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Projections of future climate for U.S. national assessments: past, present, future
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PHYSICS OF E+ E- COLLIDERS: PRESENT, FUTURE AND FAR FUTURE
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Biophysical and hydrological effects of future climate change including trends in CO 2 , in the St. Joseph River watershed, Eastern Corn Belt
Future climate change has the potential to significantly impact crop growth, both directly due to CO 2 enhancement and indirectly, through temperature and moisture impacts. Here, this work investigates the biophysical and hydrological effects of future climate change, including trends in CO 2 , in the St. Joseph River watershed, Eastern Corn Belt. In this study, the Soil and Water Assessment Tool (SWAT) was first modified to take dynamic CO 2 concentration as input. A regional crop leaf development curve from Landsat TM imagery was also used to adjust model performance in corn leaf area development for the historical period. A multi-objective calibration strategy was employed to ensure acceptable simulation of streamflow, seasonal crop growth and interannual crop yield simultaneously. The model was then driven by future climate change and CO 2 data from three Atmosphere-Ocean General Circulation Models (AOGCMs), which are GFDL-CM2.1.1, NCAR-PCM1.3, and UKMO-HADCM3.1, under three Special Report on Emissions Scenarios (SRES), including B1, A1 B and A2 to investigate crop and streamflow response in two future periods: the near future (2021–2050) and the far future (2061–2090). The St. Joseph River watershed is expected to experience more winter and spring precipitation, but slightly decreasing summer precipitation. Due to increasing temperature and decreasing summer moisture, more drought stress is predicted. Both annual total aeration and drought stress are projected to be more variable in both future periods. Although future CO 2 enhancement will benefit the crop growth and final yield by improving radiation use efficiency (RUE) and reducing drought stresses, simulated corn yield still decreased by 6% in the near future period, and 16% in the far future period due to the combined effect of both climate change and CO 2 enhancement. Streamflow redistribution is also predicted in the future. Stream discharge is projected to increase for the whole flow range in the near future period. For the far future period, high flows are expected to decrease, while low flows are expected to increase, indicating more hydrologic drought and flood events in the St. Joseph River watershed.
Spatially explicit land-energy-water future scenarios for cities: Guiding infrastructure transitions for urban sustainability
Cities control the majority of global human resource consumption and serve as hubs of major infrastructural networks. To offset their resource demands, cities derive goods and resources from regions well outside urban boundaries inducing stress and impacts on distal ecosystems. As cities grow, these stressors are likely to increase, depending on choices about how resource demands will be addressed through new infrastructures; hence, city governance is extremely important to future global sustainability. Yet, to support effective decision-making and infrastructure transitions, developing tangible city-scale alternative future scenarios is needed. We present a methodology for developing plausible spatially explicit alternative futures for city infrastructures and discuss the tradeoffs in land, energy, carbon, and water resources among alternative future pathways. We first estimate future city populations and urban boundaries and characterize future land cover scenarios. Future population along with residential housing and commercial characteristics are used to estimate current and future electricity and water demand. We characterize the energysheds of cities, which then become the spatial template for designing future electricity production scenarios. Future electricity mixes and spatial distributions of powerplants provide wide-ranging tradeoffs in carbon reduction, water use reduction, and land usage. Additionally, we explore future alternatives for meeting water supply demands. With this work, we emphasize the importance of translating scenarios into physical on-the-ground relevance in order to ensure transparent communication among city- and utility-governance. Unless spatially explicit future infrastructure scenarios are provided, we believe city-level goals will become difficult to implement, or even worse, result in unintended consequences on regional natural resources.
Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States
This report is the second in a series of Electrification Futures Study (EFS) publications, a multi-year research project to explore widespread electrification in the future energy system of the United States. The report presents scenarios of electric end-use technology adoption and resulting electricity consumption in the United States. The scenarios are used to characterize changes to end-use sectors under futures with increased electrification and to quantify how those changes might impact the amount and shape of electricity consumption. These scenarios were developed primarily using expert judgment and an energy system accounting framework. Of course, technology adoption will ultimately depend on a set of complex considerations that are not fully assessed using our methodology, but which are discussed extensively throughout the report. These interacting factors include technology and fuel cost trade-offs, infrastructure needs, environmental policies, and consumer preference. Within each of these factors are barriers that might challenge increased electrification - such as higher upfront costs or unfamiliarity with new electric technologies - but also opportunities that could yield greater adoption - such as increased productivity or expanded value streams enabled by electric and/or grid-connected technologies. Understanding and quantifying these factors are needed to both evaluate the implications of a potential increased electrification future and to influence the degree of future electrification. Overall, the report represents an initial step to inform researchers and decision-makers with data and context to plan for a potential future in which electricity powers an expanded share of the U.S. energy economy.This report is the second in a series of Electrification Futures Study (EFS) publications, a multi-year research project to explore widespread electrification in the future energy system of the United States. The report presents scenarios of electric end-use technology adoption in all major sectors - transportation, commercial and residential buildings, and industry - and resulting electricity consumption in the United States. The scenarios are used to characterize changes to end-use sectors under futures with increased electrification and to quantify how those changes might impact the amount and shape of electricity consumption. These scenarios were developed primarily using expert judgment and an energy system accounting framework. Of course, technology adoption will ultimately depend on a set of complex considerations that are not fully assessed using our methodology, but which are discussed extensively throughout the report. These interacting factors include technology and fuel cost trade-offs, infrastructure needs, environmental policies, and consumer preference. Within each of these factors are barriers that might challenge increased electrification - such as higher upfront costs or unfamiliarity with new electric technologies - but also opportunities that could yield greater adoption, such as increased productivity or expanded value streams enabled by electric and grid-connected technologies. Understanding and quantifying these factors are needed to both evaluate the implications of a potential increased electrification future and to influence the degree of future electrification. Overall, the report represents an initial step to inform researchers and decision-makers with data and context to plan for a potential future in which electricity powers an expanded share of the U.S. energy economy.
The Effect of Future Ambient Air Pollution on Human Premature Mortality to 2100 Using Output from the ACCMIP Model Ensemble
Ambient air pollution from ground-level ozone and fine particulate matter (PM(sub 2.5)) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry climate models simulated future concentrations of ozone and PM(sub 2.5) at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air-pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM(sub 2.5) relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM(sub 2.5) in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths per year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382000 (121000 to 728000) deaths per year in 2000 to between 1.09 and 2.36 million deaths per year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM(sub 2.5) concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between 2.39 and 1.31 million deaths per year for the four RCPs. The global mortality burden of PM(sub 2.5) is estimated to decrease from 1.70 (1.30 to 2.10) million deaths per year in 2000 to between 0.95 and 1.55 million deaths per year in 2100 for the four RCPs due to the combined effect of decreases in PM(sub 2.5) concentrations and changes in population and baseline mortality rates. Trends in future air-pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.
Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling
Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average
Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS
The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.
Impacts of Future Climate and Emission Changes on U.S. Air Quality
Changes in climate and emissions will affect future air quality. In this work, simulations of present (2001-2005) and future (2026-2030) regional air quality are conducted with the newly released CMAQ version 5.0 to examine the individual and combined impacts of simulated future climate and anthropogenic emission projections on air quality over the U.S. Current (2001-2005) meteorological and chemical predictions are evaluated against observational data to assess the model’s capability in reproducing the seasonal differences. Overall, WRF and CMAQ perform reasonably well. Increased temperatures (up to 3.18 °C) and decreased ventilation (up to 157 m in planetary boundary layer height) are found in both future winter and summer, with more prominent changes in winter. Increases in future temperatures result in increased isoprene and terpene emissions in winter and summer, driving the increase in maximum 8-h average O3 (up to 5.0 ppb) over the eastern U.S. in winter while decreases in NOx emissions drive the decrease in O3 over most of the U.S. in summer. Future concentrations of PM2.5 in winter and summer and many of its components including organic matter in winter, ammonium and nitrate in summer, and sulfate in winter and summer, decrease due to decreases in primary anthropogenic emissions and the concentrations of secondary anthropogenic pollutants and increased precipitation in winter. Future winter and summer dry and wet deposition fluxes are spatially variable and increase with increasing surface resistance and precipitation (e.g., NH4+ and NO3- dry and wet deposition fluxes increase in winter over much of the U.S.), respectively, and decrease with a decrease in ambient particulate concentrations (e.g., SO42- dry and wet deposition fluxes decrease over the eastern U.S. in summer and winter). Sensitivity simulations show that anthropogenic emission projections dominate over changes in climate in their impacts on the U.S. air quality in the near future. Changes in some regions/species, however, are dominated by climate and/or both climate and anthropogenic emissions, especially in future years that are marked by meteorological conditions conducive to poor air quality.
The Role of Tropical Cyclone—Ocean Interactions in Future Changes in Hurricane Katrina
Tropical cyclone (TC) intensity and precipitation are projected to increase in the future. However, some projections are based on atmosphere‐only models in which sea surface temperatures are prescribed, whereas projections based on global atmosphere‐ocean coupled models can be subject to long‐term ocean biases. We investigated the role of TC‐ocean interactions in future changes in TC intensity and precipitation in Hurricane Katrina. We performed four‐member ensembles using convection‐permitting atmosphere‐only and atmosphere‐ocean regional models for the historical climate and four future climates. We found that although future TC intensity and precipitation increased regardless of ocean coupling, ocean coupling dampened the future minimum sea‐level pressure decrease by half and amplified future precipitation scaling. Compared to future changes in upper‐ocean temperature, changes in salinity contributed little to future changes in TC intensity. This study highlights the importance of considering TC‐ocean interactions to reduce uncertainty in the magnitude of future TC intensity and precipitation projections.
cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures
Long-term electric power sector planning and capacity expansion is a key area of interest to stakeholders across a wide range of organizations because it helps in making informed decisions about investments in infrastructure within the context of potential future vulnerabilities under various natural and human stressors. Future power plant siting costs will depend on a number of factors including the characteristics of the electricity capacity expansion and electricity demand (e.g., fuel mix of future electric power capacity, and the magnitude and geographic distribution of electricity demand growth) as well as the geographic location of power plants. Electricity technology capacity expansion plans modeled to represent alternate future conditions meeting a set of scenario assumptions are traditionally compared against historical trends which may not be consistent with current and future conditions. We present the `cerf` Python package (a.k.a., the Capacity Expansion Regional Feasibility model) which helps evaluate the feasibility and structure of future, scenario-driven electricity capacity expansion plans by siting power plants in areas that have been deemed the least cost option while considering dynamic future conditions. We can use `cerf` to gain insight to research topics such as: 1) under what conditions future projected electricity expansion plans from models such as GCAM-USA are possible to achieve, 2) where and which on-the-ground barriers to siting (e.g., protected areas, cooling water availability) may influence our ability to achieve certain expansions, and 3) how electricity infrastructure build-outs and value may evolve into the future when considering locational marginal pricing (LMP) based on the supply and demand of electricity from a grid operations model.
Ventenata dubia projected to expand in the western United States despite future novel conditions
Abstract Distributions of both native and invasive species are expected to shift under future climate. Species distribution models (SDMs) are often used to explore future habitats, but sources of uncertainty including novel climate conditions may reduce the reliability of future projections. We explore the potential spread of the invasive annual grass ventenata ( Ventenata dubia ) in the western United States under both current and future climate scenarios using boosted regression tree models and 30 global climate models (GCMs). We quantify novel climate conditions, prediction variability arising from both the SDMs and GCMs, and the agreement among GCMs. Results demonstrate that currently suitable habitat is concentrated inside the invaded range of the northwest, but substantial habitat exists outside the invaded range in the Southern Rockies and southwestern US mountains. Future suitability projections vary greatly among GCMs, but GCMs commonly projected decreased suitability in the invaded range and increased suitability along higher elevations of interior mountainous areas. Climate novelty did not appear to undermine the prediction reliability in many cases where the climate–species relationship was fully represented by the occurrence data. GCM‐derived variability resulting from variation in future cool season precipitation and temperature seasonality was greatest in the Rocky Mountains. SDM‐derived variability was higher in currently suitable habitat, and few GCMs projections agreed that these areas would contain future suitable habitat. However, while prediction variability was high, many GCM projections agreed that parts of the Rocky, Wasatch, and Uinta Mountains would contain highly suitable habitat in the future. As disturbances in the interior mountains occur in coming decades, reducing some natural barriers to invasion, land managers, and conservationists will need to monitor for ventenata in post‐disturbance environments. Changes to invasion potential may not play out for several decades, but results related to current potential may have applications for early detection and rapid response planning.
Projecting future climate change impacts on heat-related mortality in large urban areas in China
Highlights: • The study projects future heat-related mortality risk in 51 major cities in China. • The projections are based on high resolution climate and population projections. • Future heat mortality risk is likely to be considerable under all emission scenarios. • Climate mitigation and heat risk management are needed to reduce such risk. Global climate change is anticipated to raise overall temperatures and has the potential to increase future mortality attributable to heat. Urban areas are particularly vulnerable to heat because of high concentrations of susceptible people. As the world's largest developing country, China has experienced noticeable changes in climate, partially evidenced by frequent occurrence of extreme heat in urban areas, which could expose millions of residents to summer heat stress that may result in increased health risk, including mortality. While there is a growing literature on future impacts of extreme temperatures on public health, projecting changes in future health outcomes associated with climate warming remains challenging and underexplored, particularly in developing countries. This is an exploratory study aimed at projecting future heat-related mortality risk in major urban areas in China. We focus on the 51 largest Chinese cities that include about one third of the total population in China, and project the potential changes in heat-related mortality based on 19 different global-scale climate models and three Representative Concentration Pathways (RCPs). City-specific risk estimates for high temperature and all-cause mortality were used to estimate annual heat-related mortality over two future twenty-year time periods. We estimated that for the 20-year period in Mid-21st century (2041–2060) relative to 1970–2000, incidence of excess heat-related mortality in the 51 cities to be approximately 37,800 (95% CI: 31,300–43,500), 31,700 (95% CI: 26,200–36,600) and 25,800 (95% CI: 21,300–29,800) deaths per year under RCP8.5, RCP4.5 and RCP2.6, respectively. Slowing climate change through the most stringent emission control scenario RCP2.6, relative to RCP8.5, was estimated to avoid 12,900 (95% CI: 10,800–14,800) deaths per year in the 51 cities in the 2050s, and 35,100 (95% CI: 29,200–40,100) deaths per year in the 2070s. The highest mortality risk is primarily in cities located in the North, East and Central regions of China. Population adaptation to heat is likely to reduce excess heat mortality, but the extent of adaptation is still unclear. Future heat mortality risk attributable to exposure to elevated warm season temperature is likely to be considerable in China's urban centers, with substantial geographic variations. Climate mitigation and heat risk management are needed to reduce such risk and produce substantial public health benefits.
Estimation of Future PM2.5- and Ozone-related Mortality over the Continental United States in a Changing Climate: An application of High-resolution Dynamical Downscaling Technique
This paper evaluates the PM2.5- and ozone-related mortality at present (2000s) and in the future (2050s) over the continental United States by using the Environmental Benefits Mapping and Analysis Program (BenMAP-CE). Atmospheric chemical fields are simulated by WRF/CMAQ (horizontal resolution: 12 × 12km), applying the dynamical downscaling technique from global climate-chemistry models under the Representative Concentration Pathways scenario (RCP 8.5). Future air quality results predict that the annual mean PM2.5 concentrations in continental US will decrease nationwide, especially in the eastern US and west coast. However, the ozone concentration is projected to decrease in the Eastern US but increase in the Western US. Future mortality is evaluated under two scenarios (1) holding future population and baseline incidence rate at the present level and (2) decreasing the future baseline incidence rate but increasing the future population. For PM2.5, the entire continental US presents a decreasing trend of PM2.5-related mortality by the 2050s in Scenario (1), primarily resulting from the emissions reduction. While in Scenario (2), almost half of the continental states show a rising tendency of PM2.5-related mortality, due to the dominant influence of population growth. In particular, the highest PM2.5-related deaths and the biggest discrepancy between present and future PM2.5-related deaths will both occur in California in 2050s. For the ozone-related premature mortality, the simulation shows nation-wide rising tendency in 2050s under both two scenarios, mainly due to the increase of ozone concentration and population in the future. Furthermore, the uncertainty analysis shows that the effect of the all causes mortality is much larger than for specific causes. This assessment is the result of the accumulated uncertainty of generating datasets. The uncertainty range of ozone-related all cause premature mortality is narrower than the PM2.5-related all cause mortality, due to its smaller standard deviation of beta parameter.
Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB
Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.
Life Cycle Assessment of Future Photovoltaic Electricity Production from Residential-scale Systems Operated in Europe
The photovoltaics (PV) industry is growing rapidly to meet the increasing demand of green power. As the industry grows, the manufacturing processes and the material and energy efficiencies of PV cells and panels are improving. To assess the impacts of this trend, future scenarios of single-crystalline (single-Si, also known as mono-crystalline) silicon and cadmium-telluride (CdTe) PV systems installed on European residences were established. Assessment of the improvement potential of PV electricity-generating technologies such as single-Si and CdTe could be considered in long-term energy strategy decisions. This study aims to provide scenario-based information about the environmental per-formance of single-Si and CdTe PV modules produced and operated in the far future (2030 to 2050). The deployment application assessed considers European residential roofs. We made scenario-dependent projections of key parameters for single-Si and CdTe PV panels manufactured in 2050. The parameters included cell efficiency, module efficiency, wafer thickness, cutting losses, kerf losses, silver use, glass thickness and operational lifetime. This study combines developments for these parameters with projections of the environmental performance of electricity mixes in the main manufacturing countries/regions (European Union, China and the United States of America) and with projections of the environ-mental performance of basic material production (aluminium, copper, magnesium, nickel, pig iron, zinc, clinker and flat glass) in the far future. The three scenarios used in the assessment of future PV electricity were categorized into three classes: 'business as usual' (BAU), 'realistic improvement' (REAL) and 'optimistic improvement' (OPT). It estimates the current life cycle greenhouse gas emissions of single-Si PV electricity produced on the roofs of European residences to be approximately 80 grams CO2-equi-valent per kWh (g CO2-eq per kWh). Based on the projected changes to key parameters and the background system, life cycle greenhouse gas (GHG) emissions could be reduced to 65% (scenario BAU), 31% (scenario REAL) and 18% (scenario OPT) of that value in the far future. Results for other life cycle assessment (LCA) metrics are also assessed in this study such as: non-renewable cumulative energy demand, acidification potential, human toxicity potential, photochemical ozone creation potential, particulate matter formation potential, and land use. We calculated the total energy payback time (EPBT) and the non-renewable energy payback time (NREPBT) in the far future of single-crystalline silicon-based PV panels operated in Europe by dividing the estimate of non-renewable cumulative energy demand of PV electricity for the given scenario by the non-renewable cumulative energy demand of the scenario-dependent national and regional non-renewable residual electricity mixes. The study estimates the payback time could be reduced from 2.4 years today to 1.7, 1.2 and 1.2 years (scenarios BAU, REAL and OPT, respectively) in the far future, based on the assumptions and projections in the analysis. It finally estimates the current life cycle greenhouse gas emissions of CdTe PV electricity produced on the roofs of European residences to be approximately 30 g CO2-eq per kWh. Based on the projected changes to key parameters and the background system, life cycle GHG emissions could be reduced to 70% (scenario BAU), 44% (scenario REAL) and 32% (scenario OPT) of that value in the far future.
Analyzing the Impact of Future Weather Data on Energy Consumption in Weatherization Assistant
This study supports the mission of the U.S. Department of Energy’s Weatherization Assistance Program (WAP), which aims to increase the energy efficiency of dwellings and reduce their total residential expenditures. Specifically, we examine how projected future climate conditions may affect residential building energy performance by integrating future weather data into the National Energy Audit Tool (NEAT). Since WAP evaluates the cost-effectiveness of retrofit measures over lifespans of up to 30 years, accounting for evolving climate conditions is increasingly important. To reflect future household energy demands, this study replaces historically based Typical Meteorological Year (TMY3) weather inputs with Future Typical Meteorological Year (fTMY) datasets derived from global climate model (GCM) projections. A simulation-based framework was established to enable NEAT analysis under future weather conditions. This workflow involves converting EPW-format weather files into JSON inputs compatible with NEAT and generating degree-hour metrics needed for load calculations. The fTMY dataset used in this study was developed by Oak Ridge National Laboratory through downscaling of six GCMs under different emission scenarios and covers the period from 2020 to 2100. In contrast, the TMY3 dataset is based on historical weather data from 1961 to 1990. Simulations were conducted for benchmark single-family prototype buildings across ASHRAE climate zones 1–7, which cover all regions of the U.S. except the subarctic Zone 8 in northern Alaska, evaluating both heating and cooling loads under TMY3 and fTMY conditions. Four foundation types were tested, while heating systems were standardized, as NEAT does not differentiate thermal energy load by HVAC system type in its load calculations. Results show that fTMY weather input consistently yield lower heating loads and higher cooling loads across most locations, aligning with expected climate warming trends. Notably, colder regions such as zones 6A, 6B, and 7 experience marked reductions in heating load, while warmer and transitional zones, such as 2A (Lufkin, TX) and 3C (San Francisco, CA), have substantial increases in cooling loads. Although this study does not directly assess the performance of retrofit measures under future climate conditions, it provides a critical foundation for doing so. By quantifying shifts in baseline (i.e., pre-retrofit case) energy loads between historical and future weather files, the study highlights the importance of integrating climate-responsive data into audit tools. These findings will inform future efforts to evaluate the long-term effectiveness and cost-effectiveness of weatherization measures under changing climate conditions.