cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1011581.v1
title: Ranking of top five flights by O<sub>3</sub> impact for two different metrics

description: <b>Table 2.</b> Ranking of top five flights by O<sub>3</sub> impact for two different metrics. The first section shows ranking by total impact, i.e. the contribution of each flight to the total ozone perturbation. The second section shows ranking by FOF, i.e. ozone impact normalized by fuel burn and scaled by the lowest impact flight. Return trips were not counted (only the higher of a return-trip pair are counted). <strong>Abstract</strong> Aviation NO<sub><em>x</em></sub> emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO<sub><em>x</em></sub> on O<sub>3</sub> in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O<sub>3</sub> formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O<sub>3</sub> production due to aviation NO<sub><em>x</em></sub> emissions, which is the first application of an adjoint to this problem. We find that total aviation NO<sub><em>x</em></sub> emitted in October causes 40% more O<sub>3</sub> than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O<sub>3</sub> per unit NO<sub><em>x</em></sub> than European or North American emissions. Using this sensitivity approach, the O<sub>3</sub> burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O<sub>3</sub>-producing flights have origins or destinations in New Zealand or Australia. The top ranked O<sub>3</sub>-producing flights normalized by fuel burn cause 157 times more normalized O<sub>3</sub> formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO<sub><em>x</em></sub> emissions.
DOI: 10.6084/m9.figshare.1011582.v1
title: Total ozone impact normalized by seat-km

description: <b>Table 3.</b> Total ozone impact normalized by seat-km. <strong>Abstract</strong> Aviation NO<sub><em>x</em></sub> emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO<sub><em>x</em></sub> on O<sub>3</sub> in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O<sub>3</sub> formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O<sub>3</sub> production due to aviation NO<sub><em>x</em></sub> emissions, which is the first application of an adjoint to this problem. We find that total aviation NO<sub><em>x</em></sub> emitted in October causes 40% more O<sub>3</sub> than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O<sub>3</sub> per unit NO<sub><em>x</em></sub> than European or North American emissions. Using this sensitivity approach, the O<sub>3</sub> burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O<sub>3</sub>-producing flights have origins or destinations in New Zealand or Australia. The top ranked O<sub>3</sub>-producing flights normalized by fuel burn cause 157 times more normalized O<sub>3</sub> formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO<sub><em>x</em></sub> emissions.
DOI: 10.6084/m9.figshare.1011580
title: Pathways of O<sub>3</sub> production and loss considered in the chemical pathway analysis

description: <b>Table 1.</b> Pathways of O<sub>3</sub> production and loss considered in the chemical pathway analysis. <strong>Abstract</strong> Aviation NO<sub><em>x</em></sub> emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO<sub><em>x</em></sub> on O<sub>3</sub> in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O<sub>3</sub> formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O<sub>3</sub> production due to aviation NO<sub><em>x</em></sub> emissions, which is the first application of an adjoint to this problem. We find that total aviation NO<sub><em>x</em></sub> emitted in October causes 40% more O<sub>3</sub> than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O<sub>3</sub> per unit NO<sub><em>x</em></sub> than European or North American emissions. Using this sensitivity approach, the O<sub>3</sub> burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O<sub>3</sub>-producing flights have origins or destinations in New Zealand or Australia. The top ranked O<sub>3</sub>-producing flights normalized by fuel burn cause 157 times more normalized O<sub>3</sub> formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO<sub><em>x</em></sub> emissions.
DOI: 10.6084/m9.figshare.1011580.v1
title: Pathways of O<sub>3</sub> production and loss considered in the chemical pathway analysis

description: <b>Table 1.</b> Pathways of O<sub>3</sub> production and loss considered in the chemical pathway analysis. <strong>Abstract</strong> Aviation NO<sub><em>x</em></sub> emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO<sub><em>x</em></sub> on O<sub>3</sub> in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O<sub>3</sub> formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O<sub>3</sub> production due to aviation NO<sub><em>x</em></sub> emissions, which is the first application of an adjoint to this problem. We find that total aviation NO<sub><em>x</em></sub> emitted in October causes 40% more O<sub>3</sub> than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O<sub>3</sub> per unit NO<sub><em>x</em></sub> than European or North American emissions. Using this sensitivity approach, the O<sub>3</sub> burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O<sub>3</sub>-producing flights have origins or destinations in New Zealand or Australia. The top ranked O<sub>3</sub>-producing flights normalized by fuel burn cause 157 times more normalized O<sub>3</sub> formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO<sub><em>x</em></sub> emissions.
DOI: 10.6084/m9.figshare.1011570
title: Per annum growth rates of exogenous drivers for the historical period 1961–2006

description: <b>Table 1.</b> Per annum growth rates of exogenous drivers for the historical period 1961–2006. <strong>Abstract</strong> Global agricultural models are becoming indispensable in the debate over climate change impacts and mitigation policies. Therefore, it is becoming increasingly important to validate these models and identify critical areas for improvement. In this letter, we illustrate both the opportunities and the challenges in undertaking such model validation, using the SIMPLE model of global agriculture. We look back at the long run historical period 1961–2006 and, using a few key historical drivers—population, incomes and total factor productivity—we find that SIMPLE is able to accurately reproduce historical changes in cropland use, crop price, crop production and average crop yields at the global scale. Equally important is our investigation into how the specific assumptions embedded in many agricultural models will likely influence these results. We find that those global models which are largely biophysical—thereby ignoring the price responsiveness of demand and supply—are likely to understate changes in crop production, while failing to capture the changes in cropland use and crop price. Likewise, global models which incorporate economic responses, but do so based on limited time series estimates of these responses, are likely to understate land use change and overstate price changes.
DOI: 10.6084/m9.figshare.1011558
title: Statistical representation for the <em>T</em><sub>2 m</sub> (° C), WS<sub>10 m</sub> (m s<sup>−1</sup>), and WD<sub>10 m</sub> (deg) at the urban and rural stations

description: <b>Table 2.</b> Statistical representation for the <em>T</em><sub>2 m</sub> (° C), WS<sub>10 m</sub> (m s<sup>−1</sup>), and WD<sub>10 m</sub> (deg) at the urban and rural stations. RMSE is the root-mean square error, MAE is the mean absolute error, and MAPE is the mean absolute percentage error. <strong>Abstract</strong> Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ~53% of diurnally averaged total electric demand, ranging from ~35% during early morning to ~65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.
DOI: 10.6084/m9.figshare.1011557
title: Periods simulated with the WRF model

description: <b>Table 1.</b> Periods simulated with the WRF model. A spin-up of 7 h was considered for each run. <strong>Abstract</strong> Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ~53% of diurnally averaged total electric demand, ranging from ~35% during early morning to ~65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.
DOI: 10.6084/m9.figshare.1011557.v1
title: Periods simulated with the WRF model

description: <b>Table 1.</b> Periods simulated with the WRF model. A spin-up of 7 h was considered for each run. <strong>Abstract</strong> Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ~53% of diurnally averaged total electric demand, ranging from ~35% during early morning to ~65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.
DOI: 10.6084/m9.figshare.1011544
title: Descriptive statistics

description: <b>Table 1.</b> Descriptive statistics. (Notes: the R&D rec. and the R&D high variables are dummy variables equal to 1 if the associated expert's cost estimate refers to the recommended and high R&D scenarios, respectively. The average values of the variables therefore represent the share of cost estimate referring to that specific R&D scenario within our sample.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.
DOI: 10.6084/m9.figshare.1011545
title: Estimates of expert's elicited 50th percentile of overnight capital cost

description: <b>Table 2.</b> Estimates of expert's elicited 50th percentile of overnight capital cost. <em>Y</em> = ln(<em>p</em>50). (Note: robust <em>p</em>-values in brackets.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.
DOI: 10.6084/m9.figshare.1011546
title: Estimates of effects on variation in nuclear costs

description: <b>Table 3.</b> Estimates of effects on variation in nuclear costs. <em>Y</em> = ln[(<em>p</em>90 − <em>p</em>10)/<em>p</em>50]. (Note: robust <em>p</em>-values in brackets.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.
DOI: 10.6084/m9.figshare.1011544.v1
title: Descriptive statistics

description: <b>Table 1.</b> Descriptive statistics. (Notes: the R&D rec. and the R&D high variables are dummy variables equal to 1 if the associated expert's cost estimate refers to the recommended and high R&D scenarios, respectively. The average values of the variables therefore represent the share of cost estimate referring to that specific R&D scenario within our sample.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.
DOI: 10.6084/m9.figshare.1011546.v1
title: Estimates of effects on variation in nuclear costs

description: <b>Table 3.</b> Estimates of effects on variation in nuclear costs. <em>Y</em> = ln[(<em>p</em>90 − <em>p</em>10)/<em>p</em>50]. (Note: robust <em>p</em>-values in brackets.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.
DOI: 10.6084/m9.figshare.1011545.v1
title: Estimates of expert's elicited 50th percentile of overnight capital cost

description: <b>Table 2.</b> Estimates of expert's elicited 50th percentile of overnight capital cost. <em>Y</em> = ln(<em>p</em>50). (Note: robust <em>p</em>-values in brackets.) <strong>Abstract</strong> Characterization of the anticipated performance of energy technologies to inform policy decisions increasingly relies on expert elicitation. Knowledge about how elicitation design factors impact the probabilistic estimates emerging from these studies is, however, scarce. We focus on nuclear power, a large-scale low-carbon power option, for which future cost estimates are important for the design of energy policies and climate change mitigation efforts. We use data from three elicitations in the USA and in Europe and assess the role of government research, development, and demonstration (RD&D) investments on expected nuclear costs in 2030. We show that controlling for expert, technology, and design characteristics increases experts' implied public RD&D elasticity of expected costs by 25%. Public sector and industry experts' cost expectations are 14% and 32% higher, respectively than academics. US experts are more optimistic than their EU counterparts, with median expected costs 22% lower. On average, a doubling of public RD&D is expected to result in an 8% cost reduction, but the uncertainty is large. The difference between the 90th and 10th percentile estimates is on average 58% of the experts' median estimates. Public RD&D investments do not affect uncertainty ranges, but US experts are less confident about costs than Europeans.