenhouse gases and air pollutants) through 2050 for the United States, using a 2019 base year to define existing asset characteristics. Enabling the creation and evaluation of scenario-based projections for dynamic environmental intensity metrics in a decarbonizing electricity sector can inform life cycle and other environmental assessment studies that evaluate impact over time, in addition to highlighting particular opportunities and risks associated with the timing and location of long-lived capital investments as the fossil fuel electricity generator fleet turns over. Model results can also be used to contextualize utilities’ decarbonization commitments and timelines against their asset bases.