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Residential GHG emissions in the United States are driven in part by a housing stock where on-site fossil combustion is common, home sizes are large by international standards, energy efficiency potential is large, and electricity generation in many regions is GHG-intensive. In this analysis we assess decarbonization pathways for the United States residential sector to 2060, through 108 scenarios describing housing stock evolution, new housing characteristics, renovation levels, and clean electricity. The lowest emission scenarios rely on very rapid decarbonization of electricity supply alongside extensive renovations to existing homes—focused on improving thermal envelopes and heat pump electrification of heating. Reducing the size, increasing the multifamily share, and increasing the electrification of new homes provide further emission cuts, and combining all strategies enables emissions reductions of 91% between 2020 and 2050. Construction becomes the main source of emissions in the most ambitious scenarios, motivating increased attention on reducing embodied emissions.

Through energy efficiency and demand flexibility, buildings can play a prominent role in mitigating the urgent and severe consequences associated with a changing climate. In this issue of Joule, the paper by Langevin et al. provides clear insight into the magnitude of impact buildings can have.

Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt™ DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.

Summary Buildings are responsible for 36% of CO2 emissions in the United States and will thus be integral to climate change mitigation; yet, no studies have comprehensively assessed the potential long-term CO2 emissions reductions from the U.S. buildings sector against national goals in a way that can be regularly updated in the future. We use Scout, a reproducible and granular model of U.S. building energy use, to investigate the potential for the U.S. buildings sector to reduce CO2 emissions 80% by 2050, consistent with the U.S. Mid-Century Strategy. We find that a combination of aggressive efficiency measures, electrification, and high renewable energy penetration can reduce CO2 emissions by 72%–78% relative to 2005 levels, just short of the target. Results are sufficiently disaggregated by technology and end use to inform targeted building energy policy approaches and establish a foundation for continual reassessment of technology development pathways that drive significant long-term emissions reductions.

Los Angeles County (LAC) is a large urbanized region with 9.7 million residents (as of 2010) and aging infrastructure. Population forecasts indicate that LAC will become home to an additional 1.2–3.1 million residents through 2060. Additionally, climate forecasts based upon representative concentration pathway (RCP) scenarios 4.5 and 8.5 indicate that average air temperatures will increase by 1–4 °C (2–7 °F) in the region. Both of these factors are expected to result in higher summertime peak electricity demand due to growth in the number of buildings, the percentage of installed air conditioners (ACs), and the additional cooling load on those air conditioners. In order to understand potential power reliability issues, and support infrastructure planning efforts, a long-term peak demand forecast was developed using hourly residential and commercial (R&C) building energy models. Peak hour electricity demand was estimated to increase from 9.5 to 12.8 GW for R&C sectors, to 13.0–17.3 GW (2–36%) and 14.7–19.2 GW (16–51%) by 2060 for the population forecasts from the California Department of Finance and the Southern California Association of Governments respectively. While marginal increases in ambient air temperature due to climate change accounted for only 4–8% of future increases in peak demand, differences in annual maximum temperatures within the 20-year periods affected results by 40–66% indicating a high sensitivity to heat waves. Population growth of at least 1 million people is anticipated to occur mostly in the northern cities of Palmdale, Lancaster, and Santa Clarita, bringing an additional 0.4–1 GW of peak demand in those regions. Building and AC efficiency are anticipated to improve as national and state efficiency standards increase, and as older, less efficient units are replaced; this could offset some of the projected increases in peak demand. Additionally, development of shared wall, multi-family dwelling units could enable population growth of up to 3 million people without increasing peak demand.

Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. This paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStockTM and ResStockTM), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

Vehicle border crossings between Mexico and the United States generate significant amounts of air pollution, which can pose health threats to personnel at the ports of entry (POEs) as well as drivers, pedestrians, and local inhabitants. Although these health risks could be substantial, there is little previous work quantifying detailed emission profiles at POEs. Using the Mariposa POE in Nogales, Arizona as a case study, light-duty and heavy-duty vehicle emissions were analyzed with the objective of identifying effective emission reduction strategies such as inspection streamlining, physical infrastructure improvements, and fuel switching. Historical traffic information as well as field data were used to establish a simulation model of vehicle movement in VISSIM. Four simulation scenarios with varied congestion levels were considered to represent real-world seasonal changes in traffic volume. Four additional simulations captured varying levels of expedited processing procedures. The VISSIM output was analyzed using the EPA’s MOVES emission simulation software for conventional air pollutants. For the highest congestion scenario, which includes a 200% increase in vehicle volume, total emissions increase by around 460% for PM2.5 and NOx, and 540% for CO, SO2, GHGs, and NMHC over uncongested conditions for a two-hour period. Expedited processing and queue reduction can reduce emissions in this highest congestion scenario by as much as 16% for PM2.5, 18% for NOx, 20% for NMHC, 7% for SO2 and 15% for GHGs and CO. Other potential mitigation strategies examined include fleet upgrades, fuel switching, and fuel upgrades. Adoption of some or all of these changes would not only reduce emissions at the Mariposa POE, but would have air-quality benefits for nearby populations in both the US and Mexico. Fleet-level changes could have far-reaching improvements in air quality on both sides of the border.

Within residential electricity consumption there exists significant variability from home-to-home due to the differences in building thermal properties, appliances, and inhabitants. Electricity analyses at sub-city scales using predefined geographies, such census tracts, might artificially split areas with homogenous characteristics leading to analyses that don't effectively contrast the drivers of energy use. The objective of this study is to use the spatial relationships between demographics, building types, and electricity consumption to form new geographies with less variability for use in residential energy assessment. Using Los Angeles and New York City as case studies, differences in energy use variability within predefined geographies (e.g., census tract) are compared to geographies defined by clustering on socio-technical characteristics. Socio-technical clustering, regardless of the chosen subset of variables, reduces the energy consumption variability over pre-defined geopolitical boundaries with high statistical significance (p

In order to combat the threat of climate change, countries have begun to implement policies which restrict GHG emissions in the electricity sector. However, the development of national electricity mixes should also be sensitive to resource availability, geo-political forces, human health impacts, and social equity concerns. Policy focused on GHG goals could potentially lead to adverse consequences in other areas. To explore the impact of “climate-centric” policy making on long-term electricity mix changes, we develop two cases for Peru and Spain analyzing their changing electricity grids in the period 1989–2013. We perform a Life Cycle Assessment of annual electricity production to catalogue the improvements in GHG emissions relative to other environmental impacts. We conclude that policies targeting GHG reductions might have the co-benefit of also reducing air pollution and toxicity at the expense of other important environmental performance indicators such as water depletion. Moreover, as of 2013, both countries generate approximately equal GHG emissions per kWh, and relatively low emission rates of other pollutants compared to nations of similar development levels. Although climate-centric policy can lead to some positive environmental outcomes in certain areas, energy policy-making should be holistic and include other aspects of sustainability and vulnerability.

Building stocks constitute enduring components of urban infrastructure systems, but little research exists on their residence time or changing environmental impacts. Using Los Angeles County, California, as a case study, a framework is developed for assessing the changes of building stocks in cities (i.e., a generalizable framework for estimating the construction and deconstruction rates), the residence time of buildings and their materials, and the associated embedded environmental impacts. In Los Angeles, previous land-use decisions prove not easily reversible, and past building stock investments may continue to constrain the energy performance of buildings. The average age of the building stock has increased steadily since 1920 and more rapidly after the post–World War II construction surge in the 1950s. Buildings will likely endure for 60 years or longer, making this infrastructure a quasi-permanent investment. The long residence time, combined with the physical limitations on outward growth, suggest that the Los Angeles building stock is unlikely to have substantial spatial expansion in the future. The construction of buildings requires a continuous investment in material, monetary, and energetic resources, resulting in environmental impacts. The long residence time of structures implies a commitment to use and maintain the infrastructure, potentially creating barriers to an urban area's ability to improve energy efficiency. The immotility of buildings, coupled with future environmental goals, indicates that urban areas will be best positioned by instituting strategies that ensure reductions in life cycle (construction, use, and demolition) environmental impacts

Metropolitan greenhouse gas and air emissions inventories can better account for the variability in vehicle movement, fleet composition, and infrastructure that exists within and between regions, to develop more accurate information for environmental goals. With emerging access to high quality data, new methods are needed for informing transportation emissions assessment practitioners of the relevant vehicle and infrastructure characteristics that should be prioritized in modeling to improve the accuracy of inventories. The sensitivity of light and heavy-duty vehicle greenhouse gas (GHG) and conventional air pollutant (CAP) emissions to speed, weight, age, and roadway gradient are examined with second-by-second velocity profiles on freeway and arterial roads under free-flow and congestion scenarios. By creating upper and lower bounds for each factor, the potential variability which could exist in transportation emissions assessments is estimated. When comparing the effects of changes in these characteristics across U.S. cities against average characteristics of the U.S. fleet and infrastructure, significant variability in emissions is found to exist. GHGs from light-duty vehicles could vary by -2%-11% and CAP by -47%-228% when compared to the baseline. For heavy-duty vehicles, the variability is -21%-55% and -32%-174%, respectively. The results show that cities should more aggressively pursue the integration of emerging big data into regional transportation emissions modeling, and the integration of these data is likely to impact GHG and CAP inventories and how aggressively policies should be implemented to meet reductions. A web-tool is developed to aide cities in improving emissions uncertainty.

This synthesis article presents an overview of an urban metabolism (UM) approach using mixed methods and multiple sources of data for Los Angeles, California. We examine electric energy use in buildings and greenhouse gas emissions from electricity, and calculate embedded infrastructure life cycle effects, water use and solid waste streams in an attempt to better understand the urban flows and sinks in the Los Angeles region (city and county). This quantification is being conducted to help policy-makers better target energy conservation and efficiency programs, pinpoint best locations for distributed solar generation, and support the development of policies for greater environmental sustainability. It provides a framework to which many more UM flows can be added to create greater understanding of the study area's resource dependencies. Going forward, together with policy analysis, UM can help untangle the complex intertwined resource dependencies that cities must address as they attempt to increase their environmental sustainability.

There is significant interest in reducing urban growth impacts yet little information exists to comprehensively estimate the energy and air quality tradeoffs. An integrated transportation and land-use life-cycle assessment framework is developed to quantify the long-term impacts from residential infill, using the Phoenix light rail system as a case study. The results show that (1) significant reductions in life-cycle energy use, greenhouse gas emissions, respiratory, and smog impacts are possible; (2) building construction, vehicle manufacturing, and energy feedstock effects are significant; and (3) marginal benefits from reduced automobile use and potential household behavior changes exceed marginal costs from new rail service.